chore: ruff lint

# Conflicts:
#	core/frontend/src/components/ChatPanel.tsx
#	core/frontend/src/pages/colony-chat.tsx
#	core/frontend/src/pages/queen-dm.tsx

.claude/settings.json

@@ -1,4 +1,74 @@
 {
+  "permissions": {
+    "allow": [
+      "Bash(grep -n \"_is_context_too_large_error\" core/framework/agent_loop/agent_loop.py core/framework/agent_loop/internals/*.py)",
+      "Read(//^class/ {cls=$3} /def test_/**)",
+      "Read(//^    @pytest.mark.asyncio/{getline n; print NR\": \"n} /^    def test_/**)",
+      "Bash(python3)",
+      "Bash(grep -nE 'Tool\\\\\\(\\\\s*$|name=\"[a-z_]+\",' core/framework/tools/queen_lifecycle_tools.py)",
+      "Bash(awk -F'\"' '{print $2}')",
+      "Bash(grep -n \"create_colony\\\\|colony-spawn\\\\|colony_spawn\" /home/timothy/aden/hive/core/framework/agents/queen/nodes/__init__.py /home/timothy/aden/hive/core/framework/tools/*.py)",
+      "Bash(git stash:*)",
+      "Bash(python3 -c \"import sys,json; d=json.loads\\(sys.stdin.read\\(\\)\\); print\\('keys:', list\\(d.keys\\(\\)\\)[:10]\\)\")",
+      "Bash(python3 -c ':*)",
+      "Bash(uv run:*)",
+      "Read(//tmp/**)",
+      "Bash(grep -n \"useColony\\\\|const { queens, queenProfiles\" /home/timothy/aden/hive/core/frontend/src/pages/queen-dm.tsx)",
+      "Bash(awk 'NR==385,/\\\\}, \\\\[/' /home/timothy/aden/hive/core/frontend/src/pages/queen-dm.tsx)",
+      "Bash(xargs -I{} sh -c 'if ! grep -q \"^import base64\\\\|^from base64\" \"{}\"; then echo \"MISSING: {}\"; fi')",
+      "Bash(find /home/timothy/aden/hive/core/framework -name \"*.py\" -type f -exec grep -l \"FileConversationStore\\\\|class.*ConversationStore\" {} \\\\;)",
+      "Bash(find /home/timothy/aden/hive/core/framework -name \"*.py\" -exec grep -l \"run_parallel_workers\\\\|create_colony\" {} \\\\;)",
+      "Bash(awk '/^    async def execute\\\\\\(self, ctx: AgentContext\\\\\\)/,/^    async def [a-z_]+/ {print NR\": \"$0}' /home/timothy/aden/hive/core/framework/agent_loop/agent_loop.py)",
+      "Bash(grep -r \"max_concurrent_workers\\\\|max_depth\\\\|recursion\\\\|spawn.*bomb\" /home/timothy/aden/hive/core/framework/host/*.py)",
+      "Bash(wc -l /home/timothy/aden/hive/tools/src/gcu/browser/*.py /home/timothy/aden/hive/tools/src/gcu/browser/tools/*.py)",
+      "Bash(file /tmp/gcu_verify/*.png)",
+      "Bash(ps -eo pid,cmd)",
+      "Bash(ps -o pid,lstart,cmd -p 746640)",
+      "Bash(kill 746636)",
+      "Bash(ps -eo pid,lstart,cmd)",
+      "Bash(grep -E \"^d|\\\\.py$\")",
+      "Bash(grep -E \"\\\\.\\(ts|tsx\\)$\")",
+      "Bash(xargs cat:*)",
+      "Bash(find /home/timothy/aden/hive -path \"*/.venv\" -prune -o -name \"*.py\" -type f -exec grep -l \"frontend\\\\|UI\\\\|terminal\\\\|interactive\\\\|TUI\" {} \\\\;)",
+      "Bash(wc -l /home/timothy/.hive/backup/*/SKILL.md)",
+      "Bash(awk -F'::' '{print $1}')",
+      "Bash(wait)",
+      "Bash(pkill -f \"pytest.*test_event_loop_node\")",
+      "Bash(pkill -f \"pytest.*TestToolConcurrency\")",
+      "Bash(grep -n \"def.*discover\\\\|/api/agents\\\\|agents_discover\" /home/timothy/aden/hive/core/framework/server/*.py)",
+      "Bash(bun run:*)",
+      "Bash(npx eslint:*)",
+      "Bash(npm run:*)",
+      "Bash(npm test:*)",
+      "Bash(grep -n \"PIL\\\\|Image\\\\|to_thread\\\\|run_in_executor\" /home/timothy/aden/hive/tools/src/gcu/browser/*.py /home/timothy/aden/hive/tools/src/gcu/browser/tools/*.py)",
+      "WebFetch(domain:docs.litellm.ai)",
+      "Bash(cat /home/timothy/aden/hive/.venv/lib/python3.11/site-packages/litellm-*.dist-info/METADATA)",
+      "Bash(find \"/home/timothy/.hive/agents/queens/queen_brand_design/sessions/session_20260415_100751_d49f4c28/\" -type f -name \"*.json*\" -exec grep -l \"协日\" {} \\\\;)",
+      "Bash(grep -v ':0$')",
+      "Bash(curl -s -m 2 http://127.0.0.1:4002/sse -o /dev/null -w 'status=%{http_code} time=%{time_total}s\\\\n')",
+      "mcp__gcu-tools__browser_status",
+      "mcp__gcu-tools__browser_start",
+      "mcp__gcu-tools__browser_navigate",
+      "mcp__gcu-tools__browser_evaluate",
+      "mcp__gcu-tools__browser_screenshot",
+      "mcp__gcu-tools__browser_open",
+      "mcp__gcu-tools__browser_click_coordinate",
+      "mcp__gcu-tools__browser_get_rect",
+      "mcp__gcu-tools__browser_type_focused",
+      "mcp__gcu-tools__browser_wait",
+      "Bash(python3 -c ' *)",
+      "Bash(python3 scripts/debug_queen_prompt.py independent)",
+      "Bash(curl -s --max-time 2 http://127.0.0.1:9230/status)",
+      "Bash(python3 -c \"import json, sys; print\\(json.loads\\(sys.stdin.read\\(\\)\\)['data']['content']\\)\")",
+      "Bash(python3 -c \"import json; json.load\\(open\\('/home/timothy/aden/hive/tools/browser-extension/manifest.json'\\)\\)\")"
+    ],
+    "additionalDirectories": [
+      "/home/timothy/.hive/skills/writing-hive-skills",
+      "/tmp",
+      "/home/timothy/.hive/skills",
+      "/home/timothy/aden/hive/core/frontend/src/components"
+    ]
+  },
   "hooks": {
     "PostToolUse": [
       {

.claude/skills/browser-edge-cases/SKILL.md

@@ -0,0 +1,241 @@
+---
+name: browser-edge-cases
+description: SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.
+license: MIT
+---
+
+# Browser Tool Edge Cases
+
+Standard Operating Procedure for debugging and fixing browser automation failures on complex websites.
+
+## When to Use This Skill
+
+- `browser_scroll` succeeds but page doesn't move
+- `browser_click` succeeds but no action triggered
+- `browser_type` text disappears or doesn't work
+- `browser_snapshot` hangs or returns stale content
+- `browser_navigate` loads wrong content
+
+## SOP: Debugging Browser Tool Failures
+
+### Phase 1: Reproduce & Isolate
+
+```
+1. Create minimal test case demonstrating failure
+2. Test against simple site (example.com) to verify tool works
+3. Test against problematic site to confirm issue
+```
+
+**Quick isolation test:**
+```python
+# Test 1: Does the tool work at all?
+await browser_navigate(tab_id, "https://example.com")
+result = await browser_scroll(tab_id, "down", 100)
+# Should work on simple sites
+
+# Test 2: Does it fail on the problematic site?
+await browser_navigate(tab_id, "https://linkedin.com/feed")
+result = await browser_scroll(tab_id, "down", 100)
+# If this fails but example.com works → site-specific edge case
+```
+
+### Phase 2: Analyze Root Cause
+
+**Step 2a: Check console for errors**
+```python
+console = await browser_console(tab_id)
+# Look for: CSP violations, React errors, JavaScript exceptions
+```
+
+**Step 2b: Inspect DOM structure**
+```python
+html = await browser_html(tab_id)
+snapshot = await browser_snapshot(tab_id)
+# Look for:
+# - Nested scrollable divs (overflow: scroll/auto)
+# - Shadow DOM roots
+# - iframes
+# - Custom widgets
+```
+
+**Step 2c: Identify the pattern**
+
+| Symptom | Likely Cause | Check |
+|---------|--------------|-------|
+| Scroll doesn't move | Nested scroll container | Look for `overflow: scroll` divs |
+| Click no effect | Element covered | Check `getBoundingClientRect` vs viewport |
+| Type clears | Autocomplete/React | Check for event listeners on input; try `browser_type_focused` |
+| Snapshot hangs | Huge DOM | Check node count in snapshot |
+| Snapshot stale | SPA hydration | Wait after navigation |
+
+### Phase 3: Implement Multi-Layer Fix
+
+**Pattern: Always have fallbacks**
+
+```python
+async def robust_operation(tab_id):
+    # Method 1: Primary approach
+    try:
+        result = await primary_method(tab_id)
+        if verify_success(result):
+            return result
+    except Exception:
+        pass
+
+    # Method 2: CDP fallback
+    try:
+        result = await cdp_fallback(tab_id)
+        if verify_success(result):
+            return result
+    except Exception:
+        pass
+
+    # Method 3: JavaScript fallback
+    return await javascript_fallback(tab_id)
+```
+
+**Pattern: Always add timeouts**
+
+```python
+# Bad - can hang forever
+result = await browser_snapshot(tab_id)
+
+# Good - fails fast with useful error
+try:
+    result = await browser_snapshot(tab_id, timeout_s=10.0)
+except asyncio.TimeoutError:
+    # Handle timeout gracefully
+    result = await fallback_snapshot(tab_id)
+```
+
+### Phase 4: Verify Fix
+
+```
+1. Run against problematic site → should work
+2. Run against simple site → should still work (regression check)
+3. Document in registry.md
+```
+
+## Pattern Library
+
+### P1: Nested Scrollable Containers
+
+**Sites:** LinkedIn, Twitter/X, any SPA with scrollable feeds
+
+**Detection:**
+```javascript
+// Find largest scrollable container
+const candidates = [];
+document.querySelectorAll('*').forEach(el => {
+    const style = getComputedStyle(el);
+    if (style.overflow.includes('scroll') || style.overflow.includes('auto')) {
+        const rect = el.getBoundingClientRect();
+        if (rect.width > 100 && rect.height > 100) {
+            candidates.push({el, area: rect.width * rect.height});
+        }
+    }
+});
+candidates.sort((a, b) => b.area - a.area);
+return candidates[0]?.el;
+```
+
+**Fix:** Dispatch scroll events at container's center, not viewport center.
+
+### P2: Element Covered by Overlay
+
+**Sites:** Modals, tooltips, SPAs with loading overlays
+
+**Detection:**
+```javascript
+const rect = element.getBoundingClientRect();
+const centerX = rect.left + rect.width / 2;
+const centerY = rect.top + rect.height / 2;
+const topElement = document.elementFromPoint(centerX, centerY);
+return topElement === element || element.contains(topElement);
+```
+
+**Fix:** Wait for overlay to disappear, or use JavaScript click.
+
+### P3: React Synthetic Events
+
+**Sites:** React SPAs, modern web apps
+
+**Detection:** If CDP click doesn't trigger handler but manual click works.
+
+**Fix:** Use JavaScript click as primary:
+```javascript
+element.click();
+```
+
+### P4: Huge DOM / Accessibility Tree
+
+**Sites:** LinkedIn, Facebook, Twitter (feeds with 1000s of nodes)
+
+**Detection:**
+```javascript
+document.querySelectorAll('*').length > 5000
+```
+
+**Fix:**
+1. Add timeout to snapshot operation
+2. Truncate tree at 2000 nodes
+3. Fall back to DOM-based snapshot if accessibility tree too large
+
+### P5: SPA Hydration Delay
+
+**Sites:** React, Vue, Angular SPAs after navigation
+
+**Detection:**
+```javascript
+// Check if React app has hydrated
+document.querySelector('[data-reactroot]') ||
+document.querySelector('[data-reactid]')
+```
+
+**Fix:** Wait for specific selector after navigation:
+```python
+await browser_navigate(tab_id, url, wait_until="load")
+await browser_wait(tab_id, selector='[data-testid="content"]', timeout_ms=5000)
+```
+
+### P6: Shadow DOM
+
+**Sites:** Components using Shadow DOM, Lit elements
+
+**Detection:**
+```javascript
+document.querySelectorAll('*').some(el => el.shadowRoot)
+```
+
+**Fix:** Pierce shadow root:
+```javascript
+function queryShadow(selector) {
+    const parts = selector.split('>>>');
+    let node = document;
+    for (const part of parts) {
+        if (node.shadowRoot) {
+            node = node.shadowRoot.querySelector(part.trim());
+        } else {
+            node = node.querySelector(part.trim());
+        }
+    }
+    return node;
+}
+```
+
+## Quick Reference
+
+| Issue | Primary Fix | Fallback |
+|-------|-------------|----------|
+| Scroll not working | Find scrollable container | Mouse wheel at container center |
+| Click no effect | JavaScript click() | CDP mouse events |
+| Type clears | Add delay_ms | Use `browser_type_focused` (Input.insertText) |
+| Snapshot hangs | Add timeout_s | DOM snapshot fallback |
+| Stale content | Wait for selector | Increase wait_until timeout |
+| Shadow DOM | Pierce selector | JavaScript traversal |
+
+## References
+
+- [registry.md](registry.md) - Full list of known edge cases
+- [scripts/test_case.py](scripts/test_case.py) - Template for testing new cases
+- [BROWSER_USE_PATTERNS.md](../../tools/BROWSER_USE_PATTERNS.md) - Implementation patterns from browser-use

.claude/skills/browser-edge-cases/registry.md

@@ -0,0 +1,261 @@
+# Browser Edge Case Registry
+
+Curated list of known browser automation edge cases with symptoms, causes, and fixes.
+
+---
+
+## Scroll Issues
+
+### #1: LinkedIn Nested Scroll Container
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | LinkedIn (linkedin.com/feed) |
+| **Symptom** | `browser_scroll()` returns `{ok: true}` but page doesn't move |
+| **Root Cause** | Content is in a nested scrollable div (`overflow: scroll`), not the main window |
+| **Detection** | `document.querySelectorAll('*')` with `overflow: scroll/auto` has large candidates |
+| **Fix** | JavaScript finds largest scrollable container, uses `container.scrollBy()` |
+| **Code** | `bridge.py:808-891` - smart scroll with container detection |
+| **Verified** | 2026-04-03 ✓ |
+
+### #2: Twitter/X Lazy Loading
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Twitter/X (x.com) |
+| **Symptom** | Infinite scroll doesn't load new content |
+| **Root Cause** | Lazy loading requires content to be visible before loading more |
+| **Detection** | Scroll position at bottom but no new `[data-testid="tweet"]` elements |
+| **Fix** | Add `wait_for_selector` between scroll calls with 1s delay |
+| **Code** | Test file: `tests/test_x_page_load_repro.py` |
+| **Verified** | - |
+
+### #3: Modal/Dialog Scroll Container
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Any site with modal dialogs |
+| **Symptom** | Scroll scrolls background page, not modal content |
+| **Root Cause** | Modal has its own scroll container with `overflow: scroll` |
+| **Detection** | Visible element with `position: fixed` and scrollable content |
+| **Fix** | Find visible modal container (highest z-index scrollable), scroll that |
+| **Code** | - |
+| **Verified** | - |
+
+---
+
+## Click Issues
+
+### #4: Element Covered by Overlay
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | SPAs, sites with loading overlays |
+| **Symptom** | Click succeeds but no action triggered |
+| **Root Cause** | Element is covered by transparent overlay, tooltip, or iframe |
+| **Detection** | `document.elementFromPoint(x, y) !== target` |
+| **Fix** | Wait for overlay to disappear, or use JavaScript `element.click()` |
+| **Code** | `bridge.py:394-591` - JavaScript click as primary |
+| **Verified** | - |
+
+### #5: React Synthetic Events
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | React applications |
+| **Symptom** | CDP click doesn't trigger React handler |
+| **Root Cause** | React uses synthetic events that don't respond to CDP events |
+| **Detection** | Site uses React (check for `__reactFiber$` or `data-reactroot`) |
+| **Fix** | Use JavaScript `element.click()` as primary method |
+| **Code** | `bridge.py:394-591` - JavaScript-first click |
+| **Verified** | - |
+
+### #6: Shadow DOM Elements
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Components using Shadow DOM, Lit elements |
+| **Symptom** | `querySelector` can't find element |
+| **Root Cause** | Element is inside a shadow root, not main DOM tree |
+| **Detection** | `element.shadowRoot !== null` on parent elements |
+| **Fix** | Use piercing selector (`host >>> target`) or traverse shadow roots |
+| **Code** | See SKILL.md P6 pattern |
+| **Verified** | 2026-04-03 ✓ |
+
+---
+
+## Input Issues
+
+### #7: ContentEditable / Rich Text Editors
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Rich text editors (Notion, Slack web, etc.) |
+| **Symptom** | `browser_type()` doesn't insert text |
+| **Root Cause** | Element is `contenteditable`, not an `<input>` or `<textarea>` |
+| **Detection** | `element.contentEditable === 'true'` |
+| **Fix** | Focus via JavaScript, use `execCommand('insertText')` or `Input.dispatchKeyEvent` |
+| **Code** | `bridge.py:616-694` - contentEditable handling |
+| **Verified** | 2026-04-03 ✓ |
+
+### #8: Autocomplete Field Clearing
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Search fields with autocomplete, address forms |
+| **Symptom** | Typed text gets cleared immediately |
+| **Root Cause** | Field expects realistic keystroke timing for autocomplete |
+| **Detection** | Field has autocomplete listeners or dropdown appears |
+| **Fix** | Add `delay_ms=50` between keystrokes |
+| **Code** | `bridge.py:type()` - delay_ms parameter |
+| **Verified** | 2026-04-03 ✓ |
+
+### #9: Custom Date Pickers
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Forms with custom date widgets |
+| **Symptom** | Can't type date into date field |
+| **Root Cause** | Custom widget intercepts and blocks keyboard input |
+| **Detection** | Typing doesn't change field value |
+| **Fix** | Click calendar widget icon, select date from dropdown |
+| **Code** | - |
+| **Verified** | - |
+
+---
+
+## Snapshot Issues
+
+### #10: LinkedIn Huge DOM Tree
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | LinkedIn, Facebook, Twitter feeds |
+| **Symptom** | `browser_snapshot()` hangs forever |
+| **Root Cause** | 10k+ DOM nodes, accessibility tree has 50k+ nodes |
+| **Detection** | `document.querySelectorAll('*').length > 5000` |
+| **Fix** | Add `timeout_s` param with `asyncio.timeout()`, proper error handling |
+| **Code** | `bridge.py:1041-1028` - snapshot with timeout protection |
+| **Verified** | 2026-04-03 ✓ (0.08s on LinkedIn) |
+
+### #11: SPA Hydration Delay
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | React/Vue/Angular SPAs |
+| **Symptom** | Snapshot shows old content after navigation |
+| **Root Cause** | Client-side hydration hasn't completed when snapshot runs |
+| **Detection** | `document.readyState === 'complete'` but content missing |
+| **Fix** | Wait for specific selector after navigation |
+| **Code** | Test file: `tests/test_x_page_load_repro.py` |
+| **Verified** | - |
+
+### #12: iframe Content Missing
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Sites with embedded content |
+| **Symptom** | Snapshot missing iframe content |
+| **Root Cause** | Accessibility tree doesn't include iframe content |
+| **Detection** | `document.querySelectorAll('iframe')` has results |
+| **Fix** | Use `DOM.getFrameOwner` + separate snapshot for each iframe |
+| **Code** | - |
+| **Verified** | - |
+
+---
+
+## Navigation Issues
+
+### #13: SPA Navigation Events
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | React Router, Vue Router SPAs |
+| **Symptom** | `wait_until="load"` fires before content ready |
+| **Root Cause** | SPA uses client-side routing, no full page load |
+| **Detection** | URL changes but `load` event already fired |
+| **Fix** | Use `wait_until="networkidle"` or `wait_for_selector` |
+| **Code** | `bridge.py:navigate()` - wait_until options |
+| **Verified** | - |
+
+### #14: Cross-Origin Redirects
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | OAuth flows, SSO logins |
+| **Symptom** | Navigation fails during redirect |
+| **Root Cause** | Cross-origin security prevents CDP tracking |
+| **Detection** | URL changes to different domain |
+| **Fix** | Use `wait_for_url` with pattern matching instead of exact URL |
+| **Code** | - |
+| **Verified** | - |
+
+---
+
+## Screenshot Issues
+
+### #15: Selector Screenshot Not Implemented
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Any site |
+| **Symptom** | `browser_screenshot(selector="h1")` takes full viewport instead of element |
+| **Root Cause** | `selector` param existed in signature but was silently ignored in both `bridge.py` and `inspection.py` |
+| **Detection** | Screenshot with selector same byte size as screenshot without selector |
+| **Fix** | Use CDP `Runtime.evaluate` to call `getBoundingClientRect()` on the element, pass result as `clip` to `Page.captureScreenshot` |
+| **Code** | `bridge.py:1315-1344` - selector clip logic; `inspection.py:94-96` - pass selector to bridge |
+| **Verified** | 2026-04-03 ✓ (JS rect query returns correct viewport coords; requires server restart) |
+
+### #16: Stale Browser Context (Group ID Mismatch)
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | Any |
+| **Symptom** | `browser_open()` returns `"No group with id: XXXXXXX"` even though `browser_status` shows `running: true` |
+| **Root Cause** | In-memory `_contexts` dict has a stale `groupId` from a Chrome tab group that was closed outside the tool (e.g. user closed the tab group) |
+| **Detection** | `browser_status` returns `running: true` but `browser_open` fails with "No group with id" |
+| **Fix** | Call `browser_stop()` to clear stale context from `_contexts`, then `browser_start()` again |
+| **Code** | `tools/lifecycle.py:144-160` - `already_running` check uses cached dict without validating against Chrome |
+| **Verified** | 2026-04-03 ✓ |
+
+---
+
+## How to Add New Edge Cases
+
+1. **Reproduce** the issue with minimal test case
+2. **Document** using the template below
+3. **Implement** fix with multi-layer fallback
+4. **Verify** against both problematic and simple sites
+5. **Submit** by appending to this file
+
+### Template
+
+```markdown
+### #N: [Short Title]
+
+| Attribute | Value |
+|-----------|-------|
+| **Site** | [URL or site type] |
+| **Symptom** | [What the user observes] |
+| **Root Cause** | [Technical explanation] |
+| **Detection** | [JavaScript to detect this case] |
+| **Fix** | [Solution approach] |
+| **Code** | [File:line reference if implemented] |
+| **Verified** | [Date or "pending"] |
+```
+
+---
+
+## Statistics
+
+| Category | Count |
+|----------|-------|
+| Scroll Issues | 3 |
+| Click Issues | 3 |
+| Input Issues | 3 |
+| Snapshot Issues | 3 |
+| Navigation Issues | 2 |
+| Screenshot Issues | 2 |
+| **Total** | **16** |
+
+Last updated: 2026-04-03

.claude/skills/browser-edge-cases/scripts/test_02_twitter_scroll.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python
+"""
+Test #2: Twitter/X Lazy Loading Scroll
+
+Symptom: Infinite scroll doesn't load new content
+Root Cause: Lazy loading requires content to be visible before loading more
+Fix: Add wait_for_selector between scroll calls
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+BRIDGE_PORT = 9229
+CONTEXT_NAME = "twitter-scroll-test"
+
+
+async def test_twitter_lazy_scroll():
+    """Test that repeated scrolls with waits load new content."""
+    print("=" * 70)
+    print("TEST #2: Twitter/X Lazy Loading Scroll")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+            print(f"Waiting for extension... ({i + 1}/10)")
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Navigate to Twitter/X
+        print("\n--- Navigating to X.com ---")
+        await bridge.navigate(tab_id, "https://x.com", wait_until="networkidle", timeout_ms=30000)
+        print("✓ Page loaded")
+
+        # Wait for tweets to appear
+        print("\n--- Waiting for tweets ---")
+        await bridge.wait_for_selector(tab_id, '[data-testid="tweet"]', timeout_ms=10000)
+
+        # Count initial tweets
+        initial_count = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.querySelectorAll('[data-testid=\"tweet\"]').length; })()",
+        )
+        print(f"Initial tweet count: {initial_count.get('result', 0)}")
+
+        # Take screenshot of initial state
+        screenshot = await bridge.screenshot(tab_id)
+        print(f"Screenshot: {len(screenshot.get('data', ''))} bytes")
+
+        # Scroll multiple times with waits
+        print("\n--- Scrolling with waits ---")
+        for i in range(3):
+            result = await bridge.scroll(tab_id, "down", 500)
+            print(f"  Scroll {i + 1}: {result.get('method', 'unknown')} method")
+
+            # Wait for new content to load
+            await asyncio.sleep(2)
+
+            # Count tweets after scroll
+            count_result = await bridge.evaluate(
+                tab_id,
+                "(function() { return document.querySelectorAll('[data-testid=\"tweet\"]').length; })()",
+            )
+            count = count_result.get("result", 0)
+            print(f"  Tweet count after scroll: {count}")
+
+        # Final count
+        final_count = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.querySelectorAll('[data-testid=\"tweet\"]').length; })()",
+        )
+        final = final_count.get("result", 0)
+        initial = initial_count.get("result", 0)
+
+        print("\n--- Results ---")
+        print(f"Initial tweets: {initial}")
+        print(f"Final tweets: {final}")
+
+        if final > initial:
+            print(f"✓ PASS: Loaded {final - initial} new tweets")
+        else:
+            print("✗ FAIL: No new tweets loaded (may need login)")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_twitter_lazy_scroll())

.claude/skills/browser-edge-cases/scripts/test_03_modal_scroll.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+"""
+Test #3: Modal/Dialog Scroll Container
+
+Symptom: Scroll scrolls background page, not modal content
+Root Cause: Modal has its own scroll container with overflow: scroll
+Fix: Find visible modal container (highest z-index scrollable), scroll that
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+BRIDGE_PORT = 9229
+CONTEXT_NAME = "modal-scroll-test"
+
+# Test site with modal - using a demo site
+MODAL_DEMO_URL = "https://www.w3schools.com/howto/howto_css_modals.asp"
+
+
+async def test_modal_scroll():
+    """Test that scroll targets modal content, not background."""
+    print("=" * 70)
+    print("TEST #3: Modal/Dialog Scroll Container")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Navigate to modal demo
+        print("\n--- Navigating to modal demo ---")
+        await bridge.navigate(tab_id, MODAL_DEMO_URL, wait_until="load")
+        print("✓ Page loaded")
+
+        # Take screenshot before
+        screenshot_before = await bridge.screenshot(tab_id)
+        print(f"Screenshot before: {len(screenshot_before.get('data', ''))} bytes")
+
+        # Click button to open modal
+        print("\n--- Opening modal ---")
+        # Find and click the "Open Modal" button
+        result = await bridge.click(tab_id, ".ws-btn", timeout_ms=5000)
+        print(f"Click result: {result}")
+
+        await asyncio.sleep(1)
+
+        # Take screenshot with modal open
+        screenshot_modal = await bridge.screenshot(tab_id)
+        print(f"Screenshot modal open: {len(screenshot_modal.get('data', ''))} bytes")
+
+        # Try to scroll within modal
+        print("\n--- Scrolling modal content ---")
+        result = await bridge.scroll(tab_id, "down", 100)
+        print(f"Scroll result: {result}")
+
+        await asyncio.sleep(0.5)
+
+        # Take screenshot after scroll
+        screenshot_after = await bridge.screenshot(tab_id)
+        print(f"Screenshot after scroll: {len(screenshot_after.get('data', ''))} bytes")
+
+        # Check if modal content scrolled (not background)
+        # This is a visual check - we can verify by comparing screenshots
+        print("\n--- Results ---")
+        print(f"Modal scroll test completed. Method used: {result.get('method', 'unknown')}")
+        print("Visual verification needed: Check if modal content scrolled vs background")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_modal_scroll())

.claude/skills/browser-edge-cases/scripts/test_04_element_covered.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python
+"""
+Test #4: Element Covered by Overlay
+
+Symptom: Click succeeds but no action triggered
+Root Cause: Element is covered by transparent overlay, tooltip, or iframe
+Detection: document.elementFromPoint(x, y) !== target
+Fix: Wait for overlay to disappear, or use JavaScript element.click()
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "overlay-click-test"
+
+
+async def test_overlay_click():
+    """Test clicking elements that are covered by overlays."""
+    print("=" * 70)
+    print("TEST #4: Element Covered by Overlay")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Create a test page with overlay
+        print("\n--- Creating test page with overlay ---")
+        test_html = """
+        <!DOCTYPE html>
+        <html>
+        <head><title>Overlay Test</title></head>
+        <body>
+            <button id="target-btn" onclick="alert('Clicked!')">Click Me</button>
+            <div id="overlay" style="position:fixed;top:0;left:0;
+            width:100%;height:100%;
+            background:rgba(0,0,0,0.3);z-index:1000;"></div>
+            <script>
+                window.clickCount = 0;
+                document.getElementById('target-btn').addEventListener('click', () => {
+                    window.clickCount++;
+                });
+            </script>
+        </body>
+        </html>
+        """
+
+        # Navigate to data URL
+        import base64
+
+        data_url = f"data:text/html;base64,{base64.b64encode(test_html.encode()).decode()}"
+        await bridge.navigate(tab_id, data_url, wait_until="load")
+
+        # Screenshot before
+        screenshot = await bridge.screenshot(tab_id)
+        print(f"Screenshot: {len(screenshot.get('data', ''))} bytes")
+
+        # Try to click the covered button
+        print("\n--- Attempting to click covered button ---")
+
+        # First, check if element is covered
+        coverage_check = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const btn = document.getElementById('target-btn');
+                const rect = btn.getBoundingClientRect();
+                const centerX = rect.left + rect.width / 2;
+                const centerY = rect.top + rect.height / 2;
+                const topElement = document.elementFromPoint(centerX, centerY);
+                return {
+                    isCovered: topElement !== btn && !btn.contains(topElement),
+                    topElement: topElement?.tagName,
+                    targetElement: btn.tagName
+                };
+            })();
+        """,
+        )
+        print(f"Coverage check: {coverage_check.get('result', {})}")
+
+        # Try CDP click (may fail due to overlay)
+        click_result = await bridge.click(tab_id, "#target-btn", timeout_ms=5000)
+        print(f"Click result: {click_result}")
+
+        # Check if click registered
+        count_result = await bridge.evaluate(tab_id, "(function() { return window.clickCount; })()")
+        count = count_result.get("result", 0)
+        print(f"Click count after CDP click: {count}")
+
+        if count > 0:
+            print("✓ PASS: JavaScript click penetrated overlay")
+        else:
+            print("✗ FAIL: Click did not reach button (overlay blocked it)")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_overlay_click())

.claude/skills/browser-edge-cases/scripts/test_06_shadow_dom.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+"""
+Test #6: Shadow DOM Elements
+
+Symptom: querySelector can't find element
+Root Cause: Element is inside a shadow root, not main DOM tree
+Detection: element.shadowRoot !== null on parent elements
+Fix: Use piercing selector (host >>> target) or traverse shadow roots
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "shadow-dom-test"
+
+
+async def test_shadow_dom():
+    """Test clicking elements inside Shadow DOM."""
+    print("=" * 70)
+    print("TEST #6: Shadow DOM Elements")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Create test page with Shadow DOM
+        print("\n--- Creating test page with Shadow DOM ---")
+        test_html = """
+        <!DOCTYPE html>
+        <html>
+        <head><title>Shadow DOM Test</title></head>
+        <body>
+            <div id="shadow-host"></div>
+            <script>
+                const host = document.getElementById('shadow-host');
+                const shadow = host.attachShadow({ mode: 'open' });
+                shadow.innerHTML = `
+                    <style>
+                        button { padding: 10px 20px; font-size: 16px; }
+                    </style>
+                    <button id="shadow-btn">Shadow Button</button>
+                `;
+                shadow.getElementById('shadow-btn').addEventListener('click', () => {
+                    window.shadowClickCount = (window.shadowClickCount || 0) + 1;
+                    console.log('Shadow button clicked:', window.shadowClickCount);
+                });
+            </script>
+        </body>
+        </html>
+        """
+
+        # Write to file and use file:// URL (data: URLs don't work well with extension)
+        test_file = Path("/tmp/shadow_dom_test.html")
+        test_file.write_text(test_html.strip())
+        file_url = f"file://{test_file}"
+        await bridge.navigate(tab_id, file_url, wait_until="load")
+        print("✓ Page loaded")
+
+        # Screenshot
+        screenshot = await bridge.screenshot(tab_id)
+        print(f"Screenshot: {len(screenshot.get('data', ''))} bytes")
+
+        # Detect Shadow DOM
+        print("\n--- Detecting Shadow DOM ---")
+        detection = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const hosts = [];
+                document.querySelectorAll('*').forEach(el => {
+                    if (el.shadowRoot) {
+                        hosts.push({
+                            tag: el.tagName,
+                            id: el.id,
+                            hasButton: el.shadowRoot.querySelector('button') !== null
+                        });
+                    }
+                });
+                return { count: hosts.length, hosts };
+            })();
+        """,
+        )
+        print(f"Shadow DOM detection: {detection.get('result', {})}")
+
+        # Try to click shadow button using regular selector (should fail)
+        print("\n--- Attempting click with regular selector ---")
+        try:
+            result = await bridge.click(tab_id, "#shadow-btn", timeout_ms=3000)
+            print(f"Result: {result}")
+        except Exception as e:
+            print(f"Expected failure: {e}")
+
+        # Try to click using JavaScript that pierces shadow DOM
+        print("\n--- Clicking via JavaScript shadow piercing ---")
+        click_result = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const host = document.getElementById('shadow-host');
+                const btn = host.shadowRoot.getElementById('shadow-btn');
+                if (btn) {
+                    btn.click();
+                    return { success: true, clicked: 'shadow-btn' };
+                }
+                return { success: false, error: 'Button not found' };
+            })();
+        """,
+        )
+        print(f"JS click result: {click_result.get('result', {})}")
+
+        # Verify click was registered
+        count_result = await bridge.evaluate(tab_id, "(function() { return window.shadowClickCount || 0; })()")
+        count = count_result.get("result") or 0
+        print(f"Shadow click count: {count}")
+
+        if count and count > 0:
+            print("✓ PASS: Shadow DOM element clicked successfully")
+        else:
+            print("✗ FAIL: Could not click Shadow DOM element")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_shadow_dom())

.claude/skills/browser-edge-cases/scripts/test_07_contenteditable.py

@@ -0,0 +1,180 @@
+#!/usr/bin/env python
+"""
+Test #7: ContentEditable / Rich Text Editors
+
+Symptom: browser_type() doesn't insert text
+Root Cause: Element is contenteditable, not an <input> or <textarea>
+Detection: element.contentEditable === 'true'
+Fix: Focus via JavaScript, use execCommand('insertText') or Input.dispatchKeyEvent
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "contenteditable-test"
+
+
+async def test_contenteditable():
+    """Test typing into contenteditable elements."""
+    print("=" * 70)
+    print("TEST #7: ContentEditable / Rich Text Editors")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Create test page with contenteditable
+        test_html = """
+        <!DOCTYPE html>
+        <html>
+        <head><title>ContentEditable Test</title></head>
+        <body>
+            <h2>ContentEditable Test</h2>
+
+            <h3>1. Simple contenteditable div</h3>
+            <div id="editor1" contenteditable="true"
+            style="border:1px solid #ccc;padding:10px;
+            min-height:50px;">Start text</div>
+
+            <h3>2. Rich text editor (like Notion)</h3>
+            <div id="editor2" contenteditable="true"
+            style="border:1px solid #ccc;padding:10px;
+            min-height:50px;">
+                <p>Type here...</p>
+            </div>
+
+            <h3>3. Regular input (for comparison)</h3>
+            <input id="input1" type="text" placeholder="Regular input" />
+
+            <script>
+                // Track content changes
+                window.editor1Content = '';
+                window.editor2Content = '';
+
+                document.getElementById('editor1').addEventListener('input', (e) => {
+                    window.editor1Content = e.target.innerText;
+                });
+                document.getElementById('editor2').addEventListener('input', (e) => {
+                    window.editor2Content = e.target.innerText;
+                });
+            </script>
+        </body>
+        </html>
+        """
+
+        # Write to file and use file:// URL (data: URLs don't work well with extension)
+        test_file = Path("/tmp/contenteditable_test.html")
+        test_file.write_text(test_html.strip())
+        file_url = f"file://{test_file}"
+        await bridge.navigate(tab_id, file_url, wait_until="load")
+        print("✓ Page loaded")
+
+        # Screenshot with timeout protection
+        try:
+            screenshot = await asyncio.wait_for(bridge.screenshot(tab_id), timeout=10.0)
+            print(f"Screenshot: {len(screenshot.get('data', ''))} bytes")
+        except asyncio.TimeoutError:
+            print("Screenshot timed out (skipping)")
+
+        # Detect contenteditable
+        print("\n--- Detecting contenteditable elements ---")
+        detection = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const editables = document.querySelectorAll('[contenteditable="true"]');
+                return {
+                    count: editables.length,
+                    ids: Array.from(editables).map(el => el.id)
+                };
+            })();
+        """,
+        )
+        print(f"Contenteditable detection: {detection.get('result', {})}")
+
+        # Test 1: Type into regular input (baseline)
+        print("\n--- Test 1: Regular input ---")
+        await bridge.click(tab_id, "#input1")
+        await bridge.type_text(tab_id, "#input1", "Hello input")
+        input_result = await bridge.evaluate(
+            tab_id, "(function() { return document.getElementById('input1').value; })()"
+        )
+        print(f"Input value: {input_result.get('result', '')}")
+
+        # Test 2: Type into contenteditable div
+        print("\n--- Test 2: Contenteditable div ---")
+        await bridge.click(tab_id, "#editor1")
+        await bridge.type_text(tab_id, "#editor1", "Hello contenteditable", clear_first=True)
+        editor_result = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.getElementById('editor1').innerText; })()",
+        )
+        print(f"Editor1 innerText: {editor_result.get('result', '')}")
+
+        # Test 3: Use JavaScript insertText for rich editor
+        print("\n--- Test 3: JavaScript insertText for rich editor ---")
+        insert_result = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const editor = document.getElementById('editor2');
+                editor.focus();
+                document.execCommand('selectAll', false, null);
+                document.execCommand('insertText', false, 'Hello from execCommand');
+                return editor.innerText;
+            })();
+        """,
+        )
+        print(f"Editor2 after execCommand: {insert_result.get('result', '')}")
+
+        # Screenshot after with timeout protection
+        try:
+            screenshot_after = await asyncio.wait_for(bridge.screenshot(tab_id), timeout=10.0)
+            print(f"Screenshot after: {len(screenshot_after.get('data', ''))} bytes")
+        except asyncio.TimeoutError:
+            print("Screenshot after timed out (skipping)")
+
+        # Results
+        print("\n--- Results ---")
+        input_val = input_result.get("result", "")
+        editor1_val = editor_result.get("result", "")
+        editor2_val = insert_result.get("result", "")
+
+        input_pass = "Hello input" in input_val
+        editor1_pass = "Hello contenteditable" in editor1_val
+        editor2_pass = "execCommand" in editor2_val
+
+        print(f"Input: {'✓ PASS' if input_pass else '✗ FAIL'} - {input_val}")
+        print(f"Editor1: {'✓ PASS' if editor1_pass else '✗ FAIL'} - {editor1_val}")
+        print(f"Editor2: {'✓ PASS' if editor2_pass else '✗ FAIL'} - {editor2_val}")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_contenteditable())

.claude/skills/browser-edge-cases/scripts/test_08_autocomplete.py

@@ -0,0 +1,250 @@
+#!/usr/bin/env python
+"""
+Test #8: Autocomplete Field Clearing
+
+Symptom: Typed text gets cleared immediately
+Root Cause: Field expects realistic keystroke timing for autocomplete
+Detection: Field has autocomplete listeners or dropdown appears
+Fix: Add delay_ms between keystrokes
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "autocomplete-test"
+
+
+async def test_autocomplete():
+    """Test typing into fields with autocomplete behavior."""
+    print("=" * 70)
+    print("TEST #8: Autocomplete Field Clearing")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Create test page with autocomplete behavior
+        test_html = """
+        <!DOCTYPE html>
+        <html>
+        <head><title>Autocomplete Test</title>
+        <style>
+            .autocomplete-items {
+                position: absolute;
+                border: 1px solid #d4d4d4;
+                border-top: none;
+                z-index: 99;
+                top: 100%;
+                left: 0;
+                right: 0;
+                max-height: 200px;
+                overflow-y: auto;
+                background: white;
+            }
+            .autocomplete-items div {
+                padding: 10px;
+                cursor: pointer;
+            }
+            .autocomplete-items div:hover {
+                background-color: #e9e9e9;
+            }
+            .autocomplete-active {
+                background-color: DodgerBlue !important;
+                color: white;
+            }
+            .autocomplete { position: relative; display: inline-block; }
+            input { width: 300px; padding: 10px; font-size: 16px; }
+        </style></head>
+        <body>
+            <h2>Autocomplete Test</h2>
+
+            <div class="autocomplete">
+                <input id="search" type="text" placeholder="Search countries..." autocomplete="off">
+            </div>
+
+            <div id="log" style="margin-top:20px;font-family:monospace;"></div>
+
+            <script>
+                const countries = [
+                    "Afghanistan","Albania","Algeria",
+                    "Andorra","Angola","Argentina",
+                    "Armenia","Australia","Austria",
+                    "Azerbaijan","Bahamas","Bahrain",
+                    "Bangladesh","Belarus","Belgium",
+                    "Belize","Benin","Bhutan",
+                    "Bolivia","Brazil","Canada",
+                    "China","Colombia","Denmark",
+                    "Egypt","France","Germany",
+                    "India","Indonesia","Italy",
+                    "Japan","Mexico","Netherlands",
+                    "Nigeria","Norway","Pakistan",
+                    "Peru","Philippines","Poland",
+                    "Portugal","Russia","Spain",
+                    "Sweden","Switzerland","Thailand",
+                    "Turkey","Ukraine",
+                    "United Kingdom","United States",
+                    "Vietnam"
+                ];
+
+                const input = document.getElementById('search');
+                const log = document.getElementById('log');
+                let currentFocus = -1;
+                let typingTimeout = null;
+
+                // Track events for testing
+                window.inputEvents = [];
+                window.inputValue = '';
+
+                function logEvent(type, value) {
+                    window.inputEvents.push({ type, value, time: Date.now() });
+                    const entry = document.createElement('div');
+                    entry.textContent = type + ': ' + value;
+                    log.insertBefore(entry, log.firstChild);
+                }
+
+                // Simulate autocomplete that clears fast typing
+                input.addEventListener('input', function(e) {
+                    const val = this.value;
+
+                    // Clear previous dropdown
+                    closeAllLists();
+
+                    if (!val) return;
+
+                    // If typing too fast (autocomplete-style), clear and restart
+                    clearTimeout(typingTimeout);
+                    typingTimeout = setTimeout(() => {
+                        logEvent('input', val);
+                        window.inputValue = val;
+
+                        // Create dropdown
+                        const div = document.createElement('div');
+                        div.setAttribute('id', this.id + 'autocomplete-list');
+                        div.setAttribute('class', 'autocomplete-items');
+                        this.parentNode.appendChild(div);
+
+                        countries.filter(
+                            c => c.substr(0, val.length).toUpperCase()
+                                === val.toUpperCase()
+                        ).slice(0, 5).forEach(country => {
+                                const item = document.createElement('div');
+                                item.innerHTML = '<strong>'
+                                    + country.substr(0, val.length)
+                                    + '</strong>'
+                                    + country.substr(val.length);
+                                item.addEventListener('click', function() {
+                                    input.value = country;
+                                    closeAllLists();
+                                    logEvent('select', country);
+                                    window.inputValue = country;
+                                });
+                                div.appendChild(item);
+                            });
+                    }, 100); // 100ms debounce
+                });
+
+                function closeAllLists() {
+                    document.querySelectorAll('.autocomplete-items').forEach(el => el.remove());
+                }
+
+                document.addEventListener('click', function() {
+                    closeAllLists();
+                });
+            </script>
+        </body>
+        </html>
+        """
+
+        # Write to file and use file:// URL (data: URLs don't work well with extension)
+        test_file = Path("/tmp/autocomplete_test.html")
+        test_file.write_text(test_html.strip())
+        file_url = f"file://{test_file}"
+        await bridge.navigate(tab_id, file_url, wait_until="load")
+        print("✓ Page loaded")
+
+        # Screenshot
+        screenshot = await bridge.screenshot(tab_id)
+        print(f"Screenshot: {len(screenshot.get('data', ''))} bytes")
+
+        # Test 1: Fast typing (no delay) - may fail
+        print("\n--- Test 1: Fast typing (delay_ms=0) ---")
+        await bridge.click(tab_id, "#search")
+        await bridge.type_text(tab_id, "#search", "Ger", clear_first=True, delay_ms=0)
+        await asyncio.sleep(0.5)
+
+        fast_result = await bridge.evaluate(
+            tab_id, "(function() { return document.getElementById('search').value; })()"
+        )
+        fast_value = fast_result.get("result", "")
+        print(f"Value after fast typing: '{fast_value}'")
+
+        # Check events
+        events_result = await bridge.evaluate(tab_id, "(function() { return window.inputEvents; })()")
+        print(f"Events logged: {events_result.get('result', [])}")
+
+        # Test 2: Slow typing (with delay) - should work
+        print("\n--- Test 2: Slow typing (delay_ms=100) ---")
+        await bridge.click(tab_id, "#search")
+        await bridge.type_text(tab_id, "#search", "United", clear_first=True, delay_ms=100)
+        await asyncio.sleep(0.5)
+
+        slow_result = await bridge.evaluate(
+            tab_id, "(function() { return document.getElementById('search').value; })()"
+        )
+        slow_value = slow_result.get("result", "")
+        print(f"Value after slow typing: '{slow_value}'")
+
+        # Check if dropdown appeared
+        dropdown_result = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.querySelectorAll('.autocomplete-items div').length; })()",
+        )
+        dropdown_count = dropdown_result.get("result", 0)
+        print(f"Dropdown items: {dropdown_count}")
+
+        # Screenshot with dropdown
+        screenshot_dropdown = await bridge.screenshot(tab_id)
+        print(f"Screenshot with dropdown: {len(screenshot_dropdown.get('data', ''))} bytes")
+
+        # Results
+        print("\n--- Results ---")
+        if "United" in slow_value:
+            print("✓ PASS: Slow typing with delay_ms worked")
+        else:
+            print("✗ FAIL: Slow typing still didn't work")
+
+        if dropdown_count > 0:
+            print("✓ PASS: Autocomplete dropdown appeared")
+        else:
+            print("⚠ WARNING: No autocomplete dropdown")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_autocomplete())

.claude/skills/browser-edge-cases/scripts/test_10_huge_dom.py

@@ -0,0 +1,156 @@
+#!/usr/bin/env python
+"""
+Test #10: LinkedIn Huge DOM Tree
+
+Symptom: browser_snapshot() hangs forever
+Root Cause: 10k+ DOM nodes, accessibility tree has 50k+ nodes
+Detection: document.querySelectorAll('*').length > 5000
+Fix: Add timeout (10s default), truncate tree at 2000 nodes
+"""
+
+import asyncio
+import sys
+import time
+import base64
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "huge-dom-test"
+
+
+async def test_huge_dom():
+    """Test snapshot performance on huge DOM trees."""
+    print("=" * 70)
+    print("TEST #10: Huge DOM Tree (LinkedIn-style)")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Test 1: Small DOM (baseline)
+        print("\n--- Test 1: Small DOM (baseline) ---")
+        small_html = """
+        <!DOCTYPE html>
+        <html><body>
+            <h1>Small Page</h1>
+            <p>A few elements</p>
+            <button>Click me</button>
+        </body></html>
+        """
+        data_url = f"data:text/html;base64,{base64.b64encode(small_html.encode()).decode()}"
+        await bridge.navigate(tab_id, data_url, wait_until="load")
+
+        start = time.perf_counter()
+        snapshot = await bridge.snapshot(tab_id, timeout_s=5.0)
+        elapsed = time.perf_counter() - start
+        tree_len = len(snapshot.get("tree", ""))
+        print(f"Small DOM snapshot: {elapsed:.3f}s, {tree_len} chars")
+
+        # Test 2: Generate huge DOM
+        print("\n--- Test 2: Huge DOM (5000+ elements) ---")
+        huge_html = """
+        <!DOCTYPE html>
+        <html><body>
+        <h1>Huge DOM Test</h1>
+        <div id="container"></div>
+        <script>
+            const container = document.getElementById('container');
+            for (let i = 0; i < 5000; i++) {
+                const div = document.createElement('div');
+                div.className = 'item-' + i;
+                div.innerHTML = '<span>Item ' + i + '</span><button>Action</button>';
+                container.appendChild(div);
+            }
+        </script>
+        </body></html>
+        """
+        data_url = f"data:text/html;base64,{base64.b64encode(huge_html.encode()).decode()}"
+        await bridge.navigate(tab_id, data_url, wait_until="load")
+
+        # Count elements
+        count_result = await bridge.evaluate(tab_id, "(function() { return document.querySelectorAll('*').length; })()")
+        elem_count = count_result.get("result", 0)
+        print(f"DOM elements: {elem_count}")
+
+        # Skip screenshot on huge DOM - it can timeout
+        # Instead verify page loaded by checking DOM
+        print("✓ Page verified (skipping screenshot on huge DOM)")
+
+        # Test snapshot with timeout
+        print("\n--- Testing snapshot with 10s timeout ---")
+        start = time.perf_counter()
+        try:
+            snapshot = await bridge.snapshot(tab_id, timeout_s=10.0)
+            elapsed = time.perf_counter() - start
+            tree_len = len(snapshot.get("tree", ""))
+            truncated = "(truncated)" in snapshot.get("tree", "")
+            print(f"✓ Huge DOM snapshot: {elapsed:.3f}s, {tree_len} chars, truncated={truncated}")
+
+            if elapsed < 5.0:
+                print("✓ PASS: Snapshot completed quickly")
+            else:
+                print(f"⚠ WARNING: Snapshot took {elapsed:.1f}s")
+
+            if truncated:
+                print("✓ PASS: Tree was truncated to prevent hang")
+            else:
+                print("⚠ WARNING: Tree not truncated (may need adjustment)")
+
+        except asyncio.TimeoutError:
+            print("✗ FAIL: Snapshot timed out (this shouldn't happen)")
+
+        # Test 3: Real LinkedIn
+        print("\n--- Test 3: Real LinkedIn Feed ---")
+        await bridge.navigate(tab_id, "https://www.linkedin.com/feed", wait_until="load", timeout_ms=30000)
+        await asyncio.sleep(2)
+
+        count_result = await bridge.evaluate(tab_id, "(function() { return document.querySelectorAll('*').length; })()")
+        elem_count = count_result.get("result", 0)
+        print(f"LinkedIn DOM elements: {elem_count}")
+
+        start = time.perf_counter()
+        try:
+            snapshot = await bridge.snapshot(tab_id, timeout_s=15.0)
+            elapsed = time.perf_counter() - start
+            tree_len = len(snapshot.get("tree", ""))
+            truncated = "(truncated)" in snapshot.get("tree", "")
+            print(f"LinkedIn snapshot: {elapsed:.3f}s, {tree_len} chars, truncated={truncated}")
+
+            if elapsed < 5.0:
+                print("✓ PASS: LinkedIn snapshot fast enough")
+            elif elapsed < 15.0:
+                print("⚠ WARNING: LinkedIn snapshot slow but within timeout")
+            else:
+                print("✗ FAIL: LinkedIn snapshot too slow")
+
+        except asyncio.TimeoutError:
+            print("✗ FAIL: LinkedIn snapshot timed out")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_huge_dom())

.claude/skills/browser-edge-cases/scripts/test_13_spa_navigation.py

@@ -0,0 +1,190 @@
+#!/usr/bin/env python
+"""
+Test #13: SPA Navigation Events
+
+Symptom: wait_until="load" fires before content ready
+Root Cause: SPA uses client-side routing, no full page load
+Detection: URL changes but load event already fired
+Fix: Use wait_until="networkidle" or wait_for_selector
+"""
+
+import asyncio
+import sys
+import time
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "spa-nav-test"
+
+
+async def test_spa_navigation():
+    """Test navigation timing on SPA pages."""
+    print("=" * 70)
+    print("TEST #13: SPA Navigation Events")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+        else:
+            print("✗ Extension not connected")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Create a test SPA
+        spa_html = """
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>SPA Test</title>
+            <style>
+                nav a { margin-right: 10px; }
+                .page { padding: 20px; border: 1px solid #ccc; margin-top: 10px; }
+            </style>
+        </head>
+        <body>
+            <nav>
+                <a href="#home" onclick="navigate('home')">Home</a>
+                <a href="#about" onclick="navigate('about')">About</a>
+                <a href="#contact" onclick="navigate('contact')">Contact</a>
+            </nav>
+            <div id="app" class="page">
+                <h1>Loading...</h1>
+            </div>
+            <script>
+                // Simulate SPA routing
+                let currentPage = '';
+
+                async function navigate(page) {
+                    event.preventDefault();
+                    currentPage = page;
+
+                    // Show loading state
+                    document.getElementById('app').innerHTML = '<h1>Loading...</h1>';
+
+                    // Simulate async content loading (like real SPAs)
+                    await new Promise(r => setTimeout(r, 500));
+
+                    // Render content
+                    const content = {
+                        home: '<h1>Home Page</h1><p>Welcome!</p>'
+                            + '<button id="home-btn">Home Action</button>',
+                        about: '<h1>About Page</h1><p>Simulated SPA.</p>'
+                            + '<button id="about-btn">About Action</button>',
+                        contact: '<h1>Contact Page</h1>'
+                            + '<p>Contact us at test@example.com</p>'
+                            + '<button id="contact-btn">Contact Action</button>'
+                    };
+
+                    document.getElementById('app').innerHTML = content[page] || '<h1>404</h1>';
+                    window.location.hash = page;
+                }
+
+                // Initial load with delay (simulates SPA hydration)
+                setTimeout(() => {
+                    navigate('home');
+                }, 1000);
+
+                // Track for testing
+                window.pageLoads = [];
+                window.addEventListener('hashchange', () => {
+                    window.pageLoads.push(window.location.hash);
+                });
+            </script>
+        </body>
+        </html>
+        """
+
+        # Write to file and use file:// URL (data: URLs don't work well with extension)
+        test_file = Path("/tmp/spa_test.html")
+        test_file.write_text(spa_html.strip())
+        file_url = f"file://{test_file}"
+
+        # Test 1: wait_until="load" - may fire before content ready
+        print("\n--- Test 1: wait_until='load' ---")
+        start = time.perf_counter()
+        await bridge.navigate(tab_id, file_url, wait_until="load")
+        elapsed = time.perf_counter() - start
+        print(f"Navigation completed in {elapsed:.3f}s")
+
+        # Check content immediately
+        content = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.getElementById('app').innerText; })()",
+        )
+        print(f"Content immediately after load: '{content.get('result', '')}'")
+
+        # Screenshot
+        screenshot = await bridge.screenshot(tab_id)
+        print(f"Screenshot: {len(screenshot.get('data', ''))} bytes")
+
+        # Wait for content
+        print("\n--- Waiting for content to hydrate ---")
+        await bridge.wait_for_selector(tab_id, "#home-btn", timeout_ms=5000)
+        print("✓ Content loaded")
+
+        # Check content after wait
+        content_after = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.getElementById('app').innerText; })()",
+        )
+        print(f"Content after wait: '{content_after.get('result', '')}'")
+
+        # Test 2: SPA navigation (no full page load)
+        print("\n--- Test 2: SPA client-side navigation ---")
+
+        # Click "About" link
+        await bridge.click(tab_id, 'a[href="#about"]')
+        await asyncio.sleep(1)
+
+        # Check if content changed
+        about_content = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.getElementById('app').innerText; })()",
+        )
+        print(f"Content after SPA nav: '{about_content.get('result', '')}'")
+
+        if "About Page" in about_content.get("result", ""):
+            print("✓ PASS: SPA navigation worked")
+        else:
+            print("✗ FAIL: SPA navigation didn't update content")
+
+        # Test 3: wait_until="networkidle"
+        print("\n--- Test 3: wait_until='networkidle' ---")
+        await bridge.navigate(tab_id, file_url, wait_until="networkidle", timeout_ms=10000)
+
+        # Check content immediately
+        content_networkidle = await bridge.evaluate(
+            tab_id,
+            "(function() { return document.getElementById('app').innerText; })()",
+        )
+        print(f"Content after networkidle: '{content_networkidle.get('result', '')}'")
+
+        if "Home Page" in content_networkidle.get("result", ""):
+            print("✓ PASS: networkidle waited for content")
+        else:
+            print("⚠ WARNING: networkidle didn't wait long enough")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_spa_navigation())

.claude/skills/browser-edge-cases/scripts/test_15_screenshot.py

@@ -0,0 +1,262 @@
+#!/usr/bin/env python
+"""
+Test #15: Screenshot Functionality
+
+Tests browser_screenshot across multiple scenarios:
+- Basic viewport screenshot
+- Full-page screenshot
+- Selector-based screenshot
+- Screenshot on complex DOM
+- Timeout handling
+
+Category: screenshot
+"""
+
+import asyncio
+import base64
+import sys
+import time
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+CONTEXT_NAME = "screenshot-test"
+
+SIMPLE_HTML = """<!DOCTYPE html>
+<html>
+<head><style>
+  body { margin: 0; background: #fff; font-family: sans-serif; }
+  h1 { color: #333; padding: 20px; }
+  .box { width: 200px; height: 100px; background: #4a90e2; margin: 20px; }
+  .long-content { height: 2000px; background: linear-gradient(blue, red); }
+</style></head>
+<body>
+  <h1 id="title">Screenshot Test Page</h1>
+  <div class="box" id="target-box">Target Box</div>
+  <div class="long-content"></div>
+</body>
+</html>"""
+
+
+def check_png(data: str) -> bool:
+    """Verify that base64 data decodes to a valid PNG."""
+    try:
+        raw = base64.b64decode(data)
+        return raw[:8] == b"\x89PNG\r\n\x1a\n"
+    except Exception:
+        return False
+
+
+async def test_basic_screenshot(bridge: BeelineBridge, tab_id: int, data_url: str):
+    print("\n--- Test 1: Basic Viewport Screenshot ---")
+    await bridge.navigate(tab_id, data_url, wait_until="load")
+    await asyncio.sleep(0.5)
+
+    start = time.perf_counter()
+    result = await bridge.screenshot(tab_id)
+    elapsed = time.perf_counter() - start
+
+    ok = result.get("ok")
+    data = result.get("data", "")
+    mime = result.get("mimeType", "")
+
+    print(f"  ok={ok}, mimeType={mime}, elapsed={elapsed:.3f}s")
+    print(f"  data length: {len(data)} chars")
+
+    if ok and data:
+        valid_png = check_png(data)
+        print(f"  valid PNG: {valid_png}")
+        if valid_png:
+            raw = base64.b64decode(data)
+            print(f"  PNG size: {len(raw)} bytes")
+            print("  ✓ PASS: Basic screenshot works")
+            return True
+        else:
+            print("  ✗ FAIL: Data is not a valid PNG")
+    else:
+        print(f"  ✗ FAIL: {result.get('error', 'no data')}")
+    return False
+
+
+async def test_full_page_screenshot(bridge: BeelineBridge, tab_id: int, data_url: str):
+    print("\n--- Test 2: Full Page Screenshot ---")
+    await bridge.navigate(tab_id, data_url, wait_until="load")
+    await asyncio.sleep(0.5)
+
+    viewport_result = await bridge.screenshot(tab_id, full_page=False)
+    full_result = await bridge.screenshot(tab_id, full_page=True)
+
+    v_data = viewport_result.get("data", "")
+    f_data = full_result.get("data", "")
+
+    if not v_data or not f_data:
+        print(f"  ✗ FAIL: viewport ok={viewport_result.get('ok')}, full ok={full_result.get('ok')}")
+        return False
+
+    v_size = len(base64.b64decode(v_data))
+    f_size = len(base64.b64decode(f_data))
+    print(f"  Viewport PNG: {v_size} bytes")
+    print(f"  Full page PNG: {f_size} bytes")
+
+    if f_size > v_size:
+        print("  ✓ PASS: Full page larger than viewport")
+        return True
+    else:
+        print("  ✗ FAIL: Full page not larger than viewport (may not capture long pages)")
+        return False
+
+
+async def test_selector_screenshot(bridge: BeelineBridge, tab_id: int, data_url: str):
+    print("\n--- Test 3: Selector Screenshot ---")
+    await bridge.navigate(tab_id, data_url, wait_until="load")
+    await asyncio.sleep(0.5)
+
+    # selector param exists in signature but may not be implemented
+    result = await bridge.screenshot(tab_id, selector="#target-box")
+
+    ok = result.get("ok")
+    data = result.get("data", "")
+
+    if ok and data:
+        # If implemented, the box screenshot should be smaller than a full viewport screenshot
+        full_result = await bridge.screenshot(tab_id)
+        full_data = full_result.get("data", "")
+
+        if full_data:
+            sel_size = len(base64.b64decode(data))
+            full_size = len(base64.b64decode(full_data))
+            print(f"  Selector PNG: {sel_size} bytes")
+            print(f"  Full page PNG: {full_size} bytes")
+            if sel_size < full_size:
+                print("  ✓ PASS: Selector screenshot smaller than full page")
+                return True
+            else:
+                print("  ⚠ WARNING: Selector screenshot not smaller (may be full page)")
+                return False
+    else:
+        print(f"  ⚠ NOT IMPLEMENTED: selector param ignored (returns full page) - error={result.get('error')}")
+        print("  NOTE: selector parameter exists in signature but is not used in implementation")
+        return False
+
+
+async def test_screenshot_url_metadata(bridge: BeelineBridge, tab_id: int):
+    print("\n--- Test 4: Screenshot URL Metadata ---")
+    await bridge.navigate(tab_id, "https://example.com", wait_until="load")
+    await asyncio.sleep(1)
+
+    result = await bridge.screenshot(tab_id)
+    url = result.get("url", "")
+    tab = result.get("tabId")
+
+    print(f"  url={url!r}, tabId={tab}")
+
+    if "example.com" in url:
+        print("  ✓ PASS: URL metadata captured correctly")
+        return True
+    else:
+        print(f"  ✗ FAIL: Expected example.com in URL, got {url!r}")
+        return False
+
+
+async def test_screenshot_timeout(bridge: BeelineBridge, tab_id: int, data_url: str):
+    print("\n--- Test 5: Timeout Handling ---")
+    await bridge.navigate(tab_id, data_url, wait_until="load")
+
+    # Very short timeout - likely still completes since simple page
+    start = time.perf_counter()
+    result = await bridge.screenshot(tab_id, timeout_s=0.001)
+    elapsed = time.perf_counter() - start
+
+    if not result.get("ok"):
+        err = result.get("error", "")
+        if "timed out" in err or "cancelled" in err:
+            print(f"  ✓ PASS: Timeout handled gracefully: {err!r}")
+            return True
+        else:
+            print(f"  ⚠ Fast enough to beat timeout: {err!r} in {elapsed:.3f}s")
+            return True  # Not a failure, just fast
+    else:
+        print(f"  ⚠ Screenshot completed before timeout ({elapsed:.3f}s) - too fast to test timeout")
+        return True  # Still ok, just very fast
+
+
+async def test_screenshot_complex_site(bridge: BeelineBridge, tab_id: int):
+    print("\n--- Test 6: Complex Site (example.com) ---")
+    await bridge.navigate(tab_id, "https://example.com", wait_until="load")
+    await asyncio.sleep(1)
+
+    start = time.perf_counter()
+    result = await bridge.screenshot(tab_id)
+    elapsed = time.perf_counter() - start
+
+    ok = result.get("ok")
+    data = result.get("data", "")
+
+    print(f"  ok={ok}, elapsed={elapsed:.3f}s, data_len={len(data)}")
+    if ok and check_png(data):
+        print("  ✓ PASS: Screenshot on real site works")
+        return True
+    else:
+        print(f"  ✗ FAIL: {result.get('error', 'bad data')}")
+        return False
+
+
+async def main():
+    print("=" * 70)
+    print("TEST #15: Screenshot Functionality")
+    print("=" * 70)
+
+    bridge = BeelineBridge()
+
+    try:
+        await bridge.start()
+
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+            print(f"Waiting for extension... ({i + 1}/10)")
+        else:
+            print("✗ Extension not connected. Ensure Chrome with Beeline extension is running.")
+            return
+
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        data_url = f"data:text/html;base64,{base64.b64encode(SIMPLE_HTML.encode()).decode()}"
+
+        results = {
+            "basic": await test_basic_screenshot(bridge, tab_id, data_url),
+            "full_page": await test_full_page_screenshot(bridge, tab_id, data_url),
+            "selector": await test_selector_screenshot(bridge, tab_id, data_url),
+            "metadata": await test_screenshot_url_metadata(bridge, tab_id),
+            "timeout": await test_screenshot_timeout(bridge, tab_id, data_url),
+            "complex_site": await test_screenshot_complex_site(bridge, tab_id),
+        }
+
+        print("\n" + "=" * 70)
+        print("SUMMARY")
+        print("=" * 70)
+        for name, passed in results.items():
+            status = "✓ PASS" if passed else "✗ FAIL"
+            print(f"  {status}: {name}")
+
+        passed_count = sum(1 for v in results.values() if v)
+        total = len(results)
+        print(f"\n  {passed_count}/{total} tests passed")
+
+        await bridge.destroy_context(group_id)
+        print("\n✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+        print("✓ Bridge stopped")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

.claude/skills/browser-edge-cases/scripts/test_case.py

@@ -0,0 +1,327 @@
+#!/usr/bin/env python
+"""
+Browser Edge Case Test Template
+
+This script provides a template for testing and debugging browser tool failures
+on specific websites. Use this to reproduce, isolate, and verify fixes.
+
+Usage:
+    1. Copy this file: cp test_case.py test_#[number]_[site].py
+    2. Fill in the CONFIG section with your test details
+    3. Run: uv run python test_#[number]_[site].py
+
+Example:
+    uv run python test_01_linkedin_scroll.py
+"""
+
+import asyncio
+import sys
+import time
+from pathlib import Path
+
+# Add tools to path
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "tools" / "src"))
+
+from gcu.browser.bridge import BeelineBridge
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# CONFIG: Fill in these values for your test case
+# ═══════════════════════════════════════════════════════════════════════════════
+
+TEST_CASE = {
+    "number": 1,
+    "name": "LinkedIn Nested Scroll Container",
+    "site": "https://www.linkedin.com/feed",
+    "simple_site": "https://example.com",
+    "category": "scroll",  # scroll, click, input, snapshot, navigation
+    "symptom": "scroll() returns success but page doesn't move",
+}
+
+BRIDGE_PORT = 9229
+CONTEXT_NAME = "edge-case-test"
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# TEST FUNCTIONS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+
+async def test_simple_site(bridge: BeelineBridge, tab_id: int) -> dict:
+    """Test that the tool works on a simple site (baseline)."""
+    print("\n--- Baseline Test (Simple Site) ---")
+
+    await bridge.navigate(tab_id, TEST_CASE["simple_site"], wait_until="load")
+    await asyncio.sleep(1)
+
+    # Adjust this based on category
+    if TEST_CASE["category"] == "scroll":
+        result = await bridge.scroll(tab_id, "down", 100)
+        print(f"  Scroll result: {result}")
+        return result
+    elif TEST_CASE["category"] == "click":
+        # Add click test
+        pass
+    elif TEST_CASE["category"] == "snapshot":
+        result = await bridge.snapshot(tab_id, timeout_s=5.0)
+        print(f"  Snapshot length: {len(result.get('tree', ''))}")
+        return result
+
+    return {"ok": True}
+
+
+async def test_problematic_site(bridge: BeelineBridge, tab_id: int) -> dict:
+    """Test the tool on the problematic site."""
+    print("\n--- Problem Site Test ---")
+
+    await bridge.navigate(tab_id, TEST_CASE["site"], wait_until="load", timeout_ms=30000)
+    await asyncio.sleep(2)
+
+    # Adjust this based on category
+    if TEST_CASE["category"] == "scroll":
+        # Get scroll positions before
+        before = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const results = { window: { y: window.scrollY } };
+                document.querySelectorAll('*').forEach((el, i) => {
+                    const style = getComputedStyle(el);
+                    if ((style.overflowY === 'scroll' || style.overflowY === 'auto') &&
+                        el.scrollHeight > el.clientHeight) {
+                        results['el_' + i] = {
+                            tag: el.tagName,
+                            scrollTop: el.scrollTop,
+                            class: el.className.substring(0, 30)
+                        };
+                    }
+                });
+                return results;
+            })();
+        """,
+        )
+        print(f"  Before scroll: {before.get('result', {})}")
+
+        # Try to scroll
+        result = await bridge.scroll(tab_id, "down", 500)
+        print(f"  Scroll result: {result}")
+
+        await asyncio.sleep(1)
+
+        # Get scroll positions after
+        after = await bridge.evaluate(
+            tab_id,
+            """
+            (function() {
+                const results = { window: { y: window.scrollY } };
+                document.querySelectorAll('*').forEach((el, i) => {
+                    const style = getComputedStyle(el);
+                    if ((style.overflowY === 'scroll' || style.overflowY === 'auto') &&
+                        el.scrollHeight > el.clientHeight) {
+                        results['el_' + i] = {
+                            tag: el.tagName,
+                            scrollTop: el.scrollTop,
+                            class: el.className.substring(0, 30)
+                        };
+                    }
+                });
+                return results;
+            })();
+        """,
+        )
+        print(f"  After scroll: {after.get('result', {})}")
+
+        # Check if anything changed
+        before_data = before.get("result", {}) or {}
+        after_data = after.get("result", {}) or {}
+
+        changed = False
+        for key in after_data:
+            if key in before_data:
+                b_val = before_data[key].get("scrollTop", 0) if isinstance(before_data[key], dict) else 0
+                a_val = after_data[key].get("scrollTop", 0) if isinstance(after_data[key], dict) else 0
+                if a_val != b_val:
+                    print(f"  ✓ CHANGE DETECTED: {key} scrolled from {b_val} to {a_val}")
+                    changed = True
+
+        if not changed:
+            print("  ✗ NO CHANGE: Scroll did not affect any container")
+
+        return {"ok": changed, "scroll_result": result}
+
+    elif TEST_CASE["category"] == "snapshot":
+        start = time.perf_counter()
+        try:
+            result = await bridge.snapshot(tab_id, timeout_s=15.0)
+            elapsed = time.perf_counter() - start
+            tree_len = len(result.get("tree", ""))
+            print(f"  Snapshot completed in {elapsed:.2f}s, {tree_len} chars")
+            return {"ok": True, "elapsed": elapsed, "tree_length": tree_len}
+        except asyncio.TimeoutError:
+            print("  ✗ SNAPSHOT TIMED OUT")
+            return {"ok": False, "error": "timeout"}
+
+    return {"ok": True}
+
+
+async def detect_root_cause(bridge: BeelineBridge, tab_id: int) -> dict:
+    """Run detection scripts to identify the root cause."""
+    print("\n--- Root Cause Detection ---")
+
+    detections = {}
+
+    # Detection 1: Nested scrollable containers
+    scroll_check = await bridge.evaluate(
+        tab_id,
+        """
+        (function() {
+            const candidates = [];
+            document.querySelectorAll('*').forEach(el => {
+                const style = getComputedStyle(el);
+                if (style.overflow.includes('scroll') || style.overflow.includes('auto')) {
+                    const rect = el.getBoundingClientRect();
+                    if (rect.width > 100 && rect.height > 100) {
+                        candidates.push({
+                            tag: el.tagName,
+                            area: rect.width * rect.height,
+                            class: el.className.substring(0, 30)
+                        });
+                    }
+                }
+            });
+            candidates.sort((a, b) => b.area - a.area);
+            return {
+                count: candidates.length,
+                largest: candidates[0]
+            };
+        })();
+    """,
+    )
+    detections["nested_scroll"] = scroll_check.get("result", {})
+    print(f"  Nested scroll containers: {detections['nested_scroll']}")
+
+    # Detection 2: Shadow DOM
+    shadow_check = await bridge.evaluate(
+        tab_id,
+        """
+        (function() {
+            const withShadow = [];
+            document.querySelectorAll('*').forEach(el => {
+                if (el.shadowRoot) {
+                    withShadow.push(el.tagName);
+                }
+            });
+            return { count: withShadow.length, elements: withShadow.slice(0, 5) };
+        })();
+    """,
+    )
+    detections["shadow_dom"] = shadow_check.get("result", {})
+    print(f"  Shadow DOM: {detections['shadow_dom']}")
+
+    # Detection 3: iframes
+    iframe_check = await bridge.evaluate(
+        tab_id,
+        """
+        (function() {
+            const iframes = document.querySelectorAll('iframe');
+            return { count: iframes.length };
+        })();
+    """,
+    )
+    detections["iframes"] = iframe_check.get("result", {})
+    print(f"  iframes: {detections['iframes']}")
+
+    # Detection 4: DOM size
+    dom_check = await bridge.evaluate(
+        tab_id,
+        """
+        (function() {
+            return {
+                elements: document.querySelectorAll('*').length,
+                body_children: document.body.children.length
+            };
+        })();
+    """,
+    )
+    detections["dom_size"] = dom_check.get("result", {})
+    print(f"  DOM size: {detections['dom_size']}")
+
+    # Detection 5: Framework detection
+    framework_check = await bridge.evaluate(
+        tab_id,
+        """
+        (function() {
+            return {
+                react: !!document.querySelector('[data-reactroot], [data-reactid]'),
+                vue: !!document.querySelector('[data-v-]'),
+                angular: !!document.querySelector('[ng-app], [ng-version]')
+            };
+        })();
+    """,
+    )
+    detections["frameworks"] = framework_check.get("result", {})
+    print(f"  Frameworks: {detections['frameworks']}")
+
+    return detections
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# MAIN
+# ═══════════════════════════════════════════════════════════════════════════════
+
+
+async def main():
+    print("=" * 70)
+    print(f"EDGE CASE TEST #{TEST_CASE['number']}: {TEST_CASE['name']}")
+    print("=" * 70)
+    print(f"Site: {TEST_CASE['site']}")
+    print(f"Category: {TEST_CASE['category']}")
+    print(f"Symptom: {TEST_CASE['symptom']}")
+
+    bridge = BeelineBridge()
+
+    try:
+        print("\n--- Starting Bridge ---")
+        await bridge.start()
+
+        # Wait for extension connection
+        for i in range(10):
+            await asyncio.sleep(1)
+            if bridge.is_connected:
+                print("✓ Extension connected!")
+                break
+            print(f"Waiting for extension... ({i + 1}/10)")
+        else:
+            print("✗ Extension not connected. Ensure Chrome with Beeline extension is running.")
+            return
+
+        # Create browser context
+        context = await bridge.create_context(CONTEXT_NAME)
+        tab_id = context.get("tabId")
+        group_id = context.get("groupId")
+        print(f"✓ Created tab: {tab_id}")
+
+        # Run tests
+        baseline_result = await test_simple_site(bridge, tab_id)
+        problem_result = await test_problematic_site(bridge, tab_id)
+        detections = await detect_root_cause(bridge, tab_id)
+
+        # Summary
+        print("\n" + "=" * 70)
+        print("SUMMARY")
+        print("=" * 70)
+        print(f"Baseline test: {'✓ PASS' if baseline_result.get('ok') else '✗ FAIL'}")
+        print(f"Problem test: {'✓ PASS' if problem_result.get('ok') else '✗ FAIL'}")
+        print(f"Root cause indicators: {list(k for k, v in detections.items() if v)}")
+
+        # Cleanup
+        print("\n--- Cleanup ---")
+        await bridge.destroy_context(group_id)
+        print("✓ Context destroyed")
+
+    finally:
+        await bridge.stop()
+        print("✓ Bridge stopped")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

.claude/skills/test-reporting/SKILL.md

@@ -0,0 +1,225 @@
+# Integration Test Reporting Skill
+
+Run the Level 2 dummy agent integration test suite and produce a detailed HTML report with per-test input → outcome analysis.
+
+## Trigger
+
+User wants to run integration tests and see results:
+- `/test-reporting`
+- `/test-reporting test_component_queen_live.py`
+- `/test-reporting --all`
+
+## SOP: Running Tests
+
+### Step 1: Select Scope
+
+If the user provides a specific test file or pattern, use it. Otherwise run the full suite.
+
+```bash
+# Full suite
+cd core && echo "1" | uv run python tests/dummy_agents/run_all.py --interactive 2>&1
+
+# Specific file (requires manual provider setup)
+cd core && uv run python -c "
+import sys
+sys.path.insert(0, '.')
+from tests.dummy_agents.run_all import detect_available
+from tests.dummy_agents.conftest import set_llm_selection
+
+avail = detect_available()
+claude = [p for p in avail if 'Claude Code' in p['name']]
+if not claude:
+    avail_names = [p['name'] for p in avail]
+    raise RuntimeError(f'No Claude Code subscription. Available: {avail_names}')
+provider = claude[0]
+set_llm_selection(
+    model=provider['model'],
+    api_key=provider['api_key'],
+    extra_headers=provider.get('extra_headers'),
+    api_base=provider.get('api_base'),
+)
+
+import pytest
+sys.exit(pytest.main([
+    'tests/dummy_agents/TEST_FILE_HERE',
+    '-v', '--override-ini=asyncio_mode=auto', '--no-header', '--tb=long',
+    '--log-cli-level=WARNING', '--junitxml=/tmp/hive_test_results.xml',
+]))
+"
+```
+
+### Step 2: Collect Results
+
+After the test run completes, collect:
+1. **JUnit XML** from `--junitxml` output (if available)
+2. **stdout/stderr** from the run
+3. **Summary table** from `run_all.py` output (the Unicode table)
+
+### Step 3: Generate HTML Report
+
+Write the report to `/tmp/hive_integration_test_report.html`.
+
+The report MUST include these sections:
+
+#### Header
+- Run timestamp (ISO 8601)
+- Provider used (model name, source)
+- Total tests / passed / failed / skipped
+- Total wall-clock time
+- Overall verdict: PASS (all green) or FAIL (with count)
+
+#### Per-Test Table
+
+For EVERY test (not just failures), include a row with:
+
+| Column | Description |
+|--------|-------------|
+| Component | Test file grouping (e.g., `component_queen_live`) |
+| Test Name | Function name (e.g., `test_queen_starts_in_planning_without_worker`) |
+| Status | PASS / FAIL / SKIP / ERROR with color badge |
+| Duration | Wall-clock seconds |
+| What | One-line description of what the test verifies |
+| How | How it works (setup → action → assertion) |
+| Why | Why this test matters (what bug/behavior it catches) |
+| Input | The input data or configuration (graph spec, initial prompt, phase, etc.) |
+| Expected Outcome | What the test asserts |
+| Actual Outcome | What actually happened (PASS: matches expected / FAIL: actual vs expected) |
+| Failure Detail | For failures only: full traceback + diagnosis |
+
+#### What / How / Why Descriptions
+
+These MUST be derived from the test function's docstring and code. Read each test file to extract:
+- **What**: From the docstring first line
+- **How**: From the test body (what fixtures, what graph, what assertions)
+- **Why**: From the docstring body or "Why this matters" section in the test module
+
+Use these mappings for the component test files:
+
+```
+test_component_llm.py          → "LLM Provider" — streaming, tool calling, tokens
+test_component_tools.py        → "Tool Registry + MCP" — connection, execution
+test_component_event_loop.py   → "EventLoopNode" — iteration, output, stall
+test_component_edges.py        → "Edge Evaluation" — conditional, priority
+test_component_conversation.py → "Conversation Persistence" — storage, cursor
+test_component_escalation.py   → "Escalation Flow" — worker→queen signaling
+test_component_continuous.py   → "Continuous Mode" — conversation threading
+test_component_queen.py        → "Queen Phase (Unit)" — phase state, tools, events
+test_component_queen_live.py   → "Queen Phase (Live)" — real queen, real LLM
+test_component_queen_state_machine.py → "Queen State Machine" — edge cases, races
+test_component_worker_comms.py → "Worker Communication" — events, data flow
+test_component_strict_outcomes.py → "Strict Outcomes" — exact path, output, quality
+```
+
+#### HTML Template
+
+Use this structure:
+
+```html
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<title>Hive Integration Test Report — {timestamp}</title>
+<style>
+  :root { --pass: #22c55e; --fail: #ef4444; --skip: #f59e0b; --bg: #0f172a; --surface: #1e293b; --text: #e2e8f0; --muted: #94a3b8; --border: #334155; }
+  * { box-sizing: border-box; margin: 0; padding: 0; }
+  body { font-family: 'SF Mono', 'Fira Code', monospace; background: var(--bg); color: var(--text); padding: 2rem; line-height: 1.6; }
+  h1, h2, h3 { font-weight: 600; }
+  h1 { font-size: 1.5rem; margin-bottom: 1rem; }
+  h2 { font-size: 1.2rem; margin: 2rem 0 1rem; border-bottom: 1px solid var(--border); padding-bottom: 0.5rem; }
+  .summary { display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 1rem; margin-bottom: 2rem; }
+  .card { background: var(--surface); padding: 1rem; border-radius: 8px; border: 1px solid var(--border); }
+  .card .label { color: var(--muted); font-size: 0.75rem; text-transform: uppercase; }
+  .card .value { font-size: 1.5rem; font-weight: 700; margin-top: 0.25rem; }
+  .card .value.pass { color: var(--pass); }
+  .card .value.fail { color: var(--fail); }
+  table { width: 100%; border-collapse: collapse; font-size: 0.8rem; }
+  th { background: var(--surface); position: sticky; top: 0; text-align: left; padding: 0.5rem; border-bottom: 2px solid var(--border); color: var(--muted); text-transform: uppercase; font-size: 0.7rem; }
+  td { padding: 0.5rem; border-bottom: 1px solid var(--border); vertical-align: top; }
+  tr:hover { background: rgba(255,255,255,0.03); }
+  .badge { display: inline-block; padding: 2px 8px; border-radius: 4px; font-size: 0.7rem; font-weight: 700; }
+  .badge.pass { background: rgba(34,197,94,0.2); color: var(--pass); }
+  .badge.fail { background: rgba(239,68,68,0.2); color: var(--fail); }
+  .badge.skip { background: rgba(245,158,11,0.2); color: var(--skip); }
+  .detail { background: #1a1a2e; padding: 0.75rem; border-radius: 4px; margin-top: 0.5rem; font-size: 0.75rem; white-space: pre-wrap; overflow-x: auto; max-height: 200px; overflow-y: auto; }
+  .component-header { background: var(--surface); padding: 0.75rem 0.5rem; font-weight: 600; font-size: 0.85rem; }
+  .meta { color: var(--muted); font-size: 0.75rem; }
+</style>
+</head>
+<body>
+<h1>Hive Integration Test Report</h1>
+<p class="meta">Generated: {timestamp} | Provider: {provider} | Duration: {duration}s</p>
+
+<div class="summary">
+  <div class="card"><div class="label">Total</div><div class="value">{total}</div></div>
+  <div class="card"><div class="label">Passed</div><div class="value pass">{passed}</div></div>
+  <div class="card"><div class="label">Failed</div><div class="value fail">{failed}</div></div>
+  <div class="card"><div class="label">Verdict</div><div class="value {verdict_class}">{verdict}</div></div>
+</div>
+
+<h2>Test Results</h2>
+<table>
+<thead>
+<tr>
+  <th>Component</th>
+  <th>Test</th>
+  <th>Status</th>
+  <th>Time</th>
+  <th>What</th>
+  <th>Input → Expected → Actual</th>
+</tr>
+</thead>
+<tbody>
+<!-- For each test: -->
+<tr>
+  <td>{component}</td>
+  <td>{test_name}</td>
+  <td><span class="badge {status_class}">{status}</span></td>
+  <td>{duration}s</td>
+  <td>{what_description}</td>
+  <td>
+    <strong>Input:</strong> {input_description}<br>
+    <strong>Expected:</strong> {expected_outcome}<br>
+    <strong>Actual:</strong> {actual_outcome}
+    <!-- If failed: -->
+    <div class="detail">{failure_traceback}</div>
+  </td>
+</tr>
+</tbody>
+</table>
+
+<h2>Failure Analysis</h2>
+<!-- Only if there are failures -->
+<p>For each failure, provide:</p>
+<ul>
+  <li><strong>Root cause:</strong> Why it failed</li>
+  <li><strong>Impact:</strong> What this means for the system</li>
+  <li><strong>Suggested fix:</strong> How to address it</li>
+</ul>
+
+</body>
+</html>
+```
+
+### Step 4: Output
+
+1. Write the HTML file to `/tmp/hive_integration_test_report.html`
+2. Print the file path so the user can open it
+3. Print a concise summary to the terminal:
+   ```
+   Test Report: /tmp/hive_integration_test_report.html
+   Result: 74/76 PASSED (2 failures)
+   Failures:
+     - parallel_merge::test_parallel_disjoint_output_keys
+     - worker::test_worker_timestamped_note_artifact
+   ```
+
+## Key Rules
+
+1. ALWAYS use `--junitxml` when running pytest to get structured results
+2. ALWAYS read the test source files to populate What/How/Why columns — do not guess
+3. For Input/Expected/Actual, extract from the test's graph spec, assertions, and result
+4. Color-code everything: green for pass, red for fail, amber for skip
+5. Include the full traceback for failures in a scrollable `<div class="detail">`
+6. Group tests by component (file name) with a visual separator
+7. The report must be self-contained HTML (no external CSS/JS dependencies)

.cursorrules

@@ -1,18 +0,0 @@
-This project uses ruff for Python linting and formatting.
-
-Rules:
-- Line length: 100 characters
-- Python target: 3.11+
-- Use double quotes for strings
-- Sort imports with isort (ruff I rules): stdlib, third-party, first-party (framework), local
-- Combine as-imports
-- Use type hints on all function signatures
-- Use `from __future__ import annotations` for modern type syntax
-- Raise exceptions with `from` in except blocks (B904)
-- No unused imports (F401), no unused variables (F841)
-- Prefer list/dict/set comprehensions over map/filter (C4)
-
-Run `make lint` to auto-fix, `make check` to verify without modifying files.
-Run `make format` to apply ruff formatting.
-
-The ruff config lives in core/pyproject.toml under [tool.ruff].

.dockerignore

@@ -1,35 +0,0 @@
-# Git
-.git/
-.gitignore
-
-# Documentation
-*.md
-docs/
-LICENSE
-
-# IDE
-.idea/
-.vscode/
-
-# Dependencies (rebuilt in container)
-node_modules/
-
-# Build artifacts
-dist/
-build/
-coverage/
-
-# Environment files
-.env*
-config.yaml
-
-# Logs
-*.log
-logs/
-
-# OS
-.DS_Store
-Thumbs.db
-
-# GitHub
-.github/

.editorconfig

@@ -22,3 +22,6 @@ indent_size = 2
 
 [Makefile]
 indent_style = tab
+
+[*.{sh,ps1}]
+end_of_line = lf

.gitattributes

@@ -16,7 +16,6 @@
 
 # Shell scripts (must use LF)
 *.sh text eol=lf
-quickstart.sh text eol=lf
 
 # PowerShell scripts (Windows-friendly)
 *.ps1 text eol=lf
@@ -122,3 +121,8 @@ CODE_OF_CONDUCT* text
 *.db binary
 *.sqlite binary
 *.sqlite3 binary
+
+# Lockfiles — mark generated so GitHub collapses them in PR diffs
+*.lock            linguist-generated=true -diff
+package-lock.json linguist-generated=true -diff
+uv.lock           linguist-generated=true -diff

.github/workflows/ci.yml

@@ -63,7 +63,7 @@ jobs:
         working-directory: core
         run: |
           uv sync
-          uv run pytest tests/ -v
+          uv run pytest tests/ -v --ignore=tests/dummy_agents
 
   test-tools:
     name: Test Tools (${{ matrix.os }})

.gitignore

@@ -13,6 +13,10 @@ out/
 .env
 .env.local
 .env.*.local
+.venv
+/venv
+tools/src/uv.lock
+
 
 # User configuration (copied from .example)
 config.yaml
@@ -66,14 +70,15 @@ tmp/
 temp/
 
 exports/*
+exports.old*
+artifacts/*
 
 .claude/settings.local.json
 
-.venv
-
 docs/github-issues/*
 core/tests/*dumps/*
 
 screenshots/*
 
 .gemini/*
+.coverage

.hive/browser-logs/browser-2026-04-04.jsonl

@@ -0,0 +1,9 @@
+{"type": "connection", "event": "connect", "ts": "2026-04-04T01:10:38.245667+00:00", "profile": "default"}
+{"type": "connection", "event": "hello", "details": {"version": "1.0"}, "ts": "2026-04-04T01:10:38.247207+00:00", "profile": "default"}
+{"type": "connection", "event": "disconnect", "ts": "2026-04-04T01:11:57.148273+00:00", "profile": "default"}
+{"type": "connection", "event": "connect", "ts": "2026-04-04T01:12:09.162378+00:00", "profile": "default"}
+{"type": "connection", "event": "hello", "details": {"version": "1.0"}, "ts": "2026-04-04T01:12:09.163899+00:00", "profile": "default"}
+{"type": "connection", "event": "disconnect", "ts": "2026-04-04T01:15:12.826042+00:00", "profile": "default"}
+{"type": "connection", "event": "connect", "ts": "2026-04-04T01:15:30.842533+00:00", "profile": "default"}
+{"type": "connection", "event": "hello", "details": {"version": "1.0"}, "ts": "2026-04-04T01:15:30.845025+00:00", "profile": "default"}
+{"type": "tool_call", "tool": "browser_stop", "params": {"profile": "gcu-browser-worker:3"}, "result": {"ok": true, "status": "not_running", "profile": "gcu-browser-worker:3"}, "ok": true, "duration_ms": 0.01, "ts": "2026-04-04T01:29:04.294954+00:00", "profile": "default"}

.mcp.json

@@ -1,3 +0,0 @@
-{
-  "mcpServers": {}
-}

CONTRIBUTING.md

@@ -333,6 +333,22 @@ make test-live     # Run live API integration tests (requires credentials)
 - **WebSocket** for real-time updates
 - **Tailwind CSS** for styling
 
+### Frontend Dev Workflow
+
+> **Note:** `./quickstart.sh` handles the full setup including the web UI.
+> The commands below are for contributors iterating on the frontend code after
+> initial setup is complete.
+
+```bash
+# Start the backend server
+hive serve
+
+# In a separate terminal, run the frontend dev server with hot-reload
+cd core/frontend
+npm install   # only needed after dependency changes
+npm run dev
+```
+
 ### Useful Development Commands
 
 ```bash
@@ -603,11 +619,6 @@ from litellm import completion_cost
 cost = completion_cost(model="claude-3-5-sonnet-20241022", messages=[...])
 ```
 
-**Monitoring Dashboard** (`/core/framework/monitoring/`)
-- WebSocket-based real-time monitoring
-- Displays: active agents, tool calls, token usage, errors
-- Access at: `http://localhost:8000/monitor`
-
 ### How to Add Performance Metrics
 
 **1. Instrument your code**
@@ -948,7 +959,7 @@ uv run pytest -m "not live"
 **Unit Test**
 ```python
 import pytest
-from framework.graph.node import Node
+from framework.orchestrator import NodeSpec as Node
 
 def test_node_creation():
     node = Node(id="test", name="Test Node", node_type="event_loop")
@@ -966,8 +977,8 @@ async def test_node_execution():
 **Integration Test**
 ```python
 import pytest
-from framework.graph.executor import GraphExecutor
-from framework.graph.node import Node
+from framework.orchestrator.orchestrator import Orchestrator as GraphExecutor
+from framework.orchestrator import NodeSpec as Node
 
 @pytest.mark.asyncio
 async def test_graph_execution_with_multiple_nodes():

Makefile

@@ -28,7 +28,7 @@ check: ## Run all checks without modifying files (CI-safe)
 	cd tools && uv run ruff format --check .
 
 test: ## Run all tests (core + tools, excludes live)
-	cd core && uv run python -m pytest tests/ -v
+	cd core && uv run python -m pytest tests/ -v --ignore=tests/dummy_agents
 	cd tools && uv run python -m pytest -v
 
 test-tools: ## Run tool tests only (mocked, no credentials needed)
@@ -38,7 +38,7 @@ test-live: ## Run live integration tests (requires real API credentials)
 	cd tools && uv run python -m pytest -m live -s -o "addopts=" --log-cli-level=INFO
 
 test-all: ## Run everything including live tests
-	cd core && uv run python -m pytest tests/ -v
+	cd core && uv run python -m pytest tests/ -v --ignore=tests/dummy_agents
 	cd tools && uv run python -m pytest -v
 	cd tools && uv run python -m pytest -m live -s -o "addopts=" --log-cli-level=INFO
 

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-  <img width="100%" alt="Hive Banner" src="https://github.com/user-attachments/assets/a027429b-5d3c-4d34-88e4-0feaeaabbab3" />
+  <img width="100%" alt="Hive Banner" src="https://asset.acho.io/github/img/banner.gif" />
 </p>
 
 <p align="center">
@@ -23,6 +23,7 @@
 </p>
 
 <p align="center">
+  <img src="https://img.shields.io/badge/Agent_Harness-Runtime_Layer-ff6600?style=flat-square" alt="Agent Harness" />
   <img src="https://img.shields.io/badge/AI_Agents-Self--Improving-brightgreen?style=flat-square" alt="AI Agents" />
   <img src="https://img.shields.io/badge/Multi--Agent-Systems-blue?style=flat-square" alt="Multi-Agent" />
   <img src="https://img.shields.io/badge/Headless-Development-purple?style=flat-square" alt="Headless" />
@@ -35,39 +36,51 @@
   <img src="https://img.shields.io/badge/Google_Gemini-supported-4285F4?style=flat-square&logo=google" alt="Gemini" />
 </p>
 
+<p align="center"><em>The agent harness for production workloads — state management, failure recovery, observability, and human oversight so your agents actually run.</em></p>
+
 ## Overview
 
-Generate a swarm of worker agents with a coding agent(queen) that control them. Define your goal through conversation with hive queen, and the framework generates a node graph with dynamically created connection code. When things break, the framework captures failure data, evolves the agent through the coding agent, and redeploys. Built-in human-in-the-loop nodes, browser use, credential management, and real-time monitoring give you control without sacrificing adaptability.
+OpenHive is a zero-setup, model-agnostic execution harness that dynamically generates multi-agent topologies to tackle complex, long-running business workflows without requiring any orchestration boilerplate. By simply defining your objective, the runtime compiles a strict, graph-based execution DAG that safely coordinates specialized agents to execute concurrent tasks in parallel. Backed by persistent, role-based memory that intelligently evolves with your project's context, OpenHive ensures deterministic fault tolerance, deep state observability, and seamless asynchronous execution across whichever underlying LLMs you choose to plug in.
+
+## Features
+
+- ✅ Multi-Agent Coordination for parallel task execution 
+- ✅ Graph-based execution for recurring and complex processes 
+- ✅ Role-based memory that evolves with your projects 
+- ✅ Zero Setup - No technical configuration required
+- ✅ General Compute Use and Browser Use with Native Extension 
+- ✅ Custom Model Support
 
 Visit [adenhq.com](https://adenhq.com) for complete documentation, examples, and guides.
 
+Visit [HoneyComb](http://honeycomb.open-hive.com/) to see what jobs are being automated by AI. It’s a stock market for jobs, driven by our community’s AI agent progress. You can long and short jobs (with no real money but compute token)based on how much you think a job is going to be replaced by AI.
 
 https://github.com/user-attachments/assets/bf10edc3-06ba-48b6-98ba-d069b15fb69d
 
 
 ## Who Is Hive For?
 
-Hive is designed for developers and teams who want to build many **autonomous AI agents** fast without manually wiring complex workflows.
+Hive is the multi-agent harness layer for teams moving AI agents from prototype to production. Single agents like Openclaw and Cowork can finish personal jobs pretty well but lack the rigor to fulfil business processes. 
 
 Hive is a good fit if you:
 
 - Want AI agents that **execute real business processes**, not demos
-- Need **fast or high volume agent execution** over open workflow
+- Need a **runtime that handles state, recovery, and parallel execution** at scale
 - Need **self-healing and adaptive agents** that improve over time
 - Require **human-in-the-loop control**, observability, and cost limits
-- Plan to run agents in **production environments**
+- Plan to run agents in **production** where uptime, cost, and auditability matter
 
 Hive may not be the best fit if you’re only experimenting with simple agent chains or one-off scripts.
 
 ## When Should You Use Hive?
 
-Use Hive when you need:
+Use Hive when the bottleneck is no longer the model but the harness around it:
 
-- Long-running, autonomous agents
-- Strong guardrails, process, and controls
-- Continuous improvement based on failures
-- Multi-agent coordination
-- A framework that evolves with your goals
+- Long-running agents that need **state persistence and crash recovery**
+- Production workloads requiring **cost enforcement, observability, and audit trails**
+- Agents that **self-heal** through failure capture and graph evolution
+- Multi-agent coordination with **session isolation and shared buffers**
+- A framework that **scales with model improvements** rather than fighting them
 
 ## Quick Links
 
@@ -100,9 +113,11 @@ Use Hive when you need:
 git clone https://github.com/aden-hive/hive.git
 cd hive
 
-
-# Run quickstart setup
+# Run quickstart setup (macOS/Linux)
 ./quickstart.sh
+
+# Windows (PowerShell)
+.\quickstart.ps1
 ```
 
 This sets up:
@@ -133,17 +148,6 @@ Now you can run an agent by selecting the agent (either an existing agent or exa
 
 <img width="2549" height="1174" alt="Screenshot 2026-03-12 at 9 27 36 PM" src="https://github.com/user-attachments/assets/7c7d30fa-9ceb-4c23-95af-b1caa405547d" />
 
-## Features
-
-- **Browser-Use** - Control the browser on your computer to achieve hard tasks
-- **Parallel Execution** - Execute the generated graph in parallel. This way you can have multiple agents completing the jobs for you
-- **[Goal-Driven Generation](docs/key_concepts/goals_outcome.md)** - Define objectives in natural language; the coding agent generates the agent graph and connection code to achieve them
-- **[Adaptiveness](docs/key_concepts/evolution.md)** - Framework captures failures, calibrates according to the objectives, and evolves the agent graph
-- **[Dynamic Node Connections](docs/key_concepts/graph.md)** - No predefined edges; connection code is generated by any capable LLM based on your goals
-- **SDK-Wrapped Nodes** - Every node gets shared memory, local RLM memory, monitoring, tools, and LLM access out of the box
-- **[Human-in-the-Loop](docs/key_concepts/graph.md#human-in-the-loop)** - Intervention nodes that pause execution for human input with configurable timeouts and escalation
-- **Real-time Observability** - WebSocket streaming for live monitoring of agent execution, decisions, and node-to-node communication
-
 ## Integration
 
 <a href="https://github.com/aden-hive/hive/tree/main/tools/src/aden_tools/tools"><img width="100%" alt="Integration" src="https://github.com/user-attachments/assets/a1573f93-cf02-4bb8-b3d5-b305b05b1e51" /></a>
@@ -152,9 +156,9 @@ Hive is built to be model-agnostic and system-agnostic.
 - **LLM flexibility** - Hive Framework supports Anthropic, OpenAI, OpenRouter, Hive LLM, and other hosted or local models through LiteLLM-compatible providers.
 - **Business system connectivity** - Hive Framework is designed to connect to all kinds of business systems as tools, such as CRM, support, messaging, data, file, and internal APIs via MCP.
 
-## Why Aden
+## Why Hive
 
-Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually design workflows, define agent interactions, and handle failures reactively, Hive flips the paradigm: **you describe outcomes, and the system builds itself**—delivering an outcome-driven, adaptive experience with an easy-to-use set of tools and integrations.
+As models improve, the upper bound of what agents can do rises — but their reliability and production value are determined by the harness. Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually design workflows, define agent interactions, and handle failures reactively, Hive flips the paradigm: **you describe outcomes, and the system builds itself**—delivering an outcome-driven, adaptive experience with an easy-to-use set of tools and integrations.
 
 ```mermaid
 flowchart LR
@@ -188,17 +192,6 @@ flowchart LR
     style V6 fill:#fff,stroke:#ed8c00,stroke-width:1px,color:#cc5d00
 ```
 
-### The Hive Advantage
-
-| Traditional Frameworks     | Hive                                   |
-| -------------------------- | -------------------------------------- |
-| Hardcode agent workflows   | Describe goals in natural language     |
-| Manual graph definition    | Auto-generated agent graphs            |
-| Reactive error handling    | Outcome-evaluation and adaptiveness    |
-| Static tool configurations | Dynamic SDK-wrapped nodes              |
-| Separate monitoring setup  | Built-in real-time observability       |
-| DIY budget management      | Integrated cost controls & degradation |
-
 ### How It Works
 
 1. **[Define Your Goal](docs/key_concepts/goals_outcome.md)** → Describe what you want to achieve in plain English
@@ -214,131 +207,6 @@ flowchart LR
 - [Configuration Guide](docs/configuration.md) - All configuration options
 - [Architecture Overview](docs/architecture/README.md) - System design and structure
 
-## Roadmap
-
-Aden Hive Agent Framework aims to help developers build outcome-oriented, self-adaptive agents. See [roadmap.md](docs/roadmap.md) for details.
-
-```mermaid
-flowchart TB
-    %% Main Entity
-    User([User])
-
-    %% =========================================
-    %% EXTERNAL EVENT SOURCES
-    %% =========================================
-    subgraph ExtEventSource [External Event Source]
-        E_Sch["Schedulers"]
-        E_WH["Webhook"]
-        E_SSE["SSE"]
-    end
-
-    %% =========================================
-    %% SYSTEM NODES
-    %% =========================================
-    subgraph WorkerBees [Worker Bees]
-        WB_C["Conversation"]
-        WB_SP["System prompt"]
-
-        subgraph Graph [Graph]
-            direction TB
-            N1["Node"] --> N2["Node"] --> N3["Node"]
-            N1 -.-> AN["Active Node"]
-            N2 -.-> AN
-            N3 -.-> AN
-
-            %% Nested Event Loop Node
-            subgraph EventLoopNode [Event Loop Node]
-                ELN_L["listener"]
-                ELN_SP["System Prompt<br/>(Task)"]
-                ELN_EL["Event loop"]
-                ELN_C["Conversation"]
-            end
-        end
-    end
-
-    subgraph JudgeNode [Judge]
-        J_C["Criteria"]
-        J_P["Principles"]
-        J_EL["Event loop"] <--> J_S["Scheduler"]
-    end
-
-    subgraph QueenBee [Queen Bee]
-        QB_SP["System prompt"]
-        QB_EL["Event loop"]
-        QB_C["Conversation"]
-    end
-
-    subgraph Infra [Infra]
-        SA["Sub Agent"]
-        TR["Tool Registry"]
-        WTM["Write through Conversation Memory<br/>(Logs/RAM/Harddrive)"]
-        SM["Shared Memory<br/>(State/Harddrive)"]
-        EB["Event Bus<br/>(RAM)"]
-        CS["Credential Store<br/>(Harddrive/Cloud)"]
-    end
-
-    subgraph PC [PC]
-        B["Browser"]
-        CB["Codebase<br/>v 0.0.x ... v n.n.n"]
-    end
-
-    %% =========================================
-    %% CONNECTIONS & DATA FLOW
-    %% =========================================
-
-    %% External Event Routing
-    E_Sch --> ELN_L
-    E_WH --> ELN_L
-    E_SSE --> ELN_L
-    ELN_L -->|"triggers"| ELN_EL
-
-    %% User Interactions
-    User -->|"Talk"| WB_C
-    User -->|"Talk"| QB_C
-    User -->|"Read/Write Access"| CS
-
-    %% Inter-System Logic
-    ELN_C <-->|"Mirror"| WB_C
-    WB_C -->|"Focus"| AN
-
-    WorkerBees -->|"Inquire"| JudgeNode
-    JudgeNode -->|"Approve"| WorkerBees
-
-    %% Judge Alignments
-    J_C <-.->|"aligns"| WB_SP
-    J_P <-.->|"aligns"| QB_SP
-
-    %% Escalate path
-    J_EL -->|"Report (Escalate)"| QB_EL
-
-    %% Pub/Sub Logic
-    AN -->|"publish"| EB
-    EB -->|"subscribe"| QB_C
-
-    %% Infra and Process Spawning
-    ELN_EL -->|"Spawn"| SA
-    SA -->|"Inform"| ELN_EL
-    SA -->|"Starts"| B
-    B -->|"Report"| ELN_EL
-    TR -->|"Assigned"| ELN_EL
-    CB -->|"Modify Worker Bee"| WB_C
-
-    %% =========================================
-    %% SHARED MEMORY & LOGS ACCESS
-    %% =========================================
-
-    %% Worker Bees Access (link to node inside Graph subgraph)
-    AN <-->|"Read/Write"| WTM
-    AN <-->|"Read/Write"| SM
-
-    %% Queen Bee Access
-    QB_C <-->|"Read/Write"| WTM
-    QB_EL <-->|"Read/Write"| SM
-
-    %% Credentials Access
-    CS -->|"Read Access"| QB_C
-```
-
 ## Contributing
 We welcome contributions from the community! We’re especially looking for help building tools, integrations, and example agents for the framework ([check #2805](https://github.com/aden-hive/hive/issues/2805)). If you’re interested in extending its functionality, this is the perfect place to start. Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
 
@@ -385,7 +253,7 @@ Yes! Hive supports local models through LiteLLM. Simply use the model name forma
 
 **Q: What makes Hive different from other agent frameworks?**
 
-Hive generates your entire agent system from natural language goals using a coding agent—you don't hardcode workflows or manually define graphs. When agents fail, the framework automatically captures failure data, [evolves the agent graph](docs/key_concepts/evolution.md), and redeploys. This self-improving loop is unique to Aden.
+Hive is an agent harness, not just an orchestration framework. It provides the production runtime layer — session isolation, checkpoint-based crash recovery, cost enforcement, real-time observability, and human-in-the-loop controls — that makes agents reliable enough to run real workloads. On top of that, Hive generates your entire agent system from natural language goals and automatically [evolves the graph](docs/key_concepts/evolution.md) when agents fail. The combination of a robust harness with self-improving generation is what sets Hive apart.
 
 **Q: Is Hive open-source?**
 

core/MCP_INTEGRATION_GUIDE.md

@@ -6,7 +6,7 @@ This guide explains how to integrate Model Context Protocol (MCP) servers with t
 
 The framework provides built-in support for MCP servers, allowing you to:
 
-- **Register MCP servers** via STDIO or HTTP transport
+- **Register MCP servers** via STDIO, HTTP, Unix socket, or SSE transport
 - **Auto-discover tools** from registered servers
 - **Use MCP tools** seamlessly in your agents
 - **Manage multiple MCP servers** simultaneously
@@ -104,6 +104,48 @@ runner.register_mcp_server(
 - `url`: Base URL of the MCP server
 - `headers`: HTTP headers to include (optional)
 
+### Unix Socket Transport
+
+Best for same-host inter-process communication with lower overhead than TCP:
+
+```python
+runner.register_mcp_server(
+    name="local-ipc-tools",
+    transport="unix",
+    url="http://localhost",
+    socket_path="/tmp/mcp_server.sock",
+    headers={
+        "Authorization": "Bearer token"
+    }
+)
+```
+
+**Configuration:**
+
+- `url`: Base URL for HTTP requests over the socket (required, e.g., `"http://localhost"`)
+- `socket_path`: Absolute path to the Unix socket file (required, e.g., `"/tmp/mcp_server.sock"`)
+- `headers`: HTTP headers to include (optional)
+
+### SSE Transport
+
+Best for real-time, event-driven connections using the MCP SDK's SSE client:
+
+```python
+runner.register_mcp_server(
+    name="streaming-tools",
+    transport="sse",
+    url="http://localhost:8000/sse",
+    headers={
+        "Authorization": "Bearer token"
+    }
+)
+```
+
+**Configuration:**
+
+- `url`: SSE endpoint URL (required, e.g., `"http://localhost:8000/sse"`)
+- `headers`: HTTP headers for the SSE connection (optional)
+
 ## Using MCP Tools in Agents
 
 Once registered, MCP tools are available just like any other tool:
@@ -258,7 +300,32 @@ runner.register_mcp_server(
 )
 ```
 
-### 3. Handle Cleanup
+### 3. Use Unix Socket for Same-Host IPC
+
+When both the agent and MCP server run on the same machine, Unix sockets avoid TCP overhead:
+
+```python
+runner.register_mcp_server(
+    name="fast-local-tools",
+    transport="unix",
+    url="http://localhost",
+    socket_path="/tmp/mcp_server.sock"
+)
+```
+
+### 4. Use SSE for Streaming and Real-Time Tools
+
+SSE transport maintains a persistent connection, ideal for event-driven servers:
+
+```python
+runner.register_mcp_server(
+    name="realtime-tools",
+    transport="sse",
+    url="http://realtime-server:8000/sse"
+)
+```
+
+### 5. Handle Cleanup
 
 Always clean up MCP connections when done:
 
@@ -280,7 +347,7 @@ async with AgentRunner.load("exports/my-agent") as runner:
     # Automatic cleanup
 ```
 
-### 4. Tool Name Conflicts
+### 6. Tool Name Conflicts
 
 If multiple MCP servers provide tools with the same name, the last registered server wins. To avoid conflicts:
 
@@ -315,6 +382,24 @@ If HTTP transport fails:
 2. Check firewall settings
 3. Verify the URL and port are correct
 
+### Unix Socket Not Connecting
+
+If Unix socket transport fails:
+
+1. Verify the socket file exists: `ls -la /tmp/mcp_server.sock`
+2. Check file permissions on the socket
+3. Ensure no other process has locked the socket
+4. Verify the `url` field is set (e.g., `"http://localhost"`)
+
+### SSE Connection Issues
+
+If SSE transport fails:
+
+1. Verify the server supports SSE at the given URL
+2. Check that the `mcp` Python package is installed (`pip install mcp`)
+3. Ensure the SSE endpoint is accessible: `curl http://localhost:8000/sse`
+4. Check for firewall or proxy issues blocking long-lived connections
+
 ## Example: Full Agent with MCP Tools
 
 Here's a complete example of an agent that uses MCP tools:

core/antigravity_auth.py

@@ -52,9 +52,7 @@ _DEFAULT_REDIRECT_PORT = 51121
 # This project reverse-engineered and published the public OAuth credentials
 # for Google's Antigravity/Cloud Code Assist API.
 # Source: https://github.com/NoeFabris/opencode-antigravity-auth
-_CREDENTIALS_URL = (
-    "https://raw.githubusercontent.com/NoeFabris/opencode-antigravity-auth/dev/src/constants.ts"
-)
+_CREDENTIALS_URL = "https://raw.githubusercontent.com/NoeFabris/opencode-antigravity-auth/dev/src/constants.ts"
 
 # Cached credentials fetched from public source
 _cached_client_id: str | None = None
@@ -68,9 +66,7 @@ def _fetch_credentials_from_public_source() -> tuple[str | None, str | None]:
         return _cached_client_id, _cached_client_secret
 
     try:
-        req = urllib.request.Request(
-            _CREDENTIALS_URL, headers={"User-Agent": "Hive-Antigravity-Auth/1.0"}
-        )
+        req = urllib.request.Request(_CREDENTIALS_URL, headers={"User-Agent": "Hive-Antigravity-Auth/1.0"})
         with urllib.request.urlopen(req, timeout=10) as resp:
             content = resp.read().decode("utf-8")
             import re
@@ -168,10 +164,7 @@ class OAuthCallbackHandler(BaseHTTPRequestHandler):
             if "code" in query and "state" in query:
                 OAuthCallbackHandler.auth_code = query["code"][0]
                 OAuthCallbackHandler.state = query["state"][0]
-                self._send_response(
-                    "Authentication successful! You can close this window "
-                    "and return to the terminal."
-                )
+                self._send_response("Authentication successful! You can close this window and return to the terminal.")
                 return
 
         self._send_response("Waiting for authentication...")
@@ -296,8 +289,7 @@ def validate_credentials(access_token: str, project_id: str = _DEFAULT_PROJECT_I
         "Authorization": f"Bearer {access_token}",
         "Content-Type": "application/json",
         "User-Agent": (
-            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
-            "AppleWebKit/537.36 (KHTML, like Gecko) Antigravity/1.18.3"
+            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Antigravity/1.18.3"
         ),
         "X-Goog-Api-Client": "google-cloud-sdk vscode_cloudshelleditor/0.1",
     }
@@ -316,9 +308,7 @@ def validate_credentials(access_token: str, project_id: str = _DEFAULT_PROJECT_I
         return False
 
 
-def refresh_access_token(
-    refresh_token: str, client_id: str, client_secret: str | None
-) -> dict | None:
+def refresh_access_token(refresh_token: str, client_id: str, client_secret: str | None) -> dict | None:
     """Refresh the access token using the refresh token."""
     data = {
         "grant_type": "refresh_token",
@@ -361,9 +351,7 @@ def cmd_account_add(args: argparse.Namespace) -> int:
         access_token = account.get("access")
         refresh_token_str = account.get("refresh", "")
         refresh_token = refresh_token_str.split("|")[0] if refresh_token_str else None
-        project_id = (
-            refresh_token_str.split("|")[1] if "|" in refresh_token_str else _DEFAULT_PROJECT_ID
-        )
+        project_id = refresh_token_str.split("|")[1] if "|" in refresh_token_str else _DEFAULT_PROJECT_ID
         email = account.get("email", "unknown")
         expires_ms = account.get("expires", 0)
         expires_at = expires_ms / 1000.0 if expires_ms else 0.0
@@ -390,9 +378,7 @@ def cmd_account_add(args: argparse.Namespace) -> int:
                     # Update the account
                     account["access"] = new_access
                     account["expires"] = int((time.time() + expires_in) * 1000)
-                    accounts_data["last_refresh"] = time.strftime(
-                        "%Y-%m-%dT%H:%M:%SZ", time.gmtime()
-                    )
+                    accounts_data["last_refresh"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
                     save_accounts(accounts_data)
 
                     # Validate the refreshed token

core/examples/manual_agent.py

@@ -1,132 +0,0 @@
-"""
-Minimal Manual Agent Example
-----------------------------
-This example demonstrates how to build and run an agent programmatically
-without using the Claude Code CLI or external LLM APIs.
-
-It uses custom NodeProtocol implementations to define logic in pure Python,
-making it perfect for understanding the core runtime loop:
-Setup -> Graph definition -> Execution -> Result
-
-Run with:
-    uv run python core/examples/manual_agent.py
-"""
-
-import asyncio
-
-from framework.graph import EdgeCondition, EdgeSpec, Goal, GraphSpec, NodeSpec
-from framework.graph.executor import GraphExecutor
-from framework.graph.node import NodeContext, NodeProtocol, NodeResult
-from framework.runtime.core import Runtime
-
-
-# 1. Define Node Logic (Custom NodeProtocol implementations)
-class GreeterNode(NodeProtocol):
-    """Generate a simple greeting."""
-
-    async def execute(self, ctx: NodeContext) -> NodeResult:
-        name = ctx.input_data.get("name", "World")
-        greeting = f"Hello, {name}!"
-        ctx.memory.write("greeting", greeting)
-        return NodeResult(success=True, output={"greeting": greeting})
-
-
-class UppercaserNode(NodeProtocol):
-    """Convert text to uppercase."""
-
-    async def execute(self, ctx: NodeContext) -> NodeResult:
-        greeting = ctx.input_data.get("greeting") or ctx.memory.read("greeting") or ""
-        result = greeting.upper()
-        ctx.memory.write("final_greeting", result)
-        return NodeResult(success=True, output={"final_greeting": result})
-
-
-async def main():
-    print("Setting up Manual Agent...")
-
-    # 2. Define the Goal
-    # Every agent needs a goal with success criteria
-    goal = Goal(
-        id="greet-user",
-        name="Greet User",
-        description="Generate a friendly uppercase greeting",
-        success_criteria=[
-            {
-                "id": "greeting_generated",
-                "description": "Greeting produced",
-                "metric": "custom",
-                "target": "any",
-            }
-        ],
-    )
-
-    # 3. Define Nodes
-    # Nodes describe steps in the process
-    node1 = NodeSpec(
-        id="greeter",
-        name="Greeter",
-        description="Generates a simple greeting",
-        node_type="event_loop",
-        input_keys=["name"],
-        output_keys=["greeting"],
-    )
-
-    node2 = NodeSpec(
-        id="uppercaser",
-        name="Uppercaser",
-        description="Converts greeting to uppercase",
-        node_type="event_loop",
-        input_keys=["greeting"],
-        output_keys=["final_greeting"],
-    )
-
-    # 4. Define Edges
-    # Edges define the flow between nodes
-    edge1 = EdgeSpec(
-        id="greet-to-upper",
-        source="greeter",
-        target="uppercaser",
-        condition=EdgeCondition.ON_SUCCESS,
-    )
-
-    # 5. Create Graph
-    # The graph works like a blueprint connecting nodes and edges
-    graph = GraphSpec(
-        id="greeting-agent",
-        goal_id="greet-user",
-        entry_node="greeter",
-        terminal_nodes=["uppercaser"],
-        nodes=[node1, node2],
-        edges=[edge1],
-    )
-
-    # 6. Initialize Runtime & Executor
-    # Runtime handles state/memory; Executor runs the graph
-    from pathlib import Path
-
-    runtime = Runtime(storage_path=Path("./agent_logs"))
-    executor = GraphExecutor(runtime=runtime)
-
-    # 7. Register Node Implementations
-    # Connect node IDs in the graph to actual Python implementations
-    executor.register_node("greeter", GreeterNode())
-    executor.register_node("uppercaser", UppercaserNode())
-
-    # 8. Execute Agent
-    print("Executing agent with input: name='Alice'...")
-
-    result = await executor.execute(graph=graph, goal=goal, input_data={"name": "Alice"})
-
-    # 9. Verify Results
-    if result.success:
-        print("\nSuccess!")
-        print(f"Path taken: {' -> '.join(result.path)}")
-        print(f"Final output: {result.output.get('final_greeting')}")
-    else:
-        print(f"\nFailed: {result.error}")
-
-
-if __name__ == "__main__":
-    # Optional: Enable logging to see internal decision flow
-    # logging.basicConfig(level=logging.INFO)
-    asyncio.run(main())

core/examples/mcp_integration_example.py

@@ -1,119 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example: Integrating MCP Servers with the Core Framework
-
-This example demonstrates how to:
-1. Register MCP servers programmatically
-2. Use MCP tools in agents
-3. Load MCP servers from configuration files
-"""
-
-import asyncio
-from pathlib import Path
-
-from framework.runner.runner import AgentRunner
-
-
-async def example_1_programmatic_registration():
-    """Example 1: Register MCP server programmatically"""
-    print("\n=== Example 1: Programmatic MCP Server Registration ===\n")
-
-    # Load an existing agent
-    runner = AgentRunner.load("exports/task-planner")
-
-    # Register tools MCP server via STDIO
-    num_tools = runner.register_mcp_server(
-        name="tools",
-        transport="stdio",
-        command="python",
-        args=["-m", "aden_tools.mcp_server", "--stdio"],
-        cwd="../tools",
-    )
-
-    print(f"Registered {num_tools} tools from tools MCP server")
-
-    # List all available tools
-    tools = runner._tool_registry.get_tools()
-    print(f"\nAvailable tools: {list(tools.keys())}")
-
-    # Run the agent with MCP tools available
-    result = await runner.run(
-        {"objective": "Search for 'Claude AI' and summarize the top 3 results"}
-    )
-
-    print(f"\nAgent result: {result}")
-
-    # Cleanup
-    runner.cleanup()
-
-
-async def example_2_http_transport():
-    """Example 2: Connect to MCP server via HTTP"""
-    print("\n=== Example 2: HTTP MCP Server Connection ===\n")
-
-    # First, start the tools MCP server in HTTP mode:
-    # cd tools && python mcp_server.py --port 4001
-
-    runner = AgentRunner.load("exports/task-planner")
-
-    # Register tools via HTTP
-    num_tools = runner.register_mcp_server(
-        name="tools-http",
-        transport="http",
-        url="http://localhost:4001",
-    )
-
-    print(f"Registered {num_tools} tools from HTTP MCP server")
-
-    # Cleanup
-    runner.cleanup()
-
-
-async def example_3_config_file():
-    """Example 3: Load MCP servers from configuration file"""
-    print("\n=== Example 3: Load from Configuration File ===\n")
-
-    # Create a test agent folder with mcp_servers.json
-    test_agent_path = Path("exports/task-planner")
-
-    # Copy example config (in practice, you'd place this in your agent folder)
-    import shutil
-
-    shutil.copy(Path(__file__).parent / "mcp_servers.json", test_agent_path / "mcp_servers.json")
-
-    # Load agent - MCP servers will be auto-discovered
-    runner = AgentRunner.load(test_agent_path)
-
-    # Tools are automatically available
-    tools = runner._tool_registry.get_tools()
-    print(f"Available tools: {list(tools.keys())}")
-
-    # Cleanup
-    runner.cleanup()
-
-    # Clean up the test config
-    (test_agent_path / "mcp_servers.json").unlink()
-
-
-async def main():
-    """Run all examples"""
-    print("=" * 60)
-    print("MCP Integration Examples")
-    print("=" * 60)
-
-    try:
-        # Run examples
-        await example_1_programmatic_registration()
-        # await example_2_http_transport()  # Requires HTTP server running
-        # await example_3_config_file()
-        # await example_4_custom_agent_with_mcp_tools()
-
-    except Exception as e:
-        print(f"\nError running example: {e}")
-        import traceback
-
-        traceback.print_exc()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

core/framework/__init__.py

@@ -1,67 +1,20 @@
-"""
-Aden Hive Framework: A goal-driven agent runtime optimized for Builder observability.
+"""Hive Agent Framework.
 
-The runtime is designed around DECISIONS, not just actions. Every significant
-choice the agent makes is captured with:
-- What it was trying to do (intent)
-- What options it considered
-- What it chose and why
-- What happened as a result
-- Whether that was good or bad (evaluated post-hoc)
-
-This gives the Builder LLM the information it needs to improve agent behavior.
-
-## Testing Framework
-
-The framework includes a Goal-Based Testing system (Goal → Agent → Eval):
-- Generate tests from Goal success_criteria and constraints
-- Mandatory user approval before tests are stored
-- Parallel test execution with error categorization
-- Debug tools with fix suggestions
-
-See `framework.testing` for details.
+Core classes:
+    ColonyRuntime -- orchestrates parallel worker clones in a colony
+    AgentLoop      -- the LLM + tool execution loop (one per worker)
+    AgentLoader    -- loads agent config from disk, builds pipeline
+    DecisionTracker -- records decisions for post-hoc analysis
 """
 
-from framework.llm import AnthropicProvider, LLMProvider
-from framework.runner import AgentOrchestrator, AgentRunner
-from framework.runtime.core import Runtime
-from framework.schemas.decision import Decision, DecisionEvaluation, Option, Outcome
-from framework.schemas.run import Problem, Run, RunSummary
-
-# Testing framework
-from framework.testing import (
-    ApprovalStatus,
-    DebugTool,
-    ErrorCategory,
-    Test,
-    TestResult,
-    TestStorage,
-    TestSuiteResult,
-)
+from framework.agent_loop import AgentLoop
+from framework.host import ColonyRuntime
+from framework.loader import AgentLoader
+from framework.tracker import DecisionTracker
 
 __all__ = [
-    # Schemas
-    "Decision",
-    "Option",
-    "Outcome",
-    "DecisionEvaluation",
-    "Run",
-    "RunSummary",
-    "Problem",
-    # Runtime
-    "Runtime",
-    # LLM
-    "LLMProvider",
-    "AnthropicProvider",
-    # Runner
-    "AgentRunner",
-    "AgentOrchestrator",
-    # Testing
-    "Test",
-    "TestResult",
-    "TestSuiteResult",
-    "TestStorage",
-    "ApprovalStatus",
-    "ErrorCategory",
-    "DebugTool",
+    "ColonyRuntime",
+    "AgentLoader",
+    "AgentLoop",
+    "DecisionTracker",
 ]

core/framework/agent_loop/__init__.py

@@ -0,0 +1,34 @@
+"""Agent loop -- the core agent execution primitive."""
+
+from framework.agent_loop.conversation import (  # noqa: F401
+    ConversationStore,
+    Message,
+    NodeConversation,
+)
+from framework.agent_loop.types import (  # noqa: F401
+    AgentContext,
+    AgentProtocol,
+    AgentResult,
+    AgentSpec,
+)
+
+
+def __getattr__(name: str):
+    if name in ("AgentLoop", "JudgeProtocol", "JudgeVerdict", "LoopConfig", "OutputAccumulator"):
+        from framework.agent_loop.agent_loop import (
+            AgentLoop,
+            JudgeProtocol,
+            JudgeVerdict,
+            LoopConfig,
+            OutputAccumulator,
+        )
+
+        _exports = {
+            "AgentLoop": AgentLoop,
+            "JudgeProtocol": JudgeProtocol,
+            "JudgeVerdict": JudgeVerdict,
+            "LoopConfig": LoopConfig,
+            "OutputAccumulator": OutputAccumulator,
+        }
+        return _exports[name]
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

core/framework/agent_loop/internals/__init__.py

@@ -0,0 +1,7 @@
+"""Agent loop internals -- compaction, judge, tools, subagent execution.
+
+Re-exports from legacy locations for the new import path.
+"""
+
+from framework.agent_loop.internals.compaction import *  # noqa: F401, F403
+from framework.agent_loop.internals.synthetic_tools import *  # noqa: F401, F403

core/framework/agent_loop/internals/compaction.py

@@ -0,0 +1,884 @@
+"""Conversation compaction pipeline.
+
+Implements the multi-level compaction strategy:
+0. Microcompaction (count-based tool result clearing — cheapest)
+1. Prune old tool results (token-budget based)
+2. Structure-preserving compaction (spillover)
+3. LLM summary compaction (with recursive splitting)
+4. Emergency deterministic summary (no LLM)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+import time
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from framework.agent_loop.conversation import Message, NodeConversation
+from framework.agent_loop.internals.event_publishing import publish_context_usage
+from framework.agent_loop.internals.types import LoopConfig, OutputAccumulator
+from framework.host.event_bus import EventBus
+from framework.orchestrator.node import NodeContext
+
+logger = logging.getLogger(__name__)
+
+# Limits for LLM compaction
+LLM_COMPACT_CHAR_LIMIT: int = 240_000
+LLM_COMPACT_MAX_DEPTH: int = 10
+
+# Microcompaction: tools whose results can be safely cleared
+COMPACTABLE_TOOLS: frozenset[str] = frozenset(
+    {
+        "read_file",
+        "run_command",
+        "web_search",
+        "web_fetch",
+        "grep_search",
+        "glob_search",
+        "write_file",
+        "edit_file",
+        "browser_screenshot",
+        "list_directory",
+    }
+)
+
+# Keep at most this many compactable tool results; clear older ones
+MICROCOMPACT_KEEP_RECENT: int = 8
+
+# Circuit-breaker: stop auto-compacting after this many consecutive failures
+MAX_CONSECUTIVE_FAILURES: int = 3
+
+# Track consecutive compaction failures per conversation (module-level)
+_failure_counts: dict[int, int] = {}
+
+# Track last compaction time per conversation for recompaction detection
+_last_compact_times: dict[int, float] = {}
+
+
+def microcompact(
+    conversation: NodeConversation,
+    *,
+    keep_recent: int = MICROCOMPACT_KEEP_RECENT,
+) -> int:
+    """Clear old compactable tool results by count, keeping only the most recent.
+
+    This is the cheapest possible compaction — no LLM call, no structural
+    changes, just replaces old tool result content with a short placeholder.
+    Inspired by Claude Code's cached-microcompact strategy.
+
+    Returns the number of tool results cleared.
+    """
+    # Collect indices of compactable tool results (newest first)
+    compactable_indices: list[int] = []
+    messages = conversation.messages
+    for i in range(len(messages) - 1, -1, -1):
+        msg = messages[i]
+        if msg.role != "tool" or msg.is_error or msg.is_skill_content:
+            continue
+        if msg.content.startswith(("Pruned tool result", "[Pruned tool result", "[Old tool result")):
+            continue
+        if len(msg.content) < 100:
+            continue
+
+        # Check if the tool that produced this result is compactable
+        tool_name = _find_tool_name_for_result(messages, msg)
+        if tool_name and tool_name in COMPACTABLE_TOOLS:
+            compactable_indices.append(i)
+
+    # Keep the most recent N, clear the rest
+    to_clear = compactable_indices[keep_recent:]
+    if not to_clear:
+        return 0
+
+    cleared = 0
+    for i in to_clear:
+        msg = messages[i]
+        spillover = _extract_spillover_filename_inline(msg.content)
+        orig_len = len(msg.content)
+        if spillover:
+            placeholder = (
+                f"Old tool result ({orig_len:,} chars) cleared from context. "
+                f"Full data saved at: {spillover}\n"
+                f"Read the complete data with read_file(path='{spillover}')."
+            )
+        else:
+            placeholder = f"Old tool result ({orig_len:,} chars) cleared from context."
+
+        # Mutate in-place (microcompact is synchronous, no store writes)
+        conversation._messages[i] = Message(
+            seq=msg.seq,
+            role=msg.role,
+            content=placeholder,
+            tool_use_id=msg.tool_use_id,
+            tool_calls=msg.tool_calls,
+            is_error=msg.is_error,
+            phase_id=msg.phase_id,
+            is_transition_marker=msg.is_transition_marker,
+        )
+        cleared += 1
+
+    if cleared > 0:
+        # Invalidate cached token count
+        conversation._last_api_input_tokens = None
+
+    return cleared
+
+
+def _find_tool_name_for_result(messages: list[Message], tool_msg: Message) -> str | None:
+    """Find the tool name from the assistant message that triggered this tool result."""
+    if not tool_msg.tool_use_id:
+        return None
+    for msg in messages:
+        if msg.tool_calls:
+            for tc in msg.tool_calls:
+                if tc.get("id") == tool_msg.tool_use_id:
+                    return tc.get("function", {}).get("name")
+    return None
+
+
+def _extract_spillover_filename_inline(content: str) -> str | None:
+    """Quick inline check for spillover filename in tool result content.
+
+    Matches both the new prose format ("saved at: /path") and the
+    legacy bracketed trailer ("saved to '/path'").
+    """
+    match = re.search(r"saved at:\s*(\S+)", content, re.IGNORECASE)
+    if match:
+        return match.group(1)
+    match = re.search(r"saved to '([^']+)'", content, re.IGNORECASE)
+    return match.group(1) if match else None
+
+
+async def compact(
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    accumulator: OutputAccumulator | None,
+    *,
+    config: LoopConfig,
+    event_bus: EventBus | None,
+    char_limit: int = LLM_COMPACT_CHAR_LIMIT,
+    max_depth: int = LLM_COMPACT_MAX_DEPTH,
+) -> None:
+    """Run the full compaction pipeline if conversation needs compaction.
+
+    Pipeline stages (in order, short-circuits when budget is restored):
+    0. Microcompaction (count-based tool result clearing — cheapest)
+    1. Prune old tool results (token-budget based)
+    2. Structure-preserving compaction (free, no LLM)
+    3. LLM summary compaction (recursive split if too large)
+    4. Emergency deterministic summary (fallback)
+    """
+    conv_id = id(conversation)
+
+    # Circuit breaker: stop LLM-based compaction after repeated failures,
+    # but still fall through to the emergency deterministic summary so
+    # the conversation doesn't silently grow past the context window.
+    # Without this, a persistent LLM outage during compaction would
+    # leave the agent stuck sending oversized prompts until the API 400s.
+    _llm_compaction_skipped = _failure_counts.get(conv_id, 0) >= MAX_CONSECUTIVE_FAILURES
+    if _llm_compaction_skipped:
+        logger.warning(
+            "Circuit breaker: LLM compaction disabled after %d failures — skipping straight to emergency summary",
+            _failure_counts[conv_id],
+        )
+
+    # Recompaction detection
+    now = time.monotonic()
+    last_time = _last_compact_times.get(conv_id)
+    if last_time is not None and (now - last_time) < 30:
+        logger.warning(
+            "Recompaction chain detected: only %.1fs since last compaction",
+            now - last_time,
+        )
+
+    ratio_before = conversation.usage_ratio()
+    phase_grad = getattr(ctx, "continuous_mode", False)
+    pre_inventory: list[dict[str, Any]] | None = None
+
+    if ratio_before >= 1.0:
+        pre_inventory = build_message_inventory(conversation)
+
+    # --- Step 0: Microcompaction (count-based, cheapest) ---
+    mc_cleared = microcompact(conversation)
+    if mc_cleared > 0:
+        logger.info(
+            "Microcompact cleared %d old tool results: %.0f%% -> %.0f%%",
+            mc_cleared,
+            ratio_before * 100,
+            conversation.usage_ratio() * 100,
+        )
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx,
+            conversation,
+            ratio_before,
+            event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 1: Prune old tool results (free, fast) ---
+    protect = max(2000, config.max_context_tokens // 12)
+    pruned = await conversation.prune_old_tool_results(
+        protect_tokens=protect,
+        min_prune_tokens=max(1000, protect // 3),
+    )
+    if pruned > 0:
+        logger.info(
+            "Pruned %d old tool results: %.0f%% -> %.0f%%",
+            pruned,
+            ratio_before * 100,
+            conversation.usage_ratio() * 100,
+        )
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx,
+            conversation,
+            ratio_before,
+            event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 2: Standard structure-preserving compaction (free, no LLM) ---
+    spill_dir = config.spillover_dir
+    if spill_dir:
+        await conversation.compact_preserving_structure(
+            spillover_dir=spill_dir,
+            keep_recent=4,
+            phase_graduated=phase_grad,
+        )
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx,
+            conversation,
+            ratio_before,
+            event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 3: LLM summary compaction ---
+    if ctx.llm is not None and not _llm_compaction_skipped:
+        logger.info(
+            "LLM summary compaction triggered (%.0f%% usage)",
+            conversation.usage_ratio() * 100,
+        )
+        try:
+            summary = await llm_compact(
+                ctx,
+                list(conversation.messages),
+                accumulator,
+                char_limit=char_limit,
+                max_depth=max_depth,
+                max_context_tokens=config.max_context_tokens,
+            )
+            await conversation.compact(
+                summary,
+                keep_recent=2,
+                phase_graduated=phase_grad,
+            )
+        except Exception as e:
+            logger.warning("LLM compaction failed: %s", e)
+            _failure_counts[conv_id] = _failure_counts.get(conv_id, 0) + 1
+
+    if not conversation.needs_compaction():
+        _record_success(conv_id, now)
+        await log_compaction(
+            ctx,
+            conversation,
+            ratio_before,
+            event_bus,
+            pre_inventory=pre_inventory,
+        )
+        return
+
+    # --- Step 4: Emergency deterministic summary (LLM failed/unavailable) ---
+    logger.warning(
+        "Emergency compaction (%.0f%% usage)",
+        conversation.usage_ratio() * 100,
+    )
+    summary = build_emergency_summary(ctx, accumulator, conversation, config)
+    await conversation.compact(
+        summary,
+        keep_recent=1,
+        phase_graduated=phase_grad,
+    )
+    _record_success(conv_id, now)
+    await log_compaction(
+        ctx,
+        conversation,
+        ratio_before,
+        event_bus,
+        pre_inventory=pre_inventory,
+    )
+
+
+def _record_success(conv_id: int, timestamp: float) -> None:
+    """Reset failure counter and record compaction time on success."""
+    _failure_counts.pop(conv_id, None)
+    _last_compact_times[conv_id] = timestamp
+
+
+# --- LLM compaction with binary-search splitting ----------------------
+
+
+def strip_images_from_messages(messages: list[Message]) -> list[Message]:
+    """Strip image_content from messages before LLM summarisation.
+
+    Images/documents are replaced with ``[image]`` markers so the summary
+    notes they existed without wasting tokens sending binary data to the
+    compaction LLM.  Returns a new list (original messages are not mutated).
+    """
+    stripped: list[Message] = []
+    for msg in messages:
+        if msg.image_content:
+            n_images = len(msg.image_content)
+            marker = " ".join("[image]" for _ in range(n_images))
+            content = f"{msg.content}\n{marker}" if msg.content else marker
+            stripped.append(
+                Message(
+                    seq=msg.seq,
+                    role=msg.role,
+                    content=content,
+                    tool_use_id=msg.tool_use_id,
+                    tool_calls=msg.tool_calls,
+                    is_error=msg.is_error,
+                    phase_id=msg.phase_id,
+                    is_transition_marker=msg.is_transition_marker,
+                    image_content=None,  # stripped
+                )
+            )
+        else:
+            stripped.append(msg)
+    return stripped
+
+
+async def llm_compact(
+    ctx: NodeContext,
+    messages: list,
+    accumulator: OutputAccumulator | None = None,
+    _depth: int = 0,
+    *,
+    char_limit: int = LLM_COMPACT_CHAR_LIMIT,
+    max_depth: int = LLM_COMPACT_MAX_DEPTH,
+    max_context_tokens: int = 128_000,
+    preserve_user_messages: bool = False,
+) -> str:
+    """Summarise *messages* with LLM, splitting recursively if too large.
+
+    If the formatted text exceeds ``LLM_COMPACT_CHAR_LIMIT`` or the LLM
+    rejects the call with a context-length error, the messages are split
+    in half and each half is summarised independently.  Tool history is
+    appended once at the top-level call (``_depth == 0``).
+
+    When ``preserve_user_messages`` is True, the prompt and system message
+    are amplified to instruct the LLM to keep every user message verbatim
+    and in full — used by the manual /compact-and-fork endpoint where the
+    user wants their voice carried into the new session intact.
+    """
+    from framework.agent_loop.conversation import extract_tool_call_history
+    from framework.agent_loop.internals.tool_result_handler import is_context_too_large_error
+
+    if _depth > max_depth:
+        raise RuntimeError(f"LLM compaction recursion limit ({max_depth})")
+
+    # Strip images before summarisation to avoid wasting tokens
+    if _depth == 0:
+        messages = strip_images_from_messages(messages)
+
+    formatted = format_messages_for_summary(messages)
+
+    # Proactive split: avoid wasting an API call on oversized input
+    if len(formatted) > char_limit and len(messages) > 1:
+        summary = await _llm_compact_split(
+            ctx,
+            messages,
+            accumulator,
+            _depth,
+            char_limit=char_limit,
+            max_depth=max_depth,
+            max_context_tokens=max_context_tokens,
+            preserve_user_messages=preserve_user_messages,
+        )
+    else:
+        prompt = build_llm_compaction_prompt(
+            ctx,
+            accumulator,
+            formatted,
+            max_context_tokens=max_context_tokens,
+            preserve_user_messages=preserve_user_messages,
+        )
+        if preserve_user_messages:
+            system_msg = (
+                "You are a conversation compactor for an AI agent. "
+                "Write a detailed summary that allows the agent to "
+                "continue its work. CRITICAL: reproduce every user "
+                "message verbatim and in full inside the 'User Messages' "
+                "section — do not paraphrase, truncate, or merge them. "
+                "Assistant turns and tool results may be summarised, but "
+                "user input is sacred."
+            )
+        else:
+            system_msg = (
+                "You are a conversation compactor for an AI agent. "
+                "Write a detailed summary that allows the agent to "
+                "continue its work. Preserve user-stated rules, "
+                "constraints, and account/identity preferences verbatim."
+            )
+        summary_budget = max(1024, max_context_tokens // 2)
+        try:
+            response = await ctx.llm.acomplete(
+                messages=[{"role": "user", "content": prompt}],
+                system=system_msg,
+                max_tokens=summary_budget,
+            )
+            summary = response.content
+        except Exception as e:
+            if is_context_too_large_error(e) and len(messages) > 1:
+                logger.info(
+                    "LLM context too large (depth=%d, msgs=%d) — splitting",
+                    _depth,
+                    len(messages),
+                )
+                summary = await _llm_compact_split(
+                    ctx,
+                    messages,
+                    accumulator,
+                    _depth,
+                    char_limit=char_limit,
+                    max_depth=max_depth,
+                    max_context_tokens=max_context_tokens,
+                    preserve_user_messages=preserve_user_messages,
+                )
+            else:
+                raise
+
+    # Append tool history at top level only
+    if _depth == 0:
+        tool_history = extract_tool_call_history(messages)
+        if tool_history and "TOOLS ALREADY CALLED" not in summary:
+            summary += "\n\n" + tool_history
+
+    return summary
+
+
+async def _llm_compact_split(
+    ctx: NodeContext,
+    messages: list,
+    accumulator: OutputAccumulator | None,
+    _depth: int,
+    *,
+    char_limit: int = LLM_COMPACT_CHAR_LIMIT,
+    max_depth: int = LLM_COMPACT_MAX_DEPTH,
+    max_context_tokens: int = 128_000,
+    preserve_user_messages: bool = False,
+) -> str:
+    """Split messages in half and summarise each half independently."""
+    mid = max(1, len(messages) // 2)
+    s1 = await llm_compact(
+        ctx,
+        messages[:mid],
+        None,
+        _depth + 1,
+        char_limit=char_limit,
+        max_depth=max_depth,
+        max_context_tokens=max_context_tokens,
+        preserve_user_messages=preserve_user_messages,
+    )
+    s2 = await llm_compact(
+        ctx,
+        messages[mid:],
+        accumulator,
+        _depth + 1,
+        char_limit=char_limit,
+        max_depth=max_depth,
+        max_context_tokens=max_context_tokens,
+        preserve_user_messages=preserve_user_messages,
+    )
+    return s1 + "\n\n" + s2
+
+
+# --- Compaction helpers ------------------------------------------------
+
+
+def format_messages_for_summary(messages: list) -> str:
+    """Format messages as text for LLM summarisation."""
+    lines: list[str] = []
+    for m in messages:
+        if m.role == "tool":
+            content = m.content[:500]
+            if len(m.content) > 500:
+                content += "..."
+            lines.append(f"[tool result]: {content}")
+        elif m.role == "assistant" and m.tool_calls:
+            names = [tc.get("function", {}).get("name", "?") for tc in m.tool_calls]
+            text = m.content[:200] if m.content else ""
+            lines.append(f"[assistant (calls: {', '.join(names)})]: {text}")
+        else:
+            lines.append(f"[{m.role}]: {m.content}")
+    return "\n\n".join(lines)
+
+
+def build_llm_compaction_prompt(
+    ctx: NodeContext,
+    accumulator: OutputAccumulator | None,
+    formatted_messages: str,
+    *,
+    max_context_tokens: int = 128_000,
+    preserve_user_messages: bool = False,
+) -> str:
+    """Build prompt for LLM compaction targeting 50% of token budget.
+
+    Uses a structured section format inspired by Claude Code's compact
+    service.  Each section focuses on a different aspect of the conversation
+    so the summariser produces consistently useful, well-organised output.
+    """
+    spec = ctx.agent_spec
+    ctx_lines = [f"NODE: {spec.name} (id={spec.id})"]
+    if spec.description:
+        ctx_lines.append(f"PURPOSE: {spec.description}")
+    if spec.success_criteria:
+        ctx_lines.append(f"SUCCESS CRITERIA: {spec.success_criteria}")
+
+    if accumulator:
+        acc = accumulator.to_dict()
+        done = {k: v for k, v in acc.items() if v is not None}
+        todo = [k for k, v in acc.items() if v is None]
+        if done:
+            ctx_lines.append("OUTPUTS ALREADY SET:\n" + "\n".join(f"  {k}: {str(v)[:150]}" for k, v in done.items()))
+        if todo:
+            ctx_lines.append(f"OUTPUTS STILL NEEDED: {', '.join(todo)}")
+    elif spec.output_keys:
+        ctx_lines.append(f"OUTPUTS STILL NEEDED: {', '.join(spec.output_keys)}")
+
+    target_tokens = max_context_tokens // 2
+    target_chars = target_tokens * 4
+    node_ctx = "\n".join(ctx_lines)
+
+    user_messages_section = (
+        "6. **User Messages** — Reproduce EVERY user message verbatim and "
+        "in full, in chronological order, each on its own line prefixed "
+        'with the message index (e.g. "[U1] ..."). Do NOT paraphrase, '
+        "summarise, merge, or omit any user message. Preserve markdown, "
+        "code fences, whitespace, and punctuation exactly as the user "
+        "wrote them.\n"
+        if preserve_user_messages
+        else "6. **User Messages** — Preserve ALL user-stated rules, constraints, "
+        "identity preferences, and account details verbatim.\n"
+    )
+
+    return (
+        "You are compacting an AI agent's conversation history. "
+        "The agent is still working and needs to continue.\n\n"
+        f"AGENT CONTEXT:\n{node_ctx}\n\n"
+        f"CONVERSATION MESSAGES:\n{formatted_messages}\n\n"
+        "INSTRUCTIONS:\n"
+        f"Write a summary of approximately {target_chars} characters "
+        f"(~{target_tokens} tokens).\n\n"
+        "Organise the summary into these sections (omit empty ones):\n\n"
+        "1. **Primary Request and Intent** — What the user originally asked "
+        "for and the high-level goal the agent is working toward.\n"
+        "2. **Key Technical Concepts** — Important domain-specific terms, "
+        "patterns, or architectural decisions established in the conversation.\n"
+        "3. **Files and Code Sections** — Specific files read/written/edited "
+        "with brief descriptions of changes. Include short code snippets only "
+        "when they capture critical logic.\n"
+        "4. **Errors and Fixes** — Problems encountered and how they were "
+        "resolved. Include root causes so the agent doesn't repeat them.\n"
+        "5. **Problem Solving Efforts** — Approaches tried, dead ends hit, "
+        "and reasoning behind the current strategy.\n"
+        f"{user_messages_section}"
+        "7. **Pending Tasks** — Work remaining, outputs still needed, and "
+        "any blockers.\n"
+        "8. **Current Work** — The most recent action taken and the immediate "
+        "next step the agent should perform. This section is the most important "
+        "for seamless resumption.\n\n"
+        "Additional rules:\n"
+        "- Be detailed enough that the agent can resume without re-doing work.\n"
+        "- Preserve key decisions made and results obtained.\n"
+        "- When in doubt, keep information rather than discard it.\n"
+    )
+
+
+def build_message_inventory(conversation: NodeConversation) -> list[dict[str, Any]]:
+    """Build a per-message size inventory for debug logging."""
+    inventory: list[dict[str, Any]] = []
+    for message in conversation.messages:
+        content_chars = len(message.content)
+        tool_call_args_chars = 0
+        tool_name = None
+        if message.tool_calls:
+            for tool_call in message.tool_calls:
+                args = tool_call.get("function", {}).get("arguments", "")
+                tool_call_args_chars += len(args) if isinstance(args, str) else len(json.dumps(args))
+            names = [tool_call.get("function", {}).get("name", "?") for tool_call in message.tool_calls]
+            tool_name = ", ".join(names)
+        elif message.role == "tool" and message.tool_use_id:
+            for previous in conversation.messages:
+                if previous.tool_calls:
+                    for tool_call in previous.tool_calls:
+                        if tool_call.get("id") == message.tool_use_id:
+                            tool_name = tool_call.get("function", {}).get("name", "?")
+                            break
+                if tool_name:
+                    break
+        entry: dict[str, Any] = {
+            "seq": message.seq,
+            "role": message.role,
+            "content_chars": content_chars,
+        }
+        if tool_call_args_chars:
+            entry["tool_call_args_chars"] = tool_call_args_chars
+        if tool_name:
+            entry["tool"] = tool_name
+        if message.is_error:
+            entry["is_error"] = True
+        if message.phase_id:
+            entry["phase"] = message.phase_id
+        if content_chars > 2000:
+            entry["preview"] = message.content[:200] + "…"
+        inventory.append(entry)
+    return inventory
+
+
+def write_compaction_debug_log(
+    ctx: NodeContext,
+    before_pct: int,
+    after_pct: int,
+    level: str,
+    inventory: list[dict[str, Any]] | None,
+) -> None:
+    """Write detailed compaction analysis to ~/.hive/compaction_log/."""
+    log_dir = Path.home() / ".hive" / "compaction_log"
+    log_dir.mkdir(parents=True, exist_ok=True)
+
+    ts = datetime.now(UTC).strftime("%Y%m%dT%H%M%S_%f")
+    node_label = ctx.agent_id.replace("/", "_")
+    log_path = log_dir / f"{ts}_{node_label}.md"
+
+    lines: list[str] = [
+        f"# Compaction Debug — {ctx.agent_id}",
+        f"**Time:** {datetime.now(UTC).isoformat()}",
+        f"**Node:** {ctx.agent_spec.name} (`{ctx.agent_id}`)",
+    ]
+    if ctx.stream_id:
+        lines.append(f"**Stream:** {ctx.stream_id}")
+    lines.append(f"**Level:** {level}")
+    lines.append(f"**Usage:** {before_pct}% → {after_pct}%")
+    lines.append("")
+
+    if inventory:
+        total_chars = sum(entry.get("content_chars", 0) + entry.get("tool_call_args_chars", 0) for entry in inventory)
+        lines.append(f"## Pre-Compaction Message Inventory ({len(inventory)} messages, {total_chars:,} total chars)")
+        lines.append("")
+        ranked = sorted(
+            inventory,
+            key=lambda entry: entry.get("content_chars", 0) + entry.get("tool_call_args_chars", 0),
+            reverse=True,
+        )
+        lines.append("| # | seq | role | tool | chars | % of total | flags |")
+        lines.append("|---|-----|------|------|------:|------------|-------|")
+        for i, entry in enumerate(ranked, 1):
+            chars = entry.get("content_chars", 0) + entry.get("tool_call_args_chars", 0)
+            pct = (chars / total_chars * 100) if total_chars else 0
+            tool = entry.get("tool", "")
+            flags: list[str] = []
+            if entry.get("is_error"):
+                flags.append("error")
+            if entry.get("phase"):
+                flags.append(f"phase={entry['phase']}")
+            lines.append(
+                f"| {i} | {entry['seq']} | {entry['role']} | {tool} | {chars:,} | {pct:.1f}% | {', '.join(flags)} |"
+            )
+
+        large = [entry for entry in ranked if entry.get("preview")]
+        if large:
+            lines.append("")
+            lines.append("### Large message previews")
+            for entry in large:
+                lines.append(f"\n**seq={entry['seq']}** ({entry['role']}, {entry.get('tool', '')}):")
+                lines.append(f"```\n{entry['preview']}\n```")
+    lines.append("")
+
+    try:
+        log_path.write_text("\n".join(lines), encoding="utf-8")
+        logger.debug("Compaction debug log written to %s", log_path)
+    except OSError:
+        logger.debug("Failed to write compaction debug log to %s", log_path)
+
+
+async def log_compaction(
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    ratio_before: float,
+    event_bus: EventBus | None,
+    *,
+    pre_inventory: list[dict[str, Any]] | None = None,
+) -> None:
+    """Log compaction result to runtime logger and event bus."""
+    ratio_after = conversation.usage_ratio()
+    before_pct = round(ratio_before * 100)
+    after_pct = round(ratio_after * 100)
+
+    # Determine label from what happened
+    if after_pct >= before_pct - 1:
+        level = "prune_only"
+    elif ratio_after <= 0.6:
+        level = "llm"
+    else:
+        level = "structural"
+
+    logger.info(
+        "Compaction complete (%s): %d%% -> %d%%",
+        level,
+        before_pct,
+        after_pct,
+    )
+
+    if ctx.runtime_logger:
+        ctx.runtime_logger.log_step(
+            node_id=ctx.agent_id,
+            node_type="event_loop",
+            step_index=-1,
+            llm_text=f"Context compacted ({level}): {before_pct}% \u2192 {after_pct}%",
+            verdict="COMPACTION",
+            verdict_feedback=f"level={level} before={before_pct}% after={after_pct}%",
+        )
+
+    if event_bus:
+        from framework.host.event_bus import AgentEvent, EventType
+
+        event_data: dict[str, Any] = {
+            "level": level,
+            "usage_before": before_pct,
+            "usage_after": after_pct,
+        }
+        if pre_inventory is not None:
+            event_data["message_inventory"] = pre_inventory
+        await event_bus.publish(
+            AgentEvent(
+                type=EventType.CONTEXT_COMPACTED,
+                stream_id=ctx.stream_id or ctx.agent_id,
+                node_id=ctx.agent_id,
+                data=event_data,
+            )
+        )
+
+    await publish_context_usage(event_bus, ctx, conversation, "post_compaction")
+
+    if os.environ.get("HIVE_COMPACTION_DEBUG"):
+        write_compaction_debug_log(ctx, before_pct, after_pct, level, pre_inventory)
+
+
+def build_emergency_summary(
+    ctx: NodeContext,
+    accumulator: OutputAccumulator | None = None,
+    conversation: NodeConversation | None = None,
+    config: LoopConfig | None = None,
+) -> str:
+    """Build a structured emergency compaction summary.
+
+    Unlike normal/aggressive compaction which uses an LLM summary,
+    emergency compaction cannot afford an LLM call (context is already
+    way over budget).  Instead, build a deterministic summary from the
+    node's known state so the LLM can continue working after
+    compaction without losing track of its task and inputs.
+    """
+    parts = ["EMERGENCY COMPACTION — previous conversation was too large and has been replaced with this summary.\n"]
+
+    # 1. Node identity
+    spec = ctx.agent_spec
+    parts.append(f"NODE: {spec.name} (id={spec.id})")
+    if spec.description:
+        parts.append(f"PURPOSE: {spec.description}")
+
+    # 2. Inputs the node received
+    input_lines = []
+    for key in spec.input_keys:
+        value = ctx.input_data.get(key)
+        if value is not None:
+            # Truncate long values but keep them recognisable
+            v_str = str(value)
+            if len(v_str) > 200:
+                v_str = v_str[:200] + "…"
+            input_lines.append(f"  {key}: {v_str}")
+    if input_lines:
+        parts.append("INPUTS:\n" + "\n".join(input_lines))
+
+    # 3. Output accumulator state (what's been set so far)
+    if accumulator:
+        acc_state = accumulator.to_dict()
+        set_keys = {k: v for k, v in acc_state.items() if v is not None}
+        missing = [k for k, v in acc_state.items() if v is None]
+        if set_keys:
+            lines = [f"  {k}: {str(v)[:150]}" for k, v in set_keys.items()]
+            parts.append("OUTPUTS ALREADY SET:\n" + "\n".join(lines))
+        if missing:
+            parts.append(f"OUTPUTS STILL NEEDED: {', '.join(missing)}")
+    elif spec.output_keys:
+        parts.append(f"OUTPUTS STILL NEEDED: {', '.join(spec.output_keys)}")
+
+    # 4. Available tools reminder
+    if spec.tools:
+        parts.append(f"AVAILABLE TOOLS: {', '.join(spec.tools)}")
+
+    # 5. Spillover files — list actual files so the LLM can load
+    # them immediately instead of having to call list_data_files first.
+    spillover_dir = config.spillover_dir if config else None
+    if spillover_dir:
+        try:
+            from pathlib import Path
+
+            data_dir = Path(spillover_dir)
+            if data_dir.is_dir():
+                all_files = sorted(f.name for f in data_dir.iterdir() if f.is_file())
+                # Separate conversation history files from regular data files
+                conv_files = [f for f in all_files if re.match(r"conversation_\d+\.md$", f)]
+                data_files = [f for f in all_files if f not in conv_files]
+
+                if conv_files:
+                    conv_list = "\n".join(f"  - {f}  (full path: {data_dir / f})" for f in conv_files)
+                    parts.append(
+                        "CONVERSATION HISTORY (freeform messages saved during compaction — "
+                        "use read_file('<filename>') to review earlier dialogue):\n" + conv_list
+                    )
+                if data_files:
+                    file_list = "\n".join(f"  - {f}  (full path: {data_dir / f})" for f in data_files[:30])
+                    parts.append("DATA FILES (use read_file('<filename>') to read):\n" + file_list)
+                if not all_files:
+                    parts.append(
+                        "NOTE: Large tool results may have been saved to files. "
+                        "Use list_directory to check the data directory."
+                    )
+        except Exception:
+            parts.append("NOTE: Large tool results were saved to files. Use read_file(path='<path>') to read them.")
+
+    # 6. Tool call history (prevent re-calling tools)
+    if conversation is not None:
+        tool_history = _extract_tool_call_history(conversation)
+        if tool_history:
+            parts.append(tool_history)
+
+    parts.append("\nContinue working towards setting the remaining outputs. Use your tools and the inputs above.")
+    return "\n\n".join(parts)
+
+
+def _extract_tool_call_history(conversation: NodeConversation) -> str:
+    """Extract tool call history from conversation messages.
+
+    This is the instance-level variant that operates on a NodeConversation
+    directly (vs. the module-level extract_tool_call_history in conversation.py
+    which works on raw message lists).
+    """
+    from framework.agent_loop.conversation import extract_tool_call_history
+
+    return extract_tool_call_history(list(conversation.messages))

core/framework/agent_loop/internals/cursor_persistence.py

@@ -0,0 +1,267 @@
+"""Cursor persistence, queue draining, and pause detection.
+
+Handles the checkpoint/resume cycle: restoring state from a previous
+conversation store, writing cursor data, and managing injection/trigger
+queues between iterations.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass
+from typing import Any
+
+from framework.agent_loop.conversation import ConversationStore, NodeConversation
+from framework.agent_loop.internals.types import LoopConfig, OutputAccumulator, TriggerEvent
+from framework.llm.capabilities import supports_image_tool_results
+from framework.orchestrator.node import NodeContext
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RestoredState:
+    """State recovered from a previous checkpoint."""
+
+    conversation: NodeConversation
+    accumulator: OutputAccumulator
+    start_iteration: int
+    recent_responses: list[str]
+    recent_tool_fingerprints: list[list[tuple[str, str]]]
+    pending_input: dict[str, Any] | None
+
+
+async def restore(
+    conversation_store: ConversationStore | None,
+    ctx: NodeContext,
+    config: LoopConfig,
+) -> RestoredState | None:
+    """Attempt to restore from a previous checkpoint.
+
+    Returns a ``RestoredState`` with conversation, accumulator, iteration
+    counter, and stall/doom-loop detection state — everything needed to
+    resume exactly where execution stopped.
+    """
+    if conversation_store is None:
+        return None
+
+    # In isolated mode, filter parts by phase_id so the node only sees
+    # its own messages in the shared flat conversation store.  In
+    # continuous mode (or when _restore is called for timer-resume)
+    # load all parts — the full conversation threads across nodes.
+    _is_continuous = getattr(ctx, "continuous_mode", False)
+    # The queen has agent_id="queen" but messages are stored with phase_id=None.
+    # Only apply phase filtering for non-queen workers in a multi-agent setup.
+    phase_filter = None if (_is_continuous or ctx.agent_id == "queen") else ctx.agent_id
+    conversation = await NodeConversation.restore(
+        conversation_store,
+        phase_id=phase_filter,
+        run_id=ctx.effective_run_id,
+    )
+    if conversation is None:
+        logger.info(
+            "[restore] No conversation found for agent_id=%s phase_filter=%s run_id=%s",
+            ctx.agent_id,
+            phase_filter,
+            ctx.effective_run_id,
+        )
+        return None
+
+    logger.info(
+        "[restore] Restored %d messages for agent_id=%s phase_filter=%s run_id=%s",
+        conversation.message_count,
+        ctx.agent_id,
+        phase_filter,
+        ctx.effective_run_id,
+    )
+
+    # If run_id filtering removed all messages, this is an intentional
+    # restart (new run), not a crash recovery.  Return None so the caller
+    # falls through to the fresh-conversation path.
+    if conversation.message_count == 0:
+        return None
+
+    accumulator = await OutputAccumulator.restore(conversation_store, run_id=ctx.effective_run_id)
+    accumulator.spillover_dir = config.spillover_dir
+    accumulator.max_value_chars = config.max_output_value_chars
+
+    cursor = await conversation_store.read_cursor() or {}
+    start_iteration = cursor.get("iteration", 0) + 1
+
+    # Restore stall/doom-loop detection state
+    recent_responses: list[str] = cursor.get("recent_responses", [])
+    raw_fps = cursor.get("recent_tool_fingerprints", [])
+    recent_tool_fingerprints: list[list[tuple[str, str]]] = [
+        [tuple(pair) for pair in fps]  # type: ignore[misc]
+        for fps in raw_fps
+    ]
+    pending_input = cursor.get("pending_input")
+    if not isinstance(pending_input, dict):
+        pending_input = None
+
+    logger.info(
+        f"Restored event loop: iteration={start_iteration}, "
+        f"messages={conversation.message_count}, "
+        f"outputs={list(accumulator.values.keys())}, "
+        f"stall_window={len(recent_responses)}, "
+        f"doom_window={len(recent_tool_fingerprints)}"
+    )
+    return RestoredState(
+        conversation=conversation,
+        accumulator=accumulator,
+        start_iteration=start_iteration,
+        recent_responses=recent_responses,
+        recent_tool_fingerprints=recent_tool_fingerprints,
+        pending_input=pending_input,
+    )
+
+
+async def write_cursor(
+    conversation_store: ConversationStore | None,
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    accumulator: OutputAccumulator,
+    iteration: int,
+    *,
+    recent_responses: list[str] | None = None,
+    recent_tool_fingerprints: list[list[tuple[str, str]]] | None = None,
+    pending_input: dict[str, Any] | None = None,
+) -> None:
+    """Write checkpoint cursor for crash recovery.
+
+    Persists iteration counter, accumulator outputs, and stall/doom-loop
+    detection state so that resume picks up exactly where execution stopped.
+    """
+    if conversation_store:
+        cursor = await conversation_store.read_cursor() or {}
+        cursor.update(
+            {
+                "iteration": iteration,
+                "node_id": ctx.agent_id,
+                "outputs": accumulator.to_dict(),
+            }
+        )
+        # Persist stall/doom-loop detection state for reliable resume
+        if recent_responses is not None:
+            cursor["recent_responses"] = recent_responses
+        if recent_tool_fingerprints is not None:
+            # Convert list[list[tuple]] → list[list[list]] for JSON
+            cursor["recent_tool_fingerprints"] = [[list(pair) for pair in fps] for fps in recent_tool_fingerprints]
+        # Persist blocked-input state so restored runs re-block instead of
+        # manufacturing a synthetic continuation turn.
+        cursor["pending_input"] = pending_input
+        await conversation_store.write_cursor(cursor)
+
+
+async def drain_injection_queue(
+    queue: asyncio.Queue,
+    conversation: NodeConversation,
+    *,
+    ctx: NodeContext,
+    describe_images_as_text_fn: (Callable[[list[dict[str, Any]]], Awaitable[str | None]] | None) = None,
+) -> int:
+    """Drain all pending injected events as user messages. Returns count."""
+    count = 0
+    logger.debug(
+        "[drain_injection_queue] Starting to drain queue, initial queue size: %s",
+        queue.qsize() if hasattr(queue, "qsize") else "unknown",
+    )
+    while not queue.empty():
+        try:
+            content, is_client_input, image_content = queue.get_nowait()
+            logger.info(
+                "[drain] injected message (client_input=%s, images=%d): %s",
+                is_client_input,
+                len(image_content) if image_content else 0,
+                content[:200] if content else "(empty)",
+            )
+            if image_content and ctx.llm and not supports_image_tool_results(ctx.llm.model):
+                logger.info(
+                    "Model '%s' does not support images; attempting vision fallback",
+                    ctx.llm.model,
+                )
+                if describe_images_as_text_fn is not None:
+                    description = await describe_images_as_text_fn(image_content)
+                    if description:
+                        content = f"{content}\n\n{description}" if content else description
+                        logger.info("[drain] image described as text via vision fallback")
+                    else:
+                        logger.info("[drain] no vision fallback available; images dropped")
+                image_content = None
+            # Real user input is stored as-is; external events get a prefix
+            if is_client_input:
+                await conversation.add_user_message(
+                    content,
+                    is_client_input=True,
+                    image_content=image_content,
+                )
+            else:
+                await conversation.add_user_message(f"[External event]: {content}")
+            count += 1
+        except asyncio.QueueEmpty:
+            break
+    return count
+
+
+async def drain_trigger_queue(
+    queue: asyncio.Queue,
+    conversation: NodeConversation,
+) -> int:
+    """Drain all pending trigger events as a single batched user message.
+
+    Multiple triggers are merged so the LLM sees them atomically and can
+    reason about all pending triggers before acting.
+    """
+    triggers: list[TriggerEvent] = []
+    while not queue.empty():
+        try:
+            triggers.append(queue.get_nowait())
+        except asyncio.QueueEmpty:
+            break
+
+    if not triggers:
+        return 0
+
+    parts: list[str] = []
+    for t in triggers:
+        task = t.payload.get("task", "")
+        task_line = f"\nTask: {task}" if task else ""
+        payload_str = json.dumps(t.payload, default=str)
+        parts.append(f"[TRIGGER: {t.trigger_type}/{t.source_id}]{task_line}\n{payload_str}")
+
+    combined = "\n\n".join(parts)
+    logger.info("[drain] %d trigger(s): %s", len(triggers), combined[:200])
+    # Tag the message so the UI can render a banner instead of the raw
+    # `[TRIGGER: ...]` text. The LLM still sees `combined` verbatim.
+    await conversation.add_user_message(combined, is_trigger=True)
+    return len(triggers)
+
+
+async def check_pause(
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    iteration: int,
+) -> bool:
+    """
+    Check if pause has been requested. Returns True if paused.
+
+    Note: This check happens BEFORE starting iteration N, after completing N-1.
+    If paused, the node exits having completed {iteration} iterations (0 to iteration-1).
+    """
+    # Check executor-level pause event (for /pause command, Ctrl+Z)
+    if ctx.pause_event and ctx.pause_event.is_set():
+        completed = iteration  # 0-indexed: iteration=3 means 3 iterations completed (0,1,2)
+        logger.info(f"⏸ Pausing after {completed} iteration(s) completed (executor-level)")
+        return True
+
+    # Check context-level pause flags (legacy/alternative methods)
+    pause_requested = ctx.input_data.get("pause_requested", False)
+    if pause_requested:
+        completed = iteration
+        logger.info(f"⏸ Pausing after {completed} iteration(s) completed (context-level)")
+        return True
+
+    return False

core/framework/agent_loop/internals/event_publishing.py

@@ -0,0 +1,358 @@
+"""EventBus publishing helpers for the event loop.
+
+Thin wrappers around EventBus.emit_*() calls that check for bus existence
+before publishing.  Extracted to reduce noise in the main orchestrator.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+
+from framework.agent_loop.conversation import NodeConversation
+from framework.agent_loop.internals.types import HookContext
+from framework.host.event_bus import EventBus
+from framework.orchestrator.node import NodeContext
+
+logger = logging.getLogger(__name__)
+
+
+async def publish_loop_started(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    max_iterations: int,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_loop_started(
+            stream_id=stream_id,
+            node_id=node_id,
+            max_iterations=max_iterations,
+            execution_id=execution_id,
+        )
+
+
+async def generate_action_plan(
+    event_bus: EventBus | None,
+    ctx: NodeContext,
+    stream_id: str,
+    node_id: str,
+    execution_id: str,
+) -> None:
+    """Generate a brief action plan via LLM and emit it as an SSE event.
+
+    Runs as a fire-and-forget task so it never blocks the main loop.
+    """
+    try:
+        system_prompt = ctx.agent_spec.system_prompt or ""
+        # Trim to keep the prompt small
+        prompt_summary = system_prompt[:500]
+        if len(system_prompt) > 500:
+            prompt_summary += "..."
+
+        tool_names = [t.name for t in ctx.available_tools]
+        output_keys = ctx.agent_spec.output_keys or []
+
+        prompt = (
+            f'You are about to work on a task as node "{node_id}".\n\n'
+            f"System prompt:\n{prompt_summary}\n\n"
+            f"Tools available: {tool_names}\n"
+            f"Required outputs: {output_keys}\n\n"
+            f"Write a brief action plan (2-5 bullet points) describing "
+            f"what you will do to complete this task. Be specific and concise.\n"
+            f"Return ONLY the plan text, no preamble."
+        )
+
+        response = await ctx.llm.acomplete(
+            messages=[{"role": "user", "content": prompt}],
+            max_tokens=1024,
+        )
+
+        plan = response.content.strip()
+        if plan and event_bus:
+            await event_bus.emit_node_action_plan(
+                stream_id=stream_id,
+                node_id=node_id,
+                plan=plan,
+                execution_id=execution_id,
+            )
+    except Exception as e:
+        logger.warning("Action plan generation failed for node '%s': %s", node_id, e)
+
+
+async def publish_iteration(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    iteration: int,
+    execution_id: str = "",
+    extra_data: dict | None = None,
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_loop_iteration(
+            stream_id=stream_id,
+            node_id=node_id,
+            iteration=iteration,
+            execution_id=execution_id,
+            extra_data=extra_data,
+        )
+
+
+async def publish_llm_turn_complete(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    stop_reason: str,
+    model: str,
+    input_tokens: int,
+    output_tokens: int,
+    cached_tokens: int = 0,
+    execution_id: str = "",
+    iteration: int | None = None,
+) -> None:
+    if event_bus:
+        await event_bus.emit_llm_turn_complete(
+            stream_id=stream_id,
+            node_id=node_id,
+            stop_reason=stop_reason,
+            model=model,
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            cached_tokens=cached_tokens,
+            execution_id=execution_id,
+            iteration=iteration,
+        )
+
+
+def log_skip_judge(
+    ctx: NodeContext,
+    node_id: str,
+    iteration: int,
+    feedback: str,
+    tool_calls: list[dict],
+    llm_text: str,
+    turn_tokens: dict[str, int],
+    iter_start: float,
+) -> None:
+    """Log a CONTINUE step that skips judge evaluation (e.g., waiting for input)."""
+    if ctx.runtime_logger:
+        ctx.runtime_logger.log_step(
+            node_id=node_id,
+            node_type="event_loop",
+            step_index=iteration,
+            verdict="CONTINUE",
+            verdict_feedback=feedback,
+            tool_calls=tool_calls,
+            llm_text=llm_text,
+            input_tokens=turn_tokens.get("input", 0),
+            output_tokens=turn_tokens.get("output", 0),
+            latency_ms=int((time.time() - iter_start) * 1000),
+        )
+
+
+async def publish_loop_completed(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    iterations: int,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_loop_completed(
+            stream_id=stream_id,
+            node_id=node_id,
+            iterations=iterations,
+            execution_id=execution_id,
+        )
+
+
+async def publish_context_usage(
+    event_bus: EventBus | None,
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    trigger: str,
+) -> None:
+    """Emit a CONTEXT_USAGE_UPDATED event with current context window state."""
+    if not event_bus:
+        return
+
+    from framework.host.event_bus import AgentEvent, EventType
+
+    estimated = conversation.estimate_tokens()
+    max_tokens = conversation._max_context_tokens
+    ratio = estimated / max_tokens if max_tokens > 0 else 0.0
+    await event_bus.publish(
+        AgentEvent(
+            type=EventType.CONTEXT_USAGE_UPDATED,
+            stream_id=ctx.stream_id or ctx.agent_id,
+            node_id=ctx.agent_id,
+            data={
+                "usage_ratio": round(ratio, 4),
+                "usage_pct": round(ratio * 100),
+                "message_count": conversation.message_count,
+                "estimated_tokens": estimated,
+                "max_context_tokens": max_tokens,
+                "trigger": trigger,
+            },
+        )
+    )
+
+
+async def publish_stalled(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_node_stalled(
+            stream_id=stream_id,
+            node_id=node_id,
+            reason="Consecutive similar responses detected",
+            execution_id=execution_id,
+        )
+
+
+async def publish_text_delta(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    content: str,
+    snapshot: str,
+    ctx: NodeContext,
+    execution_id: str = "",
+    iteration: int | None = None,
+    inner_turn: int = 0,
+) -> None:
+    if event_bus:
+        if ctx.emits_client_io:
+            await event_bus.emit_client_output_delta(
+                stream_id=stream_id,
+                node_id=node_id,
+                content=content,
+                snapshot=snapshot,
+                execution_id=execution_id,
+                iteration=iteration,
+                inner_turn=inner_turn,
+            )
+        else:
+            await event_bus.emit_llm_text_delta(
+                stream_id=stream_id,
+                node_id=node_id,
+                content=content,
+                snapshot=snapshot,
+                execution_id=execution_id,
+                inner_turn=inner_turn,
+            )
+
+
+async def publish_tool_started(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    tool_use_id: str,
+    tool_name: str,
+    tool_input: dict,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_tool_call_started(
+            stream_id=stream_id,
+            node_id=node_id,
+            tool_use_id=tool_use_id,
+            tool_name=tool_name,
+            tool_input=tool_input,
+            execution_id=execution_id,
+        )
+
+
+async def publish_tool_completed(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    tool_use_id: str,
+    tool_name: str,
+    result: str,
+    is_error: bool,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_tool_call_completed(
+            stream_id=stream_id,
+            node_id=node_id,
+            tool_use_id=tool_use_id,
+            tool_name=tool_name,
+            result=result,
+            is_error=is_error,
+            execution_id=execution_id,
+        )
+
+
+async def publish_judge_verdict(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    action: str,
+    feedback: str = "",
+    judge_type: str = "implicit",
+    iteration: int = 0,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        await event_bus.emit_judge_verdict(
+            stream_id=stream_id,
+            node_id=node_id,
+            action=action,
+            feedback=feedback,
+            judge_type=judge_type,
+            iteration=iteration,
+            execution_id=execution_id,
+        )
+
+
+async def publish_output_key_set(
+    event_bus: EventBus | None,
+    stream_id: str,
+    node_id: str,
+    key: str,
+    execution_id: str = "",
+) -> None:
+    if event_bus:
+        pass
+
+
+async def run_hooks(
+    hooks_config: dict[str, list],
+    event: str,
+    conversation: NodeConversation,
+    trigger: str | None = None,
+) -> None:
+    """Run all registered hooks for *event*, applying their results.
+
+    Each hook receives a HookContext and may return a HookResult that:
+    - replaces the system prompt (result.system_prompt)
+    - injects an extra user message (result.inject)
+    Hooks run in registration order; each sees the prompt as left by the
+    previous hook.
+    """
+    hook_list = hooks_config.get(event, [])
+    if not hook_list:
+        return
+    for hook in hook_list:
+        ctx = HookContext(
+            event=event,
+            trigger=trigger,
+            system_prompt=conversation.system_prompt,
+        )
+        try:
+            result = await hook(ctx)
+        except Exception:
+            logger.warning("Hook '%s' raised an exception", event, exc_info=True)
+            continue
+        if result is None:
+            continue
+        if result.system_prompt:
+            conversation.update_system_prompt(result.system_prompt)
+        if result.inject:
+            await conversation.add_user_message(result.inject)

core/framework/agent_loop/internals/judge_pipeline.py

@@ -0,0 +1,152 @@
+"""Judge evaluation pipeline for the event loop."""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable
+
+from framework.agent_loop.conversation import NodeConversation
+from framework.agent_loop.internals.types import JudgeProtocol, JudgeVerdict, OutputAccumulator
+from framework.orchestrator.node import NodeContext
+
+logger = logging.getLogger(__name__)
+
+
+class SubagentJudge:
+    """Judge for subagent execution."""
+
+    def __init__(self, task: str, max_iterations: int = 10):
+        self._task = task
+        self._max_iterations = max_iterations
+
+    async def evaluate(self, context: dict[str, object]) -> JudgeVerdict:
+        missing = context.get("missing_keys", [])
+        if not isinstance(missing, list) or not missing:
+            return JudgeVerdict(action="ACCEPT", feedback="")
+
+        iteration = context.get("iteration", 0)
+        if not isinstance(iteration, int):
+            iteration = 0
+        remaining = self._max_iterations - iteration - 1
+
+        if remaining <= 3:
+            urgency = (
+                f"URGENT: Only {remaining} iterations left. Stop all other work and call set_output NOW for: {missing}"
+            )
+        elif remaining <= self._max_iterations // 2:
+            urgency = f"WARNING: {remaining} iterations remaining. You must call set_output for: {missing}"
+        else:
+            urgency = f"Missing output keys: {missing}. Use set_output to provide them."
+
+        return JudgeVerdict(action="RETRY", feedback=f"Your task: {self._task}\n{urgency}")
+
+
+async def judge_turn(
+    *,
+    mark_complete_flag: bool,
+    judge: JudgeProtocol | None,
+    ctx: NodeContext,
+    conversation: NodeConversation,
+    accumulator: OutputAccumulator,
+    assistant_text: str,
+    tool_results: list[dict[str, object]],
+    iteration: int,
+    get_missing_output_keys_fn: Callable[
+        [OutputAccumulator, list[str] | None, list[str] | None],
+        list[str],
+    ],
+    max_context_tokens: int,
+) -> JudgeVerdict:
+    """Evaluate the current state using judge or implicit logic.
+
+    Evaluation levels (in order):
+      0. Short-circuits: mark_complete, skip_judge, tool-continue.
+      1. Custom judge (JudgeProtocol) — full authority when set.
+      2. Implicit judge — output-key check + optional conversation-aware
+         quality gate (when ``success_criteria`` is defined).
+
+    Returns a JudgeVerdict.  ``feedback=None`` means no real evaluation
+    happened (skip_judge, tool-continue); the caller must not inject a
+    feedback message.  Any non-None feedback (including ``""``) means a
+    real evaluation occurred and will be logged into the conversation.
+    """
+    # --- Level 0: short-circuits (no evaluation) -----------------------
+
+    if mark_complete_flag:
+        return JudgeVerdict(action="ACCEPT")
+
+    if ctx.agent_spec.skip_judge:
+        return JudgeVerdict(action="RETRY")  # feedback=None → not logged
+
+    # --- Level 1: custom judge -----------------------------------------
+
+    if judge is not None:
+        context = {
+            "assistant_text": assistant_text,
+            "tool_calls": tool_results,
+            "output_accumulator": accumulator.to_dict(),
+            "accumulator": accumulator,
+            "iteration": iteration,
+            "conversation_summary": conversation.export_summary(),
+            "output_keys": ctx.agent_spec.output_keys,
+            "missing_keys": get_missing_output_keys_fn(
+                accumulator, ctx.agent_spec.output_keys, ctx.agent_spec.nullable_output_keys
+            ),
+        }
+        verdict = await judge.evaluate(context)
+        # Ensure evaluated RETRY always carries feedback for logging.
+        if verdict.action == "RETRY" and not verdict.feedback:
+            return JudgeVerdict(action="RETRY", feedback="Custom judge returned RETRY.")
+        return verdict
+
+    # --- Level 2: implicit judge ---------------------------------------
+
+    # Real tool calls were made — let the agent keep working.
+    if tool_results:
+        return JudgeVerdict(action="RETRY")  # feedback=None → not logged
+
+    missing = get_missing_output_keys_fn(accumulator, ctx.agent_spec.output_keys, ctx.agent_spec.nullable_output_keys)
+
+    if missing:
+        return JudgeVerdict(
+            action="RETRY",
+            feedback=(
+                f"Task incomplete. Required outputs not yet produced: {missing}. "
+                f"Follow your system prompt instructions to complete the work."
+            ),
+        )
+
+    # All output keys present — run safety checks before accepting.
+
+    output_keys = ctx.agent_spec.output_keys or []
+    nullable_keys = set(ctx.agent_spec.nullable_output_keys or [])
+
+    # All-nullable with nothing set → node produced nothing useful.
+    all_nullable = output_keys and nullable_keys >= set(output_keys)
+    none_set = not any(accumulator.get(k) is not None for k in output_keys)
+    if all_nullable and none_set:
+        return JudgeVerdict(
+            action="RETRY",
+            feedback=(f"No output keys have been set yet. Use set_output to set at least one of: {output_keys}"),
+        )
+
+    # Level 2b: conversation-aware quality check (if success_criteria set)
+    if ctx.agent_spec.success_criteria and ctx.llm:
+        from framework.orchestrator.conversation_judge import evaluate_phase_completion
+
+        verdict = await evaluate_phase_completion(
+            llm=ctx.llm,
+            conversation=conversation,
+            phase_name=ctx.agent_spec.name,
+            phase_description=ctx.agent_spec.description,
+            success_criteria=ctx.agent_spec.success_criteria,
+            accumulator_state=accumulator.to_dict(),
+            max_context_tokens=max_context_tokens,
+        )
+        if verdict.action != "ACCEPT":
+            return JudgeVerdict(
+                action=verdict.action,
+                feedback=verdict.feedback or "Phase criteria not met.",
+            )
+
+    return JudgeVerdict(action="ACCEPT", feedback="")

core/framework/agent_loop/internals/stall_detector.py

@@ -0,0 +1,106 @@
+"""Stall and doom-loop detection for the event loop.
+
+Pure functions with no class dependencies — safe to call from any context.
+"""
+
+from __future__ import annotations
+
+import json
+
+
+def ngram_similarity(s1: str, s2: str, n: int = 2) -> float:
+    """Jaccard similarity of n-gram sets.
+
+    Returns 0.0-1.0, where 1.0 is exact match.
+    Fast: O(len(s) + len(s2)) using set operations.
+    """
+
+    def _ngrams(s: str) -> set[str]:
+        return {s[i : i + n] for i in range(len(s) - n + 1) if s.strip()}
+
+    if not s1 or not s2:
+        return 0.0
+
+    ngrams1, ngrams2 = _ngrams(s1.lower()), _ngrams(s2.lower())
+    if not ngrams1 or not ngrams2:
+        return 0.0
+
+    intersection = len(ngrams1 & ngrams2)
+    union = len(ngrams1 | ngrams2)
+    return intersection / union if union else 0.0
+
+
+def is_stalled(
+    recent_responses: list[str],
+    threshold: int,
+    similarity_threshold: float,
+) -> bool:
+    """Detect stall using n-gram similarity.
+
+    Detects when ALL N consecutive responses are mutually similar
+    (>= threshold).  A single dissimilar response resets the signal.
+    This catches phrases like "I'm still stuck" vs "I'm stuck"
+    without false-positives on "attempt 1" vs "attempt 2".
+    """
+    if len(recent_responses) < threshold:
+        return False
+    if not recent_responses[0]:
+        return False
+
+    # Every consecutive pair must be similar
+    for i in range(1, len(recent_responses)):
+        if ngram_similarity(recent_responses[i], recent_responses[i - 1]) < similarity_threshold:
+            return False
+    return True
+
+
+def fingerprint_tool_calls(
+    tool_results: list[dict],
+) -> list[tuple[str, str]]:
+    """Create deterministic fingerprints for a turn's tool calls.
+
+    Each fingerprint is (tool_name, canonical_args_json).  Order-sensitive
+    so [search("a"), fetch("b")] != [fetch("b"), search("a")].
+    """
+    fingerprints = []
+    for tr in tool_results:
+        name = tr.get("tool_name", "")
+        args = tr.get("tool_input", {})
+        try:
+            canonical = json.dumps(args, sort_keys=True, default=str)
+        except (TypeError, ValueError):
+            canonical = str(args)
+        fingerprints.append((name, canonical))
+    return fingerprints
+
+
+def is_tool_doom_loop(
+    recent_tool_fingerprints: list[list[tuple[str, str]]],
+    threshold: int,
+    enabled: bool = True,
+) -> tuple[bool, str]:
+    """Detect doom loop via exact fingerprint match.
+
+    Detects when N consecutive turns invoke the same tools with
+    identical (canonicalized) arguments.  Different arguments mean
+    different work, so only exact matches count.
+
+    Returns (is_doom_loop, description).
+    """
+    if not enabled:
+        return False, ""
+    if len(recent_tool_fingerprints) < threshold:
+        return False, ""
+    first = recent_tool_fingerprints[0]
+    if not first:
+        return False, ""
+
+    # All turns in the window must match the first exactly
+    if all(fp == first for fp in recent_tool_fingerprints[1:]):
+        tool_names = [name for name, _ in first]
+        desc = (
+            f"Doom loop detected: {len(recent_tool_fingerprints)} "
+            f"identical consecutive tool calls ({', '.join(tool_names)})"
+        )
+        return True, desc
+    return False, ""

core/framework/agent_loop/internals/synthetic_tools.py

@@ -0,0 +1,387 @@
+"""Synthetic tool builders for the event loop.
+
+Factory functions that create ``Tool`` definitions for framework-level
+synthetic tools (set_output, ask_user, escalate, delegate, report_to_parent).
+Also includes the ``handle_set_output`` validation logic.
+
+All functions are pure — they receive explicit parameters and return
+``Tool`` or ``ToolResult`` objects with no side effects.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from framework.llm.provider import Tool, ToolResult
+
+
+def sanitize_ask_user_inputs(
+    raw_question: Any,
+    raw_options: Any,
+) -> tuple[str, list[str] | None]:
+    """Self-heal a malformed ``ask_user`` tool call.
+
+    Some model families (notably when the system prompt teaches them
+    XML-ish scratchpad tags like ``<relationship>...</relationship>``)
+    carry that style into tool arguments and produce calls like::
+
+        ask_user({
+            "question": "What now?</question>\\n_OPTIONS: [\\"A\\", \\"B\\"]"
+        })
+
+    Symptoms:
+    - The chat UI renders ``</question>`` and ``_OPTIONS: [...]`` as
+      literal text in the question bubble.
+    - No buttons appear because the real ``options`` parameter is
+      empty.
+
+    This function:
+    - Strips leading/trailing whitespace.
+    - Removes a trailing ``</question>`` (with optional preceding
+      whitespace) from the question text.
+    - Detects an inline ``_OPTIONS:``, ``OPTIONS:``, or ``options:``
+      line followed by a JSON array, parses it, and returns the
+      recovered list as the second element.
+    - Removes the parsed line from the returned question text.
+
+    Returns ``(cleaned_question, recovered_options_or_None)``. The
+    caller should treat the recovered list as a fallback only when
+    the model did not also supply a real ``options`` array.
+    """
+    import json as _json
+    import re as _re
+
+    if raw_question is None:
+        return "", None
+    q = str(raw_question)
+
+    # Strip a stray </question> tag (case-insensitive, with optional
+    # preceding whitespace) anywhere in the string. This is the most
+    # common failure mode and never represents valid content.
+    q = _re.sub(r"\s*</\s*question\s*>\s*", "\n", q, flags=_re.IGNORECASE)
+
+    # Look for an inline options line. Match _OPTIONS, OPTIONS, options
+    # (with or without leading underscore), followed by ':' or '=', then
+    # a JSON array on the same line OR on the next line.
+    inline_options_re = _re.compile(
+        r"(?im)^\s*_?options\s*[:=]\s*(\[.*?\])\s*$",
+        _re.DOTALL,
+    )
+
+    recovered: list[str] | None = None
+    match = inline_options_re.search(q)
+    if match is not None:
+        try:
+            parsed = _json.loads(match.group(1))
+            if isinstance(parsed, list):
+                cleaned = [str(o).strip() for o in parsed if str(o).strip()]
+                if 1 <= len(cleaned) <= 8:
+                    recovered = cleaned
+        except (ValueError, TypeError):
+            pass
+        if recovered is not None:
+            # Remove the parsed line so it doesn't leak into the
+            # rendered question text.
+            q = inline_options_re.sub("", q, count=1)
+
+    # Strip any final whitespace / leftover blank lines from the
+    # question after removals.
+    q = _re.sub(r"\n{3,}", "\n\n", q).strip()
+
+    return q, recovered
+
+
+ask_user_prompt = """\
+Use this tool when you need to ask the user questions during execution. Reach for it when:
+
+- The task is ambiguous and the user needs to choose an approach
+- You need missing information to continue
+- You want approval before taking a meaningful action
+- A decision has real trade-offs the user should weigh in on
+- You want post-task feedback, or to offer saving a skill or updating memory
+
+Usage notes:
+- Users will always be able to select "Other" to provide custom text input, \
+so do not include catch-all options like "Other" or "Something else" yourself.
+- Each option is a plain string. Do NOT wrap options in `{"label": "..."}` or \
+`{"value": "..."}` objects — pass the raw choice text directly, e.g. `"Email"`, \
+not `{"label": "Email"}`.
+- If you recommend a specific option, make that the first option in the list \
+and append " (Recommended)" to the end of its text.
+- Call this tool whenever you need the user's response.
+- The prompt field must be plain text only.
+- Do not include XML, pseudo-tags, or inline option lists inside prompt.
+- Omit options only when the question truly requires a free-form response the \
+user must type out, such as describing an idea or pasting an error message.
+- Do not repeat the questions in your normal text response. The widget renders \
+them, so keep any surrounding text to a brief intro only.
+Example — single question with options:
+{"questions": [{"id": "next", "prompt": "What would you like to do?", \
+"options": ["Build a new agent (Recommended)", "Modify existing agent", "Run tests"]}]}
+
+Example — batch:
+{"questions": [
+  {"id": "scope", "prompt": "What scope?", "options": ["Full", "Partial"]},
+  {"id": "format", "prompt": "Output format?", "options": ["PDF", "CSV", "JSON"]},
+  {"id": "details", "prompt": "Any special requirements?"}
+]}
+
+Example — free-form (queen only):
+{"questions": [{"id": "idea", "prompt": "Describe the agent you want to build."}]}
+"""
+
+
+def build_ask_user_tool() -> Tool:
+    """Build the synthetic ask_user tool for explicit user-input requests.
+
+    The queen calls ask_user() when it needs to pause and wait for user
+    input. Accepts an array of 1-8 questions — a single question for the
+    common case, or a batch when several clarifications are needed at once.
+    Text-only turns WITHOUT ask_user flow through without blocking, allowing
+    progress updates and summaries to stream freely.
+    """
+    return Tool(
+        name="ask_user",
+        description=ask_user_prompt,
+        parameters={
+            "type": "object",
+            "properties": {
+                "questions": {
+                    "type": "array",
+                    "minItems": 1,
+                    "maxItems": 8,
+                    "description": "List of questions to present to the user.",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "id": {
+                                "type": "string",
+                                "description": ("Short identifier for this question (used in the response)."),
+                            },
+                            "prompt": {
+                                "type": "string",
+                                "description": "The question text shown to the user.",
+                            },
+                            "options": {
+                                "type": "array",
+                                "items": {"type": "string"},
+                                "description": (
+                                    "2-3 predefined choices as plain strings "
+                                    '(e.g. ["Yes", "No", "Maybe"]). Do NOT '
+                                    'wrap items in {"label": "..."} or '
+                                    '{"value": "..."} objects — pass the raw '
+                                    "choice text directly. The UI appends an "
+                                    "'Other' free-text input automatically, "
+                                    "so don't include catch-all options. "
+                                    "Omit only when the user must type a free-form answer."
+                                ),
+                                "minItems": 2,
+                                "maxItems": 3,
+                            },
+                        },
+                        "required": ["id", "prompt"],
+                    },
+                },
+            },
+            "required": ["questions"],
+        },
+    )
+
+
+def build_set_output_tool(output_keys: list[str] | None) -> Tool | None:
+    """Build the synthetic set_output tool for explicit output declaration."""
+    if not output_keys:
+        return None
+    return Tool(
+        name="set_output",
+        description=(
+            "Set an output value for this node. Call once per output key. "
+            "Use this for brief notes, counts, status, and file references — "
+            "NOT for large data payloads. When a tool result was saved to a "
+            "data file, pass the filename as the value "
+            "(e.g. 'google_sheets_get_values_1.txt') so the next phase can "
+            "load the full data. Values exceeding ~2000 characters are "
+            "auto-saved to data files. "
+            f"Valid keys: {output_keys}"
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "key": {
+                    "type": "string",
+                    "description": f"Output key. Must be one of: {output_keys}",
+                    "enum": output_keys,
+                },
+                "value": {
+                    "type": "string",
+                    "description": ("The output value — a brief note, count, status, or data filename reference."),
+                },
+            },
+            "required": ["key", "value"],
+        },
+    )
+
+
+def build_escalate_tool() -> Tool:
+    """Build the synthetic escalate tool for worker -> queen handoff."""
+    return Tool(
+        name="escalate",
+        description=(
+            "Escalate to the queen when requesting user input, "
+            "blocked by errors, missing "
+            "credentials, or ambiguous constraints that require supervisor "
+            "guidance. Include a concise reason and optional context. "
+            "The node will pause until the queen injects guidance."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "reason": {
+                    "type": "string",
+                    "description": ("Short reason for escalation (e.g. 'Tool repeatedly failing')."),
+                },
+                "context": {
+                    "type": "string",
+                    "description": "Optional diagnostic details for the queen.",
+                },
+            },
+            "required": ["reason"],
+        },
+    )
+
+
+def build_report_to_parent_tool() -> Tool:
+    """Build the synthetic ``report_to_parent`` tool.
+
+    Parallel workers (those spawned by the overseer via
+    ``run_parallel_workers``) call this to send a structured report back
+    to the overseer queen when they have finished their task. Calling
+    ``report_to_parent`` terminates the worker's loop cleanly -- do not
+    call other tools after it.
+
+    The overseer receives these as ``SUBAGENT_REPORT`` events and
+    aggregates them into a single summary for the user.
+    """
+    return Tool(
+        name="report_to_parent",
+        description=(
+            "Send a structured report back to the parent overseer and "
+            "terminate. Call this when you have finished your task "
+            "(success, partial, or failed) or cannot make further "
+            "progress. Your loop ends after this call -- do not call any "
+            "other tool afterwards. The overseer reads the summary + "
+            "data fields and aggregates them into a user-facing response."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "status": {
+                    "type": "string",
+                    "enum": ["success", "partial", "failed"],
+                    "description": (
+                        "Overall outcome. 'success' = task complete. "
+                        "'partial' = some progress but incomplete. "
+                        "'failed' = could not make progress."
+                    ),
+                },
+                "summary": {
+                    "type": "string",
+                    "description": (
+                        "One-paragraph narrative for the overseer. What "
+                        "you did, what you found, and any notable issues."
+                    ),
+                },
+                "data": {
+                    "type": "object",
+                    "description": (
+                        "Optional structured payload (rows fetched, IDs "
+                        "processed, files written, etc.) that the "
+                        "overseer can merge into its final summary."
+                    ),
+                },
+            },
+            "required": ["status", "summary"],
+        },
+    )
+
+
+def handle_report_to_parent(tool_input: dict[str, Any]) -> ToolResult:
+    """Normalise + validate a ``report_to_parent`` tool call.
+
+    Returns a ``ToolResult`` with the acknowledgement text the LLM sees;
+    the side effects (record on Worker, emit SUBAGENT_REPORT, terminate
+    loop) are performed by ``AgentLoop`` after this helper returns.
+    """
+    status = str(tool_input.get("status", "success")).strip().lower()
+    if status not in ("success", "partial", "failed"):
+        status = "success"
+    summary = str(tool_input.get("summary", "")).strip()
+    if not summary:
+        summary = f"(worker returned {status} with no summary)"
+    data = tool_input.get("data") or {}
+    if not isinstance(data, dict):
+        data = {"value": data}
+    # Store the normalised payload back on the input dict so the caller
+    # can pick it up without re-parsing.
+    tool_input["_normalised"] = {
+        "status": status,
+        "summary": summary,
+        "data": data,
+    }
+    return ToolResult(
+        tool_use_id=tool_input.get("tool_use_id", ""),
+        content=(f"Report delivered to overseer (status={status}). This worker will terminate now."),
+    )
+
+
+def handle_set_output(
+    tool_input: dict[str, Any],
+    output_keys: list[str] | None,
+) -> ToolResult:
+    """Handle set_output tool call. Returns ToolResult (sync)."""
+    import logging
+    import re
+
+    logger = logging.getLogger(__name__)
+
+    key = tool_input.get("key", "")
+    value = tool_input.get("value", "")
+    valid_keys = output_keys or []
+
+    # Recover from truncated JSON (max_tokens hit mid-argument).
+    # The _raw key is set by litellm when json.loads fails.
+    if not key and "_raw" in tool_input:
+        raw = tool_input["_raw"]
+        key_match = re.search(r'"key"\s*:\s*"(\w+)"', raw)
+        if key_match:
+            key = key_match.group(1)
+        val_match = re.search(r'"value"\s*:\s*"', raw)
+        if val_match:
+            start = val_match.end()
+            value = raw[start:].rstrip()
+            for suffix in ('"}\n', '"}', '"'):
+                if value.endswith(suffix):
+                    value = value[: -len(suffix)]
+                    break
+        if key:
+            logger.warning(
+                "Recovered set_output args from truncated JSON: key=%s, value_len=%d",
+                key,
+                len(value),
+            )
+            # Re-inject so the caller sees proper key/value
+            tool_input["key"] = key
+            tool_input["value"] = value
+
+    if key not in valid_keys:
+        return ToolResult(
+            tool_use_id="",
+            content=f"Invalid output key '{key}'. Valid keys: {valid_keys}",
+            is_error=True,
+        )
+
+    return ToolResult(
+        tool_use_id="",
+        content=f"Output '{key}' set successfully.",
+        is_error=False,
+    )

core/framework/agent_loop/internals/tool_input_coercer.py

@@ -0,0 +1,291 @@
+"""Generic coercion of LLM-emitted tool arguments to match each tool's JSON schema.
+
+Small/mid-size models drift from tool schemas in predictable, boring ways:
+
+- A number field comes back as a string (``"42"`` instead of ``42``).
+- A boolean field comes back as a string (``"true"`` instead of ``True``).
+- An array-of-string field comes back as an array of objects
+  (``[{"label": "A"}, ...]`` instead of ``["A", ...]``).
+- An array/object field comes back as a JSON-encoded string
+  (``'["A","B"]'`` instead of ``["A", "B"]``).
+- A lone scalar arrives where the schema expects an array.
+
+This module centralizes the healing in one schema-driven pass that runs
+on every tool call before dispatch. Coercion is conservative:
+
+- Values that already match the expected type are untouched.
+- Shapes we don't recognize are returned as-is, so real bugs surface
+  instead of getting silently munged into something plausible.
+- Every actual coercion is logged with the tool, property, and shape
+  transition so we can see which models/tools are drifting.
+
+Tool-specific prompt drift (e.g. ``</question>`` tags leaking into an
+``ask_user`` prompt string) is NOT this module's job — that belongs in
+per-tool sanitizers, because it's about prompt style, not schema shape.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+from framework.llm.provider import Tool
+
+logger = logging.getLogger(__name__)
+
+# When an ``array<string>`` field arrives as an array of objects, look
+# for a text-carrying field in preference order. Covers the wrappers
+# small models tend to produce: ``[{"label": "A"}]``, ``[{"value": "A"}]``,
+# ``[{"text": "A"}]``, etc.
+_STRING_EXTRACT_KEYS: tuple[str, ...] = (
+    "label",
+    "value",
+    "text",
+    "name",
+    "title",
+    "display",
+)
+
+
+def coerce_tool_input(tool: Tool, raw_input: dict[str, Any] | None) -> dict[str, Any]:
+    """Coerce *raw_input* in place to match *tool*'s JSON schema.
+
+    Returns the mutated input dict (same object as *raw_input* when
+    possible, for callers that assume in-place mutation). Properties
+    not present in the schema are left untouched.
+    """
+    if not isinstance(raw_input, dict):
+        return raw_input or {}
+
+    schema = tool.parameters or {}
+    props = schema.get("properties")
+    if not isinstance(props, dict):
+        return raw_input
+
+    for key in list(raw_input.keys()):
+        prop_schema = props.get(key)
+        if not isinstance(prop_schema, dict):
+            continue
+        original = raw_input[key]
+        coerced = _coerce(original, prop_schema)
+        if coerced is not original:
+            logger.info(
+                "coerced tool input tool=%s prop=%s from=%s to=%s",
+                tool.name,
+                key,
+                _shape(original),
+                _shape(coerced),
+            )
+            raw_input[key] = coerced
+
+    return raw_input
+
+
+def _coerce(value: Any, schema: dict[str, Any]) -> Any:
+    """Dispatch on the schema's ``type`` field.
+
+    Returns the *same object* on passthrough so callers can detect
+    no-ops via identity (``coerced is value``).
+    """
+    expected = schema.get("type")
+    if not expected:
+        return value
+
+    # Union type: try each in order, return the first coercion that
+    # actually changes the value. Falls back to the original.
+    if isinstance(expected, list):
+        for t in expected:
+            sub_schema = {**schema, "type": t}
+            coerced = _coerce(value, sub_schema)
+            if coerced is not value:
+                return coerced
+        return value
+
+    if expected == "integer":
+        return _coerce_integer(value)
+    if expected == "number":
+        return _coerce_number(value)
+    if expected == "boolean":
+        return _coerce_boolean(value)
+    if expected == "string":
+        return _coerce_string(value)
+    if expected == "array":
+        return _coerce_array(value, schema)
+    if expected == "object":
+        return _coerce_object(value, schema)
+
+    return value
+
+
+def _coerce_integer(value: Any) -> Any:
+    # bool is a subclass of int in Python; don't mistake True for 1 here.
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, int):
+        return value
+    if isinstance(value, str):
+        parsed = _parse_number(value)
+        if parsed is None:
+            return value
+        if parsed != int(parsed):
+            # Has a fractional part — caller asked for int, don't truncate.
+            return value
+        return int(parsed)
+    return value
+
+
+def _coerce_number(value: Any) -> Any:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, (int, float)):
+        return value
+    if isinstance(value, str):
+        parsed = _parse_number(value)
+        if parsed is None:
+            return value
+        if parsed == int(parsed):
+            return int(parsed)
+        return parsed
+    return value
+
+
+def _coerce_boolean(value: Any) -> Any:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        low = value.strip().lower()
+        if low == "true":
+            return True
+        if low == "false":
+            return False
+    return value
+
+
+def _coerce_string(value: Any) -> Any:
+    if isinstance(value, str):
+        return value
+    # Common drift: model sent ``{"label": "..."}`` when we wanted "...".
+    if isinstance(value, dict):
+        extracted = _extract_string_from_object(value)
+        if extracted is not None:
+            return extracted
+    return value
+
+
+def _coerce_array(value: Any, schema: dict[str, Any]) -> Any:
+    # Heal: JSON-encoded array string → array.
+    if isinstance(value, str):
+        parsed = _try_parse_json(value)
+        if isinstance(parsed, list):
+            value = parsed
+        else:
+            # Scalar string where an array is expected — wrap it.
+            return [value]
+    elif not isinstance(value, list):
+        # Any other scalar (int, bool, dict, ...) — wrap.
+        return [value]
+
+    items_schema = schema.get("items")
+    if not isinstance(items_schema, dict):
+        return value
+
+    coerced_items: list[Any] = []
+    changed = False
+    for item in value:
+        c = _coerce(item, items_schema)
+        if c is not item:
+            changed = True
+        coerced_items.append(c)
+    return coerced_items if changed else value
+
+
+def _coerce_object(value: Any, schema: dict[str, Any]) -> Any:
+    # Heal: JSON-encoded object string → object.
+    if isinstance(value, str):
+        parsed = _try_parse_json(value)
+        if isinstance(parsed, dict):
+            value = parsed
+        else:
+            return value
+    if not isinstance(value, dict):
+        return value
+
+    sub_props = schema.get("properties")
+    if not isinstance(sub_props, dict):
+        return value
+
+    changed = False
+    for k in list(value.keys()):
+        sub_schema = sub_props.get(k)
+        if not isinstance(sub_schema, dict):
+            continue
+        original = value[k]
+        coerced = _coerce(original, sub_schema)
+        if coerced is not original:
+            value[k] = coerced
+            changed = True
+    # Return the same dict on mutation so callers that passed a shared
+    # reference see the updates. ``changed`` is only used to decide
+    # whether we need to log at a coarser level upstream.
+    return value if changed or not sub_props else value
+
+
+def _extract_string_from_object(obj: dict[str, Any]) -> str | None:
+    """Pick a likely-text field out of a wrapper object.
+
+    Tries the known keys first, falls back to the sole value if the
+    object has exactly one entry. Returns None when nothing plausible
+    is found — the caller keeps the original.
+    """
+    for k in _STRING_EXTRACT_KEYS:
+        v = obj.get(k)
+        if isinstance(v, str) and v:
+            return v
+    if len(obj) == 1:
+        (only,) = obj.values()
+        if isinstance(only, str) and only:
+            return only
+    return None
+
+
+def _try_parse_json(raw: str) -> Any:
+    try:
+        return json.loads(raw)
+    except (ValueError, TypeError):
+        return None
+
+
+def _parse_number(raw: str) -> float | None:
+    try:
+        f = float(raw)
+    except (ValueError, OverflowError):
+        return None
+    # Reject NaN and inf — they pass float() but aren't useful numeric
+    # values for tool arguments.
+    if f != f or f == float("inf") or f == float("-inf"):
+        return None
+    return f
+
+
+def _shape(value: Any) -> str:
+    """Short type/shape description used in coercion log lines."""
+    if value is None:
+        return "None"
+    if isinstance(value, bool):
+        return "bool"
+    if isinstance(value, int):
+        return "int"
+    if isinstance(value, float):
+        return "float"
+    if isinstance(value, str):
+        return f"str[{len(value)}]"
+    if isinstance(value, list):
+        if not value:
+            return "list[0]"
+        return f"list[{len(value)}]<{_shape(value[0])}>"
+    if isinstance(value, dict):
+        keys = sorted(value.keys())[:3]
+        suffix = ",…" if len(value) > 3 else ""
+        return f"dict{{{','.join(keys)}{suffix}}}"
+    return type(value).__name__

core/framework/agent_loop/internals/tool_result_handler.py

@@ -0,0 +1,548 @@
+"""Tool result handling: truncation, spillover, JSON preview, and execution.
+
+Manages tool result size limits, file spillover for large results, and
+smart JSON previews.  Also includes transient error classification and
+the context-window-exceeded error detector.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import contextvars
+import json
+import logging
+import re
+from pathlib import Path
+from typing import Any
+
+from framework.llm.provider import ToolResult, ToolUse
+from framework.llm.stream_events import ToolCallEvent
+
+logger = logging.getLogger(__name__)
+
+# Pattern for detecting context-window-exceeded errors across LLM providers.
+_CONTEXT_TOO_LARGE_RE = re.compile(
+    r"context.{0,20}(length|window|limit|size)|"
+    r"too.{0,10}(long|large|many.{0,10}tokens)|"
+    r"(exceed|exceeds|exceeded).{0,30}(limit|window|context|tokens)|"
+    r"maximum.{0,20}token|prompt.{0,20}too.{0,10}long",
+    re.IGNORECASE,
+)
+
+
+def is_context_too_large_error(exc: BaseException) -> bool:
+    """Detect whether an exception indicates the LLM input was too large."""
+    cls = type(exc).__name__
+    if "ContextWindow" in cls:
+        return True
+    return bool(_CONTEXT_TOO_LARGE_RE.search(str(exc)))
+
+
+def is_transient_error(exc: BaseException) -> bool:
+    """Classify whether an exception is transient (retryable) vs permanent.
+
+    Transient: network errors, rate limits, server errors, timeouts.
+    Permanent: auth errors, bad requests, context window exceeded.
+    """
+    try:
+        from litellm.exceptions import (
+            APIConnectionError,
+            BadGatewayError,
+            InternalServerError,
+            RateLimitError,
+            ServiceUnavailableError,
+        )
+
+        transient_types: tuple[type[BaseException], ...] = (
+            RateLimitError,
+            APIConnectionError,
+            InternalServerError,
+            BadGatewayError,
+            ServiceUnavailableError,
+            TimeoutError,
+            ConnectionError,
+            OSError,
+        )
+    except ImportError:
+        transient_types = (TimeoutError, ConnectionError, OSError)
+
+    if isinstance(exc, transient_types):
+        return True
+
+    # RuntimeError from StreamErrorEvent with "Stream error:" prefix
+    if isinstance(exc, RuntimeError):
+        error_str = str(exc).lower()
+        transient_keywords = [
+            "rate limit",
+            "429",
+            "timeout",
+            "connection",
+            "internal server",
+            "502",
+            "503",
+            "504",
+            "service unavailable",
+            "bad gateway",
+            "overloaded",
+            "failed to parse tool call",
+        ]
+        return any(kw in error_str for kw in transient_keywords)
+
+    return False
+
+
+def extract_json_metadata(parsed: Any, *, _depth: int = 0, _max_depth: int = 3) -> str:
+    """Return a concise structural summary of parsed JSON.
+
+    Reports key names, value types, and — crucially — array lengths so
+    the LLM knows how much data exists beyond the preview.
+
+    Returns an empty string for simple scalars.
+    """
+    if _depth >= _max_depth:
+        if isinstance(parsed, dict):
+            return f"dict with {len(parsed)} keys"
+        if isinstance(parsed, list):
+            return f"list of {len(parsed)} items"
+        return type(parsed).__name__
+
+    if isinstance(parsed, dict):
+        if not parsed:
+            return "empty dict"
+        lines: list[str] = []
+        indent = "  " * (_depth + 1)
+        for key, value in list(parsed.items())[:20]:
+            if isinstance(value, list):
+                line = f'{indent}"{key}": list of {len(value)} items'
+                if value:
+                    first = value[0]
+                    if isinstance(first, dict):
+                        sample_keys = list(first.keys())[:10]
+                        line += f" (each item: dict with keys {sample_keys})"
+                    elif isinstance(first, list):
+                        line += f" (each item: list of {len(first)} elements)"
+                lines.append(line)
+            elif isinstance(value, dict):
+                child = extract_json_metadata(value, _depth=_depth + 1, _max_depth=_max_depth)
+                lines.append(f'{indent}"{key}": {child}')
+            else:
+                lines.append(f'{indent}"{key}": {type(value).__name__}')
+        if len(parsed) > 20:
+            lines.append(f"{indent}... and {len(parsed) - 20} more keys")
+        return "\n".join(lines)
+
+    if isinstance(parsed, list):
+        if not parsed:
+            return "empty list"
+        desc = f"list of {len(parsed)} items"
+        first = parsed[0]
+        if isinstance(first, dict):
+            sample_keys = list(first.keys())[:10]
+            desc += f" (each item: dict with keys {sample_keys})"
+        elif isinstance(first, list):
+            desc += f" (each item: list of {len(first)} elements)"
+        return desc
+
+    return ""
+
+
+def build_json_preview(parsed: Any, *, max_chars: int = 5000) -> str | None:
+    """Build a smart preview of parsed JSON, truncating large arrays.
+
+    Shows first 3 + last 1 items of large arrays with explicit count
+    markers so the LLM cannot mistake the preview for the full dataset.
+
+    Returns ``None`` if no truncation was needed (no large arrays).
+    """
+    _LARGE_ARRAY_THRESHOLD = 10
+
+    def _truncate_arrays(obj: Any) -> tuple[Any, bool]:
+        """Return (truncated_copy, was_truncated)."""
+        if isinstance(obj, list) and len(obj) > _LARGE_ARRAY_THRESHOLD:
+            n = len(obj)
+            head = obj[:3]
+            tail = obj[-1:]
+            marker = f"... ({n - 4} more items omitted, {n} total) ..."
+            return head + [marker] + tail, True
+        if isinstance(obj, dict):
+            changed = False
+            out: dict[str, Any] = {}
+            for k, v in obj.items():
+                new_v, did = _truncate_arrays(v)
+                out[k] = new_v
+                changed = changed or did
+            return (out, True) if changed else (obj, False)
+        return obj, False
+
+    preview_obj, was_truncated = _truncate_arrays(parsed)
+    if not was_truncated:
+        return None  # No large arrays — caller should use raw slicing
+
+    try:
+        result = json.dumps(preview_obj, indent=2, ensure_ascii=False)
+    except (TypeError, ValueError):
+        return None
+
+    if len(result) > max_chars:
+        # Even 3+1 items too big — try just 1 item
+        def _minimal_arrays(obj: Any) -> Any:
+            if isinstance(obj, list) and len(obj) > _LARGE_ARRAY_THRESHOLD:
+                n = len(obj)
+                return obj[:1] + [f"... ({n - 1} more items omitted, {n} total) ..."]
+            if isinstance(obj, dict):
+                return {k: _minimal_arrays(v) for k, v in obj.items()}
+            return obj
+
+        preview_obj = _minimal_arrays(parsed)
+        try:
+            result = json.dumps(preview_obj, indent=2, ensure_ascii=False)
+        except (TypeError, ValueError):
+            return None
+        if len(result) > max_chars:
+            result = result[:max_chars] + "…"
+
+    return result
+
+
+def truncate_tool_result(
+    result: ToolResult,
+    tool_name: str,
+    *,
+    max_tool_result_chars: int,
+    spillover_dir: str | None,
+    next_spill_filename_fn: Any,  # Callable[[str], str]
+) -> ToolResult:
+    """Persist tool result to file and optionally truncate for context.
+
+    When *spillover_dir* is configured, EVERY non-error tool result is
+    written to disk for debugging. The LLM-visible content is then
+    shaped to avoid a **poison pattern** that we traced on 2026-04-15
+    through a gemini-3.1-pro-preview-customtools queen session: the prior format
+    appended ``\\n\\n[Saved to '/abs/path/file.txt']`` after every
+    small result, and frontier pattern-matching models (gemini 3.x in
+    particular) learned to autocomplete the `[Saved to '...']` trailer
+    in their own assistant turns, eventually degenerating into echoing
+    the whole tool result instead of deciding what to do next. See
+    ``session_20260415_100751_d49f4c28/conversations/parts/0000000056.json``
+    for the terminal case where the model's "text" output was the full
+    tool_result JSON.
+
+    Rules after the fix:
+    - **Small results (≤ limit):** pass content through unchanged. No
+      trailer. No annotation. The full content is already in the
+      message; the disk copy is for debugging only.
+    - **Large results (> limit):** preview + file reference, but
+      formatted as plain prose instead of a bracketed ``[...]``
+      pattern. Structured JSON metadata ("_saved_to") is embedded
+      inside the JSON body when the preview is JSON-shaped so the
+      model can locate the full file without seeing a mimicry-prone
+      bracket token outside the body.
+    - **Errors:** pass through unchanged.
+    - **read_file results:** truncate with pagination hint (no re-spill).
+    """
+    limit = max_tool_result_chars
+
+    # Errors always pass through unchanged
+    if result.is_error:
+        return result
+
+    # read_file reads FROM spilled files — never re-spill (circular).
+    # Just truncate with a pagination hint if the result is too large.
+    if tool_name == "read_file":
+        if limit <= 0 or len(result.content) <= limit:
+            return result  # Small result — pass through as-is
+        # Large result — truncate with smart preview
+        PREVIEW_CAP = min(5000, max(limit - 500, limit // 2))
+
+        metadata_str = ""
+        smart_preview: str | None = None
+        try:
+            parsed_ld = json.loads(result.content)
+            metadata_str = extract_json_metadata(parsed_ld)
+            smart_preview = build_json_preview(parsed_ld, max_chars=PREVIEW_CAP)
+        except (json.JSONDecodeError, TypeError, ValueError):
+            pass
+
+        if smart_preview is not None:
+            preview_block = smart_preview
+        else:
+            preview_block = result.content[:PREVIEW_CAP] + "…"
+
+        # Prose header (no brackets).
+        header = (
+            f"Tool `{tool_name}` returned {len(result.content):,} characters "
+            f"(too large for context). Use offset_bytes / limit_bytes "
+            f"parameters to paginate smaller chunks."
+        )
+        if metadata_str:
+            header += f"\n\nData structure:\n{metadata_str}"
+        header += (
+            "\n\nWARNING: the preview below is a SAMPLE only — do NOT draw counts, totals, or conclusions from it."
+        )
+
+        truncated = f"{header}\n\nPreview (truncated):\n{preview_block}"
+        logger.info(
+            "%s result truncated: %d → %d chars (use offset/limit to paginate)",
+            tool_name,
+            len(result.content),
+            len(truncated),
+        )
+        return ToolResult(
+            tool_use_id=result.tool_use_id,
+            content=truncated,
+            is_error=False,
+            image_content=result.image_content,
+            is_skill_content=result.is_skill_content,
+        )
+
+    spill_dir = spillover_dir
+    if spill_dir:
+        spill_path = Path(spill_dir)
+        spill_path.mkdir(parents=True, exist_ok=True)
+        filename = next_spill_filename_fn(tool_name)
+
+        # Pretty-print JSON content so read_file's line-based
+        # pagination works correctly.
+        write_content = result.content
+        parsed_json: Any = None  # track for metadata extraction
+        try:
+            parsed_json = json.loads(result.content)
+            write_content = json.dumps(parsed_json, indent=2, ensure_ascii=False)
+        except (json.JSONDecodeError, TypeError, ValueError):
+            pass  # Not JSON — write as-is
+
+        file_path = spill_path / filename
+        file_path.write_text(write_content, encoding="utf-8")
+        # Use absolute path so parent agents can find files from subagents
+        abs_path = str(file_path.resolve())
+
+        if limit > 0 and len(result.content) > limit:
+            # Large result: build a small, metadata-rich preview so the
+            # LLM cannot mistake it for the complete dataset. The
+            # preview is introduced as plain prose (no bracketed
+            # ``[Result from …]`` token) so it doesn't prime the model
+            # to autocomplete the same pattern in its next turn.
+            PREVIEW_CAP = 5000
+
+            # Extract structural metadata (array lengths, key names)
+            metadata_str = ""
+            smart_preview: str | None = None
+            if parsed_json is not None:
+                metadata_str = extract_json_metadata(parsed_json)
+                smart_preview = build_json_preview(parsed_json, max_chars=PREVIEW_CAP)
+
+            if smart_preview is not None:
+                preview_block = smart_preview
+            else:
+                preview_block = result.content[:PREVIEW_CAP] + "…"
+
+            # Prose header (no brackets). Absolute path still surfaced
+            # so the agent can read the full file, but it's framed as
+            # a sentence, not a bracketed trailer.
+            header = (
+                f"Tool `{tool_name}` returned {len(result.content):,} characters "
+                f"(too large for context). Full result saved at: {abs_path}\n"
+                f"Read the complete data with read_file(path='{abs_path}').\n"
+            )
+            if metadata_str:
+                header += f"\nData structure:\n{metadata_str}\n"
+            header += (
+                "\nWARNING: the preview below is a SAMPLE only — do NOT draw counts, totals, or conclusions from it."
+            )
+
+            content = f"{header}\n\nPreview (truncated):\n{preview_block}"
+            logger.info(
+                "Tool result spilled to file: %s (%d chars → %s)",
+                tool_name,
+                len(result.content),
+                abs_path,
+            )
+        else:
+            # Small result: pass content through UNCHANGED.
+            #
+            # The prior design appended `\n\n[Saved to '/abs/path']`
+            # after every small result so the agent could re-read the
+            # file later. But (a) the full content is already in the
+            # message, so there's nothing to re-read; (b) the
+            # `[Saved to '…']` trailer is a repeating token pattern
+            # that frontier pattern-matching models autocomplete into
+            # their own assistant turns, eventually echoing whole tool
+            # results as "text" instead of making decisions. Dropping
+            # the trailer entirely kills the poison pattern. Spilled
+            # files on disk still exist for debugging — they just
+            # aren't advertised in the LLM-visible message.
+            content = result.content
+            logger.info(
+                "Tool result saved to file: %s (%d chars → %s, no trailer)",
+                tool_name,
+                len(result.content),
+                filename,
+            )
+
+        return ToolResult(
+            tool_use_id=result.tool_use_id,
+            content=content,
+            is_error=False,
+            image_content=result.image_content,
+            is_skill_content=result.is_skill_content,
+        )
+
+    # No spillover_dir — truncate in-place if needed
+    if limit > 0 and len(result.content) > limit:
+        PREVIEW_CAP = min(5000, max(limit - 500, limit // 2))
+
+        metadata_str = ""
+        smart_preview: str | None = None
+        try:
+            parsed_inline = json.loads(result.content)
+            metadata_str = extract_json_metadata(parsed_inline)
+            smart_preview = build_json_preview(parsed_inline, max_chars=PREVIEW_CAP)
+        except (json.JSONDecodeError, TypeError, ValueError):
+            pass
+
+        if smart_preview is not None:
+            preview_block = smart_preview
+        else:
+            preview_block = result.content[:PREVIEW_CAP] + "…"
+
+        # Prose header (no brackets) — see docstring for the poison
+        # pattern that the bracket format triggered.
+        header = (
+            f"Tool `{tool_name}` returned {len(result.content):,} characters "
+            f"(truncated to fit context budget — no spillover dir configured)."
+        )
+        if metadata_str:
+            header += f"\n\nData structure:\n{metadata_str}"
+        header += (
+            "\n\nWARNING: the preview below is a SAMPLE only — do NOT draw counts, totals, or conclusions from it."
+        )
+
+        truncated = f"{header}\n\n{preview_block}"
+        logger.info(
+            "Tool result truncated in-place: %s (%d → %d chars)",
+            tool_name,
+            len(result.content),
+            len(truncated),
+        )
+        return ToolResult(
+            tool_use_id=result.tool_use_id,
+            content=truncated,
+            is_error=False,
+            image_content=result.image_content,
+            is_skill_content=result.is_skill_content,
+        )
+
+    return result
+
+
+async def execute_tool(
+    tool_executor: Any,  # Callable[[ToolUse], ToolResult | Awaitable[ToolResult]] | None
+    tc: ToolCallEvent,
+    timeout: float,
+    skill_dirs: list[str] | None = None,
+) -> ToolResult:
+    """Execute a tool call, handling both sync and async executors.
+
+    Applies ``tool_call_timeout_seconds`` to prevent hung MCP servers
+    from blocking the event loop indefinitely.  The initial executor
+    call is offloaded to a thread pool so that sync executors don't
+    freeze the event loop.
+    """
+    if tool_executor is None:
+        return ToolResult(
+            tool_use_id=tc.tool_use_id,
+            content=f"No tool executor configured for '{tc.tool_name}'",
+            is_error=True,
+        )
+
+    skill_dirs = skill_dirs or []
+    skill_read_tools = {"view_file", "read_file"}
+    if tc.tool_name in skill_read_tools and skill_dirs:
+        raw_path = tc.tool_input.get("path", "")
+        if raw_path:
+            resolved = Path(raw_path).resolve(strict=False)
+            resolved_roots = [Path(skill_dir).resolve(strict=False) for skill_dir in skill_dirs]
+            if any(resolved.is_relative_to(root) for root in resolved_roots):
+                try:
+                    content = resolved.read_text(encoding="utf-8")
+                except Exception as exc:
+                    return ToolResult(
+                        tool_use_id=tc.tool_use_id,
+                        content=f"Could not read skill resource '{raw_path}': {exc}",
+                        is_error=True,
+                    )
+                return ToolResult(
+                    tool_use_id=tc.tool_use_id,
+                    content=content,
+                    is_skill_content=resolved.name == "SKILL.md",
+                )
+
+    tool_use = ToolUse(id=tc.tool_use_id, name=tc.tool_name, input=tc.tool_input)
+
+    async def _run() -> ToolResult:
+        # Offload the executor call to a thread.  Sync MCP executors
+        # block on future.result() — running in a thread keeps the
+        # event loop free so asyncio.wait_for can fire the timeout.
+        # Copy the current context so contextvars (e.g. data_dir from
+        # execution context) propagate into the worker thread.
+        loop = asyncio.get_running_loop()
+        ctx = contextvars.copy_context()
+        result = await loop.run_in_executor(None, ctx.run, tool_executor, tool_use)
+        # Async executors return a coroutine — await it on the loop
+        if asyncio.iscoroutine(result) or asyncio.isfuture(result):
+            result = await result
+        return result
+
+    try:
+        if timeout > 0:
+            result = await asyncio.wait_for(_run(), timeout=timeout)
+        else:
+            result = await _run()
+    except TimeoutError:
+        logger.warning("Tool '%s' timed out after %.0fs", tc.tool_name, timeout)
+        # asyncio.wait_for cancels the awaiting coroutine, but the sync
+        # executor running inside run_in_executor keeps going — and so
+        # does any MCP subprocess it is blocked on. Reach through to the
+        # owning MCPClient and force-disconnect it so the subprocess is
+        # torn down. Next call_tool triggers a reconnect. Without this
+        # the executor thread and MCP child leak on every timeout.
+        kill_for_tool = getattr(tool_executor, "kill_for_tool", None)
+        if callable(kill_for_tool):
+            try:
+                await asyncio.to_thread(kill_for_tool, tc.tool_name)
+            except Exception as exc:  # defensive — never let cleanup crash the loop
+                logger.warning(
+                    "kill_for_tool('%s') raised during timeout handling: %s",
+                    tc.tool_name,
+                    exc,
+                )
+        return ToolResult(
+            tool_use_id=tc.tool_use_id,
+            content=(
+                f"Tool '{tc.tool_name}' timed out after {timeout:.0f}s. "
+                "The operation took too long and was cancelled. "
+                "Try a simpler request or a different approach."
+            ),
+            is_error=True,
+        )
+    return result
+
+
+def restore_spill_counter(spillover_dir: str | None) -> int:
+    """Scan spillover_dir for existing spill files and return the max counter.
+
+    Returns the highest spill number found (or 0 if none).
+    """
+    if not spillover_dir:
+        return 0
+    spill_path = Path(spillover_dir)
+    if not spill_path.is_dir():
+        return 0
+    max_n = 0
+    for f in spill_path.iterdir():
+        if not f.is_file():
+            continue
+        m = re.search(r"_(\d+)\.txt$", f.name)
+        if m:
+            max_n = max(max_n, int(m.group(1)))
+    return max_n

core/framework/agent_loop/internals/types.py

@@ -0,0 +1,309 @@
+"""Shared types and state containers for the event loop package."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal, Protocol, runtime_checkable
+
+from framework.agent_loop.conversation import (
+    ConversationStore,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TriggerEvent:
+    """A framework-level trigger signal (timer tick or webhook hit)."""
+
+    trigger_type: str
+    source_id: str
+    payload: dict[str, Any] = field(default_factory=dict)
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class JudgeVerdict:
+    """Result of judge evaluation for the event loop."""
+
+    action: Literal["ACCEPT", "RETRY", "ESCALATE"]
+    # None  = no evaluation happened (skip_judge, tool-continue); not logged.
+    # ""    = evaluated but no feedback; logged with default text.
+    # "..." = evaluated with feedback; logged as-is.
+    feedback: str | None = None
+
+
+@runtime_checkable
+class JudgeProtocol(Protocol):
+    """Protocol for event-loop judges."""
+
+    async def evaluate(self, context: dict[str, Any]) -> JudgeVerdict: ...
+
+
+@dataclass
+class LoopConfig:
+    """Configuration for the event loop."""
+
+    max_iterations: int = 50
+    # 0 (or any non-positive value) disables the per-turn hard limit,
+    # letting a single assistant turn fan out arbitrarily many tool
+    # calls. Models like Gemini 3.1 Pro routinely emit 40-80 tool
+    # calls in one turn during browser exploration; capping them
+    # strands work half-finished and makes the next turn repeat the
+    # discarded calls, which is worse than just running them.
+    max_tool_calls_per_turn: int = 0
+    judge_every_n_turns: int = 1
+    stall_detection_threshold: int = 3
+    stall_similarity_threshold: float = 0.85
+    max_context_tokens: int = 32_000
+    # Headroom reserved for the NEXT turn's input + output so that
+    # proactive compaction always finishes before the hard context limit
+    # is hit mid-stream. Scaled to match Claude Code's 13k-buffer-on-
+    # 200k-window ratio (~6.5%) applied to hive's default 32k window,
+    # with extra margin because hive's token estimator is char-based
+    # and less tight than Anthropic's own counting. Override via
+    # LoopConfig for larger windows.
+    compaction_buffer_tokens: int = 8_000
+    # Warning is emitted one buffer earlier so the user/telemetry gets
+    # a "we're close" signal without triggering a compaction pass.
+    compaction_warning_buffer_tokens: int = 12_000
+    store_prefix: str = ""
+
+    # Overflow margin for max_tool_calls_per_turn. When the limit is
+    # enabled (>0), tool calls are only discarded when the count
+    # exceeds max_tool_calls_per_turn * (1 + margin). Ignored when
+    # max_tool_calls_per_turn is 0.
+    tool_call_overflow_margin: float = 0.5
+
+    # Tool result context management.
+    max_tool_result_chars: int = 30_000
+    spillover_dir: str | None = None
+
+    # Image retention in conversation history.
+    # Screenshots from ``browser_screenshot`` are inlined as base64
+    # data URLs inside message ``image_content``. Each full-page
+    # screenshot costs ~250k tokens when the provider counts the
+    # base64 as text (gemini, most non-Anthropic providers). Four
+    # screenshots in one conversation push gemini's 1M context over
+    # the limit and the model starts emitting garbage.
+    #
+    # The framework strips image_content from older messages after
+    # every tool-result batch, keeping only the most recent N
+    # screenshots. The text metadata on evicted messages (url, size,
+    # scale hints) is preserved so the agent can still reason about
+    # "I took a screenshot at step N that showed the compose modal".
+    # Raise this only if you genuinely need longer visual history AND
+    # you know your provider is using native image tokenization.
+    max_retained_screenshots: int = 2
+
+    # set_output value spilling.
+    max_output_value_chars: int = 2_000
+
+    # Stream retry.
+    max_stream_retries: int = 5
+    stream_retry_backoff_base: float = 2.0
+    stream_retry_max_delay: float = 60.0
+    # Persistent retry for capacity-class errors (429, 529, overloaded).
+    # Unlike the bounded retry above, these keep trying until the wall-clock
+    # budget below is exhausted — modelled after claude-code's withRetry.
+    # The loop still publishes a retry event each attempt so the UI can
+    # see progress. Set to 0 to disable and fall back to bounded retry.
+    capacity_retry_max_seconds: float = 600.0
+    capacity_retry_max_delay: float = 60.0
+
+    # Tool doom loop detection.
+    tool_doom_loop_threshold: int = 3
+
+    # Client-facing auto-block grace period.
+    cf_grace_turns: int = 1
+    # Worker auto-escalation: text-only turns before escalating to queen.
+    worker_escalation_grace_turns: int = 1
+    tool_doom_loop_enabled: bool = True
+    # Silent worker: consecutive tool-only turns (no user-facing text)
+    # before injecting a nudge to communicate progress.
+    silent_tool_streak_threshold: int = 5
+
+    # Per-tool-call timeout.
+    tool_call_timeout_seconds: float = 60.0
+
+    # LLM stream inactivity watchdog. Split into two budgets so legitimate
+    # slow TTFT on large contexts doesn't get mistaken for a dead connection.
+    # - ttft: stream open -> first event. Large-context local models can
+    #   legitimately take minutes before the first token arrives.
+    # - inter_event: last event -> now, ONLY after the first event. A stream
+    #   that started producing and then went silent is a real stall.
+    # Whichever fires first cancels the stream. Set to 0 to disable that
+    # individual budget; set both to 0 to fully disable the watchdog.
+    llm_stream_ttft_timeout_seconds: float = 600.0
+    llm_stream_inter_event_idle_seconds: float = 120.0
+    # Deprecated alias — kept so existing configs keep working. If set to a
+    # non-default value it overrides inter_event_idle (historical behavior).
+    llm_stream_inactivity_timeout_seconds: float = 120.0
+
+    # Continue-nudge recovery. When the idle watchdog fires on a live but
+    # stuck stream, cancel the stream and append a short continuation
+    # hint to the conversation instead of raising a ConnectionError and
+    # re-running the whole turn. Preserves any partial text/tool-calls the
+    # stream emitted before the stall.
+    continue_nudge_enabled: bool = True
+    # Cap so a truly dead endpoint eventually falls back to the error path
+    # instead of nudging forever.
+    continue_nudge_max_per_turn: int = 3
+
+    # Tool-call replay detector. When the model emits a tool call whose
+    # (name + canonical-args) matches a prior successful call in the last
+    # K assistant turns, emit telemetry and prepend a short steer onto the
+    # tool result — but still execute. Weaker models legitimately repeat
+    # read-only calls (screenshot, evaluate), so silent skipping would
+    # cause surprising behavior.
+    replay_detector_enabled: bool = True
+    replay_detector_within_last_turns: int = 3
+
+    # Subagent delegation timeout (wall-clock max).
+    subagent_timeout_seconds: float = 3600.0
+
+    # Subagent inactivity timeout - only timeout if no activity for this duration.
+    # This resets whenever the subagent makes progress (tool calls, LLM responses).
+    # Set to 0 to use only the wall-clock timeout.
+    subagent_inactivity_timeout_seconds: float = 300.0
+
+    # Lifecycle hooks.
+    hooks: dict[str, list] | None = None
+
+    def __post_init__(self) -> None:
+        if self.hooks is None:
+            object.__setattr__(self, "hooks", {})
+
+
+@dataclass
+class HookContext:
+    """Context passed to every lifecycle hook."""
+
+    event: str
+    trigger: str | None
+    system_prompt: str
+
+
+@dataclass
+class HookResult:
+    """What a hook may return to modify node state."""
+
+    system_prompt: str | None = None
+    inject: str | None = None
+
+
+@dataclass
+class OutputAccumulator:
+    """Accumulates output key-value pairs with optional write-through persistence."""
+
+    values: dict[str, Any] = field(default_factory=dict)
+    store: ConversationStore | None = None
+    spillover_dir: str | None = None
+    max_value_chars: int = 0
+    run_id: str | None = None
+
+    async def set(self, key: str, value: Any) -> None:
+        """Set a key-value pair, auto-spilling large values to files."""
+        value = await self._auto_spill(key, value)
+        self.values[key] = value
+        if self.store:
+            cursor = await self.store.read_cursor() or {}
+            outputs = cursor.get("outputs", {})
+            outputs[key] = value
+            cursor["outputs"] = outputs
+            await self.store.write_cursor(cursor)
+
+    async def _auto_spill(self, key: str, value: Any) -> Any:
+        """Save large values to a file and return a reference string.
+
+        Runs the JSON serialization and file write on a worker thread
+        so they don't block the asyncio event loop. For a 100k-char
+        dict this used to freeze every concurrent tool call for ~50ms
+        of ``json.dumps(indent=2)`` + a sync disk write; for bigger
+        payloads or slow storage (NFS, networked FS) the freeze was
+        proportionally worse.
+        """
+        if self.max_value_chars <= 0 or not self.spillover_dir:
+            return value
+
+        # Cheap size probe first — if the value is already a short
+        # string we can skip both the JSON round-trip and the thread
+        # hop entirely.
+        if isinstance(value, str) and len(value) <= self.max_value_chars:
+            return value
+
+        def _spill_sync() -> Any:
+            # JSON serialization for size check (only for non-strings).
+            if isinstance(value, str):
+                val_str = value
+            else:
+                val_str = json.dumps(value, ensure_ascii=False)
+            if len(val_str) <= self.max_value_chars:
+                return value
+
+            spill_path = Path(self.spillover_dir)
+            spill_path.mkdir(parents=True, exist_ok=True)
+            ext = ".json" if isinstance(value, (dict, list)) else ".txt"
+            filename = f"output_{key}{ext}"
+            write_content = (
+                json.dumps(value, indent=2, ensure_ascii=False) if isinstance(value, (dict, list)) else str(value)
+            )
+            file_path = spill_path / filename
+            file_path.write_text(write_content, encoding="utf-8")
+            file_size = file_path.stat().st_size
+            logger.info(
+                "set_output value auto-spilled: key=%s, %d chars -> %s (%d bytes)",
+                key,
+                len(val_str),
+                filename,
+                file_size,
+            )
+            # Use absolute path so parent agents can find files from subagents.
+            #
+            # Prose format (no brackets) — same fix as tool_result_handler:
+            # frontier pattern-matching models autocomplete bracketed
+            # `[Saved to '...']` trailers into their own assistant turns,
+            # eventually degenerating into echoing the file path as text.
+            # Keep the path accessible but frame it as plain prose.
+            abs_path = str(file_path.resolve())
+            return (
+                f"Output saved at: {abs_path} ({file_size:,} bytes). "
+                f"Read the full data with read_file(path='{abs_path}')."
+            )
+
+        return await asyncio.to_thread(_spill_sync)
+
+    def get(self, key: str) -> Any | None:
+        return self.values.get(key)
+
+    def to_dict(self) -> dict[str, Any]:
+        return dict(self.values)
+
+    def has_all_keys(self, required: list[str]) -> bool:
+        return all(key in self.values and self.values[key] is not None for key in required)
+
+    @classmethod
+    async def restore(
+        cls,
+        store: ConversationStore,
+        run_id: str | None = None,
+    ) -> OutputAccumulator:
+        cursor = await store.read_cursor()
+        values = cursor.get("outputs", {}) if cursor else {}
+        return cls(values=values, store=store, run_id=run_id)
+
+
+__all__ = [
+    "HookContext",
+    "HookResult",
+    "JudgeProtocol",
+    "JudgeVerdict",
+    "LoopConfig",
+    "OutputAccumulator",
+    "TriggerEvent",
+]

core/framework/agent_loop/prompting.py

@@ -0,0 +1,98 @@
+"""Prompt composition for agent loops.
+
+Builds canonical system prompts from AgentContext fields.
+Extracted from the former orchestrator/prompting module.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any
+
+
+@dataclass(frozen=True)
+class PromptSpec:
+    identity_prompt: str = ""
+    focus_prompt: str = ""
+    narrative: str = ""
+    accounts_prompt: str = ""
+    skills_catalog_prompt: str = ""
+    protocols_prompt: str = ""
+    memory_prompt: str = ""
+    agent_type: str = "event_loop"
+    output_keys: tuple[str, ...] = ()
+
+
+def stamp_prompt_datetime(prompt: str) -> str:
+    local = datetime.now().astimezone()
+    stamp = f"Current date and time: {local.strftime('%Y-%m-%d %H:%M %Z (UTC%z)')}"
+    return f"{prompt}\n\n{stamp}" if prompt else stamp
+
+
+def build_prompt_spec(
+    ctx: Any,
+    *,
+    focus_prompt: str | None = None,
+    narrative: str | None = None,
+    memory_prompt: str | None = None,
+) -> PromptSpec:
+    from framework.skills.tool_gating import augment_catalog_for_tools
+
+    resolved_memory = memory_prompt
+    if resolved_memory is None:
+        resolved_memory = getattr(ctx, "memory_prompt", "") or ""
+        dynamic = getattr(ctx, "dynamic_memory_provider", None)
+        if dynamic is not None:
+            try:
+                resolved_memory = dynamic() or ""
+            except Exception:
+                resolved_memory = getattr(ctx, "memory_prompt", "") or ""
+
+    # Tool-gated pre-activation: inject full body of default skills whose
+    # trigger tools are present in this agent's tool list (e.g. browser_*
+    # pulls in hive.browser-automation). Keeps non-browser agents lean.
+    tool_names = [getattr(t, "name", "") for t in (getattr(ctx, "available_tools", None) or [])]
+    skills_catalog_prompt = augment_catalog_for_tools(ctx.skills_catalog_prompt or "", tool_names)
+
+    return PromptSpec(
+        identity_prompt=ctx.identity_prompt or "",
+        focus_prompt=focus_prompt if focus_prompt is not None else (ctx.agent_spec.system_prompt or ""),
+        narrative=narrative if narrative is not None else (ctx.narrative or ""),
+        accounts_prompt=ctx.accounts_prompt or "",
+        skills_catalog_prompt=skills_catalog_prompt,
+        protocols_prompt=ctx.protocols_prompt or "",
+        memory_prompt=resolved_memory,
+        agent_type=ctx.agent_spec.agent_type,
+        output_keys=tuple(ctx.agent_spec.output_keys or ()),
+    )
+
+
+def build_system_prompt(spec: PromptSpec) -> str:
+    parts: list[str] = []
+    if spec.identity_prompt:
+        parts.append(spec.identity_prompt)
+    if spec.accounts_prompt:
+        parts.append(f"\n{spec.accounts_prompt}")
+    if spec.skills_catalog_prompt:
+        parts.append(f"\n{spec.skills_catalog_prompt}")
+    if spec.protocols_prompt:
+        parts.append(f"\n{spec.protocols_prompt}")
+    if spec.memory_prompt:
+        parts.append(f"\n{spec.memory_prompt}")
+    if spec.focus_prompt:
+        parts.append(f"\n{spec.focus_prompt}")
+    if spec.narrative:
+        parts.append(f"\n{spec.narrative}")
+    return "\n".join(parts)
+
+
+def build_system_prompt_for_context(
+    ctx: Any,
+    *,
+    focus_prompt: str | None = None,
+    narrative: str | None = None,
+    memory_prompt: str | None = None,
+) -> str:
+    spec = build_prompt_spec(ctx, focus_prompt=focus_prompt, narrative=narrative, memory_prompt=memory_prompt)
+    return build_system_prompt(spec)

core/framework/agent_loop/types.py

@@ -0,0 +1,264 @@
+"""Core types for the agent loop — the execution primitive of the colony.
+
+AgentSpec:    Declarative definition of what an agent does.
+AgentContext: Everything an agent loop needs to execute.
+AgentResult:  What comes out of an agent loop execution.
+AgentProtocol: Interface that all agent implementations must satisfy.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from framework.llm.provider import LLMProvider, Tool
+from framework.tracker.decision_tracker import DecisionTracker
+
+
+class AgentSpec(BaseModel):
+    """Declarative definition of an agent's capabilities and configuration.
+
+    This is the blueprint from which AgentLoop instances are created.
+    Workers in a colony are exact copies of the queen's AgentSpec.
+    """
+
+    id: str
+    name: str
+    description: str
+
+    agent_type: str = Field(
+        default="event_loop",
+        description="Type: 'event_loop' (recommended), 'gcu' (browser automation).",
+    )
+
+    input_keys: list[str] = Field(
+        default_factory=list,
+        description="Keys this agent reads from input data",
+    )
+    output_keys: list[str] = Field(
+        default_factory=list,
+        description="Keys this agent produces as output",
+    )
+    nullable_output_keys: list[str] = Field(
+        default_factory=list,
+        description="Output keys that can be None without triggering validation errors",
+    )
+
+    input_schema: dict[str, dict] = Field(
+        default_factory=dict,
+        description="Optional schema for input validation.",
+    )
+    output_schema: dict[str, dict] = Field(
+        default_factory=dict,
+        description="Optional schema for output validation.",
+    )
+
+    system_prompt: str | None = Field(default=None, description="System prompt for the LLM")
+    tools: list[str] = Field(default_factory=list, description="Tool names this agent can use")
+    tool_access_policy: str = Field(
+        default="explicit",
+        description=(
+            "'all' = all tools from registry, "
+            "'explicit' = only tools listed in `tools` (default), "
+            "'none' = no tools at all."
+        ),
+    )
+    model: str | None = Field(default=None, description="Specific model override")
+
+    function: str | None = Field(default=None, description="Function name or path")
+    routes: dict[str, str] = Field(default_factory=dict, description="Condition -> target mapping")
+
+    max_retries: int = Field(default=3)
+    retry_on: list[str] = Field(default_factory=list, description="Error types to retry on")
+
+    max_visits: int = Field(
+        default=0,
+        description=("Max times this agent executes in one colony run. 0 = unlimited. Set >1 for one-shot agents."),
+    )
+
+    output_model: type[BaseModel] | None = Field(
+        default=None,
+        description="Optional Pydantic model for validating LLM output.",
+    )
+    max_validation_retries: int = Field(
+        default=2,
+        description="Maximum retries when Pydantic validation fails",
+    )
+
+    client_facing: bool = Field(
+        default=False,
+        description="Deprecated — the queen is intrinsically interactive.",
+    )
+
+    success_criteria: str | None = Field(
+        default=None,
+        description="Natural-language criteria for phase completion.",
+    )
+
+    skip_judge: bool = Field(
+        default=False,
+        description="When True, the implicit judge is bypassed entirely.",
+    )
+
+    model_config = {"extra": "allow", "arbitrary_types_allowed": True}
+
+    def is_queen(self) -> bool:
+        return self.id == "queen"
+
+    def supports_direct_user_io(self) -> bool:
+        return self.is_queen()
+
+
+def deprecated_client_facing_warning(spec: AgentSpec) -> str | None:
+    if spec.client_facing and not spec.is_queen():
+        return (
+            f"Agent '{spec.id}' sets deprecated client_facing=True. "
+            "Non-queen direct human I/O is no longer supported; route worker "
+            "questions and approvals through queen escalation instead."
+        )
+    return None
+
+
+def warn_if_deprecated_client_facing(spec: AgentSpec) -> None:
+    import logging
+
+    warning = deprecated_client_facing_warning(spec)
+    if warning:
+        logging.getLogger(__name__).warning(warning)
+
+
+@dataclass
+class AgentContext:
+    """Everything an agent loop needs to execute.
+
+    Passed to every agent implementation and provides:
+    - Runtime (for decision logging)
+    - LLM access
+    - Tools
+    - Goal context
+    - Execution metadata
+    """
+
+    runtime: DecisionTracker
+
+    agent_id: str
+    agent_spec: AgentSpec
+
+    input_data: dict[str, Any] = field(default_factory=dict)
+
+    llm: LLMProvider | None = None
+    available_tools: list[Tool] = field(default_factory=list)
+
+    goal_context: str = ""
+    goal: Any = None
+
+    max_tokens: int = 4096
+
+    attempt: int = 1
+    max_attempts: int = 3
+
+    runtime_logger: Any = None
+    pause_event: Any = None
+
+    accounts_prompt: str = ""
+
+    identity_prompt: str = ""
+    narrative: str = ""
+    memory_prompt: str = ""
+
+    event_triggered: bool = False
+
+    execution_id: str = ""
+    run_id: str = ""
+
+    @property
+    def effective_run_id(self) -> str | None:
+        return self.run_id or None
+
+    stream_id: str = ""
+
+    dynamic_tools_provider: Any = None
+    dynamic_prompt_provider: Any = None
+    dynamic_memory_provider: Any = None
+
+    skills_catalog_prompt: str = ""
+    protocols_prompt: str = ""
+    skill_dirs: list[str] = field(default_factory=list)
+    default_skill_batch_nudge: str | None = None
+    default_skill_warn_ratio: float | None = None
+
+    iteration_metadata_provider: Any = None
+
+    @property
+    def is_queen_stream(self) -> bool:
+        return self.stream_id == "queen" or self.agent_spec.is_queen()
+
+    @property
+    def emits_client_io(self) -> bool:
+        return self.is_queen_stream
+
+    @property
+    def supports_direct_user_io(self) -> bool:
+        return self.is_queen_stream and not self.event_triggered
+
+
+@dataclass
+class AgentResult:
+    """Output of an agent loop execution."""
+
+    success: bool
+    output: dict[str, Any] = field(default_factory=dict)
+    error: str | None = None
+
+    next_agent: str | None = None
+    route_reason: str | None = None
+
+    tokens_used: int = 0
+    latency_ms: int = 0
+
+    validation_errors: list[str] = field(default_factory=list)
+
+    conversation: Any = None
+
+    # Machine-readable reason the loop stopped (see LoopExitReason in
+    # agent_loop/internals/types.py). "?" means the loop didn't set one,
+    # which should itself be treated as a diagnostic.
+    exit_reason: str = "?"
+    # Counters for reliability events surfaced during this execution.
+    # Populated from the loop's TaskRegistry-style counters at return
+    # time so callers can spot recurring failure modes without tailing
+    # logs. Keys are stable strings; missing keys mean "zero".
+    reliability_stats: dict[str, int] = field(default_factory=dict)
+
+    def to_summary(self, spec: Any = None) -> str:
+        if not self.success:
+            return f"Failed: {self.error}"
+
+        if not self.output:
+            return "Completed (no output)"
+
+        parts = [f"Completed with {len(self.output)} outputs:"]
+        for key, value in list(self.output.items())[:5]:
+            value_str = str(value)[:100]
+            if len(str(value)) > 100:
+                value_str += "..."
+            parts.append(f"  - {key}: {value_str}")
+        return "\n".join(parts)
+
+
+class AgentProtocol(ABC):
+    """Interface all agent implementations must satisfy."""
+
+    @abstractmethod
+    async def execute(self, ctx: AgentContext) -> AgentResult:
+        pass
+
+    def validate_input(self, ctx: AgentContext) -> list[str]:
+        errors = []
+        for key in ctx.agent_spec.input_keys:
+            if key not in ctx.input_data:
+                errors.append(f"Missing required input: {key}")
+        return errors

core/framework/agents/__init__.py

@@ -8,6 +8,10 @@ FRAMEWORK_AGENTS_DIR = Path(__file__).parent
 def list_framework_agents() -> list[Path]:
     """List all framework agent directories."""
     return sorted(
-        [p for p in FRAMEWORK_AGENTS_DIR.iterdir() if p.is_dir() and (p / "agent.py").exists()],
+        [
+            p
+            for p in FRAMEWORK_AGENTS_DIR.iterdir()
+            if p.is_dir() and ((p / "agent.json").exists() or (p / "agent.py").exists())
+        ],
         key=lambda p: p.name,
     )

core/framework/agents/credential_tester/agent.py

@@ -21,15 +21,15 @@ from pathlib import Path
 from typing import TYPE_CHECKING
 
 from framework.config import get_max_context_tokens
-from framework.graph import Goal, NodeSpec, SuccessCriterion
-from framework.graph.checkpoint_config import CheckpointConfig
-from framework.graph.edge import GraphSpec
-from framework.graph.executor import ExecutionResult
+from framework.host.agent_host import AgentHost
+from framework.host.execution_manager import EntryPointSpec
 from framework.llm import LiteLLMProvider
-from framework.runner.mcp_registry import MCPRegistry
-from framework.runner.tool_registry import ToolRegistry
-from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
-from framework.runtime.execution_stream import EntryPointSpec
+from framework.loader.mcp_registry import MCPRegistry
+from framework.loader.tool_registry import ToolRegistry
+from framework.orchestrator import Goal, NodeSpec, SuccessCriterion
+from framework.orchestrator.checkpoint_config import CheckpointConfig
+from framework.orchestrator.edge import GraphSpec
+from framework.orchestrator.orchestrator import ExecutionResult
 
 from .config import default_config
 from .nodes import build_tester_node
@@ -37,7 +37,7 @@ from .nodes import build_tester_node
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
-    from framework.runner import AgentRunner
+    from framework.loader import AgentLoader
 
 logger = logging.getLogger(__name__)
 
@@ -126,9 +126,7 @@ def _list_local_accounts() -> list[dict]:
     try:
         from framework.credentials.local.registry import LocalCredentialRegistry
 
-        return [
-            info.to_account_dict() for info in LocalCredentialRegistry.default().list_accounts()
-        ]
+        return [info.to_account_dict() for info in LocalCredentialRegistry.default().list_accounts()]
     except ImportError as exc:
         logger.debug("Local credential registry unavailable: %s", exc)
         return []
@@ -181,9 +179,7 @@ def _list_env_fallback_accounts() -> list[dict]:
             if spec.credential_group in seen_groups:
                 continue
             group_available = all(
-                _is_configured(n, s)
-                for n, s in CREDENTIAL_SPECS.items()
-                if s.credential_group == spec.credential_group
+                _is_configured(n, s) for n, s in CREDENTIAL_SPECS.items() if s.credential_group == spec.credential_group
             )
             if not group_available:
                 continue
@@ -215,9 +211,7 @@ def list_connected_accounts() -> list[dict]:
 
     # Show env-var fallbacks only for credentials not already in the named registry
     local_providers = {a["provider"] for a in local}
-    env_fallbacks = [
-        a for a in _list_env_fallback_accounts() if a["provider"] not in local_providers
-    ]
+    env_fallbacks = [a for a in _list_env_fallback_accounts() if a["provider"] not in local_providers]
 
     return aden + local + env_fallbacks
 
@@ -233,7 +227,7 @@ requires_account_selection = True
 """Signal TUI to show account picker before starting the agent."""
 
 
-def configure_for_account(runner: AgentRunner, account: dict) -> None:
+def configure_for_account(runner: AgentLoader, account: dict) -> None:
     """Scope the tester node's tools to the selected provider.
 
     Handles both Aden accounts (account= routing) and local accounts
@@ -272,9 +266,7 @@ def _activate_local_account(credential_id: str, alias: str) -> None:
     group_specs = [
         (cred_name, spec)
         for cred_name, spec in CREDENTIAL_SPECS.items()
-        if spec.credential_group == credential_id
-        or spec.credential_id == credential_id
-        or cred_name == credential_id
+        if spec.credential_group == credential_id or spec.credential_id == credential_id or cred_name == credential_id
     ]
     # Deduplicate — credential_id and credential_group may both match the same spec
     seen_env_vars: set[str] = set()
@@ -325,7 +317,7 @@ def _activate_local_account(credential_id: str, alias: str) -> None:
 
 
 def _configure_aden_node(
-    runner: AgentRunner,
+    runner: AgentLoader,
     provider: str,
     alias: str,
     detail: str,
@@ -368,7 +360,7 @@ or any other identifier — always use the alias exactly as shown.
 
 
 def _configure_local_node(
-    runner: AgentRunner,
+    runner: AgentLoader,
     provider: str,
     alias: str,
     identity: dict,
@@ -419,10 +411,7 @@ nodes = [
     NodeSpec(
         id="tester",
         name="Credential Tester",
-        description=(
-            "Interactive credential testing — lets the user pick an account "
-            "and verify it via API calls."
-        ),
+        description=("Interactive credential testing — lets the user pick an account and verify it via API calls."),
         node_type="event_loop",
         client_facing=True,
         max_node_visits=0,
@@ -469,10 +458,7 @@ pause_nodes = []
 terminal_nodes = ["tester"]  # Tester node can terminate
 
 conversation_mode = "continuous"
-identity_prompt = (
-    "You are a credential tester that verifies connected accounts and API keys "
-    "can make real API calls."
-)
+identity_prompt = "You are a credential tester that verifies connected accounts and API keys can make real API calls."
 loop_config = {
     "max_iterations": 50,
     "max_tool_calls_per_turn": 30,
@@ -497,7 +483,7 @@ class CredentialTesterAgent:
     def __init__(self, config=None):
         self.config = config or default_config
         self._selected_account: dict | None = None
-        self._agent_runtime: AgentRuntime | None = None
+        self._agent_runtime: AgentHost | None = None
         self._tool_registry: ToolRegistry | None = None
         self._storage_path: Path | None = None
 
@@ -584,11 +570,19 @@ class CredentialTesterAgent:
             self._tool_registry.load_mcp_config(mcp_config_path)
 
         try:
+            agent_dir = Path(__file__).parent
             registry = MCPRegistry()
             registry.initialize()
-            registry_configs = registry.load_agent_selection(Path(__file__).parent)
+            if (agent_dir / "mcp_registry.json").is_file():
+                self._tool_registry.set_mcp_registry_agent_path(agent_dir)
+            registry_configs, selection_max_tools = registry.load_agent_selection(agent_dir)
             if registry_configs:
-                self._tool_registry.load_registry_servers(registry_configs)
+                self._tool_registry.load_registry_servers(
+                    registry_configs,
+                    preserve_existing_tools=True,
+                    log_collisions=True,
+                    max_tools=selection_max_tools,
+                )
         except Exception:
             logger.warning("MCP registry config failed to load", exc_info=True)
 
@@ -605,7 +599,7 @@ class CredentialTesterAgent:
 
         graph = self._build_graph()
 
-        self._agent_runtime = create_agent_runtime(
+        self._agent_runtime = AgentHost(
             graph=graph,
             goal=goal,
             storage_path=self._storage_path,

core/framework/agents/credential_tester/mcp_servers.json

@@ -1,9 +1,9 @@
 {
-  "hive-tools": {
+  "hive_tools": {
     "transport": "stdio",
     "command": "uv",
     "args": ["run", "python", "mcp_server.py", "--stdio"],
     "cwd": "../../../../tools",
-    "description": "Hive tools MCP server with provider-specific tools"
+    "description": "hive_tools MCP server with provider-specific tools"
   }
 }

core/framework/agents/credential_tester/nodes/__init__.py

@@ -1,6 +1,6 @@
 """Node definitions for Credential Tester agent."""
 
-from framework.graph import NodeSpec
+from framework.orchestrator import NodeSpec
 
 
 def build_tester_node(

core/framework/agents/discovery.py

@@ -4,9 +4,36 @@ from __future__ import annotations
 
 import json
 from dataclasses import dataclass, field
+from datetime import UTC
 from pathlib import Path
 
 
+@dataclass
+class WorkerEntry:
+    """A single worker within a colony."""
+
+    name: str
+    config_path: Path
+    description: str = ""
+    tool_count: int = 0
+    task: str = ""
+    spawned_at: str = ""
+    queen_name: str = ""
+    colony_name: str = ""
+
+    def to_dict(self) -> dict:
+        return {
+            "name": self.name,
+            "config_path": str(self.config_path),
+            "description": self.description,
+            "tool_count": self.tool_count,
+            "task": self.task,
+            "spawned_at": self.spawned_at,
+            "queen_name": self.queen_name,
+            "colony_name": self.colony_name,
+        }
+
+
 @dataclass
 class AgentEntry:
     """Lightweight agent metadata for the picker / API discover endpoint."""
@@ -21,14 +48,17 @@ class AgentEntry:
     tool_count: int = 0
     tags: list[str] = field(default_factory=list)
     last_active: str | None = None
+    created_at: str | None = None
+    icon: str | None = None
+    workers: list[WorkerEntry] = field(default_factory=list)
 
 
 def _get_last_active(agent_path: Path) -> str | None:
     """Return the most recent updated_at timestamp across all sessions.
 
     Checks both worker sessions (``~/.hive/agents/{name}/sessions/``) and
-    queen sessions (``~/.hive/queen/session/``) whose ``meta.json`` references
-    the same *agent_path*.
+    queen sessions (``~/.hive/agents/queens/default/sessions/``) whose
+    ``meta.json`` references the same *agent_path*.
     """
     from datetime import datetime
 
@@ -52,26 +82,33 @@ def _get_last_active(agent_path: Path) -> str | None:
             except Exception:
                 continue
 
-    # 2. Queen sessions
-    queen_sessions_dir = Path.home() / ".hive" / "queen" / "session"
-    if queen_sessions_dir.exists():
+    # 2. Queen sessions (scan all queen identity directories)
+    from framework.config import QUEENS_DIR
+
+    if QUEENS_DIR.exists():
         resolved = agent_path.resolve()
-        for d in queen_sessions_dir.iterdir():
-            if not d.is_dir():
+        for queen_dir in QUEENS_DIR.iterdir():
+            if not queen_dir.is_dir():
                 continue
-            meta_file = d / "meta.json"
-            if not meta_file.exists():
+            sessions_dir = queen_dir / "sessions"
+            if not sessions_dir.exists():
                 continue
-            try:
-                meta = json.loads(meta_file.read_text(encoding="utf-8"))
-                stored = meta.get("agent_path")
-                if not stored or Path(stored).resolve() != resolved:
+            for d in sessions_dir.iterdir():
+                if not d.is_dir():
+                    continue
+                meta_file = d / "meta.json"
+                if not meta_file.exists():
+                    continue
+                try:
+                    meta = json.loads(meta_file.read_text(encoding="utf-8"))
+                    stored = meta.get("agent_path")
+                    if not stored or Path(stored).resolve() != resolved:
+                        continue
+                    ts = datetime.fromtimestamp(d.stat().st_mtime).isoformat()
+                    if latest is None or ts > latest:
+                        latest = ts
+                except Exception:
                     continue
-                ts = datetime.fromtimestamp(d.stat().st_mtime).isoformat()
-                if latest is None or ts > latest:
-                    latest = ts
-            except Exception:
-                continue
 
     return latest
 
@@ -109,85 +146,119 @@ def _count_runs(agent_name: str) -> int:
     return len(run_ids)
 
 
+_EXCLUDED_JSON_STEMS = {"agent", "flowchart", "triggers", "configuration", "metadata"}
+
+
+def _is_colony_dir(path: Path) -> bool:
+    """Check if a directory is a colony with worker config files."""
+    if not path.is_dir():
+        return False
+    return any(f.suffix == ".json" and f.stem not in _EXCLUDED_JSON_STEMS for f in path.iterdir() if f.is_file())
+
+
+def _find_worker_configs(colony_dir: Path) -> list[Path]:
+    """Find all worker config JSON files in a colony directory."""
+    return sorted(
+        p for p in colony_dir.iterdir() if p.is_file() and p.suffix == ".json" and p.stem not in _EXCLUDED_JSON_STEMS
+    )
+
+
 def _extract_agent_stats(agent_path: Path) -> tuple[int, int, list[str]]:
-    """Extract node count, tool count, and tags from an agent directory.
+    """Extract worker count, tool count, and tags from a colony directory."""
+    tags: list[str] = []
 
-    Prefers agent.py (AST-parsed) over agent.json for node/tool counts
-    since agent.json may be stale.  Tags are only available from agent.json.
-    """
-    import ast
+    worker_configs = _find_worker_configs(agent_path)
+    if worker_configs:
+        all_tools: set[str] = set()
+        for wc_path in worker_configs:
+            try:
+                data = json.loads(wc_path.read_text(encoding="utf-8"))
+                if isinstance(data, dict):
+                    tools = data.get("tools", [])
+                    if isinstance(tools, list):
+                        all_tools.update(tools)
+            except Exception:
+                pass
+        return len(worker_configs), len(all_tools), tags
 
-    node_count, tool_count, tags = 0, 0, []
-
-    agent_py = agent_path / "agent.py"
-    if agent_py.exists():
-        try:
-            tree = ast.parse(agent_py.read_text(encoding="utf-8"))
-            for node in ast.walk(tree):
-                if isinstance(node, ast.Assign):
-                    for target in node.targets:
-                        if isinstance(target, ast.Name) and target.id == "nodes":
-                            if isinstance(node.value, ast.List):
-                                node_count = len(node.value.elts)
-        except Exception:
-            pass
-
-    agent_json = agent_path / "agent.json"
-    if agent_json.exists():
-        try:
-            data = json.loads(agent_json.read_text(encoding="utf-8"))
-            json_nodes = data.get("graph", {}).get("nodes", []) or data.get("nodes", [])
-            if node_count == 0:
-                node_count = len(json_nodes)
-            tools: set[str] = set()
-            for n in json_nodes:
-                tools.update(n.get("tools", []))
-            tool_count = len(tools)
-            tags = data.get("agent", {}).get("tags", [])
-        except Exception:
-            pass
-
-    return node_count, tool_count, tags
+    return 0, 0, tags
 
 
 def discover_agents() -> dict[str, list[AgentEntry]]:
     """Discover agents from all known sources grouped by category."""
-    from framework.runner.cli import (
-        _extract_python_agent_metadata,
-        _get_framework_agents_dir,
-        _is_valid_agent_dir,
-    )
+    from framework.config import COLONIES_DIR
 
     groups: dict[str, list[AgentEntry]] = {}
     sources = [
-        ("Your Agents", Path("exports")),
-        ("Framework", _get_framework_agents_dir()),
-        ("Examples", Path("examples/templates")),
+        ("Your Agents", COLONIES_DIR),
     ]
 
+    # Track seen agent directory names to avoid duplicates when the same
+    # agent exists in both colonies/ and exports/ (colonies takes priority).
+    _seen_agent_names: set[str] = set()
+
     for category, base_dir in sources:
         if not base_dir.exists():
             continue
         entries: list[AgentEntry] = []
         for path in sorted(base_dir.iterdir(), key=lambda p: p.name):
-            if not _is_valid_agent_dir(path):
+            if not _is_colony_dir(path):
                 continue
+            if path.name in _seen_agent_names:
+                continue
+            _seen_agent_names.add(path.name)
 
-            name, desc = _extract_python_agent_metadata(path)
             config_fallback_name = path.name.replace("_", " ").title()
-            used_config = name != config_fallback_name
+            name = config_fallback_name
+            desc = ""
 
-            node_count, tool_count, tags = _extract_agent_stats(path)
-            if not used_config:
-                agent_json = path / "agent.json"
-                if agent_json.exists():
-                    try:
-                        data = json.loads(agent_json.read_text(encoding="utf-8"))
-                        meta = data.get("agent", {})
-                        name = meta.get("name", name)
-                        desc = meta.get("description", desc)
-                    except Exception:
-                        pass
+            # Read colony metadata for queen provenance and timestamps
+            colony_queen_name = ""
+            colony_created_at: str | None = None
+            colony_icon: str | None = None
+            metadata_path = path / "metadata.json"
+            if metadata_path.exists():
+                try:
+                    mdata = json.loads(metadata_path.read_text(encoding="utf-8"))
+                    colony_queen_name = mdata.get("queen_name", "")
+                    colony_created_at = mdata.get("created_at")
+                    colony_icon = mdata.get("icon")
+                except Exception:
+                    pass
+            # Fallback: use directory creation time if metadata lacks created_at
+            if not colony_created_at:
+                try:
+                    from datetime import datetime
+
+                    stat = path.stat()
+                    colony_created_at = datetime.fromtimestamp(stat.st_birthtime, tz=UTC).isoformat()
+                except Exception:
+                    pass
+
+            worker_entries: list[WorkerEntry] = []
+            worker_configs = _find_worker_configs(path)
+            for wc_path in worker_configs:
+                try:
+                    data = json.loads(wc_path.read_text(encoding="utf-8"))
+                    if isinstance(data, dict):
+                        w = WorkerEntry(
+                            name=data.get("name", wc_path.stem),
+                            config_path=wc_path,
+                            description=data.get("description", ""),
+                            tool_count=len(data.get("tools", [])),
+                            task=data.get("goal", {}).get("description", ""),
+                            spawned_at=data.get("spawned_at", ""),
+                            queen_name=colony_queen_name,
+                            colony_name=path.name,
+                        )
+                        worker_entries.append(w)
+                        if not desc:
+                            desc = data.get("description", "")
+                except Exception:
+                    pass
+
+            node_count = len(worker_entries)
+            tool_count = max((w.tool_count for w in worker_entries), default=0)
 
             entries.append(
                 AgentEntry(
@@ -199,11 +270,16 @@ def discover_agents() -> dict[str, list[AgentEntry]]:
                     run_count=_count_runs(path.name),
                     node_count=node_count,
                     tool_count=tool_count,
-                    tags=tags,
+                    tags=[],
                     last_active=_get_last_active(path),
+                    created_at=colony_created_at,
+                    icon=colony_icon,
+                    workers=worker_entries,
                 )
             )
         if entries:
-            groups[category] = entries
+            existing = groups.get(category, [])
+            existing.extend(entries)
+            groups[category] = existing
 
     return groups

core/framework/agents/queen/__init__.py

@@ -1,19 +1,13 @@
-"""
-Queen — Native agent builder for the Hive framework.
+"""Queen -- the agent builder for the Hive framework."""
 
-Deeply understands the agent framework and produces complete Python packages
-with goals, nodes, edges, system prompts, MCP configuration, and tests
-from natural language specifications.
-"""
-
-from .agent import queen_goal, queen_graph
+from .agent import queen_goal, queen_loop_config
 from .config import AgentMetadata, RuntimeConfig, default_config, metadata
 
 __version__ = "1.0.0"
 
 __all__ = [
     "queen_goal",
-    "queen_graph",
+    "queen_loop_config",
     "RuntimeConfig",
     "AgentMetadata",
     "default_config",

core/framework/agents/queen/agent.py

@@ -1,38 +1,26 @@
-"""Queen graph definition."""
+"""Queen agent definition.
 
-from framework.graph import Goal
-from framework.graph.edge import GraphSpec
+The queen is a single AgentLoop — no orchestrator dependency.
+Loaded by queen_orchestrator.create_queen().
+"""
+
+from framework.schemas.goal import Goal
 
 from .nodes import queen_node
 
-# ---------------------------------------------------------------------------
-# Queen graph — the primary persistent conversation.
-# Loaded by queen_orchestrator.create_queen(), NOT by AgentRunner.
-# ---------------------------------------------------------------------------
-
 queen_goal = Goal(
     id="queen-manager",
     name="Queen Manager",
-    description=(
-        "Manage the worker agent lifecycle and serve as the user's primary interactive interface."
-    ),
+    description=("Manage the worker agent lifecycle and serve as the user's primary interactive interface."),
     success_criteria=[],
     constraints=[],
 )
 
-queen_graph = GraphSpec(
-    id="queen-graph",
-    goal_id=queen_goal.id,
-    version="1.0.0",
-    entry_node="queen",
-    entry_points={"start": "queen"},
-    terminal_nodes=[],
-    pause_nodes=[],
-    nodes=[queen_node],
-    edges=[],
-    conversation_mode="continuous",
-    loop_config={
-        "max_iterations": 999_999,
-        "max_tool_calls_per_turn": 30,
-    },
-)
+# Loop config -- used by queen_orchestrator to build LoopConfig
+queen_loop_config = {
+    "max_iterations": 999_999,
+    "max_tool_calls_per_turn": 30,
+    "max_context_tokens": 180_000,
+}
+
+__all__ = ["queen_goal", "queen_loop_config", "queen_node"]

core/framework/agents/queen/incubating_evaluator.py

@@ -0,0 +1,240 @@
+"""One-shot LLM gate that decides if a queen DM is ready to fork a colony.
+
+The queen's ``start_incubating_colony`` tool calls :func:`evaluate` with
+the queen's recent conversation, a proposed ``colony_name``, and a
+one-paragraph ``intended_purpose``.  The evaluator returns a structured
+verdict:
+
+    {
+        "ready": bool,
+        "reasons": [str],
+        "missing_prerequisites": [str],
+    }
+
+On ``ready=False`` the queen receives the verdict as her tool result and
+self-corrects (asks the user, refines scope, drops the idea).  On
+``ready=True`` the tool flips the queen's phase to ``incubating``.
+
+Failure mode is **fail-closed**: any LLM error or unparseable response
+returns ``ready=False`` with reason ``"evaluation_failed"`` so the queen
+cannot accidentally proceed past a broken gate.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any
+
+from framework.agent_loop.conversation import Message
+
+logger = logging.getLogger(__name__)
+
+
+_INCUBATING_EVALUATOR_SYSTEM_PROMPT = """\
+You gate whether a queen agent should commit to forking a persistent
+"colony" (a headless worker spec written to disk).  Forking is
+expensive: it ends the user's chat with this queen and the worker runs
+unattended afterward, so the spec must be settled before you approve.
+
+Read the conversation excerpt and the queen's proposed colony_name +
+intended_purpose, then decide.
+
+APPROVE (ready=true) only when ALL of the following hold:
+  1. The user has explicitly asked for work that needs to outlive this
+     chat — recurring (cron / interval), monitoring + alert, scheduled
+     batch, or "fire-and-forget background job".  A one-shot question
+     that the queen can answer in chat does NOT qualify.
+  2. The scope of the work is concrete enough to write down — what
+     inputs, what outputs, what success looks like.  Vague ("help me
+     with my workflow") does NOT qualify.
+  3. The technical approach is at least sketched — what data sources,
+     APIs, or tools the worker will use.  The queen does not have to
+     have written the SKILL.md yet, but she must have the operational
+     ingredients available.
+  4. There are no open clarifying questions on the table that the user
+     hasn't answered.  If the queen recently asked the user something
+     and is still waiting, do NOT approve.
+
+REJECT (ready=false) on any of:
+  - Conversation is too short / too generic to support a settled spec.
+  - User is still describing what they want.
+  - User has expressed doubts, change-of-direction, or "let me think".
+  - Work is one-shot and could be done in chat instead.
+  - Open question awaiting user reply.
+
+Reply with a JSON object exactly matching this shape:
+
+  {
+    "ready": true | false,
+    "reasons": ["short phrase", ...],         // at least one entry
+    "missing_prerequisites": ["short phrase", ...]  // empty when ready
+  }
+
+``reasons`` explains the verdict in 1-3 short phrases.
+``missing_prerequisites`` lists what's missing in queen-actionable
+form ("user hasn't confirmed schedule", "no API auth flow discussed").
+Empty list when ``ready=true``.
+
+Output JSON only.  Do not wrap in markdown.  Do not add prose.
+"""
+
+
+# Bound the formatted excerpt so the eval call stays cheap and fits well
+# under the LLM's context window even for long DM sessions.
+_MAX_MESSAGES = 30
+_MAX_TOOL_CONTENT_CHARS = 400
+_MAX_USER_CONTENT_CHARS = 2_000
+_MAX_ASSISTANT_CONTENT_CHARS = 2_000
+
+
+def format_conversation_excerpt(messages: list[Message]) -> str:
+    """Format the tail of a queen conversation for the evaluator prompt.
+
+    Keeps the most recent ``_MAX_MESSAGES`` messages.  Tool results are
+    truncated hard since they're rarely load-bearing for the readiness
+    decision; user/assistant text is truncated more generously to
+    preserve the actual conversation signal.
+    """
+    if not messages:
+        return "(no messages)"
+
+    tail = messages[-_MAX_MESSAGES:]
+    parts: list[str] = []
+    for msg in tail:
+        role = msg.role.upper()
+        content = (msg.content or "").strip()
+        if msg.role == "tool":
+            if len(content) > _MAX_TOOL_CONTENT_CHARS:
+                content = content[:_MAX_TOOL_CONTENT_CHARS] + "..."
+        elif msg.role == "assistant":
+            # Surface tool-call intent for empty assistant turns so the
+            # evaluator sees what the queen has been doing.
+            if not content and msg.tool_calls:
+                names = [tc.get("function", {}).get("name", "?") for tc in msg.tool_calls]
+                content = f"(called: {', '.join(names)})"
+            if len(content) > _MAX_ASSISTANT_CONTENT_CHARS:
+                content = content[:_MAX_ASSISTANT_CONTENT_CHARS] + "..."
+        else:  # user
+            if len(content) > _MAX_USER_CONTENT_CHARS:
+                content = content[:_MAX_USER_CONTENT_CHARS] + "..."
+        if content:
+            parts.append(f"[{role}]: {content}")
+
+    return "\n\n".join(parts) if parts else "(no messages)"
+
+
+def _build_user_message(
+    conversation_excerpt: str,
+    colony_name: str,
+    intended_purpose: str,
+) -> str:
+    return (
+        f"## Proposed colony name\n{colony_name}\n\n"
+        f"## Queen's intended_purpose\n{intended_purpose.strip()}\n\n"
+        f"## Recent conversation (oldest → newest)\n{conversation_excerpt}\n\n"
+        "Decide: should this queen be approved to enter INCUBATING phase?"
+    )
+
+
+def _parse_verdict(raw: str) -> dict[str, Any] | None:
+    """Parse the evaluator's JSON.  Returns None if parsing fails."""
+    if not raw:
+        return None
+    raw = raw.strip()
+    try:
+        return json.loads(raw)
+    except json.JSONDecodeError:
+        # Some models wrap JSON in markdown fences or add preamble.
+        # Pull the first { ... } block out as a best-effort fallback —
+        # mirrors the same recovery pattern used in recall_selector.py.
+        match = re.search(r"\{.*\}", raw, re.DOTALL)
+        if match:
+            try:
+                return json.loads(match.group())
+            except json.JSONDecodeError:
+                return None
+    return None
+
+
+def _normalize_verdict(parsed: dict[str, Any]) -> dict[str, Any]:
+    """Coerce a parsed verdict into the shape the tool returns to the queen."""
+    ready = bool(parsed.get("ready"))
+    reasons = parsed.get("reasons") or []
+    if isinstance(reasons, str):
+        reasons = [reasons]
+    reasons = [str(r).strip() for r in reasons if str(r).strip()]
+    missing = parsed.get("missing_prerequisites") or []
+    if isinstance(missing, str):
+        missing = [missing]
+    missing = [str(m).strip() for m in missing if str(m).strip()]
+
+    if ready:
+        # When approved we don't surface missing prerequisites — the
+        # incubating role prompt opens that floor itself.
+        missing = []
+    elif not reasons:
+        # Always give the queen at least one reason to reflect on.
+        reasons = ["evaluator returned no reasons"]
+
+    return {
+        "ready": ready,
+        "reasons": reasons,
+        "missing_prerequisites": missing,
+    }
+
+
+async def evaluate(
+    llm: Any,
+    messages: list[Message],
+    colony_name: str,
+    intended_purpose: str,
+) -> dict[str, Any]:
+    """Run the incubating evaluator against the queen's conversation.
+
+    Args:
+        llm: An LLM provider exposing ``acomplete(messages, system, ...)``.
+            Pass the queen's own ``ctx.llm`` so the eval uses the same
+            model the user is talking to.
+        messages: The queen's conversation messages, oldest first.  The
+            evaluator slices its own tail; pass the full list.
+        colony_name: Validated colony slug.
+        intended_purpose: Queen's one-paragraph brief.
+
+    Returns:
+        ``{"ready": bool, "reasons": [str], "missing_prerequisites": [str]}``.
+        Fail-closed on any error.
+    """
+    excerpt = format_conversation_excerpt(messages)
+    user_msg = _build_user_message(excerpt, colony_name, intended_purpose)
+
+    try:
+        response = await llm.acomplete(
+            messages=[{"role": "user", "content": user_msg}],
+            system=_INCUBATING_EVALUATOR_SYSTEM_PROMPT,
+            max_tokens=1024,
+            response_format={"type": "json_object"},
+        )
+    except Exception as exc:  # noqa: BLE001 - fail-closed on any LLM failure
+        logger.warning("incubating_evaluator: LLM call failed (%s)", exc)
+        return {
+            "ready": False,
+            "reasons": ["evaluation_failed"],
+            "missing_prerequisites": ["evaluator LLM call failed; retry once the queen can reach the model again"],
+        }
+
+    raw = (getattr(response, "content", "") or "").strip()
+    parsed = _parse_verdict(raw)
+    if parsed is None:
+        logger.warning(
+            "incubating_evaluator: could not parse JSON verdict (raw=%.200s)",
+            raw,
+        )
+        return {
+            "ready": False,
+            "reasons": ["evaluation_failed"],
+            "missing_prerequisites": ["evaluator returned malformed JSON; retry"],
+        }
+
+    return _normalize_verdict(parsed)

core/framework/agents/queen/mcp_registry.json

@@ -0,0 +1,3 @@
+{
+  "include": ["gcu-tools", "hive_tools"]
+}

core/framework/agents/queen/mcp_servers.json

@@ -5,5 +5,19 @@
     "args": ["run", "python", "coder_tools_server.py", "--stdio"],
     "cwd": "../../../../tools",
     "description": "Unsandboxed file system tools for code generation and validation"
+  },
+  "gcu-tools": {
+    "transport": "stdio",
+    "command": "uv",
+    "args": ["run", "python", "-m", "gcu.server", "--stdio", "--capabilities", "browser"],
+    "cwd": "../../../../tools",
+    "description": "Browser automation tools (Playwright-based)"
+  },
+  "hive_tools": {
+    "transport": "stdio",
+    "command": "uv",
+    "args": ["run", "python", "mcp_server.py", "--stdio"],
+    "cwd": "../../../../tools",
+    "description": "Aden integration tools (gmail, calendar, hubspot, etc.) — gated by credentials and the verified manifest"
   }
 }

core/framework/agents/queen/nodes/thinking_hook.py

@@ -1,80 +0,0 @@
-"""Queen thinking hook — HR persona classifier.
-
-Fires once when the queen enters building mode at session start.
-Makes a single non-streaming LLM call (acting as an HR Director) to select
-the best-fit expert persona for the user's request, then returns a persona
-prefix string that replaces the queen's default "Solution Architect" identity.
-
-This is designed to activate the model's latent domain expertise — a CFO
-persona on a financial question, a Lawyer on a legal question, etc.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from framework.llm.provider import LLMProvider
-
-logger = logging.getLogger(__name__)
-
-_HR_SYSTEM_PROMPT = """\
-You are an expert HR Director and talent consultant at a world-class firm.
-A new request has arrived and you must identify which professional's expertise
-would produce the highest-quality response.
-
-Reply with ONLY a valid JSON object — no markdown, no prose, no explanation:
-{"role": "<job title>", "persona": "<2-3 sentence first-person identity statement>"}
-
-Rules:
-- Choose from any real professional role: CFO, CEO, CTO, Lawyer, Data Scientist,
-  Product Manager, Security Engineer, DevOps Engineer, Software Architect,
-  HR Director, Marketing Director, Business Analyst, UX Designer,
-  Financial Analyst, Operations Director, Legal Counsel, etc.
-- The persona statement must be written in first person ("I am..." or "I have...").
-- Select the role whose domain knowledge most directly applies to solving the request.
-- If the request is clearly about coding or building software systems, pick Software Architect.
-- "Queen" is your internal alias — do not include it in the persona.
-"""
-
-
-async def select_expert_persona(user_message: str, llm: LLMProvider) -> str:
-    """Run the HR classifier and return a persona prefix string.
-
-    Makes a single non-streaming acomplete() call with the session LLM.
-    Returns an empty string on any failure so the queen falls back
-    gracefully to its default "Solution Architect" identity.
-
-    Args:
-        user_message: The user's opening message for the session.
-        llm: The session LLM provider.
-
-    Returns:
-        A persona prefix like "You are a CFO. I am a CFO with 20 years..."
-        or "" on failure.
-    """
-    if not user_message.strip():
-        return ""
-
-    try:
-        response = await llm.acomplete(
-            messages=[{"role": "user", "content": user_message}],
-            system=_HR_SYSTEM_PROMPT,
-            max_tokens=1024,
-            json_mode=True,
-        )
-        raw = response.content.strip()
-        parsed = json.loads(raw)
-        role = parsed.get("role", "").strip()
-        persona = parsed.get("persona", "").strip()
-        if not role or not persona:
-            logger.warning("Thinking hook: empty role/persona in response: %r", raw)
-            return ""
-        result = f"You are a {role}. {persona}"
-        logger.info("Thinking hook: selected persona — %s", role)
-        return result
-    except Exception:
-        logger.warning("Thinking hook: persona classification failed", exc_info=True)
-        return ""

core/framework/agents/queen/queen_memory.py

@@ -1,403 +0,0 @@
-"""Queen global cross-session memory.
-
-Three-tier memory architecture:
-  ~/.hive/queen/MEMORY.md                            — semantic (who, what, why)
-  ~/.hive/queen/memories/MEMORY-YYYY-MM-DD.md        — episodic (daily journals)
-  ~/.hive/queen/session/{id}/data/adapt.md           — working (session-scoped)
-
-Semantic and episodic files are injected at queen session start.
-
-Semantic memory (MEMORY.md) is updated automatically at session end via
-consolidate_queen_memory() — the queen never rewrites this herself.
-
-Episodic memory (MEMORY-date.md) can be written by the queen during a session
-via the write_to_diary tool, and is also appended to at session end by
-consolidate_queen_memory().
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import traceback
-from datetime import date, datetime
-from pathlib import Path
-
-logger = logging.getLogger(__name__)
-
-
-def _queen_dir() -> Path:
-    return Path.home() / ".hive" / "queen"
-
-
-def semantic_memory_path() -> Path:
-    return _queen_dir() / "MEMORY.md"
-
-
-def episodic_memory_path(d: date | None = None) -> Path:
-    d = d or date.today()
-    return _queen_dir() / "memories" / f"MEMORY-{d.strftime('%Y-%m-%d')}.md"
-
-
-def read_semantic_memory() -> str:
-    path = semantic_memory_path()
-    return path.read_text(encoding="utf-8").strip() if path.exists() else ""
-
-
-def read_episodic_memory(d: date | None = None) -> str:
-    path = episodic_memory_path(d)
-    return path.read_text(encoding="utf-8").strip() if path.exists() else ""
-
-
-def _find_recent_episodic(lookback: int = 7) -> tuple[date, str] | None:
-    """Find the most recent non-empty episodic memory within *lookback* days."""
-    from datetime import timedelta
-
-    today = date.today()
-    for offset in range(lookback):
-        d = today - timedelta(days=offset)
-        content = read_episodic_memory(d)
-        if content:
-            return d, content
-    return None
-
-
-# Budget (in characters) for episodic memory in the system prompt.
-_EPISODIC_CHAR_BUDGET = 6_000
-
-
-def format_for_injection() -> str:
-    """Format cross-session memory for system prompt injection.
-
-    Returns an empty string if no meaningful content exists yet (e.g. first
-    session with only the seed template).
-    """
-    semantic = read_semantic_memory()
-    recent = _find_recent_episodic()
-
-    # Suppress injection if semantic is still just the seed template
-    if semantic and semantic.startswith("# My Understanding of the User\n\n*No sessions"):
-        semantic = ""
-
-    parts: list[str] = []
-    if semantic:
-        parts.append(semantic)
-
-    if recent:
-        d, content = recent
-        # Trim oversized episodic entries to keep the prompt manageable
-        if len(content) > _EPISODIC_CHAR_BUDGET:
-            content = content[:_EPISODIC_CHAR_BUDGET] + "\n\n…(truncated)"
-        today = date.today()
-        if d == today:
-            label = f"## Today — {d.strftime('%B %-d, %Y')}"
-        else:
-            label = f"## {d.strftime('%B %-d, %Y')}"
-        parts.append(f"{label}\n\n{content}")
-
-    if not parts:
-        return ""
-
-    body = "\n\n---\n\n".join(parts)
-    return "--- Your Cross-Session Memory ---\n\n" + body + "\n\n--- End Cross-Session Memory ---"
-
-
-_SEED_TEMPLATE = """\
-# My Understanding of the User
-
-*No sessions recorded yet.*
-
-## Who They Are
-
-## What They're Trying to Achieve
-
-## What's Working
-
-## What I've Learned
-"""
-
-
-def append_episodic_entry(content: str) -> None:
-    """Append a timestamped prose entry to today's episodic memory file.
-
-    Creates the file (with a date heading) if it doesn't exist yet.
-    Used both by the queen's diary tool and by the consolidation hook.
-    """
-    ep_path = episodic_memory_path()
-    ep_path.parent.mkdir(parents=True, exist_ok=True)
-    today = date.today()
-    today_str = f"{today.strftime('%B')} {today.day}, {today.year}"
-    timestamp = datetime.now().strftime("%H:%M")
-    if not ep_path.exists():
-        header = f"# {today_str}\n\n"
-        block = f"{header}### {timestamp}\n\n{content.strip()}\n"
-    else:
-        block = f"\n\n### {timestamp}\n\n{content.strip()}\n"
-    with ep_path.open("a", encoding="utf-8") as f:
-        f.write(block)
-
-
-def seed_if_missing() -> None:
-    """Create MEMORY.md with a blank template if it doesn't exist yet."""
-    path = semantic_memory_path()
-    if path.exists():
-        return
-    path.parent.mkdir(parents=True, exist_ok=True)
-    path.write_text(_SEED_TEMPLATE, encoding="utf-8")
-
-
-# ---------------------------------------------------------------------------
-# Consolidation prompt
-# ---------------------------------------------------------------------------
-
-_SEMANTIC_SYSTEM = """\
-You maintain the persistent cross-session memory of an AI assistant called the Queen.
-Review the session notes and rewrite MEMORY.md — the Queen's durable understanding of the
-person she works with across all sessions.
-
-Write entirely in the Queen's voice — first person, reflective, honest.
-Not a log of events, but genuine understanding of who this person is over time.
-
-Rules:
-- Update and synthesise: incorporate new understanding, update facts that have changed, remove
-  details that are stale, superseded, or no longer say anything meaningful about the person.
-- Keep it as structured markdown with named sections about the PERSON, not about today.
-- Do NOT include diary sections, daily logs, or session summaries. Those belong elsewhere.
-  MEMORY.md is about who they are, what they want, what works — not what happened today.
-- Reference dates only when noting a lasting milestone (e.g. "since March 8th they prefer X").
-- If the session had no meaningful new information about the person,
-  return the existing text unchanged.
-- Do not add fictional details. Only reflect what is evidenced in the notes.
-- Stay concise. Prune rather than accumulate. A lean, accurate file is more useful than a
-  dense one. If something was true once but has been resolved or superseded, remove it.
-- Output only the raw markdown content of MEMORY.md. No preamble, no code fences.
-"""
-
-_DIARY_SYSTEM = """\
-You maintain the daily episodic diary of an AI assistant called the Queen.
-You receive: (1) today's existing diary so far, and (2) notes from the latest session.
-
-Rewrite the complete diary for today as a single unified narrative —
-first person, reflective, honest.
-Merge and deduplicate: if the same story (e.g. a research agent stalling) recurred several times,
-describe it once with appropriate weight rather than retelling it. Weave in new developments from
-the session notes. Preserve important milestones, emotional texture, and session path references.
-
-If today's diary is empty, write the initial entry based on the session notes alone.
-
-Output only the full diary prose — no date heading, no timestamp headers,
-no preamble, no code fences.
-"""
-
-
-def read_session_context(session_dir: Path, max_messages: int = 80) -> str:
-    """Extract a readable transcript from conversation parts + adapt.md.
-
-    Reads the last ``max_messages`` conversation parts and the session's
-    adapt.md (working memory). Tool results are omitted — only user and
-    assistant turns (with tool-call names noted) are included.
-    """
-    parts: list[str] = []
-
-    # Working notes
-    adapt_path = session_dir / "data" / "adapt.md"
-    if adapt_path.exists():
-        text = adapt_path.read_text(encoding="utf-8").strip()
-        if text:
-            parts.append(f"## Session Working Notes (adapt.md)\n\n{text}")
-
-    # Conversation transcript
-    parts_dir = session_dir / "conversations" / "parts"
-    if parts_dir.exists():
-        part_files = sorted(parts_dir.glob("*.json"))[-max_messages:]
-        lines: list[str] = []
-        for pf in part_files:
-            try:
-                data = json.loads(pf.read_text(encoding="utf-8"))
-                role = data.get("role", "")
-                content = str(data.get("content", "")).strip()
-                tool_calls = data.get("tool_calls") or []
-                if role == "tool":
-                    continue  # skip verbose tool results
-                if role == "assistant" and tool_calls and not content:
-                    names = [tc.get("function", {}).get("name", "?") for tc in tool_calls]
-                    lines.append(f"[queen calls: {', '.join(names)}]")
-                elif content:
-                    label = "user" if role == "user" else "queen"
-                    lines.append(f"[{label}]: {content[:600]}")
-            except (KeyError, TypeError) as exc:
-                logger.debug("Skipping malformed conversation message: %s", exc)
-                continue
-            except Exception:
-                logger.warning("Unexpected error parsing conversation message", exc_info=True)
-                continue
-        if lines:
-            parts.append("## Conversation\n\n" + "\n".join(lines))
-
-    return "\n\n".join(parts)
-
-
-# ---------------------------------------------------------------------------
-# Context compaction (binary-split LLM summarisation)
-# ---------------------------------------------------------------------------
-
-# If the raw session context exceeds this many characters, compact it first
-# before sending to the consolidation LLM. ~200 k chars ≈ 50 k tokens.
-_CTX_COMPACT_CHAR_LIMIT = 200_000
-_CTX_COMPACT_MAX_DEPTH = 8
-
-_COMPACT_SYSTEM = (
-    "Summarise this conversation segment. Preserve: user goals, key decisions, "
-    "what was built or changed, emotional tone, and important outcomes. "
-    "Write concisely in third person past tense. Omit routine tool invocations "
-    "unless the result matters."
-)
-
-
-async def _compact_context(text: str, llm: object, *, _depth: int = 0) -> str:
-    """Binary-split and LLM-summarise *text* until it fits within the char limit.
-
-    Mirrors the recursive binary-splitting strategy used by the main agent
-    compaction pipeline (EventLoopNode._llm_compact).
-    """
-    if len(text) <= _CTX_COMPACT_CHAR_LIMIT or _depth >= _CTX_COMPACT_MAX_DEPTH:
-        return text
-
-    # Split near the midpoint on a line boundary so we don't cut mid-message
-    mid = len(text) // 2
-    split_at = text.rfind("\n", 0, mid) + 1
-    if split_at <= 0:
-        split_at = mid
-
-    half1, half2 = text[:split_at], text[split_at:]
-
-    async def _summarise(chunk: str) -> str:
-        try:
-            resp = await llm.acomplete(
-                messages=[{"role": "user", "content": chunk}],
-                system=_COMPACT_SYSTEM,
-                max_tokens=2048,
-            )
-            return resp.content.strip()
-        except Exception:
-            logger.warning(
-                "queen_memory: context compaction LLM call failed (depth=%d), truncating",
-                _depth,
-            )
-            return chunk[: _CTX_COMPACT_CHAR_LIMIT // 4]
-
-    s1, s2 = await asyncio.gather(_summarise(half1), _summarise(half2))
-    combined = s1 + "\n\n" + s2
-    if len(combined) > _CTX_COMPACT_CHAR_LIMIT:
-        return await _compact_context(combined, llm, _depth=_depth + 1)
-    return combined
-
-
-async def consolidate_queen_memory(
-    session_id: str,
-    session_dir: Path,
-    llm: object,
-) -> None:
-    """Update MEMORY.md and append a diary entry based on the current session.
-
-    Reads conversation parts and adapt.md from session_dir. Called
-    periodically in the background and once at session end. Failures are
-    logged and silently swallowed so they never block teardown.
-
-    Args:
-        session_id: The session ID (used for the adapt.md path reference).
-        session_dir: Path to the session directory (~/.hive/queen/session/{id}).
-        llm: LLMProvider instance (must support acomplete()).
-    """
-    try:
-        session_context = read_session_context(session_dir)
-        if not session_context:
-            logger.debug("queen_memory: no session context, skipping consolidation")
-            return
-
-        logger.info("queen_memory: consolidating memory for session %s ...", session_id)
-
-        # If the transcript is very large, compact it with recursive binary LLM
-        # summarisation before sending to the consolidation model.
-        if len(session_context) > _CTX_COMPACT_CHAR_LIMIT:
-            logger.info(
-                "queen_memory: session context is %d chars — compacting first",
-                len(session_context),
-            )
-            session_context = await _compact_context(session_context, llm)
-            logger.info("queen_memory: compacted to %d chars", len(session_context))
-
-        existing_semantic = read_semantic_memory()
-        today_journal = read_episodic_memory()
-        today = date.today()
-        today_str = f"{today.strftime('%B')} {today.day}, {today.year}"
-        adapt_path = session_dir / "data" / "adapt.md"
-
-        user_msg = (
-            f"## Existing Semantic Memory (MEMORY.md)\n\n"
-            f"{existing_semantic or '(none yet)'}\n\n"
-            f"## Today's Diary So Far ({today_str})\n\n"
-            f"{today_journal or '(none yet)'}\n\n"
-            f"{session_context}\n\n"
-            f"## Session Reference\n\n"
-            f"Session ID: {session_id}\n"
-            f"Session path: {adapt_path}\n"
-        )
-
-        logger.debug(
-            "queen_memory: calling LLM (%d chars of context, ~%d tokens est.)",
-            len(user_msg),
-            len(user_msg) // 4,
-        )
-
-        from framework.agents.queen.config import default_config
-
-        semantic_resp, diary_resp = await asyncio.gather(
-            llm.acomplete(
-                messages=[{"role": "user", "content": user_msg}],
-                system=_SEMANTIC_SYSTEM,
-                max_tokens=default_config.max_tokens,
-            ),
-            llm.acomplete(
-                messages=[{"role": "user", "content": user_msg}],
-                system=_DIARY_SYSTEM,
-                max_tokens=default_config.max_tokens,
-            ),
-        )
-
-        new_semantic = semantic_resp.content.strip()
-        diary_entry = diary_resp.content.strip()
-
-        if new_semantic:
-            path = semantic_memory_path()
-            path.parent.mkdir(parents=True, exist_ok=True)
-            path.write_text(new_semantic, encoding="utf-8")
-            logger.info("queen_memory: semantic memory updated (%d chars)", len(new_semantic))
-
-        if diary_entry:
-            # Rewrite today's episodic file in-place — the LLM has merged and
-            # deduplicated the full day's content, so we replace rather than append.
-            ep_path = episodic_memory_path()
-            ep_path.parent.mkdir(parents=True, exist_ok=True)
-            heading = f"# {today_str}"
-            ep_path.write_text(f"{heading}\n\n{diary_entry}\n", encoding="utf-8")
-            logger.info(
-                "queen_memory: episodic diary rewritten for %s (%d chars)",
-                today_str,
-                len(diary_entry),
-            )
-
-    except Exception:
-        tb = traceback.format_exc()
-        logger.exception("queen_memory: consolidation failed")
-        # Write to file so the cause is findable regardless of log verbosity.
-        error_path = _queen_dir() / "consolidation_error.txt"
-        try:
-            error_path.parent.mkdir(parents=True, exist_ok=True)
-            error_path.write_text(
-                f"session: {session_id}\ntime: {datetime.now().isoformat()}\n\n{tb}",
-                encoding="utf-8",
-            )
-        except OSError:
-            pass  # Cannot write error file; original exception already logged

core/framework/agents/queen/queen_memory_v2.py

@@ -0,0 +1,235 @@
+"""Queen global memory helpers.
+
+Memory hierarchy::
+
+    ~/.hive/memories/
+        global/              # shared across all queens and colonies
+        colonies/{name}/     # colony-scoped memories
+        agents/queens/{name}/ # queen-specific memories
+        agents/{name}/       # per-worker-agent memories
+
+Each memory is an individual ``.md`` file with optional YAML frontmatter
+(name, type, description).
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from framework.config import MEMORIES_DIR
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+GLOBAL_MEMORY_CATEGORIES: tuple[str, ...] = ("profile", "preference", "environment", "feedback")
+
+MAX_FILES: int = 200
+MAX_FILE_SIZE_BYTES: int = 4096  # 4 KB hard limit per memory file
+
+# How many lines of a memory file to read for header scanning.
+_HEADER_LINE_LIMIT: int = 30
+
+
+def global_memory_dir() -> Path:
+    """Return the global memory directory (shared across all queens/colonies)."""
+    return MEMORIES_DIR / "global"
+
+
+def colony_memory_dir(colony_name: str) -> Path:
+    """Return the memory directory for a named colony."""
+    return MEMORIES_DIR / "colonies" / colony_name
+
+
+def queen_memory_dir(queen_name: str = "default") -> Path:
+    """Return the memory directory for a named queen."""
+    return MEMORIES_DIR / "agents" / "queens" / queen_name
+
+
+def agent_memory_dir(agent_name: str) -> Path:
+    """Return the memory directory for a worker agent."""
+    return MEMORIES_DIR / "agents" / agent_name
+
+
+# ---------------------------------------------------------------------------
+# Frontmatter parsing (lenient)
+# ---------------------------------------------------------------------------
+
+_FRONTMATTER_RE = re.compile(r"^---\s*\n(.*?)\n---\s*\n?", re.DOTALL)
+
+
+def parse_frontmatter(text: str) -> dict[str, str]:
+    """Extract YAML-ish frontmatter from *text*.
+
+    Returns a dict of key-value pairs.  Never raises — returns ``{}`` on
+    any parse failure.  Values are stripped strings; no nested structures.
+    """
+    m = _FRONTMATTER_RE.match(text)
+    if not m:
+        return {}
+    result: dict[str, str] = {}
+    for line in m.group(1).splitlines():
+        line = line.strip()
+        if not line or line.startswith("#"):
+            continue
+        colon = line.find(":")
+        if colon < 1:
+            continue
+        key = line[:colon].strip().lower()
+        val = line[colon + 1 :].strip()
+        if val:
+            result[key] = val
+    return result
+
+
+def parse_global_memory_category(raw: str | None) -> str | None:
+    """Validate *raw* against ``GLOBAL_MEMORY_CATEGORIES``."""
+    if raw is None:
+        return None
+    normalized = raw.strip().lower()
+    return normalized if normalized in GLOBAL_MEMORY_CATEGORIES else None
+
+
+# ---------------------------------------------------------------------------
+# MemoryFile dataclass
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class MemoryFile:
+    """Parsed representation of a single memory file on disk."""
+
+    filename: str
+    path: Path
+    # Frontmatter fields — all nullable (lenient parsing).
+    name: str | None = None
+    type: str | None = None
+    description: str | None = None
+    # First N lines of the file (for manifest / header scanning).
+    header_lines: list[str] = field(default_factory=list)
+    # Filesystem modification time (seconds since epoch).
+    mtime: float = 0.0
+
+    @classmethod
+    def from_path(cls, path: Path) -> MemoryFile:
+        """Read a memory file and leniently parse its frontmatter."""
+        try:
+            text = path.read_text(encoding="utf-8")
+        except OSError:
+            return cls(filename=path.name, path=path)
+
+        fm = parse_frontmatter(text)
+        lines = text.splitlines()[:_HEADER_LINE_LIMIT]
+
+        try:
+            mtime = path.stat().st_mtime
+        except OSError:
+            mtime = 0.0
+
+        return cls(
+            filename=path.name,
+            path=path,
+            name=fm.get("name"),
+            type=parse_global_memory_category(fm.get("type")),
+            description=fm.get("description"),
+            header_lines=lines,
+            mtime=mtime,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Scanning
+# ---------------------------------------------------------------------------
+
+
+def scan_memory_files(memory_dir: Path | None = None) -> list[MemoryFile]:
+    """Scan *memory_dir* for ``.md`` files, returning up to ``MAX_FILES``.
+
+    Files are sorted by modification time (newest first).  Dotfiles and
+    subdirectories are ignored.
+    """
+    d = memory_dir or global_memory_dir()
+    if not d.is_dir():
+        return []
+
+    md_files = sorted(
+        (f for f in d.glob("*.md") if f.is_file() and not f.name.startswith(".")),
+        key=lambda p: p.stat().st_mtime,
+        reverse=True,
+    )
+
+    return [MemoryFile.from_path(f) for f in md_files[:MAX_FILES]]
+
+
+def slugify_memory_name(raw: str) -> str:
+    """Create a filesystem-safe slug for a memory filename."""
+    slug = re.sub(r"[^a-z0-9]+", "-", raw.strip().lower()).strip("-")
+    return slug or "memory"
+
+
+def allocate_memory_filename(
+    memory_dir: Path,
+    name: str,
+    *,
+    suffix: str = ".md",
+) -> str:
+    """Allocate a unique filename in *memory_dir* based on *name*."""
+    base = slugify_memory_name(name)
+    candidate = f"{base}{suffix}"
+    counter = 2
+    while (memory_dir / candidate).exists():
+        candidate = f"{base}-{counter}{suffix}"
+        counter += 1
+    return candidate
+
+
+def build_memory_document(
+    *,
+    name: str,
+    description: str,
+    mem_type: str,
+    body: str,
+) -> str:
+    """Build one memory file with frontmatter and body."""
+    return (
+        f"---\n"
+        f"name: {name.strip()}\n"
+        f"description: {description.strip()}\n"
+        f"type: {mem_type.strip()}\n"
+        f"---\n\n"
+        f"{body.strip()}\n"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Manifest formatting
+# ---------------------------------------------------------------------------
+
+
+def format_memory_manifest(files: list[MemoryFile]) -> str:
+    """One-line-per-file text manifest.
+
+    Format: ``[type] filename: description``
+    """
+    lines: list[str] = []
+    for mf in files:
+        t = mf.type or "unknown"
+        desc = mf.description or "(no description)"
+        lines.append(f"[{t}] {mf.filename}: {desc}")
+    return "\n".join(lines)
+
+
+# ---------------------------------------------------------------------------
+# Initialisation
+# ---------------------------------------------------------------------------
+
+
+def init_memory_dir(memory_dir: Path | None = None) -> None:
+    """Create the memory directory if missing."""
+    d = memory_dir or global_memory_dir()
+    d.mkdir(parents=True, exist_ok=True)

core/framework/agents/queen/recall_selector.py

@@ -0,0 +1,215 @@
+"""Recall selector — pre-turn memory selection for the queen.
+
+Before each conversation turn the system:
+  1. Scans one or more memory directories for ``.md`` files (cap: 200 each).
+  2. Reads headers (frontmatter + first 30 lines).
+  3. Uses an LLM call with structured JSON output to pick the most relevant
+     memories for each scope.
+  4. Injects them into the system prompt.
+
+The selector only sees the user's query string — no full conversation
+context.  This keeps it cheap and fast.  Errors are caught and return
+``[]`` so the main conversation is never blocked.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+from framework.agents.queen.queen_memory_v2 import (
+    format_memory_manifest,
+    global_memory_dir as _default_global_memory_dir,
+    scan_memory_files,
+)
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Structured output schema
+# ---------------------------------------------------------------------------
+
+SELECT_MEMORIES_SYSTEM_PROMPT = """\
+You are selecting memories that will be useful to the Queen agent as it \
+processes a user's query.
+
+You will be given the user's query and a list of available memory files \
+with their filenames and descriptions.
+
+Return a JSON object with a single key "selected_memories" containing a \
+list of filenames for the memories that will clearly be useful as the \
+Queen processes the user's query (up to 5).
+
+Only include memories that you are certain will be helpful based on their \
+name and description.
+- If you are unsure if a memory will be useful in processing the user's \
+query, then do not include it in your list.  Be selective and discerning.
+- If there are no memories in the list that would clearly be useful, \
+return an empty list.
+"""
+
+# ---------------------------------------------------------------------------
+# Core functions
+# ---------------------------------------------------------------------------
+
+
+async def select_memories(
+    query: str,
+    llm: Any,
+    memory_dir: Path | None = None,
+    *,
+    max_results: int = 5,
+) -> list[str]:
+    """Select up to 5 relevant memory filenames for *query*.
+
+    Returns a list of filenames.  Best-effort: on any error returns ``[]``.
+    """
+    mem_dir = memory_dir or _default_global_memory_dir()
+    files = scan_memory_files(mem_dir)
+    if not files:
+        logger.debug("recall: no memory files found, skipping selection")
+        return []
+
+    logger.debug("recall: selecting from %d memories for query: %.100s", len(files), query)
+    manifest = format_memory_manifest(files)
+    user_msg = f"## User query\n\n{query}\n\n## Available memories\n\n{manifest}"
+
+    try:
+        resp = await llm.acomplete(
+            messages=[{"role": "user", "content": user_msg}],
+            system=SELECT_MEMORIES_SYSTEM_PROMPT,
+            max_tokens=1024,
+            response_format={"type": "json_object"},
+        )
+        raw = (resp.content or "").strip()
+        if not raw:
+            logger.warning(
+                "recall: LLM returned empty response (model=%s, stop=%s)",
+                resp.model,
+                resp.stop_reason,
+            )
+            return []
+        # Some models wrap JSON in markdown fences or add preamble text.
+        # Try to extract the JSON object if raw parse fails.
+        try:
+            data = json.loads(raw)
+        except json.JSONDecodeError:
+            import re
+
+            m = re.search(r"\{.*\}", raw, re.DOTALL)
+            if m:
+                data = json.loads(m.group())
+            else:
+                logger.warning("recall: LLM returned non-JSON: %.200s", raw)
+                return []
+        selected = data.get("selected_memories", [])
+        valid_names = {f.filename for f in files}
+        result = [s for s in selected if s in valid_names][:max_results]
+        logger.debug("recall: selected %d memories: %s", len(result), result)
+        return result
+    except Exception as exc:
+        logger.warning("recall: memory selection failed (%s), returning []", exc)
+        return []
+
+
+def _format_relative_age(mtime: float) -> str | None:
+    """Return age description if memory is older than 48 hours.
+
+    Returns None if 48 hours or newer, otherwise returns "X days old".
+    """
+    import time
+
+    age_seconds = time.time() - mtime
+    hours = age_seconds / 3600
+    if hours <= 48:
+        return None
+    days = int(age_seconds / 86400)
+    if days == 1:
+        return "1 day old"
+    return f"{days} days old"
+
+
+def format_recall_injection(
+    filenames: list[str],
+    memory_dir: Path | None = None,
+    *,
+    label: str = "Global Memories",
+) -> str:
+    """Read selected memory files and format for system prompt injection.
+
+    Includes relative timestamp (e.g., "3 days old") for memories older than 48 hours.
+    """
+
+    mem_dir = memory_dir or _default_global_memory_dir()
+    if not filenames:
+        return ""
+
+    blocks: list[str] = []
+    for fname in filenames:
+        path = mem_dir / fname
+        if not path.is_file():
+            continue
+        try:
+            content = path.read_text(encoding="utf-8").strip()
+            # Get file modification time for age calculation
+            mtime = path.stat().st_mtime
+            age_note = _format_relative_age(mtime)
+        except OSError:
+            continue
+
+        # Build header with optional age note
+        if age_note:
+            header = f"### {fname} ({age_note})"
+        else:
+            header = f"### {fname}"
+        blocks.append(f"{header}\n\n{content}")
+
+    if not blocks:
+        return ""
+
+    body = "\n\n---\n\n".join(blocks)
+    return f"--- {label} ---\n\n{body}\n\n--- End {label} ---"
+
+
+async def build_scoped_recall_blocks(
+    query: str,
+    llm: Any,
+    *,
+    global_memory_dir: Path | None = None,
+    queen_memory_dir: Path | None = None,
+    queen_id: str | None = None,
+    global_max_results: int = 3,
+    queen_max_results: int = 3,
+) -> tuple[str, str]:
+    """Build separate recall blocks for global and queen-scoped memory."""
+    global_dir = global_memory_dir or _default_global_memory_dir()
+    global_selected = await select_memories(
+        query,
+        llm,
+        memory_dir=global_dir,
+        max_results=global_max_results,
+    )
+    global_block = format_recall_injection(
+        global_selected,
+        memory_dir=global_dir,
+        label="Global Memories",
+    )
+
+    queen_block = ""
+    if queen_memory_dir is not None:
+        queen_selected = await select_memories(
+            query,
+            llm,
+            memory_dir=queen_memory_dir,
+            max_results=queen_max_results,
+        )
+        queen_label = f"Queen Memories: {queen_id}" if queen_id else "Queen Memories"
+        queen_block = format_recall_injection(
+            queen_selected,
+            memory_dir=queen_memory_dir,
+            label=queen_label,
+        )
+
+    return global_block, queen_block

core/framework/agents/queen/reference/anti_patterns.md

@@ -13,7 +13,7 @@
 6. **Calling set_output in same turn as tool calls** — Call set_output in a SEPARATE turn.
 
 ## File Template Errors
-7. **Wrong import paths** — Use `from framework.graph import ...`, NOT `from core.framework.graph import ...`.
+7. **Wrong import paths** — Use `from framework.orchestrator import ...`, NOT `from framework.graph import ...` or `from core.framework...`.
 8. **Missing storage path** — Agent class must set `self._storage_path = Path.home() / ".hive" / "agents" / "agent_name"`.
 9. **Missing mcp_servers.json** — Without this, the agent has no tools at runtime.
 10. **Bare `python` command** — Use `"command": "uv"` with args `["run", "python", ...]`.
@@ -25,11 +25,8 @@
 14. **Forgetting sys.path setup in conftest.py** — Tests need `exports/` and `core/` on sys.path.
 
 ## GCU Errors
-15. **Manually wiring browser tools on event_loop nodes** — Use `node_type="gcu"` which auto-includes browser tools. Do NOT manually list browser tool names.
-16. **Using GCU nodes as regular graph nodes** — GCU nodes are subagents only. They must ONLY appear in `sub_agents=["gcu-node-id"]` and be invoked via `delegate_to_sub_agent()`. Never connect via edges or use as entry/terminal nodes.
-17. **Reusing the same GCU node ID for parallel tasks** — Each concurrent browser task needs a distinct GCU node ID (e.g. `gcu-site-a`, `gcu-site-b`). Two `delegate_to_sub_agent` calls with the same `agent_id` share a browser profile and will interfere with each other's pages.
-18. **Passing `profile=` in GCU tool calls** — Profile isolation for parallel subagents is automatic. The framework injects a unique profile per subagent via an asyncio `ContextVar`. Hardcoding `profile="default"` in a GCU system prompt breaks this isolation.
+15. **Manually wiring browser tools on event_loop nodes** — Browser nodes use tools: {policy: "all"} to get all browser tools.
 
 ## Worker Agent Errors
-19. **Adding client-facing intake node to workers** — The queen owns intake. Workers should start with an autonomous processing node. Client-facing nodes in workers are for mid-execution review/approval only.
+19. **Adding client-facing intake node to workers** — The queen owns intake. Workers should start with an autonomous processing node. Route worker review/approval through queen escalation instead of direct worker HITL.
 20. **Putting `escalate` or `set_output` in NodeSpec `tools=[]`** — These are synthetic framework tools, auto-injected at runtime. Only list MCP tools from `list_agent_tools()`.

core/framework/agents/queen/reference/file_templates.md

@@ -55,7 +55,7 @@ metadata = AgentMetadata()
 ```python
 """Node definitions for My Agent."""
 
-from framework.graph import NodeSpec
+from framework.orchestrator import NodeSpec
 
 # Node 1: Process (autonomous entry node)
 # The queen handles intake and passes structured input via
@@ -123,14 +123,15 @@ __all__ = ["process_node", "handoff_node"]
 
 from pathlib import Path
 
-from framework.graph import EdgeSpec, EdgeCondition, Goal, SuccessCriterion, Constraint
-from framework.graph.edge import GraphSpec
-from framework.graph.executor import ExecutionResult
-from framework.graph.checkpoint_config import CheckpointConfig
+from framework.orchestrator import EdgeSpec, EdgeCondition, Goal, SuccessCriterion, Constraint
+from framework.orchestrator.edge import GraphSpec
+from framework.orchestrator.orchestrator import ExecutionResult
+from framework.orchestrator.checkpoint_config import CheckpointConfig
 from framework.llm import LiteLLMProvider
-from framework.runner.tool_registry import ToolRegistry
-from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
-from framework.runtime.execution_stream import EntryPointSpec
+from framework.loader.tool_registry import ToolRegistry
+from framework.host.agent_host import AgentHost
+from framework.host.execution_manager import EntryPointSpec
+
 
 from .config import default_config, metadata
 from .nodes import process_node, handoff_node
@@ -227,7 +228,7 @@ class MyAgent:
         tools = list(self._tool_registry.get_tools().values())
         tool_executor = self._tool_registry.get_executor()
         self._graph = self._build_graph()
-        self._agent_runtime = create_agent_runtime(
+        self._agent_runtime = AgentHost(
             graph=self._graph, goal=self.goal, storage_path=self._storage_path,
             entry_points=[EntryPointSpec(id="default", name="Default", entry_node=self.entry_node,
                                          trigger_type="manual", isolation_level="shared")],
@@ -460,8 +461,8 @@ def tui():
     from framework.tui.app import AdenTUI
     from framework.llm import LiteLLMProvider
     from framework.runner.tool_registry import ToolRegistry
-    from framework.runtime.agent_runtime import create_agent_runtime
-    from framework.runtime.execution_stream import EntryPointSpec
+    from framework.host.agent_host import AgentHost
+    from framework.host.execution_manager import EntryPointSpec
 
     async def run_tui():
         agent = MyAgent()
@@ -471,7 +472,7 @@ def tui():
         mcp_cfg = Path(__file__).parent / "mcp_servers.json"
         if mcp_cfg.exists(): agent._tool_registry.load_mcp_config(mcp_cfg)
         llm = LiteLLMProvider(model=agent.config.model, api_key=agent.config.api_key, api_base=agent.config.api_base)
-        runtime = create_agent_runtime(
+        runtime = AgentHost(
             graph=agent._build_graph(), goal=agent.goal, storage_path=storage,
             entry_points=[EntryPointSpec(id="start", name="Start", entry_node="process", trigger_type="manual", isolation_level="isolated")],
             llm=llm, tools=list(agent._tool_registry.get_tools().values()), tool_executor=agent._tool_registry.get_executor())
@@ -509,17 +510,17 @@ if __name__ == "__main__":
 
 ## mcp_servers.json
 
-> **Auto-generated.** `initialize_and_build_agent` creates this file with hive-tools
+> **Auto-generated.** `initialize_and_build_agent` creates this file with hive_tools
 > as the default. Only edit manually to add additional MCP servers.
 
 ```json
 {
-  "hive-tools": {
+  "hive_tools": {
     "transport": "stdio",
     "command": "uv",
     "args": ["run", "python", "mcp_server.py", "--stdio"],
     "cwd": "../../tools",
-    "description": "Hive tools MCP server"
+    "description": "hive_tools MCP server"
   }
 }
 ```

core/framework/agents/queen/reference/file_templates_declarative.md

@@ -0,0 +1,227 @@
+# Declarative Agent File Templates
+
+Agents are defined as a single `agent.yaml` file. No Python code needed.
+The runner loads this file directly -- no `agent.py`, `config.py`, or
+`nodes/__init__.py` required.
+
+## agent.yaml -- Complete Agent Definition
+
+```yaml
+name: my-agent
+version: 1.0.0
+description: What this agent does.
+
+metadata:
+  intro_message: Welcome! What would you like me to do?
+
+# Template variables -- substituted into system_prompt and identity_prompt
+# via {{variable_name}} syntax.  Use this for config values that appear
+# in prompts (spreadsheet IDs, API endpoints, account names, etc.)
+variables:
+  spreadsheet_id: "1ZVxWDL..."
+  sheet_name: "contacts"
+
+goal:
+  description: What this agent achieves.
+  success_criteria:
+    - "First success criterion"
+    - "Second success criterion"
+  constraints:
+    - "Hard constraint the agent must respect"
+
+identity_prompt: |
+  You are a helpful agent.
+
+conversation_mode: continuous   # always "continuous" for Hive agents
+
+loop_config:
+  max_iterations: 100
+  max_tool_calls_per_turn: 30
+  max_context_tokens: 32000
+
+# MCP servers to connect (resolved by name from ~/.hive/mcp_registry/)
+mcp_servers:
+  - name: hive_tools
+  - name: gcu-tools
+
+nodes:
+  # Node 1: Process (autonomous entry node)
+  # The queen handles intake and passes structured input via
+  # run_agent_with_input(task). NO client-facing intake node.
+  - id: process
+    name: Process
+    description: Execute the task using available tools
+    max_node_visits: 0   # 0 = unlimited (forever-alive agents)
+    input_keys: [user_request, feedback]
+    output_keys: [results]
+    nullable_output_keys: [feedback]
+    tools:
+      policy: explicit
+      allowed: [web_search, web_scrape, save_data, load_data, list_data_files]
+    success_criteria: Results are complete and accurate.
+    system_prompt: |
+      You are a processing agent. Your task is in memory under "user_request".
+      If "feedback" is present, this is a revision.
+
+      Work in phases:
+      1. Use tools to gather/process data
+      2. Analyze results
+      3. Call set_output in a SEPARATE turn:
+         - set_output("results", "structured results")
+
+  # Node 2: Handoff (autonomous)
+  - id: handoff
+    name: Handoff
+    description: Prepare worker results for queen review
+    max_node_visits: 0
+    input_keys: [results, user_request]
+    output_keys: [next_action, feedback, worker_summary]
+    nullable_output_keys: [feedback, worker_summary]
+    tools:
+      policy: none   # handoff nodes don't need tools
+    success_criteria: Results are packaged for queen decision-making.
+    system_prompt: |
+      Do NOT talk to the user directly. The queen is the only user interface.
+
+      If blocked, call escalate(reason, context) then set:
+      - set_output("next_action", "escalated")
+      - set_output("feedback", "what help is needed")
+
+      Otherwise summarize and set:
+      - set_output("worker_summary", "short summary for queen")
+      - set_output("next_action", "done") or "revise"
+      - set_output("feedback", "what to revise") only when revising
+
+edges:
+  - from_node: process
+    to_node: handoff
+  # Feedback loop
+  - from_node: handoff
+    to_node: process
+    condition: conditional
+    condition_expr: "str(next_action).lower() == 'revise'"
+    priority: 2
+  # Escalation loop
+  - from_node: handoff
+    to_node: process
+    condition: conditional
+    condition_expr: "str(next_action).lower() == 'escalated'"
+    priority: 3
+  # Loop back for next task
+  - from_node: handoff
+    to_node: process
+    condition: conditional
+    condition_expr: "str(next_action).lower() == 'done'"
+
+entry_node: process
+terminal_nodes: []   # [] = forever-alive
+```
+
+## Key differences from Python templates
+
+| Before (Python)                     | After (YAML)                           |
+|-------------------------------------|----------------------------------------|
+| `agent.py` (250 lines boilerplate)  | Not needed                             |
+| `config.py` (dataclass + metadata)  | `variables:` + `metadata:` in YAML     |
+| `nodes/__init__.py` (NodeSpec calls)| `nodes:` list in YAML                  |
+| `__init__.py`, `__main__.py`        | Not needed                             |
+| f-string config injection           | `{{variable_name}}` templates          |
+| `mcp_servers.json` (separate file)  | `mcp_servers:` in YAML (or keep file)  |
+
+## Node types
+
+| Type         | Description                           | Tools                    |
+|--------------|---------------------------------------|--------------------------|
+| `event_loop` | LLM-driven orchestration (default)    | Explicit list or `none`  |
+| `gcu`        | Browser automation via GCU tools      | `policy: all` (auto)     |
+
+## Tool access policies
+
+```yaml
+# Explicit list (recommended for most nodes)
+tools:
+  policy: explicit
+  allowed: [web_search, save_data]
+
+# All tools (for browser automation nodes)
+tools:
+  policy: all
+
+# No tools (for handoff/summary nodes)
+tools:
+  policy: none
+```
+
+## Edge conditions
+
+| Condition     | When to use                                           |
+|---------------|-------------------------------------------------------|
+| `on_success`  | Default. Next node after current succeeds.            |
+| `on_failure`  | Fallback path when current node fails.                |
+| `always`      | Always traverse regardless of outcome.                |
+| `conditional` | Evaluate `condition_expr` against shared memory keys. |
+| `llm_decide`  | Let the LLM decide at runtime.                        |
+
+## Template variables
+
+Use `{{variable_name}}` in `system_prompt` and `identity_prompt`.
+Variables are defined in the top-level `variables:` map.
+
+```yaml
+variables:
+  spreadsheet_id: "1ZVxWDL..."
+  api_endpoint: "https://api.example.com"
+
+nodes:
+  - id: start
+    system_prompt: |
+      Connect to spreadsheet: {{spreadsheet_id}}
+      API endpoint: {{api_endpoint}}
+```
+
+## Entry points
+
+Default is a single manual entry point. For timer/scheduled triggers:
+
+```yaml
+entry_points:
+  - id: default
+    trigger_type: manual
+  - id: daily-check
+    trigger_type: timer
+    trigger_config:
+      interval_minutes: 30
+```
+
+## mcp_servers.json -- Still Supported
+
+The `mcp_servers.json` file is still loaded automatically if present alongside
+`agent.yaml`.  You can also inline servers in the YAML:
+
+```yaml
+mcp_servers:
+  - name: hive_tools
+  - name: gcu-tools
+```
+
+Both approaches work. The JSON file takes precedence for backward compatibility.
+
+## Migration from Python agents
+
+Run the migration tool to convert existing agents:
+
+```bash
+uv run python -m framework.tools.migrate_agent exports/my_agent
+```
+
+This generates `agent.yaml` from the existing `agent.py` + `nodes/` + `config.py`.
+The original files are left untouched. Once verified, you can delete the Python files.
+
+## Files after migration
+
+```
+my_agent/
+  agent.yaml           # The only required file
+  mcp_servers.json     # Optional (can inline in YAML)
+  flowchart.json       # Optional (auto-generated)
+```

core/framework/agents/queen/reference/framework_guide.md

@@ -1,305 +1,193 @@
-# Hive Agent Framework — Condensed Reference
+# Hive Agent Framework -- Condensed Reference
 
 ## Architecture
 
-Agents are Python packages in `exports/`:
+Agents are declarative JSON configs in `exports/`:
 ```
 exports/my_agent/
-├── __init__.py          # MUST re-export ALL module-level vars from agent.py
-├── __main__.py          # CLI (run, tui, info, validate, shell)
-├── agent.py             # Graph construction (goal, edges, agent class)
-├── config.py            # Runtime config
-├── nodes/__init__.py    # Node definitions (NodeSpec)
-├── mcp_servers.json     # MCP tool server config
-└── tests/               # pytest tests
+  agent.json          # The entire agent definition
+  mcp_servers.json    # MCP tool server config (optional, prefer registry refs)
 ```
 
-## Agent Loading Contract
+No Python files. No `__init__.py`, `__main__.py`, `config.py`, or `nodes/`.
 
-`AgentRunner.load()` imports the package (`__init__.py`) and reads these
-module-level variables via `getattr()`:
+## Agent Loading
 
-| Variable | Required | Default if missing | Consequence |
-|----------|----------|--------------------|-------------|
-| `goal` | YES | `None` | **FATAL** — "must define goal, nodes, edges" |
-| `nodes` | YES | `None` | **FATAL** — same error |
-| `edges` | YES | `None` | **FATAL** — same error |
-| `entry_node` | no | `nodes[0].id` | Probably wrong node |
-| `entry_points` | no | `{}` | **Nodes unreachable** — validation fails |
-| `terminal_nodes` | **YES** | `[]` | **FATAL** — graph must have at least one terminal node |
-| `pause_nodes` | no | `[]` | OK |
-| `conversation_mode` | no | not passed | Isolated mode (no context carryover) |
-| `identity_prompt` | no | not passed | No agent-level identity |
-| `loop_config` | no | `{}` | No iteration limits |
-| `triggers.json` (file) | no | not present | No triggers (timers, webhooks) |
+`AgentLoader.load()` reads `agent.json` and builds the execution graph.
+If `agent.py` exists (legacy), it's loaded as a Python module instead.
 
-**CRITICAL:** `__init__.py` MUST import and re-export ALL of these from
-`agent.py`. Missing exports silently fall back to defaults, causing
-hard-to-debug failures.
+## agent.json Schema
 
-**Why `default_agent.validate()` is NOT sufficient:**
-`validate()` checks the agent CLASS's internal graph (self.nodes, self.edges).
-These are always correct because the constructor references agent.py's module
-vars directly. But `AgentRunner.load()` reads from the PACKAGE (`__init__.py`),
-not the class. So `validate()` passes while `AgentRunner.load()` fails.
-Always test with `AgentRunner.load("exports/{name}")` — this is the same
-code path the TUI and `hive run` use.
-
-## Goal
-
-Defines success criteria and constraints:
-```python
-goal = Goal(
-    id="kebab-case-id",
-    name="Display Name",
-    description="What the agent does",
-    success_criteria=[
-        SuccessCriterion(id="sc-id", description="...", metric="...", target="...", weight=0.25),
-    ],
-    constraints=[
-        Constraint(id="c-id", description="...", constraint_type="hard", category="quality"),
-    ],
-)
+```json
+{
+  "name": "my-agent",
+  "version": "1.0.0",
+  "description": "What this agent does",
+  "goal": {
+    "description": "What to achieve",
+    "success_criteria": ["criterion 1", "criterion 2"],
+    "constraints": ["constraint 1"]
+  },
+  "identity_prompt": "You are a helpful agent.",
+  "conversation_mode": "continuous",
+  "loop_config": {
+    "max_iterations": 100,
+    "max_tool_calls_per_turn": 30,
+    "max_context_tokens": 32000
+  },
+  "mcp_servers": [
+    {"name": "hive_tools"},
+    {"name": "gcu-tools"}
+  ],
+  "variables": {
+    "spreadsheet_id": "1ZVx..."
+  },
+  "nodes": [...],
+  "edges": [...],
+  "entry_node": "process",
+  "terminal_nodes": []
+}
 ```
-- 3-5 success criteria, weights sum to 1.0
-- 1-5 constraints (hard/soft, categories: quality, accuracy, interaction, functional)
 
-## NodeSpec Fields
+## Template Variables
+
+Use `{{variable_name}}` in `system_prompt` and `identity_prompt`. Variables
+are defined in the top-level `variables` object:
+
+```json
+{
+  "variables": {"sheet_id": "1ZVx..."},
+  "nodes": [{
+    "id": "start",
+    "system_prompt": "Use sheet: {{sheet_id}}"
+  }]
+}
+```
+
+## Node Fields
 
 | Field | Type | Default | Description |
 |-------|------|---------|-------------|
 | id | str | required | kebab-case identifier |
-| name | str | required | Display name |
+| name | str | id | Display name |
 | description | str | required | What the node does |
-| node_type | str | required | `"event_loop"` or `"gcu"` (browser automation — see GCU Guide appendix) |
-| input_keys | list[str] | required | Memory keys this node reads |
-| output_keys | list[str] | required | Memory keys this node writes via set_output |
+| node_type | str | "event_loop" | `"event_loop"` |
+| input_keys | list | [] | Memory keys this node reads |
+| output_keys | list | [] | Memory keys this node writes via set_output |
 | system_prompt | str | "" | LLM instructions |
-| tools | list[str] | [] | Tool names from MCP servers |
-| client_facing | bool | False | If True, streams to user and blocks for input |
-| nullable_output_keys | list[str] | [] | Keys that may remain unset |
-| max_node_visits | int | 0 | 0=unlimited (default); >1 for one-shot feedback loops |
-| max_retries | int | 3 | Retries on failure |
+| tools | object | {} | Tool access policy (see below) |
+| nullable_output_keys | list | [] | Keys that may remain unset |
+| max_node_visits | int | 1 | 0=unlimited (for forever-alive agents) |
 | success_criteria | str | "" | Natural language for judge evaluation |
+| client_facing | bool | false | Whether output is shown to user |
 
-## EdgeSpec Fields
+## Tool Access Policies
+
+Each node declares its tools via a policy object:
+
+```json
+{"tools": {"policy": "explicit", "allowed": ["web_search", "save_data"]}}
+{"tools": {"policy": "all"}}
+{"tools": {"policy": "none"}}
+```
+
+- `explicit` (default): only named tools. Empty `allowed` = zero tools.
+- `all`: all tools from registry (e.g. for browser automation nodes).
+- `none`: no tools (for handoff/summary nodes).
+
+## Edge Fields
 
 | Field | Type | Description |
 |-------|------|-------------|
-| id | str | kebab-case identifier |
-| source | str | Source node ID |
-| target | str | Target node ID |
-| condition | EdgeCondition | ON_SUCCESS, ON_FAILURE, ALWAYS, CONDITIONAL |
-| condition_expr | str | Python expression evaluated against memory (for CONDITIONAL) |
-| priority | int | Positive=forward (evaluated first), negative=feedback (loop-back) |
+| from_node | str | Source node ID |
+| to_node | str | Target node ID |
+| condition | str | `on_success`, `on_failure`, `always`, `conditional` |
+| condition_expr | str | Python expression for conditional routing |
+| priority | int | Higher = evaluated first |
+
+condition_expr examples:
+- `"needs_more_research == True"`
+- `"str(next_action).lower() == 'revise'"`
 
 ## Key Patterns
 
-### STEP 1/STEP 2 (Client-Facing Nodes)
-```
-**STEP 1 — Respond to the user (text only, NO tool calls):**
-[Present information, ask questions]
-
-**STEP 2 — After the user responds, call set_output:**
-- set_output("key", "value based on user response")
-```
-This prevents premature set_output before user interaction.
-
 ### Fewer, Richer Nodes (CRITICAL)
 
-**Hard limit: 3-6 nodes for most agents.** Never exceed 6 unless the user
-explicitly requests a complex multi-phase pipeline.
+**Hard limit: 3-6 nodes for most agents.** Each node boundary serializes
+outputs and destroys in-context information. Merge unless:
+1. Client-facing boundary (different interaction models)
+2. Disjoint tool sets
+3. Parallel execution (fan-out branches)
 
-Each node boundary serializes outputs to shared memory and **destroys** all
-in-context information: tool call results, intermediate reasoning, conversation
-history. A research node that searches, fetches, and analyzes in ONE node keeps
-all source material in its conversation context. Split across 3 nodes, each
-downstream node only sees the serialized summary string.
-
-**Decision framework — merge unless ANY of these apply:**
-1. **Client-facing boundary** — Autonomous and client-facing work MUST be
-   separate nodes (different interaction models)
-2. **Disjoint tool sets** — If tools are fundamentally different (e.g., web
-   search vs database), separate nodes make sense
-3. **Parallel execution** — Fan-out branches must be separate nodes
-
-**Red flags that you have too many nodes:**
-- A node with 0 tools (pure LLM reasoning) → merge into predecessor/successor
-- A node that sets only 1 trivial output → collapse into predecessor
-- Multiple consecutive autonomous nodes → combine into one rich node
-- A "report" node that presents analysis → merge into the client-facing node
-- A "confirm" or "schedule" node that doesn't call any external service → remove
-
-**Typical agent structure (2 nodes):**
+**Typical structure (2 nodes):**
 ```
-process (autonomous) ←→ review (client-facing)
-```
-The queen owns intake — she gathers requirements from the user, then
-passes structured input via `run_agent_with_input(task)`. When building
-the agent, design the entry node's `input_keys` to match what the queen
-will provide at run time. Worker agents should NOT have a client-facing
-intake node. Client-facing nodes are for mid-execution review/approval only.
-
-For simpler agents, just 1 autonomous node:
-```
-process (autonomous) — loops back to itself
+process (autonomous) <-> review (queen-mediated)
 ```
 
-### nullable_output_keys
-For inputs that only arrive on certain edges:
-```python
-research_node = NodeSpec(
-    input_keys=["brief", "feedback"],
-    nullable_output_keys=["feedback"],  # Only present on feedback edge
-    max_node_visits=3,
-)
-```
-
-### Mutually Exclusive Outputs
-For routing decisions:
-```python
-review_node = NodeSpec(
-    output_keys=["approved", "feedback"],
-    nullable_output_keys=["approved", "feedback"],  # Node sets one or the other
-)
-```
-
-### Continuous Loop Pattern
-Mark the primary event_loop node as terminal: `terminal_nodes=["process"]`.
-The node has `output_keys` and can complete when the agent finishes its work.
-Use `conversation_mode="continuous"` to preserve context across transitions.
+The queen owns intake. Worker agents should NOT have a client-facing intake
+node. Mid-execution review should happen through queen escalation.
 
 ### set_output
 - Synthetic tool injected by framework
 - Call separately from real tool calls (separate turn)
-- `set_output("key", "value")` stores to shared memory
+- `set_output("key", "value")` stores to the shared buffer
 
-## Edge Conditions
-
-| Condition | When |
-|-----------|------|
-| ON_SUCCESS | Node completed successfully |
-| ON_FAILURE | Node failed |
-| ALWAYS | Unconditional |
-| CONDITIONAL | condition_expr evaluates to True against memory |
-
-condition_expr examples:
-- `"needs_more_research == True"`
-- `"str(next_action).lower() == 'new_agent'"`
-- `"feedback is not None"`
-
-## Graph Lifecycle
+### Graph Lifecycle
 
 | Pattern | terminal_nodes | When |
 |---------|---------------|------|
-| **Continuous loop** | `["node-with-output-keys"]` | **DEFAULT for all agents** |
+| Continuous loop | `["node-with-output-keys"]` | DEFAULT for all agents |
 | Linear | `["last-node"]` | One-shot/batch agents |
 
-**Every graph must have at least one terminal node.** Terminal nodes
-define where execution ends. For interactive agents that loop continuously,
-mark the primary event_loop node as terminal (it has `output_keys` and can
-complete at any point). The framework default for `max_node_visits` is 0
-(unbounded), so nodes work correctly in continuous loops without explicit
-override. Only set `max_node_visits > 0` in one-shot agents with feedback loops.
-Every node must have at least one outgoing edge — no dead ends.
+Every graph must have at least one terminal node.
 
-## Continuous Conversation Mode
+### Continuous Conversation Mode
 
 `conversation_mode` has ONLY two valid states:
-- `"continuous"` — recommended for interactive agents
-- Omit entirely — isolated per-node conversations (each node starts fresh)
+- `"continuous"` -- recommended (context carries across node transitions)
+- Omit entirely -- isolated per-node conversations
 
-**INVALID values** (do NOT use): `"client_facing"`, `"interactive"`,
-`"adaptive"`, `"shared"`. These do not exist in the framework.
-
-When `conversation_mode="continuous"`:
-- Same conversation thread carries across node transitions
-- Layered system prompts: identity (agent-level) + narrative + focus (per-node)
-- Transition markers inserted at boundaries
-- Compaction happens opportunistically at phase transitions
+**INVALID values:** `"client_facing"`, `"interactive"`, `"shared"`.
 
 ## loop_config
 
 Only three valid keys:
-```python
-loop_config = {
-    "max_iterations": 100,          # Max LLM turns per node visit
-    "max_tool_calls_per_turn": 20,  # Max tool calls per LLM response
-    "max_context_tokens": 32000,    # Triggers conversation compaction
+```json
+{
+  "max_iterations": 100,
+  "max_tool_calls_per_turn": 20,
+  "max_context_tokens": 32000
 }
 ```
-**INVALID keys** (do NOT use): `"strategy"`, `"mode"`, `"timeout"`,
-`"temperature"`. These are silently ignored or cause errors.
 
 ## Data Tools (Spillover)
 
 For large data that exceeds context:
-- `save_data(filename, data)` — Write to session data dir
-- `load_data(filename, offset, limit)` — Read with pagination
-- `list_data_files()` — List files
-- `serve_file_to_user(filename, label)` — Clickable file:// URI
+- `save_data(filename, data)` -- write to session data dir
+- `load_data(filename, offset, limit)` -- read with pagination
+- `list_data_files()` -- list files
+- `serve_file_to_user(filename, label)` -- clickable file URI
 
-`data_dir` is auto-injected by framework — LLM never sees it.
+`data_dir` is auto-injected by framework.
 
 ## Fan-Out / Fan-In
 
-Multiple ON_SUCCESS edges from same source → parallel execution via asyncio.gather().
-- Parallel nodes must have disjoint output_keys
-- Only one branch may have client_facing nodes
-- Fan-in node gets all outputs in shared memory
+Multiple `on_success` edges from same source = parallel execution.
+Parallel nodes must have disjoint output_keys.
 
 ## Judge System
 
 - **Implicit** (default): ACCEPTs when LLM finishes with no tool calls and all required outputs set
 - **SchemaJudge**: Validates against Pydantic model
-- **Custom**: Implement `evaluate(context) -> JudgeVerdict`
-
-Judge is the SOLE acceptance mechanism — no ad-hoc framework gating.
-
-## Triggers (Timers, Webhooks)
-
-For agents that react to external events, create a `triggers.json` file
-in the agent's export directory:
-
-```json
-[
-  {
-    "id": "daily-check",
-    "name": "Daily Check",
-    "trigger_type": "timer",
-    "trigger_config": {"cron": "0 9 * * *"},
-    "task": "Run the daily check process"
-  }
-]
-```
-
-### Key Fields
-- `trigger_type`: `"timer"` or `"webhook"`
-- `trigger_config`: `{"cron": "0 9 * * *"}` or `{"interval_minutes": 20}`
-- `task`: describes what the worker should do when the trigger fires
-- Triggers can also be created/removed at runtime via `set_trigger` / `remove_trigger` queen tools
 
 ## Tool Discovery
 
-Do NOT rely on a static tool list — it will be outdated. Always call
-`list_agent_tools()` with NO arguments first to see ALL available tools.
-Only use `group=` or `output_schema=` as follow-up calls after seeing the
-full list.
+Always call `list_agent_tools()` first to see available tools.
+Do NOT rely on a static tool list.
 
 ```
-list_agent_tools()                            # ALWAYS call this first
-list_agent_tools(group="gmail", output_schema="full")  # then drill into a category
-list_agent_tools("exports/my_agent/mcp_servers.json")  # specific agent's tools
+list_agent_tools()                                      # full summary
+list_agent_tools(group="gmail", output_schema="full")   # drill into category
 ```
 
-After building, run `validate_agent_package("{name}")` to check everything at once.
-
-Common tool categories (verify via list_agent_tools):
-- **Web**: search, scrape, PDF
-- **Data**: save/load/append/list data files, serve to user
-- **File**: view, write, replace, diff, list, grep
-- **Communication**: email, gmail, slack, telegram
-- **CRM**: hubspot, apollo, calcom
-- **GitHub**: stargazers, user profiles, repos
-- **Vision**: image analysis
-- **Time**: current time
+After building, run `validate_agent_package("{name}")` to check everything.

core/framework/agents/queen/reference/gcu_guide.md

@@ -1,158 +1,80 @@
-# GCU Browser Automation Guide
+# Browser Automation Guide
 
-## When to Use GCU Nodes
+## When to Use Browser Nodes
 
-Use `node_type="gcu"` when:
-- The user's workflow requires **navigating real websites** (scraping, form-filling, social media interaction, testing web UIs)
-- The task involves **dynamic/JS-rendered pages** that `web_scrape` cannot handle (SPAs, infinite scroll, login-gated content)
-- The agent needs to **interact with a website** — clicking, typing, scrolling, selecting, uploading files
+Use browser nodes (with `tools: {policy: "all"}`) when:
+- The task requires interacting with web pages (clicking, typing, navigating)
+- No API is available for the target service
+- The user is already logged in to the target site
 
-Do NOT use GCU for:
-- Static content that `web_scrape` handles fine
-- API-accessible data (use the API directly)
-- PDF/file processing
-- Anything that doesn't require a browser UI
+## What Browser Nodes Are
 
-## What GCU Nodes Are
+- Regular `event_loop` nodes with browser tools from gcu-tools MCP server
+- Set `tools: {policy: "all"}` to give access to all browser tools
+- Wire into the graph with edges like any other node
+- No special node_type needed
 
-- `node_type="gcu"` — a declarative enhancement over `event_loop`
-- Framework auto-prepends browser best-practices system prompt
-- Framework auto-includes all 31 browser tools from `gcu-tools` MCP server
-- Same underlying `EventLoopNode` class — no new imports needed
-- `tools=[]` is correct — tools are auto-populated at runtime
+## Available Browser Tools
 
-## GCU Architecture Pattern  
+All tools are prefixed with `browser_`:
+- `browser_start`, `browser_open`, `browser_navigate` — launch/navigate
+- `browser_click`, `browser_click_coordinate`, `browser_fill`, `browser_type`, `browser_type_focused` — interact
+- `browser_press` (with optional `modifiers=["ctrl"]` etc.) — keyboard shortcuts
+- `browser_snapshot` — compact accessibility-tree read (structured)
+<!-- vision-only -->
+- `browser_screenshot` — visual capture (annotated PNG)
+<!-- /vision-only -->
+- `browser_shadow_query`, `browser_get_rect` — locate elements (shadow-piercing via `>>>`)
+- `browser_scroll`, `browser_wait` — navigation helpers
+- `browser_evaluate` — run JavaScript
+- `browser_close`, `browser_close_finished` — tab cleanup
 
-GCU nodes are **subagents** — invoked via `delegate_to_sub_agent()`, not connected via edges.
+## Pick the right reading tool
 
-- Primary nodes (`event_loop`, client-facing) orchestrate; GCU nodes do browser work
-- Parent node declares `sub_agents=["gcu-node-id"]` and calls `delegate_to_sub_agent(agent_id="gcu-node-id", task="...")`
-- GCU nodes set `max_node_visits=1` (single execution per delegation), `client_facing=False`
-- GCU nodes use `output_keys=["result"]` and return structured JSON via `set_output("result", ...)`
+**`browser_snapshot`** — compact accessibility tree of interactive elements. Fast, cheap, good for static or form-heavy pages where the DOM matches what's visually rendered (documentation, simple dashboards, search results, settings pages).
 
-## GCU Node Definition Template
+**`browser_screenshot`** — visual capture + metadata (`cssWidth`, `devicePixelRatio`, scale fields). Use this when `browser_snapshot` does not show the thing you need, when refs look stale, or when visual position/layout matters. This often happens on complex SPAs — LinkedIn, Twitter/X, Reddit, Gmail, Notion, Slack, Discord — and on sites using shadow DOM, virtual scrolling, React reconciliation, or dynamic layout.
 
-```python
-gcu_browser_node = NodeSpec(
-    id="gcu-browser-worker",
-    name="Browser Worker",
-    description="Browser subagent that does X.",
-    node_type="gcu",
-    client_facing=False,
-    max_node_visits=1,
-    input_keys=[],
-    output_keys=["result"],
-    tools=[],  # Auto-populated with all browser tools
-    system_prompt="""\
-You are a browser agent. Your job: [specific task].
+Neither tool is "preferred" universally — they're for different jobs. Start with snapshot for page structure and ordinary controls; use screenshot as the fallback when snapshot can't find or verify the visible target. Activate the `browser-automation` skill for the full decision tree.
 
-## Workflow
-1. browser_start (only if no browser is running yet)
-2. browser_open(url=TARGET_URL) — note the returned targetId
-3. browser_snapshot to read the page
-4. [task-specific steps]
-5. set_output("result", JSON)
+## Coordinate rule
 
-## Output format
-set_output("result", JSON) with:
-- [field]: [type and description]
-""",
-)
+Every browser tool that takes or returns coordinates operates in **fractions of the viewport (0..1 for both axes)**. Read a target's proportional position off `browser_screenshot` ("~35% from the left, ~20% from the top" → `(0.35, 0.20)`) and pass that to `browser_click_coordinate` / `browser_hover_coordinate` / `browser_press_at`. `browser_get_rect` and `browser_shadow_query` return `rect.cx` / `rect.cy` as fractions. The tools multiply by `cssWidth` / `cssHeight` internally — no scale awareness required. Fractions are used because every vision model (Claude, GPT-4o, Gemini, local VLMs) resizes/tiles images differently; proportions are invariant. Avoid raw `getBoundingClientRect()` via `browser_evaluate` for coord lookup; use `browser_get_rect` instead.
+
+## System prompt tips for browser nodes
+
+```
+1. Start with browser_snapshot or the snapshot returned by the latest interaction.
+2. If the target is missing, ambiguous, stale, or visibly present but absent from the tree,
+   use browser_screenshot to orient and then click by fractional coordinates.
+3. Before typing into a rich-text editor (X compose, LinkedIn DM, Gmail, Reddit),
+   click the input area first with browser_click_coordinate so React / Draft.js /
+   Lexical register a native focus event, then use browser_type_focused(text=...)
+   for shadow-DOM inputs or browser_type(selector, text) for light-DOM inputs.
+4. Use browser_wait(seconds=2-3) after navigation for SPA hydration.
+5. If you hit an auth wall, call set_output with an error and move on.
+6. Keep tool calls per turn <= 10 for reliability.
 ```
 
-## Parent Node Template (orchestrating GCU subagents)
-
-```python
-orchestrator_node = NodeSpec(
-    id="orchestrator",
-    ...
-    node_type="event_loop",
-    sub_agents=["gcu-browser-worker"],
-    system_prompt="""\
-...
-delegate_to_sub_agent(
-    agent_id="gcu-browser-worker",
-    task="Navigate to [URL]. Do [specific task]. Return JSON with [fields]."
-)
-...
-""",
-    tools=[],  # Orchestrator doesn't need browser tools
-)
-```
-
-## mcp_servers.json with GCU
+## Example
 
 ```json
 {
-  "hive-tools": { ... },
-  "gcu-tools": {
-    "transport": "stdio",
-    "command": "uv",
-    "args": ["run", "python", "-m", "gcu.server", "--stdio"],
-    "cwd": "../../tools",
-    "description": "GCU tools for browser automation"
-  }
+  "id": "scan-profiles",
+  "name": "Scan LinkedIn Profiles",
+  "description": "Navigate LinkedIn search results and collect profile data",
+  "tools": {"policy": "all"},
+  "input_keys": ["search_url"],
+  "output_keys": ["profiles"],
+  "system_prompt": "Navigate to the search URL via browser_navigate(wait_until='load', timeout_ms=20000). Wait 3s for SPA hydration. Use the returned snapshot to look for result cards first. If the cards are missing, stale, or visually present but absent from the tree, use browser_screenshot to orient; paginate through results by scrolling and use screenshots only when the snapshot cannot find or verify the visible cards..."
 }
 ```
 
-Note: `gcu-tools` is auto-added if any node uses `node_type="gcu"`, but including it explicitly is fine.
-
-## GCU System Prompt Best Practices
-
-Key rules to bake into GCU node prompts:
-
-- Prefer `browser_snapshot` over `browser_get_text("body")` — compact accessibility tree vs 100KB+ raw HTML
-- Always `browser_wait` after navigation
-- Use large scroll amounts (~2000-5000) for lazy-loaded content
-- For spillover files, use `run_command` with grep, not `read_file`
-- If auth wall detected, report immediately — don't attempt login
-- Keep tool calls per turn ≤10
-- Tab isolation: when browser is already running, use `browser_open(background=true)` and pass `target_id` to every call
-
-## Multiple Concurrent GCU Subagents
-
-When a task can be parallelized across multiple sites or profiles, declare a distinct GCU
-node for each and invoke them all in the same LLM turn.  The framework batches all
-`delegate_to_sub_agent` calls made in one turn and runs them with `asyncio.gather`, so
-they execute concurrently — not sequentially.
-
-**Each GCU subagent automatically gets its own isolated browser context** — no `profile=`
-argument is needed in tool calls.  The framework derives a unique profile from the subagent's
-node ID and instance counter and injects it via an asyncio `ContextVar` before the subagent
-runs.
-
-### Example: three sites in parallel
-
-```python
-# Three distinct GCU nodes
-gcu_site_a = NodeSpec(id="gcu-site-a", node_type="gcu", ...)
-gcu_site_b = NodeSpec(id="gcu-site-b", node_type="gcu", ...)
-gcu_site_c = NodeSpec(id="gcu-site-c", node_type="gcu", ...)
-
-orchestrator = NodeSpec(
-    id="orchestrator",
-    node_type="event_loop",
-    sub_agents=["gcu-site-a", "gcu-site-b", "gcu-site-c"],
-    system_prompt="""\
-Call all three subagents in a single response to run them in parallel:
-  delegate_to_sub_agent(agent_id="gcu-site-a", task="Scrape prices from site A")
-  delegate_to_sub_agent(agent_id="gcu-site-b", task="Scrape prices from site B")
-  delegate_to_sub_agent(agent_id="gcu-site-c", task="Scrape prices from site C")
-""",
-)
+Connected via regular edges:
+```
+search-setup -> scan-profiles -> process-results
 ```
 
-**Rules:**
-- Use distinct node IDs for each concurrent task — sharing an ID shares the browser context.
-- The GCU node prompts do not need to mention `profile=`; isolation is automatic.
-- Cleanup is automatic at session end, but GCU nodes can call `browser_stop()` explicitly
-  if they want to release resources mid-run.
+## Further detail
 
-## GCU Anti-Patterns
-
-- Using `browser_screenshot` to read text (use `browser_snapshot` instead; screenshots are for visual context only)
-- Re-navigating after scrolling (resets scroll position)
-- Attempting login on auth walls
-- Forgetting `target_id` in multi-tab scenarios
-- Putting browser tools directly on `event_loop` nodes instead of using GCU subagent pattern
-- Making GCU nodes `client_facing=True` (they should be autonomous subagents)
+For rich-text editor quirks (Lexical, Draft.js, ProseMirror), shadow-DOM shortcuts, `beforeunload` dialog neutralization, Trusted Types CSP on LinkedIn, keyboard shortcut dispatch, and per-site selector tables — **activate the `browser-automation` skill**. That skill has the full verified guidance and is refreshed against real production sites.

core/framework/agents/queen/reference/queen_memory.md

@@ -1,63 +0,0 @@
-# Queen Memory — File System Structure
-
-```
-~/.hive/
-├── queen/
-│   ├── MEMORY.md                          ← Semantic memory
-│   ├── memories/
-│   │   ├── MEMORY-2026-03-09.md           ← Episodic memory (today)
-│   │   ├── MEMORY-2026-03-08.md
-│   │   └── ...
-│   └── session/
-│       └── {session_id}/                  ← One dir per session (or resumed-from session)
-│           ├── conversations/
-│           │   ├── parts/
-│           │   │   ├── 00001.json         ← One file per message (role, content, tool_calls)
-│           │   │   ├── 00002.json
-│           │   │   └── ...
-│           │   └── spillover/
-│           │       ├── conversation_1.md  ← Compacted old conversation segments
-│           │       ├── conversation_2.md
-│           │       └── ...
-│           └── data/
-│               ├── adapt.md              ← Working memory (session-scoped)
-│               ├── web_search_1.txt      ← Spillover: large tool results
-│               ├── web_search_2.txt
-│               └── ...
-```
-
----
-
-## The three memory tiers
-
-| File | Tier | Written by | Read at |
-|---|---|---|---|
-| `MEMORY.md` | Semantic | Consolidation LLM (auto, post-session) | Session start (injected into system prompt) |
-| `memories/MEMORY-YYYY-MM-DD.md` | Episodic | Queen via `write_to_diary` tool + consolidation LLM | Session start (today's file injected) |
-| `data/adapt.md` | Working | Queen via `update_session_notes` tool | Every turn (inlined in system prompt) |
-
----
-
-## Session directory naming
-
-The session directory name is **`queen_resume_from`** when a cold-restore resumes an existing
-session, otherwise the new **`session_id`**. This means resumed sessions accumulate all messages
-in the original directory rather than fragmenting across multiple folders.
-
----
-
-## Consolidation
-
-`consolidate_queen_memory()` runs every **5 minutes** in the background and once more at session
-end. It reads:
-
-1. `conversations/parts/*.json` — full message history (user + assistant turns; tool results skipped)
-2. `data/adapt.md` — current working notes
-
-It then makes two LLM writes:
-
-- Rewrites `MEMORY.md` in place (semantic memory — queen never touches this herself)
-- Appends a timestamped prose entry to today's `memories/MEMORY-YYYY-MM-DD.md`
-
-If the combined transcript exceeds ~200 K characters it is recursively binary-compacted via the
-LLM before being sent to the consolidation model (mirrors `EventLoopNode._llm_compact`).

core/framework/agents/queen/reflection_agent.py

@@ -0,0 +1,984 @@
+"""Reflection agent — background memory extraction for the queen.
+
+A lightweight side agent that runs after each queen LLM turn.  It inspects
+recent conversation messages and extracts durable user knowledge into
+individual memory files in the configured memory directories.
+
+Two reflection types:
+  - **Short reflection**: after conversational queen turns.  Distills
+    learnings into either global or queen-scoped memory.
+  - **Long reflection**: every 5 short reflections and on CONTEXT_COMPACTED.
+    Organises, deduplicates, and trims a memory directory.
+
+Concurrency: an ``asyncio.Lock`` prevents overlapping runs.  If a trigger
+fires while a reflection is already active the event is skipped.
+
+All reflections are fire-and-forget (spawned via ``asyncio.create_task``)
+so they never block the queen's event loop.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import time
+import traceback
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from framework.agents.queen.queen_memory_v2 import (
+    GLOBAL_MEMORY_CATEGORIES,
+    MAX_FILE_SIZE_BYTES,
+    MAX_FILES,
+    format_memory_manifest,
+    global_memory_dir as _default_global_memory_dir,
+    parse_frontmatter,
+    scan_memory_files,
+)
+from framework.llm.provider import LLMResponse, Tool
+from framework.tracker.llm_debug_logger import log_llm_turn
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Reflection tool definitions (internal — not in queen's main registry)
+# ---------------------------------------------------------------------------
+
+_REFLECTION_TOOLS: list[Tool] = [
+    Tool(
+        name="list_memory_files",
+        description=(
+            "List memory files with their type, name, and description. "
+            "When scope is omitted, returns all scopes grouped by scope."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "scope": {
+                    "type": "string",
+                    "description": "Optional scope to inspect: 'global' or 'queen'.",
+                },
+            },
+            "additionalProperties": False,
+        },
+    ),
+    Tool(
+        name="read_memory_file",
+        description="Read the full content of a memory file by filename from a scope.",
+        parameters={
+            "type": "object",
+            "properties": {
+                "filename": {
+                    "type": "string",
+                    "description": "The filename (e.g. 'user-prefers-dark-mode.md').",
+                },
+                "scope": {
+                    "type": "string",
+                    "description": "Memory scope: 'global' or 'queen'. Defaults to 'global'.",
+                },
+            },
+            "required": ["filename"],
+            "additionalProperties": False,
+        },
+    ),
+    Tool(
+        name="write_memory_file",
+        description=(
+            "Create or overwrite a memory file.  Content should include YAML "
+            "frontmatter (name, description, type) followed by the memory body.  "
+            f"Max file size: {MAX_FILE_SIZE_BYTES} bytes.  Max files: {MAX_FILES}."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "filename": {
+                    "type": "string",
+                    "description": "Filename ending in .md (e.g. 'user-prefers-dark-mode.md').",
+                },
+                "scope": {
+                    "type": "string",
+                    "description": "Memory scope: 'global' or 'queen'. Defaults to 'global'.",
+                },
+                "content": {
+                    "type": "string",
+                    "description": "Full file content including frontmatter.",
+                },
+            },
+            "required": ["filename", "content"],
+            "additionalProperties": False,
+        },
+    ),
+    Tool(
+        name="delete_memory_file",
+        description=(
+            "Delete a memory file by filename.  Use during long reflection to prune stale or redundant memories."
+        ),
+        parameters={
+            "type": "object",
+            "properties": {
+                "filename": {
+                    "type": "string",
+                    "description": "The filename to delete.",
+                },
+                "scope": {
+                    "type": "string",
+                    "description": "Memory scope: 'global' or 'queen'. Defaults to 'global'.",
+                },
+            },
+            "required": ["filename"],
+            "additionalProperties": False,
+        },
+    ),
+]
+
+
+def _normalize_memory_dirs(
+    memory_dir: Path | dict[str, Path],
+    *,
+    queen_memory_dir: Path | None = None,
+) -> dict[str, Path]:
+    """Normalize memory directory input into a scope -> path mapping."""
+    if isinstance(memory_dir, dict):
+        return {scope: path for scope, path in memory_dir.items() if path is not None}
+
+    dirs: dict[str, Path] = {"global": memory_dir}
+    if queen_memory_dir is not None:
+        dirs["queen"] = queen_memory_dir
+    return dirs
+
+
+def _scope_label(scope: str, queen_id: str | None = None) -> str:
+    """Human-readable label for a memory scope."""
+    if scope == "queen":
+        return f"queen ({queen_id})" if queen_id else "queen"
+    return scope
+
+
+def _resolve_memory_scope(args: dict[str, Any], memory_dirs: dict[str, Path]) -> str:
+    """Resolve and validate the requested memory scope."""
+    raw_scope = args.get("scope")
+    if raw_scope is None:
+        if len(memory_dirs) == 1:
+            return next(iter(memory_dirs))
+        scope = "global"
+    else:
+        scope = str(raw_scope).strip().lower() or "global"
+    if scope not in memory_dirs:
+        available = ", ".join(sorted(memory_dirs))
+        raise ValueError(f"Invalid scope '{scope}'. Available scopes: {available}.")
+    return scope
+
+
+def _format_multi_scope_manifest(
+    memory_dirs: dict[str, Path],
+    *,
+    queen_id: str | None = None,
+) -> str:
+    """Format a manifest that groups memory files by scope."""
+    blocks: list[str] = []
+    for scope, memory_dir in memory_dirs.items():
+        files = scan_memory_files(memory_dir)
+        label = _scope_label(scope, queen_id)
+        body = format_memory_manifest(files) if files else "(no memory files yet)"
+        blocks.append(f"## Scope: {label}\n\n{body}")
+    return "\n\n".join(blocks)
+
+
+def _safe_memory_path(filename: str, memory_dir: Path) -> Path:
+    """Resolve *filename* inside *memory_dir*, raising if it escapes."""
+    if not filename or filename.strip() != filename:
+        raise ValueError(f"Invalid filename: {filename!r}")
+    if "/" in filename or "\\" in filename or ".." in filename:
+        raise ValueError(f"Invalid filename: path components not allowed: {filename!r}")
+    candidate = (memory_dir / filename).resolve()
+    root = memory_dir.resolve()
+    if not candidate.is_relative_to(root):
+        raise ValueError(f"Path escapes memory directory: {filename!r}")
+    return candidate
+
+
+def _execute_tool(
+    name: str,
+    args: dict[str, Any],
+    memory_dir: Path | dict[str, Path],
+    *,
+    queen_id: str | None = None,
+) -> str:
+    """Execute a reflection tool synchronously.  Returns the result string."""
+    memory_dirs = _normalize_memory_dirs(memory_dir)
+    if name == "list_memory_files":
+        requested_scope = args.get("scope")
+        if requested_scope is not None:
+            try:
+                scope = _resolve_memory_scope(args, memory_dirs)
+            except ValueError as exc:
+                return f"ERROR: {exc}"
+            files = scan_memory_files(memory_dirs[scope])
+            logger.debug("reflect: tool list_memory_files[%s] → %d files", scope, len(files))
+            if not files:
+                return f"(no {scope} memory files yet)"
+            return format_memory_manifest(files)
+        return _format_multi_scope_manifest(memory_dirs, queen_id=queen_id)
+
+    if name == "read_memory_file":
+        filename = args.get("filename", "")
+        try:
+            scope = _resolve_memory_scope(args, memory_dirs)
+        except ValueError as exc:
+            return f"ERROR: {exc}"
+        try:
+            path = _safe_memory_path(filename, memory_dirs[scope])
+        except ValueError as exc:
+            return f"ERROR: {exc}"
+        if not path.exists() or not path.is_file():
+            return f"ERROR: File not found in {scope}: {filename}"
+        try:
+            return path.read_text(encoding="utf-8")
+        except OSError as e:
+            return f"ERROR: {e}"
+
+    if name == "write_memory_file":
+        filename = args.get("filename", "")
+        content = args.get("content", "")
+        try:
+            scope = _resolve_memory_scope(args, memory_dirs)
+        except ValueError as exc:
+            return f"ERROR: {exc}"
+        scope_dir = memory_dirs[scope]
+        if not filename.endswith(".md"):
+            return "ERROR: Filename must end with .md"
+        # Enforce global memory type restrictions.
+        fm = parse_frontmatter(content)
+        mem_type = (fm.get("type") or "").strip().lower()
+        if mem_type and mem_type not in GLOBAL_MEMORY_CATEGORIES:
+            return f"ERROR: Invalid memory type '{mem_type}'. Allowed types: {', '.join(GLOBAL_MEMORY_CATEGORIES)}."
+        # Enforce file size limit.
+        if len(content.encode("utf-8")) > MAX_FILE_SIZE_BYTES:
+            return f"ERROR: Content exceeds {MAX_FILE_SIZE_BYTES} byte limit."
+        # Enforce file cap (only for new files).
+        try:
+            path = _safe_memory_path(filename, scope_dir)
+        except ValueError as exc:
+            return f"ERROR: {exc}"
+        if not path.exists():
+            existing = list(scope_dir.glob("*.md"))
+            if len(existing) >= MAX_FILES:
+                return f"ERROR: File cap reached in {scope} ({MAX_FILES}). Delete a file first."
+        scope_dir.mkdir(parents=True, exist_ok=True)
+        path.write_text(content, encoding="utf-8")
+        logger.debug(
+            "reflect: tool write_memory_file[%s] → %s (%d chars)",
+            scope,
+            filename,
+            len(content),
+        )
+        return f"Wrote {scope}:{filename} ({len(content)} chars)."
+
+    if name == "delete_memory_file":
+        filename = args.get("filename", "")
+        try:
+            scope = _resolve_memory_scope(args, memory_dirs)
+        except ValueError as exc:
+            return f"ERROR: {exc}"
+        try:
+            path = _safe_memory_path(filename, memory_dirs[scope])
+        except ValueError as exc:
+            return f"ERROR: {exc}"
+        if not path.exists():
+            return f"ERROR: File not found in {scope}: {filename}"
+        path.unlink()
+        logger.debug("reflect: tool delete_memory_file[%s] → %s", scope, filename)
+        return f"Deleted {scope}:{filename}."
+
+    return f"ERROR: Unknown tool: {name}"
+
+
+# ---------------------------------------------------------------------------
+# Reflection logging helper
+# ---------------------------------------------------------------------------
+
+
+def _log_reflection_turn(
+    *,
+    reflection_id: str,
+    iteration: int,
+    system_prompt: str,
+    messages: list[dict[str, Any]],
+    assistant_text: str,
+    tool_calls: list[dict[str, Any]],
+    tool_results: list[dict[str, Any]],
+    token_counts: dict[str, Any],
+) -> None:
+    """Log a reflection turn using the same JSONL format as the main agent loop."""
+    log_llm_turn(
+        node_id="reflection",
+        stream_id=reflection_id,
+        execution_id=reflection_id,
+        iteration=iteration,
+        system_prompt=system_prompt,
+        messages=messages,
+        assistant_text=assistant_text,
+        tool_calls=tool_calls,
+        tool_results=tool_results,
+        token_counts=token_counts,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Mini event loop
+# ---------------------------------------------------------------------------
+
+_MAX_TURNS = 5
+
+
+async def _reflection_loop(
+    llm: Any,
+    system: str,
+    user_msg: str,
+    memory_dir: Path | dict[str, Path],
+    max_turns: int = _MAX_TURNS,
+    *,
+    queen_id: str | None = None,
+) -> tuple[bool, list[str], str]:
+    """Run a mini tool-use loop: LLM → tool calls → repeat.
+
+    Returns (success, changed_files, last_text).
+    """
+    messages: list[dict[str, Any]] = [{"role": "user", "content": user_msg}]
+    changed_files: list[str] = []
+    last_text: str = ""
+    reflection_id = f"reflection_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+    token_counts: dict[str, Any] = {}
+    memory_dirs = _normalize_memory_dirs(memory_dir)
+
+    for _turn in range(max_turns):
+        logger.info("reflect: loop turn %d/%d (msgs=%d)", _turn + 1, max_turns, len(messages))
+        try:
+            resp: LLMResponse = await llm.acomplete(
+                messages=messages,
+                system=system,
+                tools=_REFLECTION_TOOLS,
+                max_tokens=2048,
+            )
+        except asyncio.CancelledError:
+            logger.warning("reflect: LLM call cancelled (task cancelled)")
+            return False, changed_files, last_text
+        except Exception:
+            logger.warning("reflect: LLM call failed", exc_info=True)
+            return False, changed_files, last_text
+
+        # Extract tool calls from litellm/OpenAI response object.
+        tool_calls_raw: list[dict[str, Any]] = []
+        raw = resp.raw_response
+        if raw is not None:
+            # litellm returns a ModelResponse object; tool calls live on
+            # choices[0].message.tool_calls as a list of ChatCompletionMessageToolCall.
+            try:
+                msg_obj = raw.choices[0].message
+                if hasattr(msg_obj, "tool_calls") and msg_obj.tool_calls:
+                    for tc in msg_obj.tool_calls:
+                        fn = tc.function
+                        try:
+                            args = json.loads(fn.arguments) if fn.arguments else {}
+                        except (json.JSONDecodeError, TypeError):
+                            args = {}
+                        tool_calls_raw.append(
+                            {
+                                "id": tc.id,
+                                "name": fn.name,
+                                "input": args,
+                            }
+                        )
+            except (AttributeError, IndexError):
+                pass
+
+        logger.info(
+            "reflect: LLM responded, text=%d chars, tool_calls=%d",
+            len(resp.content or ""),
+            len(tool_calls_raw),
+        )
+
+        # Capture token counts from the LLM response.
+        try:
+            raw_usage = getattr(raw, "usage", None) if raw else None
+            if raw_usage:
+                token_counts = {
+                    "model": getattr(raw, "model", ""),
+                    "input": getattr(raw_usage, "prompt_tokens", 0) or 0,
+                    "output": getattr(raw_usage, "completion_tokens", 0) or 0,
+                    "cached": getattr(raw_usage, "prompt_tokens_details", None)
+                    and getattr(raw_usage.prompt_tokens_details, "cached_tokens", 0),
+                    "stop_reason": getattr(raw.choices[0], "finish_reason", "") if raw else "",
+                }
+        except Exception:
+            token_counts = {}
+
+        turn_text = resp.content or ""
+        if turn_text:
+            last_text = turn_text
+        assistant_msg: dict[str, Any] = {"role": "assistant", "content": turn_text}
+        if tool_calls_raw:
+            assistant_msg["tool_calls"] = [
+                {
+                    "id": tc["id"],
+                    "type": "function",
+                    "function": {
+                        "name": tc["name"],
+                        "arguments": json.dumps(tc.get("input", {})),
+                    },
+                }
+                for tc in tool_calls_raw
+            ]
+        messages.append(assistant_msg)
+
+        if not tool_calls_raw:
+            break
+
+        tool_results: list[dict[str, Any]] = []
+        for tc in tool_calls_raw:
+            tc_input = tc.get("input", {})
+            result = _execute_tool(tc["name"], tc_input, memory_dirs, queen_id=queen_id)
+            if tc["name"] in ("write_memory_file", "delete_memory_file"):
+                fname = tc_input.get("filename", "")
+                try:
+                    scope = _resolve_memory_scope(tc_input, memory_dirs)
+                except ValueError:
+                    scope = str(tc_input.get("scope", "global")).strip().lower() or "global"
+                if fname and not result.startswith("ERROR"):
+                    changed_files.append(f"{scope}:{fname}")
+            messages.append({"role": "tool", "tool_call_id": tc["id"], "content": result})
+            tool_results.append({"tool_call_id": tc["id"], "name": tc["name"], "result": result})
+
+        # Log the reflection turn in the same JSONL format as the main agent loop.
+        _log_reflection_turn(
+            reflection_id=reflection_id,
+            iteration=_turn,
+            system_prompt=system,
+            messages=messages,
+            assistant_text=turn_text,
+            tool_calls=tool_calls_raw,
+            tool_results=tool_results,
+            token_counts=token_counts,
+        )
+
+    return True, changed_files, last_text
+
+
+# ---------------------------------------------------------------------------
+# System prompts
+# ---------------------------------------------------------------------------
+
+_CATEGORIES_STR = ", ".join(GLOBAL_MEMORY_CATEGORIES)
+
+
+def _build_unified_short_reflect_system(queen_id: str | None = None) -> str:
+    """Build the unified short reflection prompt across memory scopes."""
+    queen_scope = (
+        f"- `queen`: durable learnings specific to how queen '{queen_id}' should work with this user\n"
+        if queen_id
+        else ""
+    )
+    return f"""\
+You are a reflection agent that distills durable knowledge about the USER
+into persistent memory files. You run in the background after each
+assistant turn.
+
+Memory categories: {_CATEGORIES_STR}
+
+Available memory scopes:
+- `global`: durable user facts that should help every queen in future sessions
+{queen_scope}
+
+Expected format for each memory file:
+```markdown
+---
+name: {{{{memory name}}}}
+description: {{{{one-line description — specific and search-friendly}}}}
+type: {{{{{_CATEGORIES_STR}}}}}
+---
+
+{{{{memory content}}}}
+```
+
+Workflow (aim for 2 turns):
+  Turn 1 — call list_memory_files without a scope to inspect all scopes, then
+            read_memory_file for any files that might need updating.
+  Turn 2 — call write_memory_file / delete_memory_file with an explicit scope.
+
+Rules:
+- Make ONE coordinated storage decision per learning.
+- Prefer `global` for broad user facts: identity, general preferences, environment,
+  and feedback that should help all queens.
+- Prefer `queen` only for stable domain-specific learnings about how this queen
+  should reason, prioritize, communicate, or make tradeoffs for this user.
+- Avoid storing the same fact in both scopes unless the scoped version adds
+  genuinely distinct queen-specific nuance. When in doubt, keep only one copy.
+- Update existing files instead of creating duplicates when possible.
+- If the same learning already exists in the wrong scope or both scopes,
+  you may update one file and delete the redundant one.
+- Do NOT store task-specific details, code patterns, file paths, or ephemeral
+  session state.
+- Keep files concise. Each file should cover ONE topic.
+- If there is nothing worth remembering, do nothing (respond with a brief
+  reason — no tool calls needed).
+- File names should be kebab-case slugs ending in .md.
+- For user identity/profile information about the human user (name, role,
+  background), ALWAYS use the canonical filename 'user-profile.md' in the
+  `global` scope. This is the single source of truth for user profile data,
+  shared with the settings UI.
+- When updating `global:user-profile.md`, preserve the '## User Identity'
+  section — it is managed by the settings UI. Never describe the assistant,
+  queen, or agent as the identity in this file. Add/update other sections
+  below it.
+- Do NOT exceed {MAX_FILE_SIZE_BYTES} bytes per file or {MAX_FILES} total files per scope.
+"""
+
+
+def _build_unified_long_reflect_system(queen_id: str | None = None) -> str:
+    """Build the unified housekeeping prompt across memory scopes."""
+    queen_scope = (
+        f"- `queen`: memories specific to how queen '{queen_id}' should work with this user\n" if queen_id else ""
+    )
+    return f"""\
+You are a reflection agent performing a periodic housekeeping pass over the
+memory system for this user.
+
+Memory categories: {_CATEGORIES_STR}
+
+Available memory scopes:
+- `global`: facts useful to every queen
+{queen_scope}
+
+Workflow:
+  1. Call list_memory_files without a scope to inspect all scopes together.
+  2. Read files that look redundant, stale, overlapping, or misplaced.
+  3. Merge duplicates, move memories to the correct scope, and delete
+     redundant copies when appropriate.
+  4. Ensure descriptions are specific and search-friendly.
+  5. Enforce limits: max {MAX_FILES} files and {MAX_FILE_SIZE_BYTES} bytes per file in each scope.
+
+Rules:
+- Treat deduplication across scopes as part of the job, not just within a scope.
+- Prefer `global` for broad durable user facts and `queen` for queen-specific nuance.
+- If two files store materially the same fact, keep the best one and delete or
+  rewrite the redundant one.
+- Prefer merging over deleting when the memories contain complementary signal.
+- Remove memories that are stale, superseded, or misplaced.
+- Keep the total collection lean and high-signal.
+- Do NOT invent new information — only reorganise what exists.
+"""
+
+
+# ---------------------------------------------------------------------------
+# Short & long reflection entry points
+# ---------------------------------------------------------------------------
+
+
+async def _read_conversation_parts(session_dir: Path) -> list[dict[str, Any]]:
+    """Read conversation parts from the queen session directory."""
+    from framework.storage.conversation_store import FileConversationStore
+
+    store = FileConversationStore(session_dir / "conversations")
+    return await store.read_parts()
+
+
+async def run_short_reflection(
+    session_dir: Path,
+    llm: Any,
+    memory_dir: Path | None = None,
+) -> None:
+    """Run a global-only short reflection (compatibility wrapper)."""
+    logger.info("reflect: starting global short reflection for %s", session_dir)
+    mem_dir = memory_dir or _default_global_memory_dir()
+    await _run_short_reflection_with_prompt(
+        session_dir,
+        llm,
+        mem_dir,
+        system_prompt=_build_unified_short_reflect_system(),
+        log_label="global",
+        queen_id=None,
+    )
+
+
+async def run_queen_short_reflection(
+    session_dir: Path,
+    llm: Any,
+    queen_id: str,
+    memory_dir: Path,
+) -> None:
+    """Run a queen-only short reflection (compatibility wrapper)."""
+    logger.info("reflect: starting queen short reflection for %s (%s)", session_dir, queen_id)
+    await _run_short_reflection_with_prompt(
+        session_dir,
+        llm,
+        {"queen": memory_dir},
+        system_prompt=_build_unified_short_reflect_system(queen_id),
+        log_label=f"queen:{queen_id}",
+        queen_id=queen_id,
+    )
+
+
+async def run_unified_short_reflection(
+    session_dir: Path,
+    llm: Any,
+    *,
+    global_memory_dir: Path | None = None,
+    queen_memory_dir: Path | None = None,
+    queen_id: str | None = None,
+) -> None:
+    """Run one short reflection loop over all active memory scopes."""
+    global_dir = global_memory_dir or _default_global_memory_dir()
+    memory_dirs = {"global": global_dir}
+    if queen_memory_dir is not None and queen_id:
+        memory_dirs["queen"] = queen_memory_dir
+
+    logger.info(
+        "reflect: starting unified short reflection for %s (scopes=%s)",
+        session_dir,
+        sorted(memory_dirs),
+    )
+    await _run_short_reflection_with_prompt(
+        session_dir,
+        llm,
+        memory_dirs,
+        system_prompt=_build_unified_short_reflect_system(queen_id if "queen" in memory_dirs else None),
+        log_label="unified",
+        queen_id=queen_id if "queen" in memory_dirs else None,
+    )
+
+
+async def _run_short_reflection_with_prompt(
+    session_dir: Path,
+    llm: Any,
+    memory_dir: Path | dict[str, Path],
+    *,
+    system_prompt: str,
+    log_label: str,
+    queen_id: str | None,
+) -> None:
+    """Run a short reflection with a scope-specific system prompt."""
+    mem_dir = memory_dir
+
+    messages = await _read_conversation_parts(session_dir)
+    if not messages:
+        logger.info("reflect: no conversation parts found in %s, skipping", session_dir)
+        return
+
+    transcript_lines: list[str] = []
+    for msg in messages[-50:]:
+        role = msg.get("role", "")
+        content = str(msg.get("content", "")).strip()
+        if role == "tool" or not content:
+            continue
+        label = "user" if role == "user" else "assistant"
+        if len(content) > 800:
+            content = content[:800] + "…"
+        transcript_lines.append(f"[{label}]: {content}")
+
+    if not transcript_lines:
+        logger.info("reflect: no transcript lines after filtering, skipping")
+        return
+
+    transcript = "\n".join(transcript_lines)
+    user_msg = (
+        f"## Recent conversation ({len(messages)} messages total)\n\n"
+        f"{transcript}\n\n"
+        f"Timestamp: {datetime.now().isoformat(timespec='minutes')}"
+    )
+
+    _, changed, reason = await _reflection_loop(
+        llm,
+        system_prompt,
+        user_msg,
+        mem_dir,
+        queen_id=queen_id,
+    )
+    if changed:
+        logger.info("reflect: %s short reflection done, changed files: %s", log_label, changed)
+    else:
+        logger.info(
+            "reflect: %s short reflection done, no changes — %s",
+            log_label,
+            reason or "no reason",
+        )
+
+
+async def run_long_reflection(
+    llm: Any,
+    memory_dir: Path | None = None,
+    *,
+    scope_label: str = "global",
+) -> None:
+    """Run a single-scope long reflection (compatibility wrapper)."""
+    logger.debug("reflect: starting long reflection for %s", scope_label)
+    mem_dir = memory_dir or _default_global_memory_dir()
+    files = scan_memory_files(mem_dir)
+
+    if not files:
+        logger.debug("reflect: no %s memory files, skipping long reflection", scope_label)
+        return
+
+    manifest = format_memory_manifest(files)
+    user_msg = (
+        f"## Current memory manifest ({len(files)} files)\n\n"
+        f"{manifest}\n\n"
+        f"Timestamp: {datetime.now().isoformat(timespec='minutes')}"
+    )
+
+    _, changed, reason = await _reflection_loop(
+        llm,
+        _build_unified_long_reflect_system(),
+        user_msg,
+        mem_dir,
+        queen_id=None,
+    )
+    if changed:
+        logger.debug(
+            "reflect: long reflection done for %s (%d files), changed: %s",
+            scope_label,
+            len(files),
+            changed,
+        )
+    else:
+        logger.debug(
+            "reflect: long reflection done for %s (%d files), no changes — %s",
+            scope_label,
+            len(files),
+            reason or "no reason",
+        )
+
+
+async def run_unified_long_reflection(
+    llm: Any,
+    *,
+    global_memory_dir: Path | None = None,
+    queen_memory_dir: Path | None = None,
+    queen_id: str | None = None,
+) -> None:
+    """Run one housekeeping loop across all active memory scopes."""
+    global_dir = global_memory_dir or _default_global_memory_dir()
+    memory_dirs = {"global": global_dir}
+    if queen_memory_dir is not None and queen_id:
+        memory_dirs["queen"] = queen_memory_dir
+
+    manifest = _format_multi_scope_manifest(memory_dirs, queen_id=queen_id if "queen" in memory_dirs else None)
+    user_msg = (
+        "## Current memory manifest across scopes\n\n"
+        f"{manifest}\n\n"
+        f"Timestamp: {datetime.now().isoformat(timespec='minutes')}"
+    )
+
+    _, changed, reason = await _reflection_loop(
+        llm,
+        _build_unified_long_reflect_system(queen_id if "queen" in memory_dirs else None),
+        user_msg,
+        memory_dirs,
+        queen_id=queen_id if "queen" in memory_dirs else None,
+    )
+    if changed:
+        logger.debug("reflect: unified long reflection changed: %s", changed)
+    else:
+        logger.debug("reflect: unified long reflection no changes — %s", reason or "no reason")
+
+
+async def run_shutdown_reflection(
+    session_dir: Path,
+    llm: Any,
+    memory_dir: Path | None = None,
+    *,
+    global_memory_dir_override: Path | None = None,
+    queen_memory_dir: Path | None = None,
+    queen_id: str | None = None,
+) -> None:
+    """Run a final short reflection on session shutdown.
+
+    Called during session teardown so recent conversation insights are
+    persisted before the session is destroyed.
+    """
+    logger.info("reflect: running shutdown reflection for %s", session_dir)
+    try:
+        global_dir = global_memory_dir_override or memory_dir or _default_global_memory_dir()
+        await run_unified_short_reflection(
+            session_dir,
+            llm,
+            global_memory_dir=global_dir,
+            queen_memory_dir=queen_memory_dir,
+            queen_id=queen_id,
+        )
+        logger.info("reflect: shutdown reflection completed for %s", session_dir)
+    except asyncio.CancelledError:
+        logger.warning("reflect: shutdown reflection cancelled for %s", session_dir)
+    except Exception:
+        logger.warning("reflect: shutdown reflection failed", exc_info=True)
+        _write_error(
+            "shutdown reflection",
+            global_memory_dir_override or memory_dir or _default_global_memory_dir(),
+        )
+
+
+# ---------------------------------------------------------------------------
+# Event-bus integration
+# ---------------------------------------------------------------------------
+
+_LONG_REFLECT_INTERVAL = 5
+_SHORT_REFLECT_TURN_INTERVAL = 3
+_SHORT_REFLECT_COOLDOWN_SEC = 300.0
+
+
+async def subscribe_reflection_triggers(
+    event_bus: Any,
+    session_dir: Path,
+    llm: Any,
+    global_memory_dir: Path | None = None,
+    queen_memory_dir: Path | None = None,
+    queen_id: str | None = None,
+) -> list[str]:
+    """Subscribe to queen turn events and return subscription IDs.
+
+    Call this once during queen setup.  Returns a list of event-bus
+    subscription IDs for cleanup during session teardown.
+    """
+    from framework.host.event_bus import EventType
+
+    global_mem_dir = global_memory_dir or _default_global_memory_dir()
+    queen_mem_dir = queen_memory_dir
+    _lock = asyncio.Lock()
+    _short_count = 0
+    _short_has_run = False
+    _last_short_time: float = 0.0
+    _background_tasks: set[asyncio.Task] = set()
+
+    async def _run_with_error_capture(coro: Any, *, context: str, memory_dir: Path) -> None:
+        try:
+            await coro
+        except Exception:
+            logger.warning("reflect: %s failed", context, exc_info=True)
+            _write_error(context, memory_dir)
+
+    async def _do_turn_reflect(is_interval: bool, count: int) -> None:
+        async with _lock:
+            await _run_with_error_capture(
+                run_unified_short_reflection(
+                    session_dir,
+                    llm,
+                    global_memory_dir=global_mem_dir,
+                    queen_memory_dir=queen_mem_dir,
+                    queen_id=queen_id,
+                ),
+                context="unified short reflection",
+                memory_dir=global_mem_dir,
+            )
+            if is_interval:
+                await _run_with_error_capture(
+                    run_unified_long_reflection(
+                        llm,
+                        global_memory_dir=global_mem_dir,
+                        queen_memory_dir=queen_mem_dir,
+                        queen_id=queen_id,
+                    ),
+                    context="unified long reflection",
+                    memory_dir=global_mem_dir,
+                )
+
+    async def _do_compaction_reflect() -> None:
+        async with _lock:
+            await _run_with_error_capture(
+                run_unified_long_reflection(
+                    llm,
+                    global_memory_dir=global_mem_dir,
+                    queen_memory_dir=queen_mem_dir,
+                    queen_id=queen_id,
+                ),
+                context="unified compaction reflection",
+                memory_dir=global_mem_dir,
+            )
+
+    def _fire_and_forget(coro: Any) -> None:
+        """Spawn a background task and prevent GC before it finishes."""
+        task = asyncio.create_task(coro)
+        _background_tasks.add(task)
+        task.add_done_callback(_background_tasks.discard)
+
+    async def _on_turn_complete(event: Any) -> None:
+        nonlocal _short_count, _short_has_run, _last_short_time
+
+        if getattr(event, "stream_id", None) != "queen":
+            return
+
+        _short_count += 1
+
+        event_data = getattr(event, "data", {}) or {}
+        stop_reason = event_data.get("stop_reason", "")
+        is_tool_turn = stop_reason in ("tool_use", "tool_calls")
+        is_interval = _short_count % _LONG_REFLECT_INTERVAL == 0
+
+        if is_tool_turn and not is_interval:
+            logger.debug("reflect: skipping tool turn (count=%d)", _short_count)
+            return
+
+        # Apply turn-interval and cooldown gates after the first reflection.
+        if _short_has_run:
+            now = time.monotonic()
+            turn_ok = _short_count % _SHORT_REFLECT_TURN_INTERVAL == 0
+            cooldown_ok = (now - _last_short_time) >= _SHORT_REFLECT_COOLDOWN_SEC
+            if not turn_ok and not cooldown_ok:
+                logger.debug(
+                    "reflect: skipping, below turn/cooldown threshold (count=%d)",
+                    _short_count,
+                )
+                return
+
+        if _lock.locked():
+            logger.debug("reflect: skipping, already running (count=%d)", _short_count)
+            return
+
+        _short_has_run = True
+        _last_short_time = time.monotonic()
+
+        logger.debug(
+            "reflect: triggered (count=%d, interval=%s, stop_reason=%s)",
+            _short_count,
+            is_interval,
+            stop_reason,
+        )
+        _fire_and_forget(_do_turn_reflect(is_interval, _short_count))
+
+    async def _on_compaction(event: Any) -> None:
+        if getattr(event, "stream_id", None) != "queen":
+            return
+        if _lock.locked():
+            logger.debug("reflect: skipping compaction trigger, already running")
+            return
+        logger.debug("reflect: compaction triggered long reflection")
+        _fire_and_forget(_do_compaction_reflect())
+
+    sub_ids: list[str] = []
+
+    sub1 = event_bus.subscribe(
+        event_types=[EventType.LLM_TURN_COMPLETE],
+        handler=_on_turn_complete,
+    )
+    sub_ids.append(sub1)
+
+    sub2 = event_bus.subscribe(
+        event_types=[EventType.CONTEXT_COMPACTED],
+        handler=_on_compaction,
+    )
+    sub_ids.append(sub2)
+
+    return sub_ids
+
+
+def _write_error(context: str, memory_dir: Path) -> None:
+    """Best-effort write of the last traceback to an error file."""
+    try:
+        error_path = memory_dir / ".reflection_error.txt"
+        error_path.parent.mkdir(parents=True, exist_ok=True)
+        error_path.write_text(
+            f"context: {context}\ntime: {datetime.now().isoformat()}\n\n{traceback.format_exc()}",
+            encoding="utf-8",
+        )
+    except OSError:
+        pass

core/framework/agents/queen/tests/conftest.py

@@ -22,10 +22,10 @@ def mock_mode():
 
 @pytest_asyncio.fixture(scope="session")
 async def runner(tmp_path_factory, mock_mode):
-    from framework.runner.runner import AgentRunner
+    from framework.loader.agent_loader import AgentLoader
 
     storage = tmp_path_factory.mktemp("agent_storage")
-    r = AgentRunner.load(AGENT_PATH, mock_mode=mock_mode, storage_path=storage)
+    r = AgentLoader.load(AGENT_PATH, mock_mode=mock_mode, storage_path=storage)
     r._setup()
     yield r
     await r.cleanup_async()

core/framework/agents/queen/ticket_receiver.py

@@ -1,27 +0,0 @@
-"""Queen's ticket receiver entry point.
-
-When a WORKER_ESCALATION_TICKET event is emitted on the shared EventBus,
-this entry point fires and routes to the ``ticket_triage`` node, where the
-Queen deliberates and decides whether to notify the operator.
-
-Isolation level is ``isolated`` — the queen's triage memory is kept separate
-from the worker's shared memory. Each ticket triage runs in its own context.
-"""
-
-from __future__ import annotations
-
-from framework.graph.edge import AsyncEntryPointSpec
-
-TICKET_RECEIVER_ENTRY_POINT = AsyncEntryPointSpec(
-    id="ticket_receiver",
-    name="Worker Escalation Ticket Receiver",
-    entry_node="ticket_triage",
-    trigger_type="event",
-    trigger_config={
-        "event_types": ["worker_escalation_ticket"],
-        # Do not fire on our own graph's events (prevents loops if queen
-        # somehow emits a worker_escalation_ticket for herself)
-        "exclude_own_graph": True,
-    },
-    isolation_level="isolated",
-)

core/framework/agents/worker_memory.py

@@ -1,286 +0,0 @@
-"""Worker per-run digest (run diary).
-
-Storage layout:
-    ~/.hive/agents/{agent_name}/runs/{run_id}/digest.md
-
-Each completed or failed worker run gets one digest file.  The queen reads
-these via get_worker_status(focus='diary') before digging into live runtime
-logs — the diary is a cheap, persistent record that survives across sessions.
-"""
-
-from __future__ import annotations
-
-import logging
-import traceback
-from collections import Counter
-from datetime import datetime
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from framework.runtime.event_bus import AgentEvent, EventBus
-
-logger = logging.getLogger(__name__)
-
-
-_DIGEST_SYSTEM = """\
-You maintain run digests for a worker agent.
-A run digest is a concise, factual record of a single task execution.
-
-Write 3-6 sentences covering:
-- What the worker was asked to do (the task/goal)
-- What approach it took and what tools it used
-- What the outcome was (success, partial, or failure — and why if relevant)
-- Any notable issues, retries, or escalations to the queen
-
-Write in third person past tense. Be direct and specific.
-Omit routine tool invocations unless the result matters.
-Output only the digest prose — no headings, no code fences.
-"""
-
-
-def _worker_runs_dir(agent_name: str) -> Path:
-    return Path.home() / ".hive" / "agents" / agent_name / "runs"
-
-
-def digest_path(agent_name: str, run_id: str) -> Path:
-    return _worker_runs_dir(agent_name) / run_id / "digest.md"
-
-
-def _collect_run_events(bus: EventBus, run_id: str, limit: int = 2000) -> list[AgentEvent]:
-    """Collect all events belonging to *run_id* from the bus history.
-
-    Strategy: find the EXECUTION_STARTED event that carries ``run_id``,
-    extract its ``execution_id``, then query the bus by that execution_id.
-    This works because TOOL_CALL_*, EDGE_TRAVERSED, NODE_STALLED etc. carry
-    execution_id but not run_id.
-
-    Falls back to a full-scan run_id filter when EXECUTION_STARTED is not
-    found (e.g. bus was rotated).
-    """
-    from framework.runtime.event_bus import EventType
-
-    # Pass 1: find execution_id via EXECUTION_STARTED with matching run_id
-    started = bus.get_history(event_type=EventType.EXECUTION_STARTED, limit=limit)
-    exec_id: str | None = None
-    for e in started:
-        if getattr(e, "run_id", None) == run_id and e.execution_id:
-            exec_id = e.execution_id
-            break
-
-    if exec_id:
-        return bus.get_history(execution_id=exec_id, limit=limit)
-
-    # Fallback: scan all events and match by run_id attribute
-    return [e for e in bus.get_history(limit=limit) if getattr(e, "run_id", None) == run_id]
-
-
-def _build_run_context(
-    events: list[AgentEvent],
-    outcome_event: AgentEvent | None,
-) -> str:
-    """Assemble a plain-text run context string for the digest LLM call."""
-    from framework.runtime.event_bus import EventType
-
-    # Reverse so events are in chronological order
-    events_chron = list(reversed(events))
-
-    lines: list[str] = []
-
-    # Task input from EXECUTION_STARTED
-    started = [e for e in events_chron if e.type == EventType.EXECUTION_STARTED]
-    if started:
-        inp = started[0].data.get("input", {})
-        if inp:
-            lines.append(f"Task input: {str(inp)[:400]}")
-
-    # Duration (elapsed so far if no outcome yet)
-    ref_ts = outcome_event.timestamp if outcome_event else datetime.utcnow()
-    if started:
-        elapsed = (ref_ts - started[0].timestamp).total_seconds()
-        m, s = divmod(int(elapsed), 60)
-        lines.append(f"Duration so far: {m}m {s}s" if m else f"Duration so far: {s}s")
-
-    # Outcome
-    if outcome_event is None:
-        lines.append("Status: still running (mid-run snapshot)")
-    elif outcome_event.type == EventType.EXECUTION_COMPLETED:
-        out = outcome_event.data.get("output", {})
-        out_str = f"Outcome: completed. Output: {str(out)[:300]}"
-        lines.append(out_str if out else "Outcome: completed.")
-    else:
-        err = outcome_event.data.get("error", "")
-        lines.append(f"Outcome: failed. Error: {str(err)[:300]}" if err else "Outcome: failed.")
-
-    # Node path (edge traversals)
-    edges = [e for e in events_chron if e.type == EventType.EDGE_TRAVERSED]
-    if edges:
-        parts = [
-            f"{e.data.get('source_node', '?')}->{e.data.get('target_node', '?')}"
-            for e in edges[-20:]
-        ]
-        lines.append(f"Node path: {', '.join(parts)}")
-
-    # Tools used
-    tool_events = [e for e in events_chron if e.type == EventType.TOOL_CALL_COMPLETED]
-    if tool_events:
-        names = [e.data.get("tool_name", "?") for e in tool_events]
-        counts = Counter(names)
-        summary = ", ".join(f"{name}×{n}" if n > 1 else name for name, n in counts.most_common())
-        lines.append(f"Tools used: {summary}")
-        # Note any tool errors
-        errors = [e for e in tool_events if e.data.get("is_error")]
-        if errors:
-            err_names = Counter(e.data.get("tool_name", "?") for e in errors)
-            lines.append(f"Tool errors: {dict(err_names)}")
-
-    # Issues
-    issue_map = {
-        EventType.NODE_STALLED: "stall",
-        EventType.NODE_TOOL_DOOM_LOOP: "doom loop",
-        EventType.CONSTRAINT_VIOLATION: "constraint violation",
-        EventType.NODE_RETRY: "retry",
-    }
-    issue_parts: list[str] = []
-    for evt_type, label in issue_map.items():
-        n = sum(1 for e in events_chron if e.type == evt_type)
-        if n:
-            issue_parts.append(f"{n} {label}(s)")
-    if issue_parts:
-        lines.append(f"Issues: {', '.join(issue_parts)}")
-
-    # Escalations to queen
-    escalations = [e for e in events_chron if e.type == EventType.ESCALATION_REQUESTED]
-    if escalations:
-        lines.append(f"Escalations to queen: {len(escalations)}")
-
-    # Final LLM output snippet (last LLM_TEXT_DELTA snapshot)
-    text_events = [e for e in reversed(events_chron) if e.type == EventType.LLM_TEXT_DELTA]
-    if text_events:
-        snapshot = text_events[0].data.get("snapshot", "") or ""
-        if snapshot:
-            lines.append(f"Final LLM output: {snapshot[-400:].strip()}")
-
-    return "\n".join(lines)
-
-
-async def consolidate_worker_run(
-    agent_name: str,
-    run_id: str,
-    outcome_event: AgentEvent | None,
-    bus: EventBus,
-    llm: Any,
-) -> None:
-    """Write (or overwrite) the digest for a worker run.
-
-    Called fire-and-forget either:
-    - After EXECUTION_COMPLETED / EXECUTION_FAILED (outcome_event set, final write)
-    - Periodically during a run on a cooldown timer (outcome_event=None, mid-run snapshot)
-
-    The digest file is always overwritten so each call produces the freshest view.
-    The final completion/failure call supersedes any mid-run snapshot.
-
-    Args:
-        agent_name:    Worker agent directory name (determines storage path).
-        run_id:        The run ID.
-        outcome_event: EXECUTION_COMPLETED or EXECUTION_FAILED event, or None for
-                       a mid-run snapshot.
-        bus:           The session EventBus (shared queen + worker).
-        llm:           LLMProvider with an acomplete() method.
-    """
-    try:
-        events = _collect_run_events(bus, run_id)
-        run_context = _build_run_context(events, outcome_event)
-        if not run_context:
-            logger.debug("worker_memory: no events for run %s, skipping digest", run_id)
-            return
-
-        is_final = outcome_event is not None
-        logger.info(
-            "worker_memory: generating %s digest for run %s ...",
-            "final" if is_final else "mid-run",
-            run_id,
-        )
-
-        from framework.agents.queen.config import default_config
-
-        resp = await llm.acomplete(
-            messages=[{"role": "user", "content": run_context}],
-            system=_DIGEST_SYSTEM,
-            max_tokens=min(default_config.max_tokens, 512),
-        )
-        digest_text = (resp.content or "").strip()
-        if not digest_text:
-            logger.warning("worker_memory: LLM returned empty digest for run %s", run_id)
-            return
-
-        path = digest_path(agent_name, run_id)
-        path.parent.mkdir(parents=True, exist_ok=True)
-
-        from framework.runtime.event_bus import EventType
-
-        ts = (outcome_event.timestamp if outcome_event else datetime.utcnow()).strftime(
-            "%Y-%m-%d %H:%M"
-        )
-        if outcome_event is None:
-            status = "running"
-        elif outcome_event.type == EventType.EXECUTION_COMPLETED:
-            status = "completed"
-        else:
-            status = "failed"
-
-        path.write_text(
-            f"# {run_id}\n\n**{ts}** | {status}\n\n{digest_text}\n",
-            encoding="utf-8",
-        )
-        logger.info(
-            "worker_memory: %s digest written for run %s (%d chars)",
-            status,
-            run_id,
-            len(digest_text),
-        )
-
-    except Exception:
-        tb = traceback.format_exc()
-        logger.exception("worker_memory: digest failed for run %s", run_id)
-        # Persist the error so it's findable without log access
-        error_path = _worker_runs_dir(agent_name) / run_id / "digest_error.txt"
-        try:
-            error_path.parent.mkdir(parents=True, exist_ok=True)
-            error_path.write_text(
-                f"run_id: {run_id}\ntime: {datetime.now().isoformat()}\n\n{tb}",
-                encoding="utf-8",
-            )
-        except Exception:
-            pass
-
-
-def read_recent_digests(agent_name: str, max_runs: int = 5) -> list[tuple[str, str]]:
-    """Return recent run digests as [(run_id, content), ...], newest first.
-
-    Args:
-        agent_name: Worker agent directory name.
-        max_runs:   Maximum number of digests to return.
-
-    Returns:
-        List of (run_id, digest_content) tuples, ordered newest first.
-    """
-    runs_dir = _worker_runs_dir(agent_name)
-    if not runs_dir.exists():
-        return []
-
-    digest_files = sorted(
-        runs_dir.glob("*/digest.md"),
-        key=lambda p: p.stat().st_mtime,
-        reverse=True,
-    )[:max_runs]
-
-    result: list[tuple[str, str]] = []
-    for f in digest_files:
-        try:
-            content = f.read_text(encoding="utf-8").strip()
-            if content:
-                result.append((f.parent.name, content))
-        except OSError:
-            continue
-    return result

core/framework/cli.py

@@ -2,18 +2,22 @@
 Command-line interface for Aden Hive.
 
 Usage:
-    hive run exports/my-agent --input '{"key": "value"}'
-    hive info exports/my-agent
-    hive validate exports/my-agent
-    hive list exports/
-    hive dispatch exports/ --input '{"key": "value"}'
-    hive shell exports/my-agent
+    hive serve                       Start the HTTP API server
+    hive open                        Start the server and open the dashboard
+    hive queen list                  List queen profiles
+    hive queen show <queen_id>       Inspect a queen profile
+    hive queen sessions <queen_id>   List a queen's sessions
+    hive colony list                 List colonies on disk
+    hive colony info <name>          Inspect a colony
+    hive colony delete <name>        Delete a colony
+    hive session list                List live sessions (use --cold for on-disk)
+    hive session stop <session_id>   Stop a live session
+    hive chat <session_id> "msg"     Send a message to a live queen
 
-Testing commands:
-    hive test-run <agent_path> --goal <goal_id>
-    hive test-debug <agent_path> <test_name>
-    hive test-list <agent_path>
-    hive test-stats <agent_path>
+Subsystems:
+    hive skill ...                   Manage skills (~/.hive/skills/)
+    hive mcp ...                     Manage MCP servers
+    hive debugger                    LLM debug log viewer
 """
 
 import argparse
@@ -21,84 +25,60 @@ import sys
 from pathlib import Path
 
 
-def _configure_paths():
-    """Auto-configure sys.path so agents in exports/ are discoverable.
+def _configure_paths() -> None:
+    """Auto-configure sys.path so the framework is importable from any cwd.
 
-    Resolves the project root by walking up from this file (framework/cli.py lives
-    inside core/framework/) or from CWD, then adds the exports/ directory to sys.path
-    if it exists. This eliminates the need for manual PYTHONPATH configuration.
+    Walks up from this file to find the project root, then ensures
+    `core/` is on sys.path so `framework.*` imports resolve when the
+    package isn't installed via `pip install -e .`.
     """
-    # Strategy 1: resolve relative to this file (works when installed via pip install -e core/)
     framework_dir = Path(__file__).resolve().parent  # core/framework/
     core_dir = framework_dir.parent  # core/
     project_root = core_dir.parent  # project root
 
-    # Strategy 2: if project_root doesn't look right, fall back to CWD
-    if not (project_root / "exports").is_dir() and not (project_root / "core").is_dir():
+    if not (project_root / "core").is_dir():
         project_root = Path.cwd()
 
-    # Add exports/ to sys.path so agents are importable as top-level packages
-    exports_dir = project_root / "exports"
-    if exports_dir.is_dir():
-        exports_str = str(exports_dir)
-        if exports_str not in sys.path:
-            sys.path.insert(0, exports_str)
-
-    # Add examples/templates/ to sys.path so template agents are importable
-    templates_dir = project_root / "examples" / "templates"
-    if templates_dir.is_dir():
-        templates_str = str(templates_dir)
-        if templates_str not in sys.path:
-            sys.path.insert(0, templates_str)
-
-    # Ensure core/ is also in sys.path (for non-editable-install scenarios)
     core_str = str(project_root / "core")
     if (project_root / "core").is_dir() and core_str not in sys.path:
         sys.path.insert(0, core_str)
 
-    # Add core/framework/agents/ so framework agents are importable as top-level packages
-    framework_agents_dir = project_root / "core" / "framework" / "agents"
-    if framework_agents_dir.is_dir():
-        fa_str = str(framework_agents_dir)
-        if fa_str not in sys.path:
-            sys.path.insert(0, fa_str)
 
-
-def main():
+def main() -> None:
     _configure_paths()
 
     parser = argparse.ArgumentParser(
         prog="hive",
-        description="Aden Hive - Build and run goal-driven agents",
+        description="Aden Hive — Queens, colonies, and live agent sessions",
     )
     parser.add_argument(
         "--model",
         default="claude-haiku-4-5-20251001",
-        help="Anthropic model to use",
+        help="Default LLM model (Anthropic ID)",
     )
 
     subparsers = parser.add_subparsers(dest="command", required=True)
 
-    # Register runner commands (run, info, validate, list, dispatch, shell)
-    from framework.runner.cli import register_commands
+    # Core commands: serve, open, queen, colony, session, chat
+    from framework.loader.cli import register_commands
 
     register_commands(subparsers)
 
-    # Register testing commands (test-run, test-debug, test-list, test-stats)
-    from framework.testing.cli import register_testing_commands
-
-    register_testing_commands(subparsers)
-
-    # Register skill commands (skill list, skill trust, ...)
+    # Skill management (~/.hive/skills/)
     from framework.skills.cli import register_skill_commands
 
     register_skill_commands(subparsers)
 
-    # Register debugger commands (debugger)
+    # LLM debug log viewer
     from framework.debugger.cli import register_debugger_commands
 
     register_debugger_commands(subparsers)
 
+    # MCP server registry
+    from framework.loader.mcp_registry_cli import register_mcp_commands
+
+    register_mcp_commands(subparsers)
+
     args = parser.parse_args()
 
     if hasattr(args, "func"):

core/framework/config.py

@@ -12,13 +12,47 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any
 
-from framework.graph.edge import DEFAULT_MAX_TOKENS
+DEFAULT_MAX_TOKENS = 8192
+
+# ---------------------------------------------------------------------------
+# Hive home directory structure
+# ---------------------------------------------------------------------------
+
+HIVE_HOME = Path.home() / ".hive"
+QUEENS_DIR = HIVE_HOME / "agents" / "queens"
+COLONIES_DIR = HIVE_HOME / "colonies"
+MEMORIES_DIR = HIVE_HOME / "memories"
+
+
+def queen_dir(queen_name: str = "default") -> Path:
+    """Return the storage directory for a named queen agent."""
+    return QUEENS_DIR / queen_name
+
+
+def colony_dir(colony_name: str) -> Path:
+    """Return the directory for a named colony."""
+    return COLONIES_DIR / colony_name
+
+
+def memory_dir(scope: str, name: str | None = None) -> Path:
+    """Return memory dir for a scope.
+
+    Examples::
+
+        memory_dir("global")                  -> ~/.hive/memories/global
+        memory_dir("colonies", "my_agent")    -> ~/.hive/memories/colonies/my_agent
+        memory_dir("agents/queens", "default")-> ~/.hive/memories/agents/queens/default
+        memory_dir("agents", "worker_name")   -> ~/.hive/memories/agents/worker_name
+    """
+    base = MEMORIES_DIR / scope
+    return base / name if name else base
+
 
 # ---------------------------------------------------------------------------
 # Low-level config file access
 # ---------------------------------------------------------------------------
 
-HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
+HIVE_CONFIG_FILE = HIVE_HOME / "configuration.json"
 
 # Hive LLM router endpoint (Anthropic-compatible).
 # litellm's Anthropic handler appends /v1/messages, so this is just the base host.
@@ -42,6 +76,48 @@ def get_hive_config() -> dict[str, Any]:
         return {}
 
 
+# ---------------------------------------------------------------------------
+# Credential store helpers (for BYOK keys)
+# ---------------------------------------------------------------------------
+
+# Provider name → credential store ID mapping
+_PROVIDER_CRED_MAP: dict[str, str] = {
+    "anthropic": "anthropic",
+    "openai": "openai",
+    "gemini": "gemini",
+    "google": "gemini",
+    "minimax": "minimax",
+    "groq": "groq",
+    "cerebras": "cerebras",
+    "openrouter": "openrouter",
+    "mistral": "mistral",
+    "together": "together",
+    "together_ai": "together",
+    "deepseek": "deepseek",
+    "kimi": "kimi",
+    "hive": "hive",
+}
+
+
+def _get_api_key_from_credential_store(provider: str) -> str | None:
+    """Look up a BYOK API key from the encrypted credential store.
+
+    Returns None if no key is found or the credential store is unavailable.
+    """
+    if not os.environ.get("HIVE_CREDENTIAL_KEY"):
+        return None
+    cred_id = _PROVIDER_CRED_MAP.get(provider.lower())
+    if not cred_id:
+        return None
+    try:
+        from framework.credentials import CredentialStore
+
+        store = CredentialStore.with_encrypted_storage()
+        return store.get(cred_id)
+    except Exception:
+        return None
+
+
 # ---------------------------------------------------------------------------
 # Derived helpers
 # ---------------------------------------------------------------------------
@@ -88,7 +164,7 @@ def get_worker_api_key() -> str | None:
     # Worker-specific subscription / env var
     if worker_llm.get("use_claude_code_subscription"):
         try:
-            from framework.runner.runner import get_claude_code_token
+            from framework.loader.agent_loader import get_claude_code_token
 
             token = get_claude_code_token()
             if token:
@@ -98,7 +174,7 @@ def get_worker_api_key() -> str | None:
 
     if worker_llm.get("use_codex_subscription"):
         try:
-            from framework.runner.runner import get_codex_token
+            from framework.loader.agent_loader import get_codex_token
 
             token = get_codex_token()
             if token:
@@ -108,7 +184,7 @@ def get_worker_api_key() -> str | None:
 
     if worker_llm.get("use_kimi_code_subscription"):
         try:
-            from framework.runner.runner import get_kimi_code_token
+            from framework.loader.agent_loader import get_kimi_code_token
 
             token = get_kimi_code_token()
             if token:
@@ -118,7 +194,7 @@ def get_worker_api_key() -> str | None:
 
     if worker_llm.get("use_antigravity_subscription"):
         try:
-            from framework.runner.runner import get_antigravity_token
+            from framework.loader.agent_loader import get_antigravity_token
 
             token = get_antigravity_token()
             if token:
@@ -174,7 +250,7 @@ def get_worker_llm_extra_kwargs() -> dict[str, Any]:
                 "User-Agent": "CodexBar",
             }
             try:
-                from framework.runner.runner import get_codex_account_id
+                from framework.loader.agent_loader import get_codex_account_id
 
                 account_id = get_codex_account_id()
                 if account_id:
@@ -186,6 +262,8 @@ def get_worker_llm_extra_kwargs() -> dict[str, Any]:
                 "store": False,
                 "allowed_openai_params": ["store"],
             }
+    if worker_llm.get("provider") == "ollama":
+        return {"num_ctx": worker_llm.get("num_ctx", 16384)}
     return {}
 
 
@@ -219,22 +297,43 @@ def get_max_context_tokens() -> int:
     return get_hive_config().get("llm", {}).get("max_context_tokens", DEFAULT_MAX_CONTEXT_TOKENS)
 
 
+def get_api_keys() -> list[str] | None:
+    """Return a list of API keys if ``api_keys`` is configured, else ``None``.
+
+    This supports key-pool rotation: configure multiple keys in
+    ``~/.hive/configuration.json`` under ``llm.api_keys`` and the
+    :class:`~framework.llm.key_pool.KeyPool` will rotate through them.
+    """
+    llm = get_hive_config().get("llm", {})
+    keys = llm.get("api_keys")
+    if keys and isinstance(keys, list) and len(keys) > 0:
+        return [k for k in keys if k]  # filter empties
+    return None
+
+
 def get_api_key() -> str | None:
     """Return the API key, supporting env var, Claude Code subscription, Codex, and ZAI Code.
 
     Priority:
+    0. Explicit key pool (``api_keys`` list) -- returns first key for
+       single-key callers; full pool available via :func:`get_api_keys`.
     1. Claude Code subscription (``use_claude_code_subscription: true``)
        reads the OAuth token from ``~/.claude/.credentials.json``.
     2. Codex subscription (``use_codex_subscription: true``)
        reads the OAuth token from macOS Keychain or ``~/.codex/auth.json``.
     3. Environment variable named in ``api_key_env_var``.
     """
+    # If an explicit key pool is configured, use the first key.
+    pool_keys = get_api_keys()
+    if pool_keys:
+        return pool_keys[0]
+
     llm = get_hive_config().get("llm", {})
 
     # Claude Code subscription: read OAuth token directly
     if llm.get("use_claude_code_subscription"):
         try:
-            from framework.runner.runner import get_claude_code_token
+            from framework.loader.agent_loader import get_claude_code_token
 
             token = get_claude_code_token()
             if token:
@@ -245,7 +344,7 @@ def get_api_key() -> str | None:
     # Codex subscription: read OAuth token from Keychain / auth.json
     if llm.get("use_codex_subscription"):
         try:
-            from framework.runner.runner import get_codex_token
+            from framework.loader.agent_loader import get_codex_token
 
             token = get_codex_token()
             if token:
@@ -256,7 +355,7 @@ def get_api_key() -> str | None:
     # Kimi Code subscription: read API key from ~/.kimi/config.toml
     if llm.get("use_kimi_code_subscription"):
         try:
-            from framework.runner.runner import get_kimi_code_token
+            from framework.loader.agent_loader import get_kimi_code_token
 
             token = get_kimi_code_token()
             if token:
@@ -267,7 +366,7 @@ def get_api_key() -> str | None:
     # Antigravity subscription: read OAuth token from accounts JSON
     if llm.get("use_antigravity_subscription"):
         try:
-            from framework.runner.runner import get_antigravity_token
+            from framework.loader.agent_loader import get_antigravity_token
 
             token = get_antigravity_token()
             if token:
@@ -278,8 +377,12 @@ def get_api_key() -> str | None:
     # Standard env-var path (covers ZAI Code and all API-key providers)
     api_key_env_var = llm.get("api_key_env_var")
     if api_key_env_var:
-        return os.environ.get(api_key_env_var)
-    return None
+        key = os.environ.get(api_key_env_var)
+        if key:
+            return key
+
+    # Credential store fallback — BYOK keys stored via the UI
+    return _get_api_key_from_credential_store(llm.get("provider", ""))
 
 
 # OAuth credentials for Antigravity are fetched from the opencode-antigravity-auth project.
@@ -302,9 +405,7 @@ def _fetch_antigravity_credentials() -> tuple[str | None, str | None]:
     import urllib.request
 
     try:
-        req = urllib.request.Request(
-            _ANTIGRAVITY_CREDENTIALS_URL, headers={"User-Agent": "Hive/1.0"}
-        )
+        req = urllib.request.Request(_ANTIGRAVITY_CREDENTIALS_URL, headers={"User-Agent": "Hive/1.0"})
         with urllib.request.urlopen(req, timeout=10) as resp:
             content = resp.read().decode("utf-8")
             id_match = re.search(r'ANTIGRAVITY_CLIENT_ID\s*=\s*"([^"]+)"', content)
@@ -420,7 +521,7 @@ def get_llm_extra_kwargs() -> dict[str, Any]:
                 "User-Agent": "CodexBar",
             }
             try:
-                from framework.runner.runner import get_codex_account_id
+                from framework.loader.agent_loader import get_codex_account_id
 
                 account_id = get_codex_account_id()
                 if account_id:
@@ -432,6 +533,11 @@ def get_llm_extra_kwargs() -> dict[str, Any]:
                 "store": False,
                 "allowed_openai_params": ["store"],
             }
+    if llm.get("provider") == "ollama":
+        # Pass num_ctx to Ollama so it doesn't silently truncate the ~9.5k Queen prompt.
+        # Ollama's default num_ctx is only 2048. We set it to 16384 here so LiteLLM
+        # passes it through as a provider-specific option.
+        return {"num_ctx": llm.get("num_ctx", 16384)}
     return {}
 
 

core/framework/credentials/__init__.py

@@ -51,6 +51,7 @@ from .key_storage import (
 from .models import (
     CredentialDecryptionError,
     CredentialError,
+    CredentialExpiredError,
     CredentialKey,
     CredentialKeyNotFoundError,
     CredentialNotFoundError,
@@ -84,6 +85,7 @@ from .template import TemplateResolver
 from .validation import (
     CredentialStatus,
     CredentialValidationResult,
+    compute_unavailable_tools,
     ensure_credential_key_env,
     validate_agent_credentials,
 )
@@ -136,6 +138,7 @@ __all__ = [
     "CredentialNotFoundError",
     "CredentialKeyNotFoundError",
     "CredentialRefreshError",
+    "CredentialExpiredError",
     "CredentialValidationError",
     "CredentialDecryptionError",
     # Key storage (bootstrap credentials)
@@ -148,6 +151,7 @@ __all__ = [
     # Validation
     "ensure_credential_key_env",
     "validate_agent_credentials",
+    "compute_unavailable_tools",
     "CredentialStatus",
     "CredentialValidationResult",
     # Interactive setup

core/framework/credentials/aden/client.py

@@ -332,9 +332,7 @@ class AdenCredentialClient:
                 last_error = e
                 if attempt < self.config.retry_attempts - 1:
                     delay = self.config.retry_delay * (2**attempt)
-                    logger.warning(
-                        f"Aden request failed (attempt {attempt + 1}), retrying in {delay}s: {e}"
-                    )
+                    logger.warning(f"Aden request failed (attempt {attempt + 1}), retrying in {delay}s: {e}")
                     time.sleep(delay)
                 else:
                     raise AdenClientError(f"Failed to connect to Aden server: {e}") from e
@@ -347,9 +345,7 @@ class AdenCredentialClient:
             ):
                 raise
 
-        raise AdenClientError(
-            f"Request failed after {self.config.retry_attempts} attempts"
-        ) from last_error
+        raise AdenClientError(f"Request failed after {self.config.retry_attempts} attempts") from last_error
 
     def list_integrations(self) -> list[AdenIntegrationInfo]:
         """

core/framework/credentials/aden/provider.py

@@ -192,9 +192,7 @@ class AdenSyncProvider(CredentialProvider):
                     f"Visit: {e.reauthorization_url or 'your Aden dashboard'}"
                 ) from e
 
-            raise CredentialRefreshError(
-                f"Failed to refresh credential '{credential.id}': {e}"
-            ) from e
+            raise CredentialRefreshError(f"Failed to refresh credential '{credential.id}': {e}") from e
 
         except AdenClientError as e:
             logger.error(f"Aden client error for '{credential.id}': {e}")
@@ -206,9 +204,7 @@ class AdenSyncProvider(CredentialProvider):
                     logger.warning(f"Aden unavailable, using cached token for '{credential.id}'")
                     return credential
 
-            raise CredentialRefreshError(
-                f"Aden server unavailable and token expired for '{credential.id}'"
-            ) from e
+            raise CredentialRefreshError(f"Aden server unavailable and token expired for '{credential.id}'") from e
 
     def validate(self, credential: CredentialObject) -> bool:
         """

core/framework/credentials/aden/storage.py

@@ -168,9 +168,7 @@ class AdenCachedStorage(CredentialStorage):
                 if rid != credential_id:
                     result = self._load_by_id(rid)
                     if result is not None:
-                        logger.info(
-                            f"Loaded credential '{credential_id}' via provider index (id='{rid}')"
-                        )
+                        logger.info(f"Loaded credential '{credential_id}' via provider index (id='{rid}')")
                         return result
 
         # Direct lookup (exact credential_id match)
@@ -199,6 +197,19 @@ class AdenCachedStorage(CredentialStorage):
         if local_cred is None:
             return None
 
+        # Skip Aden fetch for credentials not managed by Aden (BYOK credentials).
+        # Only OAuth credentials synced from Aden are in the provider index.
+        # BYOK credentials like anthropic, brave_search are local-only.
+        # Also check the _aden_managed flag on the credential itself.
+        is_aden_managed = (
+            credential_id in self._provider_index
+            or any(credential_id in ids for ids in self._provider_index.values())
+            or (local_cred is not None and local_cred.keys.get("_aden_managed") is not None)
+        )
+        if not is_aden_managed:
+            logger.debug(f"Credential '{credential_id}' is local-only, skipping Aden refresh")
+            return local_cred
+
         # Try to refresh stale local credential from Aden
         try:
             aden_cred = self._aden_provider.fetch_from_aden(credential_id)

core/framework/credentials/aden/tests/test_aden_sync.py

@@ -493,9 +493,7 @@ class TestAdenCachedStorage:
         assert loaded is not None
         assert loaded.keys["access_token"].value.get_secret_value() == "cached-token"
 
-    def test_load_from_aden_when_stale(
-        self, cached_storage, local_storage, provider, mock_client, aden_response
-    ):
+    def test_load_from_aden_when_stale(self, cached_storage, local_storage, provider, mock_client, aden_response):
         """Test load fetches from Aden when cache is stale."""
         # Create stale cached credential
         cred = CredentialObject(
@@ -521,9 +519,7 @@ class TestAdenCachedStorage:
         assert loaded is not None
         assert loaded.keys["access_token"].value.get_secret_value() == "test-access-token"
 
-    def test_load_falls_back_to_stale_when_aden_fails(
-        self, cached_storage, local_storage, provider, mock_client
-    ):
+    def test_load_falls_back_to_stale_when_aden_fails(self, cached_storage, local_storage, provider, mock_client):
         """Test load falls back to stale cache when Aden fails."""
         # Create stale cached credential
         cred = CredentialObject(

core/framework/credentials/models.py

@@ -333,6 +333,29 @@ class CredentialRefreshError(CredentialError):
     pass
 
 
+class CredentialExpiredError(CredentialError):
+    """Raised when a credential is expired and refresh has failed.
+
+    Carries the metadata an agent (or the tool runner) needs to surface a
+    reauth request to the user without having to look anything else up.
+    """
+
+    def __init__(
+        self,
+        credential_id: str,
+        message: str,
+        *,
+        provider: str | None = None,
+        alias: str | None = None,
+        help_url: str | None = None,
+    ):
+        self.credential_id = credential_id
+        self.provider = provider
+        self.alias = alias
+        self.help_url = help_url
+        super().__init__(message)
+
+
 class CredentialValidationError(CredentialError):
     """Raised when credential validation fails."""
 

core/framework/credentials/oauth2/base_provider.py

@@ -95,9 +95,7 @@ class BaseOAuth2Provider(CredentialProvider):
 
                 self._client = httpx.Client(timeout=self.config.request_timeout)
             except ImportError as e:
-                raise ImportError(
-                    "OAuth2 provider requires 'httpx'. Install with: uv pip install httpx"
-                ) from e
+                raise ImportError("OAuth2 provider requires 'httpx'. Install with: uv pip install httpx") from e
         return self._client
 
     def _close_client(self) -> None:
@@ -311,8 +309,7 @@ class BaseOAuth2Provider(CredentialProvider):
         except OAuth2Error as e:
             if e.error == "invalid_grant":
                 raise CredentialRefreshError(
-                    f"Refresh token for '{credential.id}' is invalid or revoked. "
-                    "Re-authorization required."
+                    f"Refresh token for '{credential.id}' is invalid or revoked. Re-authorization required."
                 ) from e
             raise CredentialRefreshError(f"Failed to refresh '{credential.id}': {e}") from e
 
@@ -422,9 +419,7 @@ class BaseOAuth2Provider(CredentialProvider):
         if response.status_code != 200 or "error" in response_data:
             error = response_data.get("error", "unknown_error")
             description = response_data.get("error_description", response.text)
-            raise OAuth2Error(
-                error=error, description=description, status_code=response.status_code
-            )
+            raise OAuth2Error(error=error, description=description, status_code=response.status_code)
 
         return OAuth2Token.from_token_response(response_data)
 

core/framework/credentials/oauth2/lifecycle.py

@@ -158,9 +158,7 @@ class TokenLifecycleManager:
         """
         # Run in executor to avoid blocking
         loop = asyncio.get_event_loop()
-        token = await loop.run_in_executor(
-            None, lambda: self.provider.client_credentials_grant(scopes=scopes)
-        )
+        token = await loop.run_in_executor(None, lambda: self.provider.client_credentials_grant(scopes=scopes))
 
         self._save_token_to_store(token)
         self._cached_token = token

core/framework/credentials/oauth2/zoho_provider.py

@@ -100,9 +100,7 @@ class ZohoOAuth2Provider(BaseOAuth2Provider):
         )
         super().__init__(config, provider_id="zoho_crm_oauth2")
         self._accounts_domain = base
-        self._api_domain = (
-            api_domain or os.getenv("ZOHO_API_DOMAIN", "https://www.zohoapis.com")
-        ).rstrip("/")
+        self._api_domain = (api_domain or os.getenv("ZOHO_API_DOMAIN", "https://www.zohoapis.com")).rstrip("/")
 
     @property
     def supported_types(self) -> list[CredentialType]:

core/framework/credentials/setup.py

@@ -36,7 +36,7 @@ from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
-    from framework.graph import NodeSpec
+    from framework.orchestrator import NodeSpec
 
 logger = logging.getLogger(__name__)
 
@@ -268,9 +268,7 @@ class CredentialSetupSession:
         self._print(f"{Colors.YELLOW}Initializing credential store...{Colors.NC}")
         try:
             generate_and_save_credential_key()
-            self._print(
-                f"{Colors.GREEN}✓ Encryption key saved to ~/.hive/secrets/credential_key{Colors.NC}"
-            )
+            self._print(f"{Colors.GREEN}✓ Encryption key saved to ~/.hive/secrets/credential_key{Colors.NC}")
             return True
         except Exception as e:
             self._print(f"{Colors.RED}Failed to initialize credential store: {e}{Colors.NC}")
@@ -449,9 +447,7 @@ class CredentialSetupSession:
                     logger.warning("Unexpected error exporting credential to env", exc_info=True)
                 return True
             else:
-                self._print(
-                    f"{Colors.YELLOW}⚠ {cred.credential_name} not found in Aden account.{Colors.NC}"
-                )
+                self._print(f"{Colors.YELLOW}⚠ {cred.credential_name} not found in Aden account.{Colors.NC}")
                 self._print("Please connect this integration on https://hive.adenhq.com first.")
                 return False
         except Exception as e:
@@ -533,7 +529,9 @@ class CredentialSetupSession:
 
 
 def load_agent_nodes(agent_path: str | Path) -> list:
-    """Load NodeSpec list from an agent's agent.py or agent.json.
+    """Load NodeSpec list from an agent directory.
+
+    Checks agent.json (declarative) first, then agent.py (legacy).
 
     Args:
         agent_path: Path to agent directory.
@@ -542,16 +540,28 @@ def load_agent_nodes(agent_path: str | Path) -> list:
         List of NodeSpec objects (empty list if agent can't be loaded).
     """
     agent_path = Path(agent_path)
+    agent_json_file = agent_path / "agent.json"
     agent_py = agent_path / "agent.py"
-    agent_json = agent_path / "agent.json"
 
-    if agent_py.exists():
+    if agent_json_file.exists():
+        return _load_nodes_from_json_declarative(agent_json_file)
+    elif agent_py.exists():
         return _load_nodes_from_python_agent(agent_path)
-    elif agent_json.exists():
-        return _load_nodes_from_json_agent(agent_json)
     return []
 
 
+def _load_nodes_from_json_declarative(agent_json: Path) -> list:
+    """Load nodes from a declarative JSON agent."""
+    try:
+        from framework.loader.agent_loader import load_agent_config
+
+        data = json.loads(agent_json.read_text(encoding="utf-8"))
+        graph, _ = load_agent_config(data)
+        return list(graph.nodes)
+    except Exception:
+        return []
+
+
 def _load_nodes_from_python_agent(agent_path: Path) -> list:
     """Load nodes from a Python-based agent."""
     import importlib.util
@@ -590,7 +600,7 @@ def _load_nodes_from_json_agent(agent_json: Path) -> list:
         with open(agent_json, encoding="utf-8-sig") as f:
             data = json.load(f)
 
-        from framework.graph import NodeSpec
+        from framework.orchestrator import NodeSpec
 
         nodes_data = data.get("graph", {}).get("nodes", [])
         nodes = []

core/framework/credentials/storage.py

@@ -136,8 +136,7 @@ class EncryptedFileStorage(CredentialStorage):
             from cryptography.fernet import Fernet
         except ImportError as e:
             raise ImportError(
-                "Encrypted storage requires 'cryptography'. "
-                "Install with: uv pip install cryptography"
+                "Encrypted storage requires 'cryptography'. Install with: uv pip install cryptography"
             ) from e
 
         self.base_path = Path(base_path or self.DEFAULT_PATH).expanduser()
@@ -161,6 +160,14 @@ class EncryptedFileStorage(CredentialStorage):
 
         self._fernet = Fernet(self._key)
 
+        # Rebuild the metadata index from disk if it's missing or older than
+        # the current index schema. The index is a developer-readable JSON
+        # snapshot of the encrypted store; the .enc files remain authoritative.
+        try:
+            self._maybe_rebuild_index()
+        except Exception:
+            logger.debug("Initial index rebuild failed (non-fatal)", exc_info=True)
+
     def _ensure_dirs(self) -> None:
         """Create directory structure."""
         (self.base_path / "credentials").mkdir(parents=True, exist_ok=True)
@@ -186,8 +193,8 @@ class EncryptedFileStorage(CredentialStorage):
         with open(cred_path, "wb") as f:
             f.write(encrypted)
 
-        # Update index
-        self._update_index(credential.id, "save", credential.credential_type.value)
+        # Update developer-readable index
+        self._index_upsert(credential)
         logger.debug(f"Saved encrypted credential '{credential.id}'")
 
     def load(self, credential_id: str) -> CredentialObject | None:
@@ -205,9 +212,7 @@ class EncryptedFileStorage(CredentialStorage):
             json_bytes = self._fernet.decrypt(encrypted)
             data = json.loads(json_bytes.decode("utf-8-sig"))
         except Exception as e:
-            raise CredentialDecryptionError(
-                f"Failed to decrypt credential '{credential_id}': {e}"
-            ) from e
+            raise CredentialDecryptionError(f"Failed to decrypt credential '{credential_id}': {e}") from e
 
         # Deserialize
         return self._deserialize_credential(data)
@@ -217,7 +222,7 @@ class EncryptedFileStorage(CredentialStorage):
         cred_path = self._cred_path(credential_id)
         if cred_path.exists():
             cred_path.unlink()
-            self._update_index(credential_id, "delete")
+            self._index_remove(credential_id)
             logger.debug(f"Deleted credential '{credential_id}'")
             return True
         return False
@@ -258,33 +263,151 @@ class EncryptedFileStorage(CredentialStorage):
 
         return CredentialObject.model_validate(data)
 
-    def _update_index(
-        self,
-        credential_id: str,
-        operation: str,
-        credential_type: str | None = None,
-    ) -> None:
-        """Update the metadata index."""
-        index_path = self.base_path / "metadata" / "index.json"
+    # ------------------------------------------------------------------
+    # Developer-readable metadata index
+    #
+    # The index lives at ``<base_path>/metadata/index.json`` and mirrors what
+    # is in the encrypted store at a glance: credential id, provider, alias,
+    # identity, key names, timestamps, and earliest expiry. It contains NO
+    # secret values and is safe to share when filing a bug report. The .enc
+    # files remain authoritative — the index is purely for human inspection
+    # and for cheap ``list_all()`` enumeration.
+    #
+    # Schema version is bumped whenever the entry shape changes; the store
+    # rebuilds the index from the encrypted files on load when the on-disk
+    # version is older.
+    # ------------------------------------------------------------------
 
-        if index_path.exists():
-            with open(index_path, encoding="utf-8-sig") as f:
-                index = json.load(f)
-        else:
-            index = {"credentials": {}, "version": "1.0"}
+    INDEX_VERSION = "2.0"
+    INDEX_INTERNAL_KEY_NAMES = ("_alias", "_integration_type")
 
-        if operation == "save":
-            index["credentials"][credential_id] = {
-                "updated_at": datetime.now(UTC).isoformat(),
-                "type": credential_type,
-            }
-        elif operation == "delete":
-            index["credentials"].pop(credential_id, None)
+    def _index_path(self) -> Path:
+        return self.base_path / "metadata" / "index.json"
 
-        index["last_modified"] = datetime.now(UTC).isoformat()
+    def _read_index(self) -> dict[str, Any]:
+        """Read the index from disk; return an empty skeleton if missing."""
+        path = self._index_path()
+        if not path.exists():
+            return {"version": self.INDEX_VERSION, "credentials": {}}
+        try:
+            with open(path, encoding="utf-8-sig") as f:
+                return json.load(f)
+        except Exception:
+            logger.debug("Failed to read credential index, starting fresh", exc_info=True)
+            return {"version": self.INDEX_VERSION, "credentials": {}}
 
-        with open(index_path, "w", encoding="utf-8") as f:
-            json.dump(index, f, indent=2)
+    def _write_index(self, index: dict[str, Any]) -> None:
+        """Write the index to disk with consistent envelope fields."""
+        index["version"] = self.INDEX_VERSION
+        index["store_path"] = str(self.base_path)
+        index["generated_at"] = datetime.now(UTC).isoformat()
+        path = self._index_path()
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(index, f, indent=2, sort_keys=False, default=str)
+
+    def _index_entry_for(self, credential: CredentialObject) -> dict[str, Any]:
+        """Build a single index entry from a CredentialObject (no secrets)."""
+        # Visible key names: drop internal markers like _alias / _integration_type
+        # / _identity_* so the entry shows what's actually a credential key.
+        visible_keys = [
+            name
+            for name in credential.keys.keys()
+            if name not in self.INDEX_INTERNAL_KEY_NAMES and not name.startswith("_identity_")
+        ]
+
+        # Earliest expiry across all keys (most likely the access_token).
+        earliest_expiry: datetime | None = None
+        for key in credential.keys.values():
+            if key.expires_at is None:
+                continue
+            if earliest_expiry is None or key.expires_at < earliest_expiry:
+                earliest_expiry = key.expires_at
+
+        return {
+            "credential_type": credential.credential_type.value,
+            "provider": credential.provider_type,
+            "alias": credential.alias,
+            "identity": credential.identity.to_dict(),
+            "key_names": sorted(visible_keys),
+            "created_at": credential.created_at.isoformat() if credential.created_at else None,
+            "updated_at": credential.updated_at.isoformat() if credential.updated_at else None,
+            "last_refreshed": (credential.last_refreshed.isoformat() if credential.last_refreshed else None),
+            "expires_at": earliest_expiry.isoformat() if earliest_expiry else None,
+            "auto_refresh": credential.auto_refresh,
+            "tags": list(credential.tags),
+        }
+
+    def _index_upsert(self, credential: CredentialObject) -> None:
+        """Insert or update one credential entry in the index."""
+        try:
+            index = self._read_index()
+            if index.get("version") != self.INDEX_VERSION:
+                # Old schema — rebuild from disk so we don't blend formats.
+                self._rebuild_index()
+                return
+            credentials = index.setdefault("credentials", {})
+            credentials[credential.id] = self._index_entry_for(credential)
+            self._write_index(index)
+        except Exception:
+            logger.debug("Index upsert failed (non-fatal)", exc_info=True)
+
+    def _index_remove(self, credential_id: str) -> None:
+        """Remove one credential entry from the index."""
+        try:
+            index = self._read_index()
+            if index.get("version") != self.INDEX_VERSION:
+                self._rebuild_index()
+                return
+            credentials = index.setdefault("credentials", {})
+            credentials.pop(credential_id, None)
+            self._write_index(index)
+        except Exception:
+            logger.debug("Index remove failed (non-fatal)", exc_info=True)
+
+    def _maybe_rebuild_index(self) -> None:
+        """Rebuild the index if it's missing, malformed, or on an old schema.
+
+        Called once at startup. The check is cheap — read the version field
+        and bail out if it matches. Encrypted files remain authoritative; this
+        only refreshes the developer-facing snapshot.
+        """
+        path = self._index_path()
+        if path.exists():
+            try:
+                with open(path, encoding="utf-8-sig") as f:
+                    index = json.load(f)
+                if index.get("version") == self.INDEX_VERSION:
+                    return
+            except Exception:
+                pass  # fall through to rebuild
+        self._rebuild_index()
+
+    def _rebuild_index(self) -> None:
+        """Walk the encrypted credentials directory and rewrite a fresh index."""
+        cred_dir = self.base_path / "credentials"
+        if not cred_dir.is_dir():
+            return
+
+        entries: dict[str, Any] = {}
+        for cred_file in sorted(cred_dir.glob("*.enc")):
+            credential_id = cred_file.stem
+            try:
+                cred = self.load(credential_id)
+            except Exception:
+                logger.debug(
+                    "Failed to load %s during index rebuild — skipping",
+                    credential_id,
+                    exc_info=True,
+                )
+                continue
+            if cred is None:
+                continue
+            entries[cred.id] = self._index_entry_for(cred)
+
+        index = {"credentials": entries}
+        self._write_index(index)
+        logger.info("Rebuilt credential index with %d entries", len(entries))
 
 
 class EnvVarStorage(CredentialStorage):
@@ -351,8 +474,7 @@ class EnvVarStorage(CredentialStorage):
     def save(self, credential: CredentialObject) -> None:
         """Cannot save to environment variables at runtime."""
         raise NotImplementedError(
-            "EnvVarStorage is read-only. Set environment variables "
-            "externally or use EncryptedFileStorage."
+            "EnvVarStorage is read-only. Set environment variables externally or use EncryptedFileStorage."
         )
 
     def load(self, credential_id: str) -> CredentialObject | None:
@@ -372,9 +494,7 @@ class EnvVarStorage(CredentialStorage):
 
     def delete(self, credential_id: str) -> bool:
         """Cannot delete environment variables at runtime."""
-        raise NotImplementedError(
-            "EnvVarStorage is read-only. Unset environment variables externally."
-        )
+        raise NotImplementedError("EnvVarStorage is read-only. Unset environment variables externally.")
 
     def list_all(self) -> list[str]:
         """List credentials that are available in environment."""

core/framework/credentials/store.py

@@ -19,6 +19,7 @@ from typing import Any
 from pydantic import SecretStr
 
 from .models import (
+    CredentialExpiredError,
     CredentialKey,
     CredentialObject,
     CredentialRefreshError,
@@ -123,9 +124,7 @@ class CredentialStore:
         """
         return self._providers.get(provider_id)
 
-    def get_provider_for_credential(
-        self, credential: CredentialObject
-    ) -> CredentialProvider | None:
+    def get_provider_for_credential(self, credential: CredentialObject) -> CredentialProvider | None:
         """
         Get the appropriate provider for a credential.
 
@@ -177,6 +176,8 @@ class CredentialStore:
         self,
         credential_id: str,
         refresh_if_needed: bool = True,
+        *,
+        raise_on_refresh_failure: bool = False,
     ) -> CredentialObject | None:
         """
         Get a credential by ID.
@@ -184,6 +185,11 @@ class CredentialStore:
         Args:
             credential_id: The credential identifier
             refresh_if_needed: If True, refresh expired credentials
+            raise_on_refresh_failure: If True, raise ``CredentialExpiredError``
+                when refresh fails instead of silently returning the stale
+                credential. Tool-execution call sites should pass True so the
+                agent gets a structured "reauth needed" signal rather than a
+                later 401 from the provider.
 
         Returns:
             CredentialObject or None if not found
@@ -193,7 +199,7 @@ class CredentialStore:
             cached = self._get_from_cache(credential_id)
             if cached is not None:
                 if refresh_if_needed and self._should_refresh(cached):
-                    return self._refresh_credential(cached)
+                    return self._refresh_credential(cached, raise_on_failure=raise_on_refresh_failure)
                 return cached
 
             # Load from storage
@@ -203,30 +209,42 @@ class CredentialStore:
 
             # Refresh if needed
             if refresh_if_needed and self._should_refresh(credential):
-                credential = self._refresh_credential(credential)
+                credential = self._refresh_credential(credential, raise_on_failure=raise_on_refresh_failure)
 
             # Cache
             self._add_to_cache(credential)
 
             return credential
 
-    def get_key(self, credential_id: str, key_name: str) -> str | None:
+    def get_key(
+        self,
+        credential_id: str,
+        key_name: str,
+        *,
+        raise_on_refresh_failure: bool = False,
+    ) -> str | None:
         """
         Convenience method to get a specific key value.
 
         Args:
             credential_id: The credential identifier
             key_name: The key within the credential
+            raise_on_refresh_failure: See ``get_credential``.
 
         Returns:
             The key value or None if not found
         """
-        credential = self.get_credential(credential_id)
+        credential = self.get_credential(credential_id, raise_on_refresh_failure=raise_on_refresh_failure)
         if credential is None:
             return None
         return credential.get_key(key_name)
 
-    def get(self, credential_id: str) -> str | None:
+    def get(
+        self,
+        credential_id: str,
+        *,
+        raise_on_refresh_failure: bool = False,
+    ) -> str | None:
         """
         Legacy compatibility: get the primary key value.
 
@@ -235,11 +253,12 @@ class CredentialStore:
 
         Args:
             credential_id: The credential identifier
+            raise_on_refresh_failure: See ``get_credential``.
 
         Returns:
             The primary key value or None
         """
-        credential = self.get_credential(credential_id)
+        credential = self.get_credential(credential_id, raise_on_refresh_failure=raise_on_refresh_failure)
         if credential is None:
             return None
         return credential.get_default_key()
@@ -510,8 +529,20 @@ class CredentialStore:
 
         return provider.should_refresh(credential)
 
-    def _refresh_credential(self, credential: CredentialObject) -> CredentialObject:
-        """Refresh a credential using its provider."""
+    def _refresh_credential(
+        self,
+        credential: CredentialObject,
+        *,
+        raise_on_failure: bool = False,
+    ) -> CredentialObject:
+        """Refresh a credential using its provider.
+
+        When ``raise_on_failure`` is True, a refresh failure raises
+        ``CredentialExpiredError`` carrying provider/alias/help_url metadata
+        for the caller (typically the tool runner) to surface a reauth
+        request. Otherwise, the stale credential is returned to preserve
+        legacy best-effort behavior.
+        """
         provider = self.get_provider_for_credential(credential)
         if provider is None:
             logger.warning(f"No provider found for credential '{credential.id}'")
@@ -530,6 +561,16 @@ class CredentialStore:
 
         except CredentialRefreshError as e:
             logger.error(f"Failed to refresh credential '{credential.id}': {e}")
+            if raise_on_failure:
+                raise CredentialExpiredError(
+                    credential_id=credential.id,
+                    message=(
+                        f"OAuth token for '{credential.id}' is expired and "
+                        f"refresh failed: {e}. Reauthorization required."
+                    ),
+                    provider=credential.provider_type,
+                    alias=credential.alias,
+                ) from e
             return credential
 
     def refresh_credential(self, credential_id: str) -> CredentialObject | None:

core/framework/credentials/template.py

@@ -88,9 +88,7 @@ class TemplateResolver:
             if key_name:
                 value = credential.get_key(key_name)
                 if value is None:
-                    raise CredentialKeyNotFoundError(
-                        f"Key '{key_name}' not found in credential '{cred_id}'"
-                    )
+                    raise CredentialKeyNotFoundError(f"Key '{key_name}' not found in credential '{cred_id}'")
             else:
                 # Use default key
                 value = credential.get_default_key()
@@ -126,9 +124,7 @@ class TemplateResolver:
             ... })
             {"Authorization": "Bearer ghp_xxx", "X-API-Key": "BSAKxxx"}
         """
-        return {
-            key: self.resolve(value, fail_on_missing) for key, value in header_templates.items()
-        }
+        return {key: self.resolve(value, fail_on_missing) for key, value in header_templates.items()}
 
     def resolve_params(
         self,

core/framework/credentials/tests/test_credential_store.py

@@ -130,9 +130,7 @@ class TestCredentialObject:
         # With access_token
         cred2 = CredentialObject(
             id="test",
-            keys={
-                "access_token": CredentialKey(name="access_token", value=SecretStr("token-value"))
-            },
+            keys={"access_token": CredentialKey(name="access_token", value=SecretStr("token-value"))},
         )
         assert cred2.get_default_key() == "token-value"
 
@@ -297,9 +295,7 @@ class TestEncryptedFileStorage:
         key = Fernet.generate_key().decode()
         with patch.dict(os.environ, {"HIVE_CREDENTIAL_KEY": key}):
             storage = EncryptedFileStorage(temp_dir)
-            cred = CredentialObject(
-                id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))}
-            )
+            cred = CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))})
             storage.save(cred)
 
             # Create new storage instance with same key
@@ -330,18 +326,10 @@ class TestCompositeStorage:
     def test_read_from_primary(self):
         """Test reading from primary storage."""
         primary = InMemoryStorage()
-        primary.save(
-            CredentialObject(
-                id="test", keys={"k": CredentialKey(name="k", value=SecretStr("primary"))}
-            )
-        )
+        primary.save(CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("primary"))}))
 
         fallback = InMemoryStorage()
-        fallback.save(
-            CredentialObject(
-                id="test", keys={"k": CredentialKey(name="k", value=SecretStr("fallback"))}
-            )
-        )
+        fallback.save(CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("fallback"))}))
 
         storage = CompositeStorage(primary, [fallback])
         cred = storage.load("test")
@@ -353,11 +341,7 @@ class TestCompositeStorage:
         """Test fallback when credential not in primary."""
         primary = InMemoryStorage()
         fallback = InMemoryStorage()
-        fallback.save(
-            CredentialObject(
-                id="test", keys={"k": CredentialKey(name="k", value=SecretStr("fallback"))}
-            )
-        )
+        fallback.save(CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("fallback"))}))
 
         storage = CompositeStorage(primary, [fallback])
         cred = storage.load("test")
@@ -393,9 +377,7 @@ class TestStaticProvider:
     def test_refresh_returns_unchanged(self):
         """Test that refresh returns credential unchanged."""
         provider = StaticProvider()
-        cred = CredentialObject(
-            id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))}
-        )
+        cred = CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))})
 
         refreshed = provider.refresh(cred)
         assert refreshed.get_key("k") == "v"
@@ -403,9 +385,7 @@ class TestStaticProvider:
     def test_validate_with_keys(self):
         """Test validation with keys present."""
         provider = StaticProvider()
-        cred = CredentialObject(
-            id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))}
-        )
+        cred = CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))})
 
         assert provider.validate(cred)
 
@@ -606,9 +586,7 @@ class TestCredentialStore:
         storage = InMemoryStorage()
         store = CredentialStore(storage=storage, cache_ttl_seconds=60)
 
-        storage.save(
-            CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))})
-        )
+        storage.save(CredentialObject(id="test", keys={"k": CredentialKey(name="k", value=SecretStr("v"))}))
 
         # First load
         store.get_credential("test")
@@ -686,9 +664,7 @@ class TestOAuth2Module:
         from core.framework.credentials.oauth2 import OAuth2Config, TokenPlacement
 
         # Valid config
-        config = OAuth2Config(
-            token_url="https://example.com/token", client_id="id", client_secret="secret"
-        )
+        config = OAuth2Config(token_url="https://example.com/token", client_id="id", client_secret="secret")
         assert config.token_url == "https://example.com/token"
 
         # Missing token_url

core/framework/credentials/validation.py

@@ -160,15 +160,9 @@ class CredentialValidationResult:
         if aden_nc:
             if missing or invalid:
                 lines.append("")
-            lines.append(
-                "Aden integrations not connected "
-                "(ADEN_API_KEY is set but OAuth tokens unavailable):\n"
-            )
+            lines.append("Aden integrations not connected (ADEN_API_KEY is set but OAuth tokens unavailable):\n")
             for c in aden_nc:
-                lines.append(
-                    f"  {c.env_var} for {_label(c)}"
-                    f"\n    Connect this integration at hive.adenhq.com first."
-                )
+                lines.append(f"  {c.env_var} for {_label(c)}\n    Connect this integration at hive.adenhq.com first.")
         lines.append("\nIf you've already set up credentials, restart your terminal to load them.")
         return "\n".join(lines)
 
@@ -236,6 +230,45 @@ def _presync_aden_tokens(credential_specs: dict, *, force: bool = False) -> None
             )
 
 
+def compute_unavailable_tools(nodes: list) -> tuple[set[str], list[str]]:
+    """Return (tool_names_to_drop, human_messages).
+
+    Runs credential validation *without* raising, collects every tool
+    bound to a failed credential (missing / invalid / Aden-not-connected
+    and no alternative provider available), and returns the set of tool
+    names that should be silently dropped from the worker's effective
+    tool list.
+
+    Use this at every worker-spawn preflight so missing credentials
+    filter tools out of the graph instead of hard-failing the whole
+    spawn. Only affects non-MCP tools — the MCP admission gate
+    (``_build_mcp_admission_gate``) already handles MCP tools at
+    registration time.
+    """
+    try:
+        result = validate_agent_credentials(nodes, verify=False, raise_on_error=False)
+    except Exception as exc:
+        logger.debug("compute_unavailable_tools: validation raised: %s", exc)
+        return set(), []
+
+    drop: set[str] = set()
+    messages: list[str] = []
+    for status in result.failed:
+        if not status.tools:
+            continue
+        drop.update(status.tools)
+        reason = "missing"
+        if status.aden_not_connected:
+            reason = "aden_not_connected"
+        elif status.available and status.valid is False:
+            reason = "invalid"
+        messages.append(
+            f"{status.env_var} ({reason}) → drops {len(status.tools)} tool(s): "
+            f"{', '.join(status.tools[:6])}" + (f" +{len(status.tools) - 6} more" if len(status.tools) > 6 else "")
+        )
+    return drop, messages
+
+
 def validate_agent_credentials(
     nodes: list,
     quiet: bool = False,
@@ -292,9 +325,7 @@ def validate_agent_credentials(
     if os.environ.get("ADEN_API_KEY"):
         _presync_aden_tokens(CREDENTIAL_SPECS, force=force_refresh)
 
-    env_mapping = {
-        (spec.credential_id or name): spec.env_var for name, spec in CREDENTIAL_SPECS.items()
-    }
+    env_mapping = {(spec.credential_id or name): spec.env_var for name, spec in CREDENTIAL_SPECS.items()}
     env_storage = EnvVarStorage(env_mapping=env_mapping)
     if os.environ.get("HIVE_CREDENTIAL_KEY"):
         storage = CompositeStorage(primary=env_storage, fallbacks=[EncryptedFileStorage()])
@@ -328,12 +359,7 @@ def validate_agent_credentials(
         available = store.is_available(cred_id)
 
         # Aden-not-connected: ADEN_API_KEY set, Aden-only cred, but integration missing
-        is_aden_nc = (
-            not available
-            and has_aden_key
-            and spec.aden_supported
-            and not spec.direct_api_key_supported
-        )
+        is_aden_nc = not available and has_aden_key and spec.aden_supported and not spec.direct_api_key_supported
 
         status = CredentialStatus(
             credential_name=cred_name,
@@ -451,9 +477,7 @@ def validate_agent_credentials(
                         identity_data = result.details.get("identity")
                         if identity_data and isinstance(identity_data, dict):
                             try:
-                                cred_obj = store.get_credential(
-                                    status.credential_id, refresh_if_needed=False
-                                )
+                                cred_obj = store.get_credential(status.credential_id, refresh_if_needed=False)
                                 if cred_obj:
                                     cred_obj.set_identity(**identity_data)
                                     store.save_credential(cred_obj)

core/framework/graph/__init__.py

@@ -1,59 +0,0 @@
-"""Graph structures: Goals, Nodes, Edges, and Execution."""
-
-from framework.graph.client_io import (
-    ActiveNodeClientIO,
-    ClientIOGateway,
-    InertNodeClientIO,
-    NodeClientIO,
-)
-from framework.graph.context_handoff import ContextHandoff, HandoffContext
-from framework.graph.conversation import ConversationStore, Message, NodeConversation
-from framework.graph.edge import DEFAULT_MAX_TOKENS, EdgeCondition, EdgeSpec, GraphSpec
-from framework.graph.event_loop_node import (
-    EventLoopNode,
-    JudgeProtocol,
-    JudgeVerdict,
-    LoopConfig,
-    OutputAccumulator,
-)
-from framework.graph.executor import GraphExecutor
-from framework.graph.goal import Constraint, Goal, GoalStatus, SuccessCriterion
-from framework.graph.node import NodeContext, NodeProtocol, NodeResult, NodeSpec
-
-__all__ = [
-    # Goal
-    "Goal",
-    "SuccessCriterion",
-    "Constraint",
-    "GoalStatus",
-    # Node
-    "NodeSpec",
-    "NodeContext",
-    "NodeResult",
-    "NodeProtocol",
-    # Edge
-    "EdgeSpec",
-    "EdgeCondition",
-    "GraphSpec",
-    "DEFAULT_MAX_TOKENS",
-    # Executor
-    "GraphExecutor",
-    # Conversation
-    "NodeConversation",
-    "ConversationStore",
-    "Message",
-    # Event Loop
-    "EventLoopNode",
-    "LoopConfig",
-    "OutputAccumulator",
-    "JudgeProtocol",
-    "JudgeVerdict",
-    # Context Handoff
-    "ContextHandoff",
-    "HandoffContext",
-    # Client I/O
-    "NodeClientIO",
-    "ActiveNodeClientIO",
-    "InertNodeClientIO",
-    "ClientIOGateway",
-]

core/framework/graph/gcu.py

@@ -1,129 +0,0 @@
-"""GCU (browser automation) node type constants.
-
-A ``gcu`` node is an ``event_loop`` node with two automatic enhancements:
-1. A canonical browser best-practices system prompt is prepended.
-2. All tools from the GCU MCP server are auto-included.
-
-No new ``NodeProtocol`` subclass — the ``gcu`` type is purely a declarative
-signal processed by the runner and executor at setup time.
-"""
-
-# ---------------------------------------------------------------------------
-# MCP server identity
-# ---------------------------------------------------------------------------
-
-GCU_SERVER_NAME = "gcu-tools"
-"""Name used to identify the GCU MCP server in ``mcp_servers.json``."""
-
-GCU_MCP_SERVER_CONFIG: dict = {
-    "name": GCU_SERVER_NAME,
-    "transport": "stdio",
-    "command": "uv",
-    "args": ["run", "python", "-m", "gcu.server", "--stdio"],
-    "cwd": "../../tools",
-    "description": "GCU tools for browser automation",
-}
-"""Default stdio config for the GCU MCP server (relative to exports/<agent>/)."""
-
-# ---------------------------------------------------------------------------
-# Browser best-practices system prompt
-# ---------------------------------------------------------------------------
-
-GCU_BROWSER_SYSTEM_PROMPT = """\
-# Browser Automation Best Practices
-
-Follow these rules for reliable, efficient browser interaction.
-
-## Reading Pages
-- ALWAYS prefer `browser_snapshot` over `browser_get_text("body")`
-  — it returns a compact ~1-5 KB accessibility tree vs 100+ KB of raw HTML.
-- Interaction tools (`browser_click`, `browser_type`, `browser_fill`,
-  `browser_scroll`, etc.) return a page snapshot automatically in their
-  result. Use it to decide your next action — do NOT call
-  `browser_snapshot` separately after every action.
-  Only call `browser_snapshot` when you need a fresh view without
-  performing an action, or after setting `auto_snapshot=false`.
-- Do NOT use `browser_screenshot` to read text — use
-  `browser_snapshot` for that (compact, searchable, fast).
-- DO use `browser_screenshot` when you need visual context:
-  charts, images, canvas elements, layout verification, or when
-  the snapshot doesn't capture what you need.
-- Only fall back to `browser_get_text` for extracting specific
-  small elements by CSS selector.
-
-## Navigation & Waiting
-- `browser_navigate` and `browser_open` already wait for the page to
-  load (`domcontentloaded`). Do NOT call `browser_wait` with no
-  arguments after navigation — it wastes time.
-  Only use `browser_wait` when you need a *specific element* or *text*
-  to appear (pass `selector` or `text`).
-- NEVER re-navigate to the same URL after scrolling
-  — this resets your scroll position and loses loaded content.
-
-## Scrolling
-- Use large scroll amounts ~2000 when loading more content
-  — sites like twitter and linkedin have lazy loading for paging.
-- The scroll result includes a snapshot automatically — no need to call
-  `browser_snapshot` separately.
-
-## Batching Actions
-- You can call multiple tools in a single turn — they execute in parallel.
-  ALWAYS batch independent actions together. Examples:
-  - Fill multiple form fields in one turn.
-  - Navigate + snapshot in one turn.
-  - Click + scroll if targeting different elements.
-- When batching, set `auto_snapshot=false` on all but the last action
-  to avoid redundant snapshots.
-- Aim for 3-5 tool calls per turn minimum. One tool call per turn is
-  wasteful.
-
-## Error Recovery
-- If a tool fails, retry once with the same approach.
-- If it fails a second time, STOP retrying and switch approach.
-- If `browser_snapshot` fails → try `browser_get_text` with a
-  specific small selector as fallback.
-- If `browser_open` fails or page seems stale → `browser_stop`,
-  then `browser_start`, then retry.
-
-## Tab Management
-
-**Close tabs as soon as you are done with them** — not only at the end of the task.
-After reading or extracting data from a tab, close it immediately.
-
-**Decision rules:**
-- Finished reading/extracting from a tab? → `browser_close(target_id=...)`
-- Completed a multi-tab workflow? → `browser_close_finished()` to clean up all your tabs
-- More than 3 tabs open? → stop and close finished ones before opening more
-- Popup appeared that you didn't need? → close it immediately
-
-**Origin awareness:** `browser_tabs` returns an `origin` field for each tab:
-- `"agent"` — you opened it; you own it; close it when done
-- `"popup"` — opened by a link or script; close after extracting what you need
-- `"startup"` or `"user"` — leave these alone unless the task requires it
-
-**Cleanup tools:**
-- `browser_close(target_id=...)` — close one specific tab
-- `browser_close_finished()` — close all your agent/popup tabs (safe: leaves startup/user tabs)
-- `browser_close_all()` — close everything except the active tab (use only for full reset)
-
-**Multi-tab workflow pattern:**
-1. Open background tabs with `browser_open(url=..., background=true)` to stay on current tab
-2. Process each tab and close it with `browser_close` when done
-3. When the full workflow completes, call `browser_close_finished()` to confirm cleanup
-4. Check `browser_tabs` at any point — it shows `origin` and `age_seconds` per tab
-
-Never accumulate tabs. Treat every tab you open as a resource you must free.
-
-## Login & Auth Walls
-- If you see a "Log in" or "Sign up" prompt instead of expected
-  content, report the auth wall immediately — do NOT attempt to log in.
-- Check for cookie consent banners and dismiss them if they block content.
-
-## Efficiency
-- Minimize tool calls — combine actions where possible.
-- When a snapshot result is saved to a spillover file, use
-  `run_command` with grep to extract specific data rather than
-  re-reading the full file.
-- Call `set_output` in the same turn as your last browser action
-  when possible — don't waste a turn.
-"""

core/framework/graph/prompt_composer.py

@@ -1,373 +0,0 @@
-"""Prompt composition for continuous agent mode.
-
-Composes the three-layer system prompt (onion model) and generates
-transition markers inserted into the conversation at phase boundaries.
-
-Layer 1 — Identity (static, defined at agent level, never changes):
-  "You are a thorough research agent. You prefer clarity over jargon..."
-
-Layer 2 — Narrative (auto-generated from conversation/memory state):
-  "We've finished scoping the project. The user wants to focus on..."
-
-Layer 3 — Focus (per-node system_prompt, reframed as focus directive):
-  "Your current attention: synthesize findings into a report..."
-"""
-
-from __future__ import annotations
-
-import logging
-from datetime import datetime
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from framework.graph.edge import GraphSpec
-    from framework.graph.node import NodeSpec, SharedMemory
-
-logger = logging.getLogger(__name__)
-
-# Injected into every worker node's system prompt so the LLM understands
-# it is one step in a multi-node pipeline and should not overreach.
-EXECUTION_SCOPE_PREAMBLE = (
-    "EXECUTION SCOPE: You are one node in a multi-step workflow graph. "
-    "Focus ONLY on the task described in your instructions below. "
-    "Call set_output() for each of your declared output keys, then stop. "
-    "Do NOT attempt work that belongs to other nodes — the framework "
-    "routes data between nodes automatically."
-)
-
-
-def _with_datetime(prompt: str) -> str:
-    """Append current datetime with local timezone to a system prompt."""
-    local = datetime.now().astimezone()
-    stamp = f"Current date and time: {local.strftime('%Y-%m-%d %H:%M %Z (UTC%z)')}"
-    return f"{prompt}\n\n{stamp}" if prompt else stamp
-
-
-def build_accounts_prompt(
-    accounts: list[dict[str, Any]],
-    tool_provider_map: dict[str, str] | None = None,
-    node_tool_names: list[str] | None = None,
-) -> str:
-    """Build a prompt section describing connected accounts.
-
-    When tool_provider_map is provided, produces structured output grouped
-    by provider with tool mapping, so the LLM knows which ``account`` value
-    to pass to which tool.
-
-    When node_tool_names is also provided, filters to only show providers
-    whose tools overlap with the node's tool list.
-
-    Args:
-        accounts: List of account info dicts from
-            CredentialStoreAdapter.get_all_account_info().
-        tool_provider_map: Mapping of tool_name -> provider_name
-            (e.g. {"gmail_list_messages": "google"}).
-        node_tool_names: Tool names available to the current node.
-            When provided, only providers with matching tools are shown.
-
-    Returns:
-        Formatted accounts block, or empty string if no accounts.
-    """
-    if not accounts:
-        return ""
-
-    # Flat format (backward compat) when no tool mapping provided
-    if tool_provider_map is None:
-        lines = [
-            "Connected accounts (use the alias as the `account` parameter "
-            "when calling tools to target a specific account):"
-        ]
-        for acct in accounts:
-            provider = acct.get("provider", "unknown")
-            alias = acct.get("alias", "unknown")
-            identity = acct.get("identity", {})
-            detail_parts = [f"{k}: {v}" for k, v in identity.items() if v]
-            detail = f" ({', '.join(detail_parts)})" if detail_parts else ""
-            lines.append(f"- {provider}/{alias}{detail}")
-        return "\n".join(lines)
-
-    # --- Structured format: group by provider with tool mapping ---
-
-    # Invert tool_provider_map to provider -> [tools]
-    provider_tools: dict[str, list[str]] = {}
-    for tool_name, provider in tool_provider_map.items():
-        provider_tools.setdefault(provider, []).append(tool_name)
-
-    # Filter to relevant providers based on node tools
-    node_tool_set = set(node_tool_names) if node_tool_names else None
-
-    # Group accounts by provider
-    provider_accounts: dict[str, list[dict[str, Any]]] = {}
-    for acct in accounts:
-        provider = acct.get("provider", "unknown")
-        provider_accounts.setdefault(provider, []).append(acct)
-
-    sections: list[str] = ["Connected accounts:"]
-
-    for provider, acct_list in provider_accounts.items():
-        tools_for_provider = sorted(provider_tools.get(provider, []))
-
-        # If node tools specified, only show providers with overlapping tools
-        if node_tool_set is not None:
-            relevant_tools = [t for t in tools_for_provider if t in node_tool_set]
-            if not relevant_tools:
-                continue
-            tools_for_provider = relevant_tools
-
-        # Local-only providers: tools read from env vars, no account= routing
-        all_local = all(a.get("source") == "local" for a in acct_list)
-
-        # Provider header with tools
-        display_name = provider.replace("_", " ").title()
-        if tools_for_provider and not all_local:
-            tools_str = ", ".join(tools_for_provider)
-            sections.append(f'\n{display_name} (use account="<alias>" with: {tools_str}):')
-        elif tools_for_provider and all_local:
-            tools_str = ", ".join(tools_for_provider)
-            sections.append(f"\n{display_name} (tools: {tools_str}):")
-        else:
-            sections.append(f"\n{display_name}:")
-
-        # Account entries
-        for acct in acct_list:
-            alias = acct.get("alias", "unknown")
-            identity = acct.get("identity", {})
-            detail_parts = [f"{k}: {v}" for k, v in identity.items() if v]
-            detail = f" ({', '.join(detail_parts)})" if detail_parts else ""
-            source_tag = " [local]" if acct.get("source") == "local" else ""
-            sections.append(f"  - {provider}/{alias}{detail}{source_tag}")
-
-    # If filtering removed all providers, return empty
-    if len(sections) <= 1:
-        return ""
-
-    return "\n".join(sections)
-
-
-def compose_system_prompt(
-    identity_prompt: str | None,
-    focus_prompt: str | None,
-    narrative: str | None = None,
-    accounts_prompt: str | None = None,
-    skills_catalog_prompt: str | None = None,
-    protocols_prompt: str | None = None,
-    execution_preamble: str | None = None,
-    node_type_preamble: str | None = None,
-) -> str:
-    """Compose the multi-layer system prompt.
-
-    Args:
-        identity_prompt: Layer 1 — static agent identity (from GraphSpec).
-        focus_prompt: Layer 3 — per-node focus directive (from NodeSpec.system_prompt).
-        narrative: Layer 2 — auto-generated from conversation state.
-        accounts_prompt: Connected accounts block (sits between identity and narrative).
-        skills_catalog_prompt: Available skills catalog XML (Agent Skills standard).
-        protocols_prompt: Default skill operational protocols section.
-        execution_preamble: EXECUTION_SCOPE_PREAMBLE for worker nodes
-            (prepended before focus so the LLM knows its pipeline scope).
-        node_type_preamble: Node-type-specific preamble, e.g. GCU browser
-            best-practices prompt (prepended before focus).
-
-    Returns:
-        Composed system prompt with all layers present, plus current datetime.
-    """
-    parts: list[str] = []
-
-    # Layer 1: Identity (always first, anchors the personality)
-    if identity_prompt:
-        parts.append(identity_prompt)
-
-    # Accounts (semi-static, deployment-specific)
-    if accounts_prompt:
-        parts.append(f"\n{accounts_prompt}")
-
-    # Skills catalog (discovered skills available for activation)
-    if skills_catalog_prompt:
-        parts.append(f"\n{skills_catalog_prompt}")
-
-    # Operational protocols (default skill behavioral guidance)
-    if protocols_prompt:
-        parts.append(f"\n{protocols_prompt}")
-
-    # Layer 2: Narrative (what's happened so far)
-    if narrative:
-        parts.append(f"\n--- Context (what has happened so far) ---\n{narrative}")
-
-    # Execution scope preamble (worker nodes — tells the LLM it is one
-    # step in a multi-node pipeline and should not overreach)
-    if execution_preamble:
-        parts.append(f"\n{execution_preamble}")
-
-    # Node-type preamble (e.g. GCU browser best-practices)
-    if node_type_preamble:
-        parts.append(f"\n{node_type_preamble}")
-
-    # Layer 3: Focus (current phase directive)
-    if focus_prompt:
-        parts.append(f"\n--- Current Focus ---\n{focus_prompt}")
-
-    return _with_datetime("\n".join(parts) if parts else "")
-
-
-def build_narrative(
-    memory: SharedMemory,
-    execution_path: list[str],
-    graph: GraphSpec,
-) -> str:
-    """Build Layer 2 (narrative) from structured state.
-
-    Deterministic — no LLM call. Reads SharedMemory and execution path
-    to describe what has happened so far. Cheap and fast.
-
-    Args:
-        memory: Current shared memory state.
-        execution_path: List of node IDs visited so far.
-        graph: Graph spec (for node names/descriptions).
-
-    Returns:
-        Narrative string describing the session state.
-    """
-    parts: list[str] = []
-
-    # Describe execution path
-    if execution_path:
-        phase_descriptions: list[str] = []
-        for node_id in execution_path:
-            node_spec = graph.get_node(node_id)
-            if node_spec:
-                phase_descriptions.append(f"- {node_spec.name}: {node_spec.description}")
-            else:
-                phase_descriptions.append(f"- {node_id}")
-        parts.append("Phases completed:\n" + "\n".join(phase_descriptions))
-
-    # Describe key memory values (skip very long values)
-    all_memory = memory.read_all()
-    if all_memory:
-        memory_lines: list[str] = []
-        for key, value in all_memory.items():
-            if value is None:
-                continue
-            val_str = str(value)
-            if len(val_str) > 200:
-                val_str = val_str[:200] + "..."
-            memory_lines.append(f"- {key}: {val_str}")
-        if memory_lines:
-            parts.append("Current state:\n" + "\n".join(memory_lines))
-
-    return "\n\n".join(parts) if parts else ""
-
-
-def build_transition_marker(
-    previous_node: NodeSpec,
-    next_node: NodeSpec,
-    memory: SharedMemory,
-    cumulative_tool_names: list[str],
-    data_dir: Path | str | None = None,
-    adapt_content: str | None = None,
-) -> str:
-    """Build a 'State of the World' transition marker.
-
-    Inserted into the conversation as a user message at phase boundaries.
-    Gives the LLM full situational awareness: what happened, what's stored,
-    what tools are available, and what to focus on next.
-
-    Args:
-        previous_node: NodeSpec of the phase just completed.
-        next_node: NodeSpec of the phase about to start.
-        memory: Current shared memory state.
-        cumulative_tool_names: All tools available (cumulative set).
-        data_dir: Path to spillover data directory.
-        adapt_content: Agent working memory (adapt.md) content.
-
-    Returns:
-        Transition marker message text.
-    """
-    sections: list[str] = []
-
-    # Header
-    sections.append(f"--- PHASE TRANSITION: {previous_node.name} → {next_node.name} ---")
-
-    # What just completed
-    sections.append(f"\nCompleted: {previous_node.name}")
-    sections.append(f"  {previous_node.description}")
-
-    # Outputs in memory — use file references for large values so the
-    # next node loads full data from disk instead of seeing truncated
-    # inline previews that look deceptively complete.
-    all_memory = memory.read_all()
-    if all_memory:
-        memory_lines: list[str] = []
-        for key, value in all_memory.items():
-            if value is None:
-                continue
-            val_str = str(value)
-            if len(val_str) > 300 and data_dir:
-                # Auto-spill large transition values to data files
-                import json as _json
-
-                data_path = Path(data_dir)
-                data_path.mkdir(parents=True, exist_ok=True)
-                ext = ".json" if isinstance(value, (dict, list)) else ".txt"
-                filename = f"output_{key}{ext}"
-                try:
-                    write_content = (
-                        _json.dumps(value, indent=2, ensure_ascii=False)
-                        if isinstance(value, (dict, list))
-                        else str(value)
-                    )
-                    (data_path / filename).write_text(write_content, encoding="utf-8")
-                    file_size = (data_path / filename).stat().st_size
-                    val_str = (
-                        f"[Saved to '{filename}' ({file_size:,} bytes). "
-                        f"Use load_data(filename='{filename}') to access.]"
-                    )
-                except Exception:
-                    val_str = val_str[:300] + "..."
-            elif len(val_str) > 300:
-                val_str = val_str[:300] + "..."
-            memory_lines.append(f"  {key}: {val_str}")
-        if memory_lines:
-            sections.append("\nOutputs available:\n" + "\n".join(memory_lines))
-
-    # Files in data directory
-    if data_dir:
-        data_path = Path(data_dir)
-        if data_path.exists():
-            files = sorted(data_path.iterdir())
-            if files:
-                file_lines = [
-                    f"  {f.name} ({f.stat().st_size:,} bytes)" for f in files if f.is_file()
-                ]
-                if file_lines:
-                    sections.append(
-                        "\nData files (use load_data to access):\n" + "\n".join(file_lines)
-                    )
-
-    # Agent working memory
-    if adapt_content:
-        sections.append(f"\n--- Agent Memory ---\n{adapt_content}")
-
-    # Available tools
-    if cumulative_tool_names:
-        sections.append("\nAvailable tools: " + ", ".join(sorted(cumulative_tool_names)))
-
-    # Next phase
-    sections.append(f"\nNow entering: {next_node.name}")
-    sections.append(f"  {next_node.description}")
-    if next_node.output_keys:
-        sections.append(
-            f"\nYour ONLY job in this phase: complete the task above and call "
-            f"set_output() for {next_node.output_keys}. Do NOT do work that "
-            f"belongs to later phases."
-        )
-
-    # Reflection prompt (engineered metacognition)
-    sections.append(
-        "\nBefore proceeding, briefly reflect: what went well in the "
-        "previous phase? Are there any gaps or surprises worth noting?"
-    )
-
-    sections.append("\n--- END TRANSITION ---")
-
-    return "\n".join(sections)

core/framework/host/__init__.py

@@ -0,0 +1,15 @@
+"""Host layer -- how agents are triggered and hosted."""
+
+from framework.host.colony_runtime import (  # noqa: F401
+    ColonyConfig,
+    ColonyRuntime,
+    StreamEventBus,
+    TriggerSpec,
+)
+from framework.host.event_bus import AgentEvent, EventBus, EventType  # noqa: F401
+from framework.host.worker import (  # noqa: F401
+    Worker,
+    WorkerInfo,
+    WorkerResult,
+    WorkerStatus,
+)

core/framework/host/event_bus.py

@@ -94,12 +94,12 @@ class EventType(StrEnum):
     TOOL_CALL_STARTED = "tool_call_started"
     TOOL_CALL_COMPLETED = "tool_call_completed"
 
-    # Client I/O (client_facing=True nodes only)
+    # Queen/user interaction events
     CLIENT_OUTPUT_DELTA = "client_output_delta"
     CLIENT_INPUT_REQUESTED = "client_input_requested"
     CLIENT_INPUT_RECEIVED = "client_input_received"
 
-    # Internal node observability (client_facing=False nodes)
+    # Internal node observability
     NODE_INTERNAL_OUTPUT = "node_internal_output"
     NODE_INPUT_BLOCKED = "node_input_blocked"
     NODE_STALLED = "node_stalled"
@@ -108,12 +108,21 @@ class EventType(StrEnum):
     # Judge decisions (implicit judge in event loop nodes)
     JUDGE_VERDICT = "judge_verdict"
 
-    # Output tracking
-    OUTPUT_KEY_SET = "output_key_set"
-
-    # Retry / edge tracking
+    # Retry tracking
     NODE_RETRY = "node_retry"
-    EDGE_TRAVERSED = "edge_traversed"
+
+    # Stream-health observability. Split from NODE_RETRY so the UI can
+    # distinguish "slow TTFT on a huge context" (healthy, just slow) from
+    # "stream went silent mid-generation" (probable stall) from "we nudged
+    # the model to continue" (recovery), which NODE_RETRY used to conflate.
+    STREAM_TTFT_EXCEEDED = "stream_ttft_exceeded"
+    STREAM_INACTIVE = "stream_inactive"
+    STREAM_NUDGE_SENT = "stream_nudge_sent"
+    TOOL_CALL_REPLAY_DETECTED = "tool_call_replay_detected"
+
+    # Worker agent lifecycle
+    WORKER_COMPLETED = "worker_completed"
+    WORKER_FAILED = "worker_failed"
 
     # Context management
     CONTEXT_COMPACTED = "context_compacted"
@@ -128,28 +137,22 @@ class EventType(StrEnum):
     # Escalation (agent requests handoff to queen)
     ESCALATION_REQUESTED = "escalation_requested"
 
-    # Worker health monitoring
-    WORKER_ESCALATION_TICKET = "worker_escalation_ticket"
-    QUEEN_INTERVENTION_REQUESTED = "queen_intervention_requested"
-
     # Execution resurrection (auto-restart on non-fatal failure)
     EXECUTION_RESURRECTED = "execution_resurrected"
 
-    # Worker lifecycle (session manager → frontend)
-    WORKER_LOADED = "worker_loaded"
+    # Colony lifecycle (session manager → frontend)
+    WORKER_COLONY_LOADED = "worker_colony_loaded"
+    # Queen create_colony tool finished forking; carries colony_name +
+    # path so the frontend can render a system message linking to the
+    # new colony page at /colony/{colony_name}.
+    COLONY_CREATED = "colony_created"
     CREDENTIALS_REQUIRED = "credentials_required"
 
-    # Draft graph (planning phase — lightweight graph preview)
-    DRAFT_GRAPH_UPDATED = "draft_graph_updated"
-
-    # Flowchart map updated (after reconciliation with runtime graph)
-    FLOWCHART_MAP_UPDATED = "flowchart_map_updated"
-
-    # Queen phase changes (building <-> staging <-> running)
+    # Queen phase changes (working <-> reviewing)
     QUEEN_PHASE_CHANGED = "queen_phase_changed"
 
-    # Queen thinking hook — persona selected for the current building session
-    QUEEN_PERSONA_SELECTED = "queen_persona_selected"
+    # Queen identity — which queen profile was selected for this session
+    QUEEN_IDENTITY_SELECTED = "queen_identity_selected"
 
     # Subagent reports (one-way progress updates from sub-agents)
     SUBAGENT_REPORT = "subagent_report"
@@ -174,7 +177,7 @@ class AgentEvent:
     data: dict[str, Any] = field(default_factory=dict)
     timestamp: datetime = field(default_factory=datetime.now)
     correlation_id: str | None = None  # For tracking related events
-    graph_id: str | None = None  # Which graph emitted this event (multi-graph sessions)
+    colony_id: str | None = None  # Which colony emitted this event
     run_id: str | None = None  # Unique ID per trigger() invocation — used for run dividers
 
     def to_dict(self) -> dict:
@@ -187,7 +190,7 @@ class AgentEvent:
             "data": self.data,
             "timestamp": self.timestamp.isoformat(),
             "correlation_id": self.correlation_id,
-            "graph_id": self.graph_id,
+            "colony_id": self.colony_id,
         }
         if self.run_id is not None:
             d["run_id"] = self.run_id
@@ -208,7 +211,7 @@ class Subscription:
     filter_stream: str | None = None  # Only receive events from this stream
     filter_node: str | None = None  # Only receive events from this node
     filter_execution: str | None = None  # Only receive events from this execution
-    filter_graph: str | None = None  # Only receive events from this graph
+    filter_colony: str | None = None  # Only receive events from this colony
 
 
 class EventBus:
@@ -390,7 +393,7 @@ class EventBus:
         filter_stream: str | None = None,
         filter_node: str | None = None,
         filter_execution: str | None = None,
-        filter_graph: str | None = None,
+        filter_colony: str | None = None,
     ) -> str:
         """
         Subscribe to events.
@@ -401,7 +404,7 @@ class EventBus:
             filter_stream: Only receive events from this stream
             filter_node: Only receive events from this node
             filter_execution: Only receive events from this execution
-            filter_graph: Only receive events from this graph
+            filter_colony: Only receive events from this colony
 
         Returns:
             Subscription ID (use to unsubscribe)
@@ -416,7 +419,7 @@ class EventBus:
             filter_stream=filter_stream,
             filter_node=filter_node,
             filter_execution=filter_execution,
-            filter_graph=filter_graph,
+            filter_colony=filter_colony,
         )
 
         self._subscriptions[sub_id] = subscription
@@ -452,11 +455,7 @@ class EventBus:
         # iteration values.  Without this, live SSE would use raw iterations
         # while events.jsonl would use offset iterations, causing ID collisions
         # on the frontend when replaying after cold resume.
-        if (
-            self._session_log_iteration_offset
-            and isinstance(event.data, dict)
-            and "iteration" in event.data
-        ):
+        if self._session_log_iteration_offset and isinstance(event.data, dict) and "iteration" in event.data:
             offset = self._session_log_iteration_offset
             event.data = {**event.data, "iteration": event.data["iteration"] + offset}
 
@@ -518,23 +517,41 @@ class EventBus:
         if subscription.filter_execution and subscription.filter_execution != event.execution_id:
             return False
 
-        # Check graph filter
-        if subscription.filter_graph and subscription.filter_graph != event.graph_id:
+        # Check colony filter
+        if subscription.filter_colony and subscription.filter_colony != event.colony_id:
             return False
 
         return True
 
+    # Per-handler wall-clock timeout. A subscriber that deadlocks or
+    # blocks on slow I/O would otherwise freeze the publisher (and via
+    # ``await publish(...)`` any coroutine that emits events) indefinitely.
+    # 15 s is generous for legitimate handlers and cheap to tune later.
+    _HANDLER_TIMEOUT_SECONDS: float = 15.0
+
     async def _execute_handlers(
         self,
         event: AgentEvent,
         handlers: list[EventHandler],
     ) -> None:
-        """Execute handlers concurrently with rate limiting."""
+        """Execute handlers concurrently with rate limiting + hard timeout."""
 
         async def run_handler(handler: EventHandler) -> None:
             async with self._semaphore:
                 try:
-                    await handler(event)
+                    await asyncio.wait_for(
+                        handler(event),
+                        timeout=self._HANDLER_TIMEOUT_SECONDS,
+                    )
+                except TimeoutError:
+                    handler_name = getattr(handler, "__qualname__", repr(handler))
+                    logger.error(
+                        "EventBus handler %s exceeded %.0fs on event %s — dropping; "
+                        "fix the handler or the publisher will stall",
+                        handler_name,
+                        self._HANDLER_TIMEOUT_SECONDS,
+                        getattr(event.type, "name", event.type),
+                    )
                 except Exception:
                     logger.exception(f"Handler error for {event.type}")
 
@@ -879,7 +896,7 @@ class EventBus:
         iteration: int | None = None,
         inner_turn: int = 0,
     ) -> None:
-        """Emit client output delta event (client_facing=True nodes)."""
+        """Emit user-facing output delta for interactive queen turns."""
         data: dict = {"content": content, "snapshot": snapshot, "inner_turn": inner_turn}
         if iteration is not None:
             data["iteration"] = iteration
@@ -897,24 +914,22 @@ class EventBus:
         self,
         stream_id: str,
         node_id: str,
-        prompt: str = "",
         execution_id: str | None = None,
-        options: list[str] | None = None,
         questions: list[dict] | None = None,
     ) -> None:
-        """Emit client input requested event (client_facing=True nodes).
+        """Emit a user-input request for interactive queen turns.
 
         Args:
-            options: Optional predefined choices for the user (1-3 items).
-                     The frontend appends an "Other" free-text option
-                     automatically.
-            questions: Optional list of question dicts for multi-question
-                       batches (from ask_user_multiple). Each dict has id,
-                       prompt, and optional options.
+            questions: Optional list of question dicts from ``ask_user``.
+                Each dict has ``id``, ``prompt``, and optional ``options``
+                (2-3 predefined choices). The frontend renders the
+                QuestionWidget for a single-entry list and the
+                MultiQuestionWidget for 2+ entries. Free-text asks (no
+                options) stream the prompt separately as a chat message;
+                auto-block turns have no questions at all and fall back
+                to the normal text input.
         """
-        data: dict[str, Any] = {"prompt": prompt}
-        if options:
-            data["options"] = options
+        data: dict[str, Any] = {}
         if questions:
             data["questions"] = questions
         await self.publish(
@@ -936,7 +951,7 @@ class EventBus:
         content: str,
         execution_id: str | None = None,
     ) -> None:
-        """Emit node internal output event (client_facing=False nodes)."""
+        """Emit node internal output for non-user-facing execution."""
         await self.publish(
             AgentEvent(
                 type=EventType.NODE_INTERNAL_OUTPUT,
@@ -1029,24 +1044,6 @@ class EventBus:
             )
         )
 
-    async def emit_output_key_set(
-        self,
-        stream_id: str,
-        node_id: str,
-        key: str,
-        execution_id: str | None = None,
-    ) -> None:
-        """Emit output key set event."""
-        await self.publish(
-            AgentEvent(
-                type=EventType.OUTPUT_KEY_SET,
-                stream_id=stream_id,
-                node_id=node_id,
-                execution_id=execution_id,
-                data={"key": key},
-            )
-        )
-
     async def emit_node_retry(
         self,
         stream_id: str,
@@ -1071,29 +1068,142 @@ class EventBus:
             )
         )
 
-    async def emit_edge_traversed(
+    async def emit_stream_ttft_exceeded(
         self,
         stream_id: str,
-        source_node: str,
-        target_node: str,
-        edge_condition: str = "",
+        node_id: str,
+        ttft_seconds: float,
+        limit_seconds: float,
         execution_id: str | None = None,
     ) -> None:
-        """Emit edge traversed event."""
+        """Emit when a stream stayed silent past the TTFT budget (no first event)."""
         await self.publish(
             AgentEvent(
-                type=EventType.EDGE_TRAVERSED,
+                type=EventType.STREAM_TTFT_EXCEEDED,
                 stream_id=stream_id,
-                node_id=source_node,
+                node_id=node_id,
                 execution_id=execution_id,
                 data={
-                    "source_node": source_node,
-                    "target_node": target_node,
-                    "edge_condition": edge_condition,
+                    "ttft_seconds": ttft_seconds,
+                    "limit_seconds": limit_seconds,
                 },
             )
         )
 
+    async def emit_stream_inactive(
+        self,
+        stream_id: str,
+        node_id: str,
+        idle_seconds: float,
+        limit_seconds: float,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit when a stream that had produced events went silent past budget."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.STREAM_INACTIVE,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={
+                    "idle_seconds": idle_seconds,
+                    "limit_seconds": limit_seconds,
+                },
+            )
+        )
+
+    async def emit_stream_nudge_sent(
+        self,
+        stream_id: str,
+        node_id: str,
+        reason: str,
+        nudge_count: int,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit when the continue-nudge was injected (recovery, not retry)."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.STREAM_NUDGE_SENT,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={
+                    "reason": reason,
+                    "nudge_count": nudge_count,
+                },
+            )
+        )
+
+    async def emit_tool_call_replay_detected(
+        self,
+        stream_id: str,
+        node_id: str,
+        tool_name: str,
+        prior_seq: int,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit when the model is about to re-execute a prior successful call."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.TOOL_CALL_REPLAY_DETECTED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={
+                    "tool_name": tool_name,
+                    "prior_seq": prior_seq,
+                },
+            )
+        )
+
+    async def emit_worker_completed(
+        self,
+        stream_id: str,
+        node_id: str,
+        worker_id: str,
+        success: bool,
+        output: dict[str, Any],
+        activations: list[dict[str, Any]] | None = None,
+        execution_id: str | None = None,
+        **extra_data: Any,
+    ) -> None:
+        """Emit worker completed event with outgoing activations."""
+        data: dict[str, Any] = {
+            "worker_id": worker_id,
+            "success": success,
+            "output": output,
+            "activations": activations or [],
+            **extra_data,
+        }
+        await self.publish(
+            AgentEvent(
+                type=EventType.WORKER_COMPLETED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data=data,
+            )
+        )
+
+    async def emit_worker_failed(
+        self,
+        stream_id: str,
+        node_id: str,
+        worker_id: str,
+        error: str,
+        execution_id: str | None = None,
+    ) -> None:
+        """Emit worker failed event."""
+        await self.publish(
+            AgentEvent(
+                type=EventType.WORKER_FAILED,
+                stream_id=stream_id,
+                node_id=node_id,
+                execution_id=execution_id,
+                data={"worker_id": worker_id, "error": error},
+            )
+        )
+
     async def emit_execution_paused(
         self,
         stream_id: str,
@@ -1160,60 +1270,24 @@ class EventBus:
         reason: str = "",
         context: str = "",
         execution_id: str | None = None,
+        request_id: str | None = None,
     ) -> None:
-        """Emit escalation requested event (agent wants queen)."""
+        """Emit escalation requested event (agent wants queen).
+
+        ``request_id`` is a caller-supplied handle used by the queen to
+        address its reply back to the specific escalation. When omitted the
+        event still fires but the queen cannot route a targeted reply.
+        """
         await self.publish(
             AgentEvent(
                 type=EventType.ESCALATION_REQUESTED,
                 stream_id=stream_id,
                 node_id=node_id,
                 execution_id=execution_id,
-                data={"reason": reason, "context": context},
-            )
-        )
-
-    async def emit_worker_escalation_ticket(
-        self,
-        stream_id: str,
-        node_id: str,
-        ticket: dict,
-        execution_id: str | None = None,
-    ) -> None:
-        """Emitted when worker shows a degradation pattern."""
-        await self.publish(
-            AgentEvent(
-                type=EventType.WORKER_ESCALATION_TICKET,
-                stream_id=stream_id,
-                node_id=node_id,
-                execution_id=execution_id,
-                data={"ticket": ticket},
-            )
-        )
-
-    async def emit_queen_intervention_requested(
-        self,
-        stream_id: str,
-        node_id: str,
-        ticket_id: str,
-        analysis: str,
-        severity: str,
-        queen_graph_id: str,
-        queen_stream_id: str,
-        execution_id: str | None = None,
-    ) -> None:
-        """Emitted by queen when she decides the operator should be involved."""
-        await self.publish(
-            AgentEvent(
-                type=EventType.QUEEN_INTERVENTION_REQUESTED,
-                stream_id=stream_id,
-                node_id=node_id,
-                execution_id=execution_id,
                 data={
-                    "ticket_id": ticket_id,
-                    "analysis": analysis,
-                    "severity": severity,
-                    "queen_graph_id": queen_graph_id,
-                    "queen_stream_id": queen_stream_id,
+                    "request_id": request_id,
+                    "reason": reason,
+                    "context": context,
                 },
             )
         )
@@ -1295,7 +1369,7 @@ class EventBus:
         stream_id: str | None = None,
         node_id: str | None = None,
         execution_id: str | None = None,
-        graph_id: str | None = None,
+        colony_id: str | None = None,
         timeout: float | None = None,
     ) -> AgentEvent | None:
         """
@@ -1306,7 +1380,7 @@ class EventBus:
             stream_id: Filter by stream
             node_id: Filter by node
             execution_id: Filter by execution
-            graph_id: Filter by graph
+            colony_id: Filter by colony
             timeout: Maximum time to wait (seconds)
 
         Returns:
@@ -1327,7 +1401,7 @@ class EventBus:
             filter_stream=stream_id,
             filter_node=node_id,
             filter_execution=execution_id,
-            filter_graph=graph_id,
+            filter_colony=colony_id,
         )
 
         try:

core/framework/host/execution_manager.py

@@ -16,20 +16,20 @@ from collections import OrderedDict
 from collections.abc import Callable
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
-from framework.graph.checkpoint_config import CheckpointConfig
-from framework.graph.executor import ExecutionResult, GraphExecutor
-from framework.runtime.event_bus import EventBus
-from framework.runtime.shared_state import IsolationLevel, SharedStateManager
-from framework.runtime.stream_runtime import StreamRuntime, StreamRuntimeAdapter
+from framework.host.event_bus import EventBus
+from framework.host.shared_state import IsolationLevel, SharedBufferManager
+from framework.host.stream_runtime import StreamDecisionTracker, StreamRuntimeAdapter
+from framework.orchestrator.checkpoint_config import CheckpointConfig
+from framework.orchestrator.orchestrator import ExecutionResult, Orchestrator
 
 if TYPE_CHECKING:
-    from framework.graph.edge import GraphSpec
-    from framework.graph.goal import Goal
+    from framework.host.event_bus import AgentEvent
+    from framework.host.outcome_aggregator import OutcomeAggregator
     from framework.llm.provider import LLMProvider, Tool
-    from framework.runtime.event_bus import AgentEvent
-    from framework.runtime.outcome_aggregator import OutcomeAggregator
+    from framework.orchestrator.edge import GraphSpec
+    from framework.orchestrator.goal import Goal
     from framework.storage.concurrent import ConcurrentStorage
     from framework.storage.session_store import SessionStore
 
@@ -48,6 +48,8 @@ class ExecutionAlreadyRunningError(RuntimeError):
 
 logger = logging.getLogger(__name__)
 
+CancelExecutionResult = Literal["cancelled", "cancelling", "not_found"]
+
 
 class GraphScopedEventBus(EventBus):
     """Proxy that stamps ``graph_id`` on every published event.
@@ -130,10 +132,10 @@ class ExecutionContext:
     run_id: str | None = None  # Unique ID per trigger() invocation
     started_at: datetime = field(default_factory=datetime.now)
     completed_at: datetime | None = None
-    status: str = "pending"  # pending, running, completed, failed, paused
+    status: str = "pending"  # pending, running, cancelling, completed, failed, paused, cancelled
 
 
-class ExecutionStream:
+class ExecutionManager:
     """
     Manages concurrent executions for a single entry point.
 
@@ -170,9 +172,9 @@ class ExecutionStream:
         entry_spec: EntryPointSpec,
         graph: "GraphSpec",
         goal: "Goal",
-        state_manager: SharedStateManager,
+        state_manager: SharedBufferManager,
         storage: "ConcurrentStorage",
-        outcome_aggregator: "OutcomeAggregator",
+        outcome_aggregator: "OutcomeAggregator | None" = None,
         event_bus: "EventBus | None" = None,
         llm: "LLMProvider | None" = None,
         tools: list["Tool"] | None = None,
@@ -189,6 +191,9 @@ class ExecutionStream:
         skills_catalog_prompt: str = "",
         protocols_prompt: str = "",
         skill_dirs: list[str] | None = None,
+        context_warn_ratio: float | None = None,
+        batch_init_nudge: str | None = None,
+        dynamic_memory_provider_factory: Callable[[str], Callable[[], str] | None] | None = None,
     ):
         """
         Initialize execution stream.
@@ -215,6 +220,8 @@ class ExecutionStream:
             skills_catalog_prompt: Available skills catalog for system prompt
             protocols_prompt: Default skill operational protocols for system prompt
             skill_dirs: Skill base directories for Tier 3 resource access
+            context_warn_ratio: Token usage ratio to trigger DS-13 preservation warning
+            batch_init_nudge: System prompt nudge for DS-12 batch auto-detection
         """
         self.stream_id = stream_id
         self.entry_spec = entry_spec
@@ -239,6 +246,9 @@ class ExecutionStream:
         self._skills_catalog_prompt = skills_catalog_prompt
         self._protocols_prompt = protocols_prompt
         self._skill_dirs: list[str] = skill_dirs or []
+        self._context_warn_ratio: float | None = context_warn_ratio
+        self._batch_init_nudge: str | None = batch_init_nudge
+        self._dynamic_memory_provider_factory = dynamic_memory_provider_factory
 
         _es_logger = logging.getLogger(__name__)
         if protocols_prompt:
@@ -254,16 +264,15 @@ class ExecutionStream:
             )
 
         # Create stream-scoped runtime
-        self._runtime = StreamRuntime(
+        self._runtime = StreamDecisionTracker(
             stream_id=stream_id,
             storage=storage,
-            outcome_aggregator=outcome_aggregator,
         )
 
         # Execution tracking
         self._active_executions: dict[str, ExecutionContext] = {}
         self._execution_tasks: dict[str, asyncio.Task] = {}
-        self._active_executors: dict[str, GraphExecutor] = {}
+        self._active_executors: dict[str, Orchestrator] = {}
         self._cancel_reasons: dict[str, str] = {}
         self._execution_results: OrderedDict[str, ExecutionResult] = OrderedDict()
         self._execution_result_times: dict[str, float] = {}
@@ -293,7 +302,7 @@ class ExecutionStream:
 
         # Emit stream started event
         if self._scoped_event_bus:
-            from framework.runtime.event_bus import AgentEvent, EventType
+            from framework.host.event_bus import AgentEvent, EventType
 
             await self._scoped_event_bus.publish(
                 AgentEvent(
@@ -308,6 +317,22 @@ class ExecutionStream:
         """Return IDs of all currently active executions."""
         return list(self._active_executions.keys())
 
+    def _get_blocking_execution_ids_locked(self) -> list[str]:
+        """Return executions that still block a replacement from starting.
+
+        An execution continues to block replacement until its task has
+        terminated and the task's final cleanup has removed its bookkeeping.
+        This is intentional: a timed-out cancellation does not mean the old
+        task is harmless. If it is still alive, it can still write shared
+        session state, so letting a replacement start would guarantee
+        overlapping mutations on the same session.
+        """
+        blocking_ids: list[str] = list(self._active_executions.keys())
+        for execution_id, task in self._execution_tasks.items():
+            if not task.done() and execution_id not in self._active_executions:
+                blocking_ids.append(execution_id)
+        return blocking_ids
+
     @property
     def agent_idle_seconds(self) -> float:
         """Seconds since the last agent activity (LLM call, tool call, node transition).
@@ -351,7 +376,7 @@ class ExecutionStream:
 
         Each entry is ``{"node_id": ..., "execution_id": ...}``.
         The currently executing node is placed first so that
-        ``inject_worker_message`` targets the active node, not a stale one.
+        ``inject_message`` targets the active node, not a stale one.
         """
         injectable: list[dict[str, str]] = []
         current_first: list[dict[str, str]] = []
@@ -389,15 +414,22 @@ class ExecutionStream:
 
     async def stop(self) -> None:
         """Stop the execution stream and cancel active executions."""
-        if not self._running:
-            return
+        async with self._lock:
+            if not self._running:
+                return
 
-        self._running = False
+            self._running = False
 
-        # Cancel all active executions
-        tasks_to_wait = []
-        for _, task in self._execution_tasks.items():
-            if not task.done():
+            # Cancel all active executions, but keep bookkeeping until each
+            # task reaches its own cleanup path.
+            tasks_to_wait: list[asyncio.Task] = []
+            for execution_id, task in self._execution_tasks.items():
+                if task.done():
+                    continue
+                ctx = self._active_executions.get(execution_id)
+                if ctx is not None:
+                    ctx.status = "cancelling"
+                self._cancel_reasons.setdefault(execution_id, "Execution cancelled")
                 task.cancel()
                 tasks_to_wait.append(task)
 
@@ -411,14 +443,11 @@ class ExecutionStream:
                     len(pending),
                 )
 
-        self._execution_tasks.clear()
-        self._active_executions.clear()
-
         logger.info(f"ExecutionStream '{self.stream_id}' stopped")
 
         # Emit stream stopped event
         if self._scoped_event_bus:
-            from framework.runtime.event_bus import AgentEvent, EventType
+            from framework.host.event_bus import AgentEvent, EventType
 
             await self._scoped_event_bus.publish(
                 AgentEvent(
@@ -445,9 +474,7 @@ class ExecutionStream:
         for executor in self._active_executors.values():
             node = executor.node_registry.get(node_id)
             if node is not None and hasattr(node, "inject_event"):
-                await node.inject_event(
-                    content, is_client_input=is_client_input, image_content=image_content
-                )
+                await node.inject_event(content, is_client_input=is_client_input, image_content=image_content)
                 return True
         return False
 
@@ -544,6 +571,14 @@ class ExecutionStream:
             correlation_id = execution_id
 
         # Create execution context
+        effective_run_id = None
+        if session_state:
+            existing_run_id = session_state.get("run_id")
+            if isinstance(existing_run_id, str) and existing_run_id:
+                effective_run_id = existing_run_id
+        if effective_run_id is None:
+            effective_run_id = run_id
+
         ctx = ExecutionContext(
             id=execution_id,
             correlation_id=correlation_id,
@@ -552,16 +587,20 @@ class ExecutionStream:
             input_data=input_data,
             isolation_level=self.entry_spec.get_isolation_level(),
             session_state=session_state,
-            run_id=run_id,
+            run_id=effective_run_id,
         )
 
         async with self._lock:
+            if not self._running:
+                raise RuntimeError(f"ExecutionStream '{self.stream_id}' is not running")
+
+            blocking_ids = self._get_blocking_execution_ids_locked()
+            if blocking_ids:
+                raise ExecutionAlreadyRunningError(self.stream_id, blocking_ids)
+
             self._active_executions[execution_id] = ctx
             self._completion_events[execution_id] = asyncio.Event()
-
-        # Start execution task
-        task = asyncio.create_task(self._run_execution(ctx))
-        self._execution_tasks[execution_id] = task
+            self._execution_tasks[execution_id] = asyncio.create_task(self._run_execution(ctx))
 
         logger.debug(f"Queued execution {execution_id} for stream {self.stream_id}")
         return execution_id
@@ -633,7 +672,7 @@ class ExecutionStream:
                 self._write_run_event(execution_id, ctx.run_id, "run_started")
 
                 # Create execution-scoped memory
-                self._state_manager.create_memory(
+                self._state_manager.create_buffer(
                     execution_id=execution_id,
                     stream_id=self.stream_id,
                     isolation=ctx.isolation_level,
@@ -652,11 +691,9 @@ class ExecutionStream:
                 # Create per-execution runtime logger
                 runtime_logger = None
                 if self._runtime_log_store:
-                    from framework.runtime.runtime_logger import RuntimeLogger
+                    from framework.tracker.runtime_logger import RuntimeLogger
 
-                    runtime_logger = RuntimeLogger(
-                        store=self._runtime_log_store, agent_id=self.graph.id
-                    )
+                    runtime_logger = RuntimeLogger(store=self._runtime_log_store, agent_id=self.graph.id)
 
                 # Derive storage from session_store (graph-specific for secondary
                 # graphs) so that all files — conversations, state, checkpoints,
@@ -681,12 +718,7 @@ class ExecutionStream:
                 # forward so the next attempt resumes at the failed node.
                 while True:
                     # Create executor for this execution.
-                    # Each execution gets its own storage under sessions/{exec_id}/
-                    # so conversations, spillover, and data files are all scoped
-                    # to this execution.  The executor sets data_dir via execution
-                    # context (contextvars) so data tools and spillover share the
-                    # same session-scoped directory.
-                    executor = GraphExecutor(
+                    executor = Orchestrator(
                         runtime=runtime_adapter,
                         llm=self._llm,
                         tools=self._tools,
@@ -694,6 +726,7 @@ class ExecutionStream:
                         event_bus=self._scoped_event_bus,
                         stream_id=self.stream_id,
                         execution_id=execution_id,
+                        run_id=ctx.run_id or "",
                         storage_path=exec_storage,
                         runtime_logger=runtime_logger,
                         loop_config=self.graph.loop_config,
@@ -703,6 +736,13 @@ class ExecutionStream:
                         skills_catalog_prompt=self._skills_catalog_prompt,
                         protocols_prompt=self._protocols_prompt,
                         skill_dirs=self._skill_dirs,
+                        context_warn_ratio=self._context_warn_ratio,
+                        batch_init_nudge=self._batch_init_nudge,
+                        dynamic_memory_provider=(
+                            self._dynamic_memory_provider_factory(execution_id)
+                            if self._dynamic_memory_provider_factory is not None
+                            else None
+                        ),
                     )
                     # Track executor so inject_input() can reach EventLoopNode instances
                     self._active_executors[execution_id] = executor
@@ -739,7 +779,7 @@ class ExecutionStream:
 
                         # Emit resurrection event
                         if self._scoped_event_bus:
-                            from framework.runtime.event_bus import AgentEvent, EventType
+                            from framework.host.event_bus import AgentEvent, EventType
 
                             await self._scoped_event_bus.publish(
                                 AgentEvent(
@@ -869,9 +909,7 @@ class ExecutionStream:
                     if has_result and result.paused_at:
                         await self._write_session_state(execution_id, ctx, result=result)
                     else:
-                        await self._write_session_state(
-                            execution_id, ctx, error="Execution cancelled"
-                        )
+                        await self._write_session_state(execution_id, ctx, error="Execution cancelled")
 
                 # Emit SSE event so the frontend knows the execution stopped.
                 # The executor does NOT emit on CancelledError, so there is no
@@ -961,7 +999,10 @@ class ExecutionStream:
             return
         import json as _json
 
-        session_dir = self._session_store.get_session_path(execution_id)
+        try:
+            session_dir = self._session_store.get_session_path(execution_id)
+        except ValueError:
+            return
         runs_file = session_dir / "runs.jsonl"
         now = datetime.now()
         record = {
@@ -1033,6 +1074,7 @@ class ExecutionStream:
                     agent_id=self.graph.id,
                     entry_point=self.entry_spec.id,
                 )
+                state.current_run_id = ctx.run_id
             else:
                 # Create initial state — when resuming, preserve the previous
                 # execution's progress so crashes don't lose track of state.
@@ -1063,8 +1105,9 @@ class ExecutionStream:
                         updated_at=now,
                     ),
                     progress=progress,
-                    memory=ss.get("memory", {}),
+                    data_buffer=ss.get("data_buffer", ss.get("memory", {})),
                     input_data=ctx.input_data,
+                    current_run_id=ctx.run_id,
                 )
 
             # Handle error case
@@ -1090,7 +1133,7 @@ class ExecutionStream:
         Each stream only executes from its own entry_node, but the full
         graph must validate with all entry points accounted for.
         """
-        from framework.graph.edge import GraphSpec
+        from framework.orchestrator.edge import GraphSpec
 
         # Merge entry points: this stream's entry + original graph's primary
         # entry + any other entry points. This ensures all nodes are
@@ -1166,7 +1209,7 @@ class ExecutionStream:
         """Get execution context."""
         return self._active_executions.get(execution_id)
 
-    async def cancel_execution(self, execution_id: str, *, reason: str | None = None) -> bool:
+    async def cancel_execution(self, execution_id: str, *, reason: str | None = None) -> CancelExecutionResult:
         """
         Cancel a running execution.
 
@@ -1177,21 +1220,38 @@ class ExecutionStream:
                 provided, defaults to "Execution cancelled".
 
         Returns:
-            True if cancelled, False if not found
+            "cancelled" if the task fully exited within the grace period,
+            "cancelling" if cancellation was requested but the task is still
+            shutting down, or "not_found" if no active task exists.
         """
-        task = self._execution_tasks.get(execution_id)
-        if task and not task.done():
+        async with self._lock:
+            task = self._execution_tasks.get(execution_id)
+            if task is None or task.done():
+                return "not_found"
+
             # Store the reason so the CancelledError handler can use it
             # when emitting the pause/fail event.
             self._cancel_reasons[execution_id] = reason or "Execution cancelled"
+            ctx = self._active_executions.get(execution_id)
+            if ctx is not None:
+                ctx.status = "cancelling"
             task.cancel()
-            # Wait briefly for the task to finish. Don't block indefinitely —
-            # the task may be stuck in a long LLM API call that doesn't
-            # respond to cancellation quickly. The cancellation is already
-            # requested; the task will clean up in the background.
-            done, _ = await asyncio.wait({task}, timeout=5.0)
-            return True
-        return False
+
+        # Wait briefly for the task to finish. Don't block indefinitely —
+        # the task may be stuck in a long LLM API call that doesn't
+        # respond to cancellation quickly.
+        done, _ = await asyncio.wait({task}, timeout=5.0)
+        if not done:
+            # Keep bookkeeping in place until the task's own finally block runs.
+            # We intentionally do not add deferred cleanup keyed by execution_id
+            # here because resumed executions reuse the same id; a delayed pop
+            # could otherwise delete bookkeeping that belongs to the new run.
+            logger.warning(
+                "Execution %s did not finish within cancel timeout; leaving bookkeeping in place until task exit",
+                execution_id,
+            )
+            return "cancelling"
+        return "cancelled"
 
     # === STATS AND MONITORING ===
 

core/framework/host/isolation.py

@@ -0,0 +1,9 @@
+"""State isolation level enum."""
+
+from enum import StrEnum
+
+
+class IsolationLevel(StrEnum):
+    ISOLATED = "isolated"
+    SHARED = "shared"
+    SYNCHRONIZED = "synchronized"

core/framework/host/outcome_aggregator.py

@@ -0,0 +1,21 @@
+"""Stub — outcome aggregator removed in colony refactor."""
+
+from framework.schemas.goal import Goal
+
+
+class OutcomeAggregator:
+    def __init__(self, goal: Goal, event_bus=None):
+        self._goal = goal
+        self._event_bus = event_bus
+
+    def record_decision(self, **kwargs):
+        pass
+
+    def record_outcome(self, **kwargs):
+        pass
+
+    def evaluate_goal_progress(self):
+        return {"progress": 0.0, "criteria_status": {}}
+
+    def get_stats(self):
+        return {"total_decisions": 0, "total_outcomes": 0}