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refactor: remove old get_agent_session_state and get_agent_session_memory tools

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This commit is contained in:
Richard Tang
2026-03-04 17:23:10 -08:00
parent 4cbd5a4c6c
commit f9e53f58af
7 changed files with 4 additions and 229 deletions
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core/framework/mcp/agent_builder_server.py
-76
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@@ -3714,82 +3714,6 @@ def list_agent_sessions(
)
@mcp.tool()
def get_agent_session_state(
agent_work_dir: Annotated[str, "Path to the agent's working directory"],
session_id: Annotated[str, "The session ID (e.g., 'session_20260208_143022_abc12345')"],
) -> str:
"""
Load full session state for a specific session.
Returns complete session data including status, progress, result,
metrics, and checkpoint info. Memory values are excluded to prevent
context bloat -- use get_agent_session_memory to retrieve memory contents.
"""
state_path = Path(agent_work_dir) / "sessions" / session_id / "state.json"
data = _read_session_json(state_path)
if data is None:
return json.dumps({"error": f"Session not found: {session_id}"})
memory = data.get("memory", {})
data["memory_keys"] = list(memory.keys()) if isinstance(memory, dict) else []
data["memory_size"] = len(memory) if isinstance(memory, dict) else 0
data.pop("memory", None)
return json.dumps(data, indent=2, default=str)
@mcp.tool()
def get_agent_session_memory(
agent_work_dir: Annotated[str, "Path to the agent's working directory"],
session_id: Annotated[str, "The session ID"],
key: Annotated[str, "Specific memory key to retrieve. Empty for all."] = "",
) -> str:
"""
Get memory contents from a session.
Memory stores intermediate results passed between nodes. Use this
to inspect what data was produced during execution.
If key is provided, returns only that memory key's value.
If key is empty, returns all memory keys and their values.
"""
state_path = Path(agent_work_dir) / "sessions" / session_id / "state.json"
data = _read_session_json(state_path)
if data is None:
return json.dumps({"error": f"Session not found: {session_id}"})
memory = data.get("memory", {})
if not isinstance(memory, dict):
memory = {}
if key:
if key not in memory:
return json.dumps(
{
"error": f"Memory key not found: '{key}'",
"available_keys": list(memory.keys()),
}
)
value = memory[key]
return json.dumps(
{
"session_id": session_id,
"key": key,
"value": value,
"value_type": type(value).__name__,
},
indent=2,
default=str,
)
return json.dumps(
{"session_id": session_id, "memory": memory, "total_keys": len(memory)},
indent=2,
default=str,
)
@mcp.tool()
def list_agent_checkpoints(
agent_work_dir: Annotated[str, "Path to the agent's working directory"],
docs/hive-coder-meta-agent-plan.md
+1 -3
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@@ -37,8 +37,6 @@ Ported from `agent_builder_server.py` lines 3484-3856. Pure filesystem reads —
| Tool | Purpose |
|------|---------|
| `list_agent_sessions(agent_name, status?, limit?)` | List sessions, filterable by status |
| `get_agent_session_state(agent_name, session_id)` | Full session state (memory excluded to prevent context bloat) |
| `get_agent_session_memory(agent_name, session_id, key?)` | Read memory contents from a session |
| `list_agent_checkpoints(agent_name, session_id)` | List checkpoints for debugging |
| `get_agent_checkpoint(agent_name, session_id, checkpoint_id?)` | Load a checkpoint's full state |
@@ -67,7 +65,7 @@ Add all 8 tools after the existing `undo_changes` tool:
# ── Meta-agent: Session & checkpoint inspection ───────────────
# _resolve_hive_agent_path(), _read_session_json(), _scan_agent_sessions(), _truncate_value()
# list_agent_sessions(), get_agent_session_state(), get_agent_session_memory()
# list_agent_sessions(), list_agent_checkpoints(), get_agent_checkpoint()
# list_agent_checkpoints(), get_agent_checkpoint()
# ── Meta-agent: Test execution ────────────────────────────────
docs/issue-queen-bee.md
+1 -1
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@@ -43,7 +43,7 @@ Dedicated tool server providing:
- **File I/O**: `read_file` (with line numbers, offset/limit), `write_file` (auto-mkdir), `edit_file` (9-strategy fuzzy matching ported from opencode), `list_directory`, `search_files` (regex)
- **Shell**: `run_command` (timeout, cwd, output truncation)
- **Git**: `undo_changes` (snapshot-based rollback)
- **Meta-agent**: `discover_mcp_tools`, `list_agents`, `list_agent_sessions`, `get_agent_session_state`, `get_agent_session_memory`, `list_agent_checkpoints`, `get_agent_checkpoint`, `run_agent_tests`
- **Meta-agent**: `discover_mcp_tools`, `list_agents`, `list_agent_sessions`, `list_agent_checkpoints`, `get_agent_checkpoint`, `run_agent_tests`
All file operations sandboxed to a configurable project root.
docs/pr-queen-bee.md
+1 -1
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@@ -16,7 +16,7 @@ The agent is deeply integrated with the framework: it can discover available MCP
- **`reference/`** — Framework guide, file templates, and anti-patterns docs embedded as agent reference material
### New: Coder Tools MCP Server (`tools/coder_tools_server.py`)
- 1500-line MCP server providing 15 tools: `read_file`, `write_file`, `edit_file` (with opencode-style 9-strategy fuzzy matching), `list_directory`, `search_files`, `run_command`, `undo_changes`, `discover_mcp_tools`, `list_agents`, `list_agent_sessions`, `get_agent_session_state`, `get_agent_session_memory`, `list_agent_checkpoints`, `get_agent_checkpoint`, `run_agent_tests`
- 1500-line MCP server providing 13 tools: `read_file`, `write_file`, `edit_file` (with opencode-style 9-strategy fuzzy matching), `list_directory`, `search_files`, `run_command`, `undo_changes`, `discover_mcp_tools`, `list_agents`, `list_agent_sessions`, `list_agent_checkpoints`, `get_agent_checkpoint`, `run_agent_tests`
- Path-scoped security: all file operations sandboxed to project root
- Git-based undo: automatic snapshots before writes with `undo_changes` rollback
docs/tui-selection-guide.md
-59
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@@ -1,59 +0,0 @@
# TUI Dashboard Guide
## Launching the TUI
There are two ways to launch the TUI dashboard:
```bash
# Browse and select an agent interactively
hive tui
# Launch the TUI for a specific agent
hive run exports/my_agent --tui
```
`hive tui` scans both `exports/` and `examples/templates/` for available agents, then presents a selection menu.
## Dashboard Panels
The TUI dashboard is divided into four areas:
- **Status Bar** - Shows the current agent name, execution state, and model in use
- **Graph Overview** - Live visualization of the agent's node graph with highlighted active node
- **Log Pane** - Scrollable event log streaming node transitions, LLM calls, and tool outputs
- **Chat REPL** - Input area for interacting with client-facing nodes (`ask_user()` prompts appear here)
## Keybindings
| Key | Action |
|---------------|-----------------------|
| `Tab` | Next panel |
| `Shift+Tab` | Previous panel |
| `Ctrl+S` | Save SVG screenshot |
| `Ctrl+O` | Command palette |
| `Q` | Quit |
## Panel Cycle Order
`Tab` cycles: **Log Pane → Graph View → Chat Input**
## Text Selection
Textual apps capture the mouse, so normal click-drag selection won't work by default. To select and copy text from any pane:
1. **Hold `Shift`** while clicking and dragging — this bypasses Textual's mouse capture and lets your terminal handle selection natively.
2. Copy with your terminal's shortcut (`Cmd+C` on macOS, `Ctrl+Shift+C` on most Linux terminals).
## Log Pane Scrolling
The log pane uses `auto_scroll=False`. New output only scrolls to the bottom when you are already at the bottom of the log. If you've scrolled up to read earlier output, it stays in place.
## Screenshots
`Ctrl+S` saves an SVG screenshot to the `screenshots/` directory with a timestamped filename. Open the SVG in any browser to view it.
## Tips
- Use `--mock` mode to explore agent execution without spending API credits: `hive run exports/my_agent --tui --mock`
- Override the default model with `--model`: `hive run exports/my_agent --model gpt-4o`
- Screenshots are saved as SVG files to `screenshots/` and can be opened in any browser
docs/worker-health-monitoring.md
+1 -1
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@@ -191,7 +191,7 @@ Both events are handled in the cross-graph filter (events from non-active graphs
## Known Gaps
**Gap 1 — Resolved.** The queen is now the full `HiveCoderAgent` graph (not a minimal hand-assembled subset). `_load_judge_and_queen` calls `HiveCoderAgent._setup(mock_mode=True)` to load hive-tools MCP, then merges those tools into the worker runtime alongside monitoring tools. When the operator connects via Ctrl+Q, they get `coder_node` with `read_file`, `write_file`, `run_command`, `restart_agent`, `get_agent_session_state`, and all other hive-tools. The `ticket_triage_node` still handles auto-triage on ticket events. `self._queen_agent` is held on the TUI instance to keep the MCP process alive.
**Gap 1 — Resolved.** The queen is now the full `HiveCoderAgent` graph (not a minimal hand-assembled subset). `_load_judge_and_queen` calls `HiveCoderAgent._setup(mock_mode=True)` to load hive-tools MCP, then merges those tools into the worker runtime alongside monitoring tools. When the operator connects via Ctrl+Q, they get `coder_node` with `read_file`, `write_file`, `run_command`, `restart_agent`, and all other hive-tools. The `ticket_triage_node` still handles auto-triage on ticket events. `self._queen_agent` is held on the TUI instance to keep the MCP process alive.
**Gap 2 — LLM-hang detection latency.**
If the worker's LLM call hangs (API never returns), no new log entries are written. The judge detects this on its next timer tick (≤2 min). Bounded latency, not zero.
tools/coder_tools_server.py
-88
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@@ -726,94 +726,6 @@ def list_agent_sessions(
)
@mcp.tool()
def get_agent_session_state(agent_name: str, session_id: str) -> str:
"""Load full session state (excluding memory to prevent context bloat).
Returns status, progress, result, metrics, and checkpoint info.
Use get_agent_session_memory to read memory contents separately.
Args:
agent_name: Agent package name (e.g. 'deep_research_agent')
session_id: Session ID (e.g. 'session_20260208_143022_abc12345')
Returns:
JSON with full session state
"""
agent_dir = _resolve_hive_agent_path(agent_name)
state_path = agent_dir / "sessions" / session_id / "state.json"
data = _read_session_json(state_path)
if data is None:
return json.dumps({"error": f"Session not found: {session_id}"})
# Exclude memory values but show keys
memory = data.get("memory", {})
data["memory_keys"] = list(memory.keys()) if isinstance(memory, dict) else []
data["memory_size"] = len(memory) if isinstance(memory, dict) else 0
data.pop("memory", None)
return json.dumps(data, indent=2, default=str)
@mcp.tool()
def get_agent_session_memory(
agent_name: str,
session_id: str,
key: str = "",
) -> str:
"""Read memory contents from a session.
Memory stores intermediate results passed between nodes. Use this
to inspect what data was produced during execution.
Args:
agent_name: Agent package name
session_id: Session ID
key: Specific memory key to retrieve. Empty for all keys.
Returns:
JSON with memory contents
"""
agent_dir = _resolve_hive_agent_path(agent_name)
state_path = agent_dir / "sessions" / session_id / "state.json"
data = _read_session_json(state_path)
if data is None:
return json.dumps({"error": f"Session not found: {session_id}"})
memory = data.get("memory", {})
if not isinstance(memory, dict):
memory = {}
if key:
if key not in memory:
return json.dumps(
{
"error": f"Memory key not found: '{key}'",
"available_keys": list(memory.keys()),
}
)
return json.dumps(
{
"session_id": session_id,
"key": key,
"value": memory[key],
"value_type": type(memory[key]).__name__,
},
indent=2,
default=str,
)
return json.dumps(
{
"session_id": session_id,
"memory": memory,
"total_keys": len(memory),
},
indent=2,
default=str,
)
@mcp.tool()
def list_agent_checkpoints(
agent_name: str,