Timothy
2001 lines
77 KiB
Python
2001 lines
77 KiB
Python
#!/usr/bin/env python3
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"""
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GitHub Outreach Pipeline Demo
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Demonstrates the full GraphExecutor framework: sequential pipeline,
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fan-out/fan-in parallel execution, feedback/callback edges, and
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client-facing HITL checkpoints — using a realistic GitHub outreach workflow.
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Pipeline:
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Intake (HITL) → Scanner → [Profiler || Scorer] → Extractor
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→ Review (HITL) ⇄ Extractor (feedback)
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→ Campaign Builder → Approval (HITL) ⇄ Campaign Builder (feedback)
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→ Sender (terminal)
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Features demonstrated:
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- Sequential pipeline (Intake → Scanner → ...)
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- Fan-out / fan-in (Scanner → [Profiler, Scorer] → Extractor)
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- Feedback edges (Review → Extractor, Approval → Campaign Builder)
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- Client-facing HITL checkpoints (Intake, Review, Approval)
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- SchemaJudge for output validation + native client_facing blocking for HITL
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- max_node_visits for feedback loop control
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Usage:
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cd /home/timothy/oss/hive/core
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python demos/github_outreach_demo.py
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Then open http://localhost:8768 in your browser.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import os
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import sys
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import tempfile
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from collections.abc import Callable
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from http import HTTPStatus
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from pathlib import Path
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from typing import Any
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import httpx
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import websockets
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from pydantic import BaseModel, ValidationError
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from websockets.http11 import Request, Response
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# Add core, tools, and hive root to path
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_CORE_DIR = Path(__file__).resolve().parent.parent
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_HIVE_DIR = _CORE_DIR.parent
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sys.path.insert(0, str(_CORE_DIR))
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sys.path.insert(0, str(_HIVE_DIR / "tools" / "src"))
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sys.path.insert(0, str(_HIVE_DIR))
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from aden_tools.credentials import CREDENTIAL_SPECS, CredentialStoreAdapter # noqa: E402
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from core.framework.credentials import CredentialStore # noqa: E402
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from framework.credentials.storage import ( # noqa: E402
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CompositeStorage,
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EncryptedFileStorage,
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EnvVarStorage,
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)
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from framework.graph.edge import EdgeCondition, EdgeSpec, GraphSpec # noqa: E402
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from framework.graph.event_loop_node import ( # noqa: E402
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EventLoopNode,
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JudgeVerdict,
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LoopConfig,
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)
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from framework.graph.executor import GraphExecutor # noqa: E402
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from framework.graph.goal import Goal # noqa: E402
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from framework.graph.node import NodeContext, NodeProtocol, NodeResult, NodeSpec # noqa: E402
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from framework.llm.litellm import LiteLLMProvider # noqa: E402
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from framework.runner.tool_registry import ToolRegistry # noqa: E402
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from framework.runtime.core import Runtime # noqa: E402
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from framework.runtime.event_bus import ( # noqa: E402
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AgentEvent,
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EventBus,
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EventType,
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)
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(message)s")
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logger = logging.getLogger("github_outreach_demo")
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# -------------------------------------------------------------------------
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# Persistent state
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# -------------------------------------------------------------------------
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STORE_DIR = Path(tempfile.mkdtemp(prefix="hive_outreach_"))
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RUNTIME = Runtime(STORE_DIR / "runtime")
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LLM = LiteLLMProvider(model="claude-haiku-4-5-20251001")
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# -------------------------------------------------------------------------
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# Credentials
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# -------------------------------------------------------------------------
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_env_mapping = {name: spec.env_var for name, spec in CREDENTIAL_SPECS.items()}
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_local_storage = CompositeStorage(
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primary=EncryptedFileStorage(),
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fallbacks=[EnvVarStorage(env_mapping=_env_mapping)],
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)
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if os.environ.get("ADEN_API_KEY"):
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try:
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from framework.credentials.aden import ( # noqa: E402
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AdenCachedStorage,
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AdenClientConfig,
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AdenCredentialClient,
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AdenSyncProvider,
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)
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_client = AdenCredentialClient(AdenClientConfig(base_url="https://api.adenhq.com"))
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_provider = AdenSyncProvider(client=_client)
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_storage = AdenCachedStorage(
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local_storage=_local_storage,
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aden_provider=_provider,
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)
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_cred_store = CredentialStore(storage=_storage, providers=[_provider], auto_refresh=True)
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_synced = _provider.sync_all(_cred_store)
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logger.info("Synced %d credentials from Aden", _synced)
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except Exception as e:
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logger.warning("Aden sync unavailable: %s", e)
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_cred_store = CredentialStore(storage=_local_storage)
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else:
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logger.info("ADEN_API_KEY not set, using local credential storage")
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_cred_store = CredentialStore(storage=_local_storage)
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CREDENTIALS = CredentialStoreAdapter(_cred_store)
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# =========================================================================
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# Pydantic Output Models (structural validation for hybrid judges)
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# =========================================================================
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class IntakeOutput(BaseModel):
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repo_url: str
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project_url: str
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scan_config: str
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min_leads: str # Minimum number of leads to collect (numeric string)
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class GitHubUser(BaseModel):
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username: str
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user_type: str # stargazer | contributor | issue_author
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class ScannerOutput(BaseModel):
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github_users: str # Filename of JSON file containing list[GitHubUser]
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class UserProfile(BaseModel):
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username: str
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name: str | None = None
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bio: str | None = None
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company: str | None = None
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languages: list[str] = []
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class ProfilerOutput(BaseModel):
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user_profiles: str # Filename of JSON file containing list[UserProfile]
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class RelevanceScore(BaseModel):
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username: str
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score: float
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reasoning: str
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class ScorerOutput(BaseModel):
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relevance_scores: str # Filename of JSON file containing list[RelevanceScore]
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class Contact(BaseModel):
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username: str
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name: str | None = None
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email: str | None = None
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twitter: str | None = None
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relevance_score: float
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class ExtractorOutput(BaseModel):
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contact_list: str # Filename of JSON file containing list[Contact]
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class DraftEmail(BaseModel):
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recipient: str
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subject: str
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body: str
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class CampaignOutput(BaseModel):
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draft_emails: str # Filename of JSON file containing list[DraftEmail]
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# =========================================================================
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# Tool Registration
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# =========================================================================
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TOOL_REGISTRY = ToolRegistry()
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# ---- MCP server tools (GitHub, web search, web scrape, email) ----
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# The aden-tools MCP server provides the official tool implementations.
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# Tools are auto-discovered via register_mcp_server() and available to
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# nodes through their NodeSpec.tools lists.
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_MCP_SERVER_PATH = str(_HIVE_DIR / "tools" / "mcp_server.py")
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_mcp_tool_count = TOOL_REGISTRY.register_mcp_server(
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{
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"name": "aden-tools",
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"transport": "stdio",
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"command": sys.executable,
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"args": [_MCP_SERVER_PATH, "--stdio"],
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"description": "Aden tools MCP server (GitHub, web search, email, etc.)",
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}
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)
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logger.info("Registered %d tools from MCP server", _mcp_tool_count)
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# ---- Demo-specific tools (context management for large data) ----
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# Agents use these to write large intermediate results to disk and pass
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# filenames between nodes, keeping the LLM conversation context small.
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_DATA_DIR = STORE_DIR / "data"
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def load_campaign_template() -> dict:
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"""Load the outreach campaign email template with placeholders."""
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return {
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"template": (
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"Subject: {project_name} - Collaboration Opportunity\n\n"
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"Hi {name},\n\n"
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"I noticed your work on {user_repo_highlights} and thought "
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"you might be interested in {project_name}. "
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"{personalized_hook}\n\n"
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"We'd love to have you involved. "
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"Would you be open to a quick chat?\n\n"
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"Best,\nThe {project_name} Team"
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),
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}
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def save_data(filename: str, data: str) -> dict:
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"""Save data to a file for later retrieval by this or downstream nodes.
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Use this to store large results (user lists, profiles, scores, emails)
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instead of passing them inline through set_output. Returns a brief
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summary with the filename to reference in set_output calls.
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Args:
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filename: Simple filename like 'github_users.json'. No paths or '..'.
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data: The string data to write (typically JSON).
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"""
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if not filename or ".." in filename or "/" in filename:
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return {"error": "Invalid filename. Use simple names like 'users.json'"}
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_DATA_DIR.mkdir(parents=True, exist_ok=True)
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path = _DATA_DIR / filename
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path.write_text(data, encoding="utf-8")
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lines = data.count("\n") + 1
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return {
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"success": True,
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"filename": filename,
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"size_bytes": len(data.encode("utf-8")),
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"lines": lines,
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"preview": data[:200] + ("..." if len(data) > 200 else ""),
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}
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def load_data(filename: str, offset: int = 0, limit: int = 50) -> dict:
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"""Load data from a previously saved file with pagination.
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Always returns a page of lines with metadata about the full file,
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so you know the shape of the data without loading it all into context.
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Args:
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filename: The filename that was used with save_data.
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offset: 0-based line number to start reading from. Default 0.
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limit: Max number of lines to return. Default 50.
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Examples:
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load_data('users.json') # first 50 lines
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load_data('users.json', offset=50, limit=50) # next 50 lines
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load_data('users.json', limit=200) # first 200 lines
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"""
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if not filename or ".." in filename or "/" in filename:
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return {"error": "Invalid filename"}
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offset = int(offset)
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limit = int(limit)
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path = _DATA_DIR / filename
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if not path.exists():
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return {"error": f"File not found: {filename}"}
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content = path.read_text(encoding="utf-8")
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size_bytes = len(content.encode("utf-8"))
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# If content is a single long line, try to pretty-print JSON so
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# line-based pagination actually works. Handles spillover files
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# written before the pretty-print fix in _truncate_tool_result().
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all_lines = content.split("\n")
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if len(all_lines) <= 2 and size_bytes > 500:
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try:
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parsed = json.loads(content)
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content = json.dumps(parsed, indent=2, ensure_ascii=False)
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all_lines = content.split("\n")
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except (json.JSONDecodeError, TypeError, ValueError):
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pass # Not JSON — keep original lines
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total = len(all_lines)
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start = min(offset, total)
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end = min(start + limit, total)
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sliced = all_lines[start:end]
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return {
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"success": True,
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"filename": filename,
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"content": "\n".join(sliced),
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"total_lines": total,
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"size_bytes": size_bytes,
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"offset": start,
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"lines_returned": len(sliced),
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"has_more": end < total,
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}
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def list_data_files() -> dict:
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"""List all data files saved during this pipeline run.
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Use this to discover what intermediate results are available
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from previous nodes in the pipeline.
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"""
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if not _DATA_DIR.exists():
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return {"files": []}
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files = []
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for f in sorted(_DATA_DIR.iterdir()):
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if f.is_file():
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files.append(
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{
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"filename": f.name,
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"size_bytes": f.stat().st_size,
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}
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)
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return {"files": files}
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TOOL_REGISTRY.register_function(load_campaign_template)
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TOOL_REGISTRY.register_function(save_data)
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TOOL_REGISTRY.register_function(load_data)
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TOOL_REGISTRY.register_function(list_data_files)
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logger.info("Tools registered: %s", ", ".join(TOOL_REGISTRY.get_registered_names()))
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# =========================================================================
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# Node Specifications
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# =========================================================================
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NODE_SPECS = {
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"intake": NodeSpec(
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id="intake",
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name="Intake",
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description="Gather repo URL, project URL, and scan configuration from the operator",
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node_type="event_loop",
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client_facing=True,
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input_keys=[],
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output_keys=["repo_url", "project_url", "scan_config", "min_leads"],
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system_prompt=(
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"You are the Intake agent for a GitHub outreach pipeline. "
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"Your job is to collect four pieces of information from the operator:\n\n"
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"1. **repo_url** — The GitHub repository URL to scan for potential contacts "
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"(e.g., https://github.com/anthropics/claude-code)\n"
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"2. **project_url** — The project URL we're promoting "
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"(e.g., https://github.com/our-org/our-project)\n"
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"3. **scan_config** — Scan parameters as a brief description "
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"(e.g., 'stargazers and contributors, last 6 months')\n"
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"4. **min_leads** — Minimum number of leads to collect (a number). "
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"Ask the operator: 'How many leads do you need at minimum?' "
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"If they say something vague like 'as many as possible', default to 50.\n\n"
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"CRITICAL: Once you have all four, you MUST call the set_output tool for EACH "
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"key. Call set_output(key='repo_url', value='...'), then "
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"set_output(key='project_url', value='...'), etc. for all four keys.\n"
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"Do NOT just say 'ready to proceed' or 'handing off' — the pipeline ONLY "
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"advances when you make actual set_output tool calls.\n"
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"For min_leads, set it as a plain number string (e.g., '100').\n"
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"Be conversational but efficient. Ask for missing information if the operator "
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"doesn't provide everything at once."
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),
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),
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"scanner": NodeSpec(
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id="scanner",
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name="Scanner",
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description="Scan a GitHub repository to find contributors, issue authors, and stargazers",
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node_type="event_loop",
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input_keys=["repo_url", "scan_config"],
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output_keys=["github_users"],
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tools=[
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"github_get_repo",
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"github_list_issues",
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"github_list_pull_requests",
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"github_list_stargazers",
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"execute_command_tool",
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"save_data",
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"load_data",
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"list_data_files",
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],
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system_prompt=(
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"You are a GitHub Scanner agent. You receive a repository URL and scan config.\n\n"
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"Parse the repository URL to extract the owner and repo name.\n"
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"Then use the available GitHub tools to discover users:\n"
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"1. Use github_get_repo to get repo metadata (stars, language, description)\n"
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"2. Use github_list_issues to find issue authors (look at each issue's user.login)\n"
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"3. Use github_list_pull_requests to find PR contributors "
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"(look at each PR's user.login)\n"
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"4. Use github_list_stargazers to find stargazers (each entry has a login field)\n\n"
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"PAGINATION — CRITICAL:\n"
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"Each GitHub list tool returns up to 100 items per page. You MUST paginate:\n"
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" - Always call with limit=100 for maximum results per page.\n"
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" - If a page returns 100 items, call again with page=2, page=3, etc.\n"
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" - Stop when a page returns fewer than 100 items (that's the last page).\n"
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" - Example: github_list_stargazers(owner, repo, page=1, limit=100)\n"
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" github_list_stargazers(owner, repo, page=2, limit=100)\n\n"
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"SPILLOVER RECOVERY:\n"
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"Tool results larger than 8KB are truncated. When you see a message like\n"
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" 'saved to tool_xxx.txt — Use load_data(filename) to read the full result'\n"
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"you MUST call load_data(filename) to retrieve the full data. The truncated\n"
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"preview only shows a fraction of the users. Use load_data with offset/limit\n"
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"to page through the file: load_data(filename, offset=0, limit=50), then\n"
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"offset=50, offset=100, etc. until has_more is false.\n\n"
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"USER COLLECTION:\n"
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"Collect unique usernames from all sources. Classify each user:\n"
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"- 'contributor' if they authored a pull request\n"
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"- 'issue_author' if they authored an issue\n"
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"- 'stargazer' if they starred the repo\n"
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"If a user appears in multiple categories, use the highest-value type "
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"(contributor > issue_author > stargazer).\n\n"
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"Respect the scan_config for any limits or filtering preferences.\n\n"
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"SHELL TOOL:\n"
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"You have execute_command_tool for running shell commands. Use it for file "
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"management tasks like merging JSON files, deduplicating data, or processing "
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"saved data with jq/python one-liners. Example:\n"
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" execute_command_tool(command='cat data/file.json | python3 -c \"import sys,json; "
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"d=json.load(sys.stdin); print(len(d))\"')\n\n"
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"CONTEXT MANAGEMENT:\n"
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"Format the result as a JSON array of objects with 'username' and 'user_type'.\n"
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"Use save_data('github_users.json', <json_string>) to write the list to a file.\n"
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"Then call set_output(key='github_users', value='github_users.json')\n"
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"This passes a filename — not the raw data — to downstream nodes, "
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"keeping the conversation context small."
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),
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),
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"profiler": NodeSpec(
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id="profiler",
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name="Profiler",
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description="Fetch detailed profiles for each discovered user",
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node_type="event_loop",
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input_keys=["github_users"],
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output_keys=["user_profiles"],
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tools=[
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"github_get_user_profile",
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"github_list_repos",
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"execute_command_tool",
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"load_data",
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"save_data",
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],
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system_prompt=(
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"You are a GitHub Profiler agent. Your input 'github_users' is a filename.\n\n"
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"WORKFLOW:\n"
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"1. Use load_data to read the user list from the input filename\n"
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"2. For each user, call github_get_user_profile to get their profile, and "
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"github_list_repos(username=<username>) to list their public repos\n"
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"3. Compile profiles into a JSON array with: username, name, bio, company, "
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"languages (from repos)\n"
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"4. Use save_data('user_profiles.json', <json>) to write results to a file\n"
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"5. Call set_output(key='user_profiles', value='user_profiles.json')\n\n"
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"SHELL TOOL:\n"
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"You have execute_command_tool for running shell commands. Use it for file "
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"management tasks like filtering JSON, extracting fields, or merging data. "
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"Example: execute_command_tool(command='python3 -c \"import json; ...')\n\n"
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"CONTEXT MANAGEMENT:\n"
|
|
"Start by loading the user list file. For large lists, use load_data with "
|
|
"offset and limit to page through users in batches "
|
|
"(e.g. load_data('github_users.json', offset=0, limit=20), then offset=20, etc.). "
|
|
"When done, save all profiles to a file and pass the filename — not the raw data."
|
|
),
|
|
),
|
|
"scorer": NodeSpec(
|
|
id="scorer",
|
|
name="Scorer",
|
|
description="Score each user's relevance to our project",
|
|
node_type="event_loop",
|
|
input_keys=["github_users", "project_url"],
|
|
output_keys=["relevance_scores"],
|
|
tools=["load_data", "save_data"],
|
|
system_prompt=(
|
|
"You are a Relevance Scorer agent. Your input 'github_users' is a filename "
|
|
"and 'project_url' is a URL string.\n\n"
|
|
"WORKFLOW:\n"
|
|
"1. Use load_data to read the user list from the github_users filename\n"
|
|
"2. For each user, assess relevance to the project based on:\n"
|
|
" - Their user_type (contributor > issue_author > stargazer)\n"
|
|
" - Assumed technical overlap with the project\n"
|
|
"3. Output a JSON array of objects with: username, score (0.0-1.0), reasoning\n"
|
|
"4. Use save_data('relevance_scores.json', <json>) to write results\n"
|
|
"5. Call set_output(key='relevance_scores', value='relevance_scores.json')\n\n"
|
|
"Score generously — this is a demo."
|
|
),
|
|
),
|
|
"extractor": NodeSpec(
|
|
id="extractor",
|
|
name="Extractor",
|
|
description="Build a curated contact list from profiles and scores",
|
|
node_type="event_loop",
|
|
input_keys=["user_profiles", "relevance_scores"],
|
|
output_keys=["contact_list"],
|
|
tools=[
|
|
"github_get_user_emails",
|
|
"web_search",
|
|
"web_scrape",
|
|
"execute_command_tool",
|
|
"load_data",
|
|
"save_data",
|
|
],
|
|
max_node_visits=3,
|
|
system_prompt=(
|
|
"You are a Contact Extractor agent. Your inputs 'user_profiles' and "
|
|
"'relevance_scores' are filenames pointing to JSON data files.\n\n"
|
|
"WORKFLOW:\n"
|
|
"1. Use load_data to read both files (profiles and scores). For large files, "
|
|
"use offset and limit to page through them incrementally "
|
|
"(e.g. load_data('file.json', offset=0, limit=30))\n"
|
|
"2. For each user with relevance score >= 0.3, enrich their contact info:\n"
|
|
" - Use github_get_user_emails(username) to find emails from their "
|
|
"public commits and profile (this is the BEST source for GitHub emails)\n"
|
|
" - If no email found, try web_search ('{username} github email')\n"
|
|
" - If results include personal sites, use web_scrape to extract details\n"
|
|
" - Look for: email addresses, Twitter/X handles, LinkedIn profiles\n"
|
|
"3. Compile a JSON array of contacts with: username, name, email, twitter, "
|
|
"relevance_score\n"
|
|
"4. Use save_data('contact_list.json', <json>) to write results\n"
|
|
"5. Call set_output(key='contact_list', value='contact_list.json')\n\n"
|
|
"SHELL TOOL:\n"
|
|
"You have execute_command_tool for running shell commands. Use it for file "
|
|
"management tasks like merging profiles and scores, deduplicating contacts, "
|
|
"or batch-processing data. Example:\n"
|
|
" execute_command_tool(command='python3 -c \"import json; profiles=json.load("
|
|
'open(\\"data/user_profiles.json\\")); print(len(profiles))"\')\n\n'
|
|
"Include all users who have at least one contact method."
|
|
),
|
|
),
|
|
"review": NodeSpec(
|
|
id="review",
|
|
name="Review",
|
|
description="Human operator reviews and approves the contact list",
|
|
node_type="event_loop",
|
|
client_facing=True,
|
|
input_keys=["contact_list"],
|
|
output_keys=["approved_contacts", "redo_extraction"],
|
|
nullable_output_keys=["approved_contacts", "redo_extraction"],
|
|
max_node_visits=3,
|
|
tools=["load_data", "save_data"],
|
|
system_prompt=(
|
|
"You are the Review agent at a human checkpoint. Your input 'contact_list' "
|
|
"is a filename pointing to a JSON data file.\n\n"
|
|
"WORKFLOW:\n"
|
|
"1. Use load_data to read the contact list from the file\n"
|
|
"2. Present the contacts to the operator in a clear, readable format\n"
|
|
" Show each contact with: name, username, email, twitter, relevance score\n"
|
|
"3. Ask the operator to either approve or request a redo\n"
|
|
"4. Based on the operator's response, call set_output EXACTLY ONCE:\n\n"
|
|
" IF APPROVED: save the approved list with save_data, then call:\n"
|
|
" set_output(key='approved_contacts', value='approved_contacts.json')\n\n"
|
|
" IF REDO REQUESTED: call:\n"
|
|
" set_output(key='redo_extraction', value='true')\n\n"
|
|
"CRITICAL RULE: Call set_output EXACTLY ONCE with EXACTLY ONE key.\n"
|
|
"NEVER call set_output twice. NEVER set both keys.\n"
|
|
"The two output keys are mutually exclusive — setting both will cause an error."
|
|
),
|
|
),
|
|
"campaign_builder": NodeSpec(
|
|
id="campaign_builder",
|
|
name="Campaign Builder",
|
|
description="Iteratively build personalized outreach emails in batches",
|
|
node_type="event_loop",
|
|
client_facing=True,
|
|
input_keys=["approved_contacts", "project_url"],
|
|
output_keys=["draft_emails"],
|
|
nullable_output_keys=["draft_emails"],
|
|
tools=["load_campaign_template", "load_data", "save_data"],
|
|
max_node_visits=5,
|
|
system_prompt=(
|
|
"You are the Campaign Builder agent. Your input 'approved_contacts' is a "
|
|
"filename and 'project_url' is a URL string.\n\n"
|
|
"ITERATIVE BATCH WORKFLOW:\n"
|
|
"You build draft emails in batches of up to 10 contacts at a time, "
|
|
"presenting each batch to the operator for review before continuing.\n\n"
|
|
"STEP 1 — SETUP:\n"
|
|
"- Use load_data to read the approved contacts from the file\n"
|
|
"- Load the campaign template using load_campaign_template\n"
|
|
"- Count total contacts and plan batches of up to 10\n\n"
|
|
"STEP 2 — DRAFT A BATCH (max 10 emails):\n"
|
|
"- For each contact in the current batch, write a personalized email:\n"
|
|
" * Fill in their name and relevant details from their profile\n"
|
|
" * Add a personalized hook based on their interests/contributions\n"
|
|
" * Format: {recipient, subject, body}\n"
|
|
"- Present the drafted emails to the operator clearly, showing:\n"
|
|
" * Batch number (e.g. 'Batch 1 of 4')\n"
|
|
" * Each email with recipient, subject, and body preview\n"
|
|
" * How many contacts remain\n\n"
|
|
"STEP 3 — ASK THE OPERATOR:\n"
|
|
"After presenting each batch, ask:\n"
|
|
" 'Create more drafts for the next batch, or submit all drafts "
|
|
"for outbound email?'\n\n"
|
|
" IF 'create more' → go back to STEP 2 for the next batch of contacts\n"
|
|
" IF 'submit' → go to STEP 4\n\n"
|
|
"STEP 4 — FINALIZE:\n"
|
|
"- Combine ALL drafted batches into a single JSON array\n"
|
|
"- Use save_data('draft_emails.json', <json>) to write them\n"
|
|
"- Call set_output(key='draft_emails', value='draft_emails.json')\n\n"
|
|
"RULES:\n"
|
|
"- Never draft more than 10 emails at once — always pause for operator input\n"
|
|
"- Keep a running total of emails drafted across batches\n"
|
|
"- Make each email feel personal and relevant\n"
|
|
"- Do NOT call set_output until the operator says to submit"
|
|
),
|
|
),
|
|
"approval": NodeSpec(
|
|
id="approval",
|
|
name="Approval",
|
|
description="Human operator reviews and approves campaign emails",
|
|
node_type="event_loop",
|
|
client_facing=True,
|
|
input_keys=["draft_emails"],
|
|
output_keys=["approved_emails", "revise_campaigns"],
|
|
nullable_output_keys=["approved_emails", "revise_campaigns"],
|
|
max_node_visits=3,
|
|
tools=["load_data", "save_data"],
|
|
system_prompt=(
|
|
"You are the Approval agent at the final human checkpoint. Your input "
|
|
"'draft_emails' is a filename pointing to a JSON data file.\n\n"
|
|
"WORKFLOW:\n"
|
|
"1. Use load_data to read the draft emails from the file\n"
|
|
"2. Present each email to the operator with: recipient, subject, and body\n"
|
|
"3. Ask the operator to either approve or request revision\n"
|
|
"4. Based on the operator's response, call set_output EXACTLY ONCE:\n\n"
|
|
" IF APPROVED: save the approved list with save_data, then call:\n"
|
|
" set_output(key='approved_emails', value='approved_emails.json')\n\n"
|
|
" IF REVISION REQUESTED: call:\n"
|
|
" set_output(key='revise_campaigns', value='true')\n\n"
|
|
"CRITICAL RULE: Call set_output EXACTLY ONCE with EXACTLY ONE key.\n"
|
|
"NEVER call set_output twice. NEVER set both keys.\n"
|
|
"The two output keys are mutually exclusive — setting both will cause an error."
|
|
),
|
|
),
|
|
"sender": NodeSpec(
|
|
id="sender",
|
|
name="Sender",
|
|
description="Send approved campaign emails",
|
|
node_type="event_loop",
|
|
input_keys=["approved_emails"],
|
|
output_keys=["send_results"],
|
|
),
|
|
}
|
|
|
|
|
|
# =========================================================================
|
|
# Edge + Graph Definitions
|
|
# =========================================================================
|
|
|
|
EDGES = [
|
|
EdgeSpec(
|
|
id="intake_to_scanner",
|
|
source="intake",
|
|
target="scanner",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
# Fan-out: scanner → profiler AND scorer (both ON_SUCCESS)
|
|
EdgeSpec(
|
|
id="scanner_to_profiler",
|
|
source="scanner",
|
|
target="profiler",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
EdgeSpec(
|
|
id="scanner_to_scorer",
|
|
source="scanner",
|
|
target="scorer",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
# Fan-in: profiler → extractor AND scorer → extractor
|
|
EdgeSpec(
|
|
id="profiler_to_extractor",
|
|
source="profiler",
|
|
target="extractor",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
EdgeSpec(
|
|
id="scorer_to_extractor",
|
|
source="scorer",
|
|
target="extractor",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
# Extractor → Review
|
|
EdgeSpec(
|
|
id="extractor_to_review",
|
|
source="extractor",
|
|
target="review",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
# Review: forward to campaign_builder OR feedback to extractor
|
|
EdgeSpec(
|
|
id="review_to_campaign",
|
|
source="review",
|
|
target="campaign_builder",
|
|
condition=EdgeCondition.CONDITIONAL,
|
|
condition_expr="output.get('approved_contacts') is not None",
|
|
priority=1,
|
|
),
|
|
EdgeSpec(
|
|
id="review_feedback",
|
|
source="review",
|
|
target="extractor",
|
|
condition=EdgeCondition.CONDITIONAL,
|
|
condition_expr="output.get('redo_extraction') is not None",
|
|
priority=-1,
|
|
),
|
|
# Campaign Builder → Approval
|
|
EdgeSpec(
|
|
id="campaign_to_approval",
|
|
source="campaign_builder",
|
|
target="approval",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
# Approval: forward to sender OR feedback to campaign_builder
|
|
EdgeSpec(
|
|
id="approval_to_sender",
|
|
source="approval",
|
|
target="sender",
|
|
condition=EdgeCondition.CONDITIONAL,
|
|
condition_expr="output.get('approved_emails') is not None",
|
|
priority=1,
|
|
),
|
|
EdgeSpec(
|
|
id="approval_feedback",
|
|
source="approval",
|
|
target="campaign_builder",
|
|
condition=EdgeCondition.CONDITIONAL,
|
|
condition_expr="output.get('revise_campaigns') is not None",
|
|
priority=-1,
|
|
),
|
|
]
|
|
|
|
GRAPH = GraphSpec(
|
|
id="github_outreach_pipeline",
|
|
goal_id="outreach_goal",
|
|
name="GitHub Outreach Pipeline",
|
|
entry_node="intake",
|
|
nodes=list(NODE_SPECS.values()),
|
|
edges=EDGES,
|
|
terminal_nodes=["sender"],
|
|
max_steps=30,
|
|
max_tokens=64000,
|
|
)
|
|
|
|
GOAL = Goal(
|
|
id="outreach_goal",
|
|
name="GitHub Outreach Campaign",
|
|
description=(
|
|
"Scan a GitHub repository to identify potential collaborators, "
|
|
"profile and score them, build a curated contact list, "
|
|
"create personalized outreach emails, and send approved campaigns."
|
|
),
|
|
)
|
|
|
|
|
|
# =========================================================================
|
|
# Sender Function (terminal node)
|
|
# =========================================================================
|
|
|
|
|
|
def _send_email_via_resend(
|
|
to: str,
|
|
subject: str,
|
|
html: str,
|
|
from_email: str,
|
|
) -> dict:
|
|
"""Send a single email via the Resend REST API."""
|
|
api_key = CREDENTIALS.get("resend") or os.getenv("RESEND_API_KEY")
|
|
if not api_key:
|
|
return {"error": "Resend API key not configured"}
|
|
|
|
# Testing override: redirect all recipients to a single address
|
|
override_to = os.getenv("EMAIL_OVERRIDE_TO")
|
|
if override_to:
|
|
subject = f"[TEST -> {to}] {subject}"
|
|
to = override_to
|
|
|
|
try:
|
|
resp = httpx.post(
|
|
"https://api.resend.com/emails",
|
|
headers={
|
|
"Authorization": f"Bearer {api_key}",
|
|
"Content-Type": "application/json",
|
|
},
|
|
json={
|
|
"from": from_email,
|
|
"to": [to] if isinstance(to, str) else to,
|
|
"subject": subject,
|
|
"html": html,
|
|
},
|
|
timeout=30.0,
|
|
)
|
|
if resp.status_code == 200:
|
|
data = resp.json()
|
|
return {
|
|
"success": True,
|
|
"id": data.get("id", ""),
|
|
}
|
|
return {
|
|
"error": (f"Resend API ({resp.status_code}): {resp.text[:200]}"),
|
|
}
|
|
except httpx.TimeoutException:
|
|
return {"error": "Email send timed out"}
|
|
except httpx.RequestError as e:
|
|
return {"error": f"Network error: {e}"}
|
|
|
|
|
|
class SenderNode(NodeProtocol):
|
|
"""Node wrapper for send_emails function."""
|
|
|
|
async def execute(self, ctx: NodeContext) -> NodeResult:
|
|
approved = ctx.input_data.get("approved_emails", "")
|
|
result_str = send_emails(approved_emails=approved)
|
|
ctx.memory.write("send_results", result_str)
|
|
return NodeResult(success=True, output={"send_results": result_str})
|
|
|
|
|
|
def send_emails(approved_emails: str = "") -> str:
|
|
"""Send approved campaign emails via Resend, or log if unconfigured.
|
|
|
|
Returns a JSON string.
|
|
"""
|
|
approved = approved_emails
|
|
if not approved:
|
|
return json.dumps({"error": "No approved emails to send"})
|
|
|
|
# Load from file if the value is a filename (file-ops pattern)
|
|
if isinstance(approved, str) and not approved.strip().startswith("["):
|
|
data_path = _DATA_DIR / approved
|
|
if data_path.exists():
|
|
approved = data_path.read_text(encoding="utf-8")
|
|
|
|
try:
|
|
emails = json.loads(approved) if isinstance(approved, str) else approved
|
|
except (json.JSONDecodeError, TypeError):
|
|
emails = [{"recipient": "unknown", "status": "parse_error"}]
|
|
|
|
from_email = os.getenv("EMAIL_FROM", "noreply@example.com")
|
|
has_resend = bool(CREDENTIALS.get("resend") or os.getenv("RESEND_API_KEY"))
|
|
|
|
results = []
|
|
for email in emails:
|
|
recipient = email.get("recipient", "unknown")
|
|
subject = email.get("subject", "")
|
|
body = email.get("body", "")
|
|
html_body = "<div style='font-family:sans-serif;'>" + body.replace("\n", "<br>") + "</div>"
|
|
|
|
if has_resend:
|
|
result = _send_email_via_resend(
|
|
to=recipient,
|
|
subject=subject,
|
|
html=html_body,
|
|
from_email=from_email,
|
|
)
|
|
sent_ok = result.get("success", False)
|
|
status = "sent" if sent_ok else "failed"
|
|
msg_id = result.get("id", "")
|
|
error = result.get("error", "")
|
|
else:
|
|
status = "logged"
|
|
msg_id = f"mock_{len(results) + 1:03d}"
|
|
error = ""
|
|
logger.info(
|
|
"(mock) Would send to %s: %s",
|
|
recipient,
|
|
subject,
|
|
)
|
|
|
|
results.append(
|
|
{
|
|
"recipient": recipient,
|
|
"subject": subject,
|
|
"status": status,
|
|
"message_id": msg_id,
|
|
"error": error,
|
|
}
|
|
)
|
|
if status == "sent":
|
|
logger.info(
|
|
"Sent to %s: %s",
|
|
recipient,
|
|
subject,
|
|
)
|
|
|
|
return json.dumps(results)
|
|
|
|
|
|
# =========================================================================
|
|
# Judges (SchemaJudge for output validation)
|
|
# =========================================================================
|
|
|
|
|
|
class SchemaJudge:
|
|
"""Judge that validates event loop output against a Pydantic model.
|
|
|
|
For internal (non-client-facing) nodes:
|
|
1. Check if required output keys are set
|
|
2. Validate accumulated values against Pydantic model
|
|
3. Optionally load referenced files and check minimum item counts
|
|
4. RETRY with structural feedback on validation failure
|
|
5. ACCEPT on valid output
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
output_model: type[BaseModel],
|
|
min_items: dict[str, int | Callable[[], int]] | None = None,
|
|
data_dir: Path | None = None,
|
|
):
|
|
self._model = output_model
|
|
self._min_items = min_items or {}
|
|
self._data_dir = data_dir
|
|
|
|
async def evaluate(self, context: dict) -> JudgeVerdict:
|
|
accumulator = context.get("output_accumulator", {})
|
|
missing = context.get("missing_keys", [])
|
|
|
|
if missing:
|
|
return JudgeVerdict(
|
|
action="RETRY",
|
|
feedback=f"Missing output keys: {missing}. Use set_output to provide them.",
|
|
)
|
|
|
|
# Try to validate against schema
|
|
try:
|
|
parsed = {}
|
|
for key, value in accumulator.items():
|
|
if value is None:
|
|
continue
|
|
if isinstance(value, str):
|
|
stripped = value.strip()
|
|
if stripped and stripped[0] in ("{", "["):
|
|
try:
|
|
parsed[key] = json.loads(value)
|
|
except json.JSONDecodeError:
|
|
parsed[key] = value
|
|
else:
|
|
parsed[key] = value
|
|
else:
|
|
parsed[key] = value
|
|
self._model.model_validate(parsed)
|
|
except ValidationError as e:
|
|
errors = "; ".join(
|
|
f"{'.'.join(str(x) for x in err['loc'])}: {err['msg']}" for err in e.errors()
|
|
)
|
|
return JudgeVerdict(
|
|
action="RETRY",
|
|
feedback=f"Output schema validation failed: {errors}. Fix and re-set outputs.",
|
|
)
|
|
|
|
# Check minimum item counts for file-based outputs
|
|
for key, min_count_or_fn in self._min_items.items():
|
|
min_count = min_count_or_fn() if callable(min_count_or_fn) else min_count_or_fn
|
|
value = accumulator.get(key)
|
|
if not value or not self._data_dir:
|
|
continue
|
|
fpath = self._data_dir / value
|
|
if not fpath.exists():
|
|
return JudgeVerdict(
|
|
action="RETRY",
|
|
feedback=f"Output file '{value}' not found. Use save_data to create it.",
|
|
)
|
|
try:
|
|
content = fpath.read_text(encoding="utf-8")
|
|
data = json.loads(content)
|
|
if isinstance(data, list) and len(data) < min_count:
|
|
return JudgeVerdict(
|
|
action="RETRY",
|
|
feedback=(
|
|
f"Insufficient results: '{value}' contains {len(data)} items "
|
|
f"but at least {min_count} are required. "
|
|
f"Use pagination (page=1, page=2, ...) on the GitHub tools "
|
|
f"and load_data on spillover files to collect more users."
|
|
),
|
|
)
|
|
except (json.JSONDecodeError, OSError):
|
|
pass # File exists but can't be parsed — let it through
|
|
|
|
return JudgeVerdict(action="ACCEPT")
|
|
|
|
|
|
# =========================================================================
|
|
# HTML Page (embedded)
|
|
# =========================================================================
|
|
|
|
HTML_PAGE = """<!DOCTYPE html>
|
|
<html lang="en">
|
|
<head>
|
|
<meta charset="utf-8">
|
|
<meta name="viewport" content="width=device-width, initial-scale=1">
|
|
<title>GitHub Outreach Pipeline</title>
|
|
<style>
|
|
* { box-sizing: border-box; margin: 0; padding: 0; }
|
|
body {
|
|
font-family: 'SF Mono', 'Fira Code', monospace;
|
|
background: #0d1117; color: #c9d1d9;
|
|
height: 100vh; display: flex; flex-direction: column;
|
|
}
|
|
header {
|
|
background: #161b22; padding: 10px 20px;
|
|
border-bottom: 1px solid #30363d;
|
|
display: flex; align-items: center; gap: 10px; flex-wrap: wrap;
|
|
}
|
|
header h1 { font-size: 14px; color: #58a6ff; font-weight: 600; }
|
|
.badge {
|
|
font-size: 10px; padding: 2px 7px; border-radius: 10px;
|
|
background: #21262d; color: #484f58;
|
|
}
|
|
.badge.active { font-weight: 600; background: #1a3a5c; color: #58a6ff; }
|
|
.badge.done { background: #1a4b2e; color: #3fb950; }
|
|
.badge.waiting { background: #1c1c1c; color: #6e7681; }
|
|
.badge.client { border: 1px solid #58a6ff33; }
|
|
.main { flex: 1; display: flex; overflow: hidden; }
|
|
.chat {
|
|
flex: 65; overflow-y: auto; padding: 12px; min-width: 0;
|
|
border-right: 1px solid #30363d;
|
|
}
|
|
.graph-panel {
|
|
flex: 35; display: flex; flex-direction: column;
|
|
padding: 12px; min-width: 260px; background: #0d1117;
|
|
}
|
|
.graph-title {
|
|
font-size: 11px; color: #8b949e; font-weight: 600;
|
|
margin-bottom: 8px; text-transform: uppercase; letter-spacing: 1px;
|
|
}
|
|
.graph-svg { flex: 1; width: 100%; }
|
|
.graph-legend {
|
|
font-size: 9px; color: #484f58; margin-top: 6px; line-height: 1.8;
|
|
}
|
|
.legend-dot {
|
|
display: inline-block; width: 7px; height: 7px;
|
|
border-radius: 50%; margin-right: 3px; vertical-align: middle;
|
|
}
|
|
.msg {
|
|
margin: 3px 0; padding: 7px 10px; border-radius: 5px;
|
|
line-height: 1.5; white-space: pre-wrap; word-wrap: break-word;
|
|
font-size: 12px; border-left: 3px solid transparent;
|
|
}
|
|
.msg.user { background: #1a3a5c; color: #58a6ff; border-left-color: #58a6ff; }
|
|
.msg.assistant { background: #161b22; color: #c9d1d9; }
|
|
.msg.event {
|
|
background: transparent; color: #8b949e; font-size: 10px;
|
|
padding: 2px 10px;
|
|
}
|
|
.msg.event.tool { border-left-color: #d29922; }
|
|
.msg.event.done { color: #3fb950; }
|
|
.msg.event.feedback { border-left-color: #f85149; color: #f85149; }
|
|
.msg .node-tag {
|
|
font-size: 9px; font-weight: 700; margin-right: 5px;
|
|
text-transform: uppercase; letter-spacing: 0.5px;
|
|
}
|
|
/* Node colors */
|
|
.node-intake .node-tag { color: #58a6ff; }
|
|
.node-scanner .node-tag { color: #d29922; }
|
|
.node-profiler .node-tag { color: #bc8cff; }
|
|
.node-scorer .node-tag { color: #bc8cff; }
|
|
.node-extractor .node-tag { color: #d29922; }
|
|
.node-review .node-tag { color: #58a6ff; }
|
|
.node-campaign_builder .node-tag { color: #58a6ff; }
|
|
.node-approval .node-tag { color: #58a6ff; }
|
|
.node-sender .node-tag { color: #3fb950; }
|
|
.result-banner {
|
|
margin: 12px 0; padding: 14px; border-radius: 8px;
|
|
background: #0a2614; border: 1px solid #3fb950;
|
|
}
|
|
.result-banner h3 {
|
|
color: #3fb950; font-size: 12px; margin-bottom: 8px; text-align: center;
|
|
}
|
|
.result-banner .report {
|
|
color: #c9d1d9; font-size: 11px; line-height: 1.5;
|
|
max-height: 300px; overflow-y: auto; white-space: pre-wrap;
|
|
}
|
|
.result-banner .tokens {
|
|
color: #484f58; font-size: 9px; text-align: center; margin-top: 6px;
|
|
}
|
|
.input-bar {
|
|
padding: 10px 16px; background: #161b22;
|
|
border-top: 1px solid #30363d; display: flex; gap: 8px;
|
|
}
|
|
.input-bar input {
|
|
flex: 1; background: #0d1117; border: 1px solid #30363d;
|
|
color: #c9d1d9; padding: 8px 12px; border-radius: 6px;
|
|
font-family: inherit; font-size: 12px; outline: none;
|
|
}
|
|
.input-bar input:focus { border-color: #58a6ff; }
|
|
.input-bar input:disabled { color: #484f58; }
|
|
.input-bar button {
|
|
background: #238636; color: #fff; border: none;
|
|
padding: 8px 16px; border-radius: 6px; cursor: pointer;
|
|
font-family: inherit; font-weight: 600; font-size: 12px;
|
|
}
|
|
.input-bar button:hover { background: #2ea043; }
|
|
.input-bar button:disabled {
|
|
background: #21262d; color: #484f58; cursor: not-allowed;
|
|
}
|
|
#status-text {
|
|
font-size: 10px; color: #8b949e; margin-left: auto;
|
|
}
|
|
/* SVG graph styles */
|
|
.graph-node rect { transition: stroke-width 0.2s, stroke 0.2s; }
|
|
.graph-node.active rect { stroke-width: 3; stroke: #58a6ff; }
|
|
.graph-node.done rect { stroke-width: 2; stroke: #3fb950; }
|
|
@keyframes waitingDash { to { stroke-dashoffset: -24; } }
|
|
.graph-node.waiting rect {
|
|
stroke: #484f58; stroke-width: 2;
|
|
stroke-dasharray: 8 4;
|
|
animation: waitingDash 1.2s linear infinite;
|
|
}
|
|
@keyframes edgePulse {
|
|
0% { stroke-opacity: 1; stroke-width: 3; }
|
|
100% { stroke-opacity: 0.3; stroke-width: 1.5; }
|
|
}
|
|
svg line.flash, svg path.flash {
|
|
stroke: #58a6ff !important;
|
|
animation: edgePulse 0.8s ease-out forwards;
|
|
}
|
|
/* Badge spinner animations */
|
|
.badge.waiting::before {
|
|
content: ''; display: inline-block;
|
|
width: 8px; height: 8px;
|
|
border: 1.5px solid #30363d; border-top-color: #6e7681;
|
|
border-radius: 50%; vertical-align: middle; margin-right: 4px;
|
|
animation: badgeSpin 0.7s linear infinite;
|
|
}
|
|
@keyframes badgeSpin { to { transform: rotate(360deg); } }
|
|
.badge.active::before {
|
|
content: ''; display: inline-block;
|
|
width: 6px; height: 6px; background: #58a6ff;
|
|
border-radius: 50%; vertical-align: middle; margin-right: 4px;
|
|
animation: badgePulse 1s ease-in-out infinite;
|
|
}
|
|
@keyframes badgePulse {
|
|
0%, 100% { opacity: 1; transform: scale(1); }
|
|
50% { opacity: 0.5; transform: scale(0.8); }
|
|
}
|
|
/* Activity cards for internal nodes */
|
|
.activity-card {
|
|
margin: 4px 0; padding: 6px 12px; border-radius: 6px;
|
|
font-size: 11px; display: flex; align-items: center; gap: 8px;
|
|
background: #161b22; border-left: 3px solid #30363d;
|
|
transition: opacity 0.3s, border-color 0.3s;
|
|
}
|
|
.activity-card.active { border-left-color: #58a6ff; }
|
|
.activity-card.done { border-left-color: #3fb950; opacity: 0.7; }
|
|
.activity-card .card-label {
|
|
font-weight: 700; font-size: 9px; letter-spacing: 0.5px;
|
|
min-width: 70px;
|
|
}
|
|
.activity-card .card-action { flex: 1; color: #8b949e; }
|
|
.activity-card .card-time {
|
|
color: #484f58; font-size: 10px; min-width: 30px; text-align: right;
|
|
}
|
|
.activity-card.node-scanner .card-label { color: #d29922; }
|
|
.activity-card.node-profiler .card-label { color: #bc8cff; }
|
|
.activity-card.node-scorer .card-label { color: #bc8cff; }
|
|
.activity-card.node-extractor .card-label { color: #d29922; }
|
|
.activity-card.node-campaign_builder .card-label { color: #58a6ff; }
|
|
</style>
|
|
</head>
|
|
<body>
|
|
<header>
|
|
<h1>GitHub Outreach Pipeline</h1>
|
|
<span id="badge-intake" class="badge client">Intake</span>
|
|
<span id="badge-scanner" class="badge">Scanner</span>
|
|
<span id="badge-profiler" class="badge">Profiler</span>
|
|
<span id="badge-scorer" class="badge">Scorer</span>
|
|
<span id="badge-extractor" class="badge">Extractor</span>
|
|
<span id="badge-review" class="badge client">Review</span>
|
|
<span id="badge-campaign_builder" class="badge">Campaign</span>
|
|
<span id="badge-approval" class="badge client">Approval</span>
|
|
<span id="badge-sender" class="badge">Sender</span>
|
|
<span id="status-text">Ready</span>
|
|
<span id="progress-info" style="font-size:10px;color:#484f58;margin-left:4px"></span>
|
|
</header>
|
|
|
|
<div class="main">
|
|
<div id="chat" class="chat"></div>
|
|
<div class="graph-panel">
|
|
<div class="graph-title">Pipeline Graph</div>
|
|
<svg class="graph-svg" viewBox="0 0 340 640" xmlns="http://www.w3.org/2000/svg">
|
|
<defs>
|
|
<marker id="arrow" markerWidth="7" markerHeight="5" refX="6" refY="2.5" orient="auto">
|
|
<polygon points="0 0, 7 2.5, 0 5" fill="#484f58"/>
|
|
</marker>
|
|
<marker id="arrow-red" markerWidth="7" markerHeight="5" refX="6" refY="2.5" orient="auto">
|
|
<polygon points="0 0, 7 2.5, 0 5" fill="#f85149"/>
|
|
</marker>
|
|
</defs>
|
|
|
|
<!-- Forward edges -->
|
|
<line id="edge-intake-scanner" x1="170" y1="42" x2="170" y2="68" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-scanner-profiler" x1="135" y1="102" x2="85" y2="128" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-scanner-scorer" x1="205" y1="102" x2="255" y2="128" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-profiler-extractor" x1="85" y1="162" x2="135" y2="188" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-scorer-extractor" x1="255" y1="162" x2="205" y2="188" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-extractor-review" x1="170" y1="222" x2="170" y2="248" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-review-campaign_builder" x1="170" y1="282" x2="170" y2="368" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-campaign_builder-approval" x1="170" y1="402" x2="170" y2="428" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
<line id="edge-approval-sender" x1="170" y1="462" x2="170" y2="548" stroke="#30363d" stroke-width="1.2" marker-end="url(#arrow)"/>
|
|
|
|
<!-- Feedback edges (curved, red dashed) -->
|
|
<path id="edge-review-feedback" d="M 130 270 Q 40 330 130 200" stroke="#f8514966" stroke-width="1" fill="none" stroke-dasharray="4 3" marker-end="url(#arrow-red)"/>
|
|
<path id="edge-approval-feedback" d="M 130 450 Q 40 510 130 380" stroke="#f8514966" stroke-width="1" fill="none" stroke-dasharray="4 3" marker-end="url(#arrow-red)"/>
|
|
|
|
<!-- Nodes -->
|
|
<g id="gnode-intake" class="graph-node">
|
|
<rect x="110" y="10" width="120" height="32" rx="6" fill="#161b22" stroke="#58a6ff" stroke-width="1.5"/>
|
|
<text x="170" y="31" fill="#58a6ff" text-anchor="middle" font-size="11" font-weight="600" font-family="SF Mono,monospace">Intake</text>
|
|
</g>
|
|
<g id="gnode-scanner" class="graph-node">
|
|
<rect x="110" y="70" width="120" height="32" rx="6" fill="#161b22" stroke="#30363d" stroke-width="1.5"/>
|
|
<text x="170" y="91" fill="#d29922" text-anchor="middle" font-size="11" font-weight="600" font-family="SF Mono,monospace">Scanner</text>
|
|
</g>
|
|
<g id="gnode-profiler" class="graph-node">
|
|
<rect x="20" y="130" width="110" height="32" rx="6" fill="#161b22" stroke="#30363d" stroke-width="1.5"/>
|
|
<text x="75" y="151" fill="#bc8cff" text-anchor="middle" font-size="10" font-weight="600" font-family="SF Mono,monospace">Profiler</text>
|
|
</g>
|
|
<g id="gnode-scorer" class="graph-node">
|
|
<rect x="210" y="130" width="110" height="32" rx="6" fill="#161b22" stroke="#30363d" stroke-width="1.5"/>
|
|
<text x="265" y="151" fill="#bc8cff" text-anchor="middle" font-size="10" font-weight="600" font-family="SF Mono,monospace">Scorer</text>
|
|
</g>
|
|
<g id="gnode-extractor" class="graph-node">
|
|
<rect x="110" y="190" width="120" height="32" rx="6" fill="#161b22" stroke="#30363d" stroke-width="1.5"/>
|
|
<text x="170" y="211" fill="#d29922" text-anchor="middle" font-size="11" font-weight="600" font-family="SF Mono,monospace">Extractor</text>
|
|
</g>
|
|
<g id="gnode-review" class="graph-node">
|
|
<rect x="110" y="250" width="120" height="32" rx="6" fill="#161b22" stroke="#58a6ff" stroke-width="1.5"/>
|
|
<text x="170" y="271" fill="#58a6ff" text-anchor="middle" font-size="11" font-weight="600" font-family="SF Mono,monospace">Review</text>
|
|
</g>
|
|
<g id="gnode-campaign_builder" class="graph-node">
|
|
<rect x="100" y="370" width="140" height="32" rx="6" fill="#161b22" stroke="#30363d" stroke-width="1.5"/>
|
|
<text x="170" y="391" fill="#d29922" text-anchor="middle" font-size="10" font-weight="600" font-family="SF Mono,monospace">Campaign Builder</text>
|
|
</g>
|
|
<g id="gnode-approval" class="graph-node">
|
|
<rect x="110" y="430" width="120" height="32" rx="6" fill="#161b22" stroke="#58a6ff" stroke-width="1.5"/>
|
|
<text x="170" y="451" fill="#58a6ff" text-anchor="middle" font-size="11" font-weight="600" font-family="SF Mono,monospace">Approval</text>
|
|
</g>
|
|
<g id="gnode-sender" class="graph-node">
|
|
<rect x="110" y="550" width="120" height="32" rx="6" fill="#161b22" stroke="#3fb950" stroke-width="1.5"/>
|
|
<text x="170" y="571" fill="#3fb950" text-anchor="middle" font-size="11" font-weight="600" font-family="SF Mono,monospace">Sender</text>
|
|
</g>
|
|
<!-- Status text below each node -->
|
|
<text id="status-intake" x="170" y="55" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-scanner" x="170" y="113" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-profiler" x="75" y="173" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-scorer" x="265" y="173" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-extractor" x="170" y="233" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-review" x="170" y="295" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-campaign_builder" x="170" y="415" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-approval" x="170" y="475" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
<text id="status-sender" x="170" y="593" fill="#484f58" text-anchor="middle"
|
|
font-size="8" font-family="SF Mono,monospace"></text>
|
|
</svg>
|
|
<div class="graph-legend">
|
|
<span class="legend-dot" style="background:#58a6ff"></span>Client-facing (HITL)
|
|
<span class="legend-dot" style="background:#d29922;margin-left:6px"></span>Internal
|
|
<span class="legend-dot" style="background:#bc8cff;margin-left:6px"></span>Parallel
|
|
<span class="legend-dot" style="background:#3fb950;margin-left:6px"></span>Terminal
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="input-bar">
|
|
<input id="input" type="text"
|
|
placeholder="Tell the Intake agent what repo to scan..." autofocus />
|
|
<button id="send" onclick="sendMsg()">Send</button>
|
|
</div>
|
|
|
|
<script>
|
|
const chat = document.getElementById('chat');
|
|
const sendBtn = document.getElementById('send');
|
|
const inputEl = document.getElementById('input');
|
|
const statusText = document.getElementById('status-text');
|
|
|
|
const nodeNames = {
|
|
intake: 'Intake', scanner: 'Scanner', profiler: 'Profiler',
|
|
scorer: 'Scorer', extractor: 'Extractor', review: 'Review',
|
|
campaign_builder: 'Campaign', approval: 'Approval', sender: 'Sender'
|
|
};
|
|
const allNodes = Object.keys(nodeNames);
|
|
|
|
let ws = null;
|
|
let started = false;
|
|
let inputEnabled = true;
|
|
const assistantEls = {};
|
|
const activityCards = {};
|
|
const cardTimers = {};
|
|
let lastCompletedNode = null;
|
|
let pipelineStartTime = null;
|
|
let progressInterval = null;
|
|
let totalIterations = 0;
|
|
|
|
const EDGE_MAP = {
|
|
'intake->scanner': 'edge-intake-scanner',
|
|
'scanner->profiler': 'edge-scanner-profiler',
|
|
'scanner->scorer': 'edge-scanner-scorer',
|
|
'profiler->extractor': 'edge-profiler-extractor',
|
|
'scorer->extractor': 'edge-scorer-extractor',
|
|
'extractor->review': 'edge-extractor-review',
|
|
'review->campaign_builder': 'edge-review-campaign_builder',
|
|
'review->extractor': 'edge-review-feedback',
|
|
'campaign_builder->approval': 'edge-campaign_builder-approval',
|
|
'approval->sender': 'edge-approval-sender',
|
|
'approval->campaign_builder': 'edge-approval-feedback',
|
|
};
|
|
|
|
const spinChars = ['\u280b','\u2819','\u2839','\u2838','\u283c',
|
|
'\u2834','\u2826','\u2827','\u2807','\u280f'];
|
|
const spinTimers = {};
|
|
|
|
inputEl.addEventListener('keydown', e => { if (e.key === 'Enter') sendMsg(); });
|
|
|
|
function setStatus(text) { statusText.textContent = text; }
|
|
|
|
function setNodeState(nid, state) {
|
|
const g = document.getElementById('gnode-' + nid);
|
|
const b = document.getElementById('badge-' + nid);
|
|
if (g) { g.classList.remove('active','done','waiting'); if (state) g.classList.add(state); }
|
|
if (b) { b.classList.remove('active','done','waiting'); if (state) b.classList.add(state); }
|
|
}
|
|
|
|
function addMsg(html, cls) {
|
|
const el = document.createElement('div');
|
|
el.className = 'msg ' + cls;
|
|
el.innerHTML = html;
|
|
chat.appendChild(el);
|
|
chat.scrollTop = chat.scrollHeight;
|
|
return el;
|
|
}
|
|
|
|
function addNodeMsg(nid, text, cls) {
|
|
const tag = '<span class="node-tag">' + (nodeNames[nid]||nid) + '</span>';
|
|
const el = addMsg(tag, 'assistant node-' + nid + ' ' + (cls||''));
|
|
const span = document.createElement('span');
|
|
span.className = 'text-content';
|
|
span.textContent = text;
|
|
el.appendChild(span);
|
|
return el;
|
|
}
|
|
|
|
function addEventMsg(nid, text, cls) {
|
|
const prefix = nid ? ('[' + (nodeNames[nid]||nid) + '] ') : '';
|
|
return addMsg(prefix + text, 'event ' + (cls||''));
|
|
}
|
|
|
|
function enableInput(placeholder) {
|
|
inputEnabled = true;
|
|
inputEl.disabled = false;
|
|
sendBtn.disabled = false;
|
|
if (placeholder) inputEl.placeholder = placeholder;
|
|
inputEl.focus();
|
|
}
|
|
|
|
function disableInput(placeholder) {
|
|
inputEnabled = false;
|
|
inputEl.disabled = true;
|
|
sendBtn.disabled = true;
|
|
if (placeholder) inputEl.placeholder = placeholder;
|
|
}
|
|
|
|
// --- SVG status text with braille spinner ---
|
|
|
|
function setNodeStatus(nid, text) {
|
|
const s = document.getElementById('status-' + nid);
|
|
if (!s) return;
|
|
if (spinTimers[nid]) { clearInterval(spinTimers[nid]); delete spinTimers[nid]; }
|
|
if (!text || text === 'idle') {
|
|
s.textContent = ''; s.setAttribute('fill', '#484f58'); return;
|
|
}
|
|
if (text.startsWith('\u2713')) {
|
|
s.textContent = text; s.setAttribute('fill', '#3fb950'); return;
|
|
}
|
|
let f = 0;
|
|
s.textContent = spinChars[0] + ' ' + text;
|
|
s.setAttribute('fill', '#8b949e');
|
|
spinTimers[nid] = setInterval(() => {
|
|
f = (f + 1) % spinChars.length;
|
|
s.textContent = spinChars[f] + ' ' + text;
|
|
}, 80);
|
|
}
|
|
|
|
// --- Edge flash ---
|
|
|
|
function flashEdge(from, to) {
|
|
const key = from + '->' + to;
|
|
const id = EDGE_MAP[key];
|
|
if (!id) return;
|
|
const el = document.getElementById(id);
|
|
if (!el) return;
|
|
el.classList.remove('flash');
|
|
void el.offsetWidth;
|
|
el.classList.add('flash');
|
|
setTimeout(() => el.classList.remove('flash'), 900);
|
|
}
|
|
|
|
// --- Activity cards for internal nodes ---
|
|
|
|
function getOrCreateActivityCard(nid) {
|
|
if (activityCards[nid]) return activityCards[nid];
|
|
const card = document.createElement('div');
|
|
card.className = 'activity-card active node-' + nid;
|
|
card.id = 'card-' + nid;
|
|
const lbl = (nodeNames[nid] || nid).toUpperCase();
|
|
card.innerHTML = '<span class="card-label">' + lbl +
|
|
'</span><span class="card-action">thinking...</span>' +
|
|
'<span class="card-time">0s</span>';
|
|
chat.appendChild(card);
|
|
chat.scrollTop = chat.scrollHeight;
|
|
activityCards[nid] = card;
|
|
const start = Date.now();
|
|
cardTimers[nid] = setInterval(() => {
|
|
const elapsed = Math.round((Date.now() - start) / 1000);
|
|
const t = card.querySelector('.card-time');
|
|
if (t) t.textContent = elapsed + 's';
|
|
}, 1000);
|
|
return card;
|
|
}
|
|
|
|
function updateActivityCard(nid, action) {
|
|
const card = getOrCreateActivityCard(nid);
|
|
const el = card.querySelector('.card-action');
|
|
if (el) el.textContent = action;
|
|
}
|
|
|
|
function completeActivityCard(nid, iterations) {
|
|
const card = activityCards[nid];
|
|
if (!card) return;
|
|
if (cardTimers[nid]) { clearInterval(cardTimers[nid]); delete cardTimers[nid]; }
|
|
card.classList.remove('active');
|
|
card.classList.add('done');
|
|
const el = card.querySelector('.card-action');
|
|
if (el) el.textContent = 'done (' + (iterations || '?') + ' iters)';
|
|
activityCards[nid] = null;
|
|
}
|
|
|
|
// --- Progress indicator ---
|
|
|
|
function updateProgress() {
|
|
const el = document.getElementById('progress-info');
|
|
if (!el || !pipelineStartTime) return;
|
|
const elapsed = Math.round((Date.now() - pipelineStartTime) / 1000);
|
|
el.textContent = '[iter ' + totalIterations + ' | ' + elapsed + 's]';
|
|
}
|
|
|
|
function startProgress() {
|
|
pipelineStartTime = Date.now();
|
|
totalIterations = 0;
|
|
updateProgress();
|
|
progressInterval = setInterval(updateProgress, 1000);
|
|
}
|
|
|
|
function stopProgress() {
|
|
if (progressInterval) { clearInterval(progressInterval); progressInterval = null; }
|
|
const el = document.getElementById('progress-info');
|
|
if (el) el.textContent = '';
|
|
}
|
|
|
|
function connect() {
|
|
ws = new WebSocket('ws://' + location.host + '/ws');
|
|
ws.onopen = () => {
|
|
setStatus('Connected');
|
|
enableInput('Tell the Intake agent what repo to scan...');
|
|
};
|
|
ws.onmessage = handleEvent;
|
|
ws.onerror = () => setStatus('Connection error');
|
|
ws.onclose = () => {
|
|
setStatus('Reconnecting...');
|
|
disableInput('Reconnecting...');
|
|
setTimeout(connect, 2000);
|
|
};
|
|
}
|
|
|
|
// Node spec metadata for client-facing detection
|
|
const NODE_SPECS = {
|
|
intake: {client_facing: true}, scanner: {client_facing: false},
|
|
profiler: {client_facing: false}, scorer: {client_facing: false},
|
|
extractor: {client_facing: false}, review: {client_facing: true},
|
|
campaign_builder: {client_facing: true}, approval: {client_facing: true},
|
|
sender: {client_facing: false}
|
|
};
|
|
|
|
function handleEvent(msg) {
|
|
const evt = JSON.parse(msg.data);
|
|
const nid = evt.node_id || '';
|
|
const isClientFacing = NODE_SPECS[nid] && NODE_SPECS[nid].client_facing;
|
|
|
|
// --- Node lifecycle ---
|
|
if (evt.type === 'node_loop_started') {
|
|
setNodeState(nid, 'active');
|
|
setNodeStatus(nid, 'thinking');
|
|
setStatus('Running: ' + (nodeNames[nid]||nid));
|
|
if (lastCompletedNode) flashEdge(lastCompletedNode, nid);
|
|
if (!isClientFacing) getOrCreateActivityCard(nid);
|
|
if (!pipelineStartTime) startProgress();
|
|
}
|
|
else if (evt.type === 'node_loop_iteration') {
|
|
setNodeState(nid, 'active');
|
|
totalIterations++;
|
|
updateProgress();
|
|
}
|
|
else if (evt.type === 'node_loop_completed') {
|
|
if (assistantEls[nid]) {
|
|
var tc = assistantEls[nid].querySelector('.text-content');
|
|
if (tc && !tc.textContent) assistantEls[nid].remove();
|
|
assistantEls[nid] = null;
|
|
}
|
|
setNodeState(nid, 'done');
|
|
setNodeStatus(nid, '\u2713 done');
|
|
if (!isClientFacing) completeActivityCard(nid, evt.iterations);
|
|
lastCompletedNode = nid;
|
|
}
|
|
else if (evt.type === 'node_started') {
|
|
setNodeState(nid, 'active');
|
|
setNodeStatus(nid, 'running');
|
|
setStatus('Running: ' + (nodeNames[nid]||nid));
|
|
if (lastCompletedNode) flashEdge(lastCompletedNode, nid);
|
|
}
|
|
|
|
// --- Awaiting user input (HITL) ---
|
|
else if (evt.type === 'awaiting_input') {
|
|
setNodeState(evt.node_id, 'waiting');
|
|
setNodeStatus(evt.node_id, 'waiting');
|
|
setStatus('Waiting: ' + (nodeNames[evt.node_id]||evt.node_id));
|
|
enableInput('Message to ' + (nodeNames[evt.node_id]||evt.node_id) + '...');
|
|
}
|
|
|
|
// --- LLM streaming ---
|
|
else if (evt.type === 'llm_text_delta') {
|
|
setNodeState(nid, 'active');
|
|
if (isClientFacing) {
|
|
if (!assistantEls[nid]) {
|
|
assistantEls[nid] = addNodeMsg(nid, '');
|
|
}
|
|
var tc = assistantEls[nid].querySelector('.text-content');
|
|
if (tc) tc.textContent += evt.content;
|
|
chat.scrollTop = chat.scrollHeight;
|
|
} else {
|
|
setNodeStatus(nid, 'streaming');
|
|
updateActivityCard(nid, 'thinking...');
|
|
}
|
|
}
|
|
else if (evt.type === 'client_output_delta') {
|
|
setNodeState(nid, 'active');
|
|
if (!assistantEls[nid]) {
|
|
assistantEls[nid] = addNodeMsg(nid, '');
|
|
}
|
|
var tc = assistantEls[nid].querySelector('.text-content');
|
|
if (tc) tc.textContent += (evt.content || '');
|
|
chat.scrollTop = chat.scrollHeight;
|
|
}
|
|
|
|
// --- Tool calls ---
|
|
else if (evt.type === 'tool_call_started') {
|
|
setNodeState(nid, 'active');
|
|
if (assistantEls[nid]) {
|
|
var tc = assistantEls[nid].querySelector('.text-content');
|
|
if (tc && !tc.textContent) assistantEls[nid].remove();
|
|
assistantEls[nid] = null;
|
|
}
|
|
if (evt.tool_name !== 'set_output') {
|
|
setNodeStatus(nid, 'tool:' + evt.tool_name);
|
|
if (!isClientFacing) updateActivityCard(nid, 'calling ' + evt.tool_name + '...');
|
|
var info = evt.tool_name + '(' + JSON.stringify(evt.tool_input||{}).slice(0,80) + ')';
|
|
addEventMsg(nid, 'TOOL ' + info, 'tool');
|
|
}
|
|
}
|
|
else if (evt.type === 'tool_call_completed') {
|
|
if (evt.tool_name === 'set_output') {
|
|
addEventMsg(nid, 'set_output: ' + (evt.output_key||''), 'done');
|
|
} else {
|
|
setNodeStatus(nid, 'thinking');
|
|
if (!isClientFacing) updateActivityCard(nid, 'processing...');
|
|
var preview = (evt.result || '').slice(0, 150);
|
|
addEventMsg(nid, 'RESULT: ' + preview, 'tool');
|
|
}
|
|
assistantEls[nid] = null;
|
|
}
|
|
|
|
// --- Feedback edge fired ---
|
|
else if (evt.type === 'feedback_edge') {
|
|
addEventMsg(evt.from_node, 'feedback \u2192 ' + (nodeNames[evt.to_node]||evt.to_node), 'feedback');
|
|
flashEdge(evt.from_node, evt.to_node);
|
|
}
|
|
|
|
// --- Pipeline complete ---
|
|
else if (evt.type === 'pipeline_done') {
|
|
setStatus('Pipeline Complete');
|
|
stopProgress();
|
|
allNodes.forEach(n => { setNodeState(n, 'done'); setNodeStatus(n, '\u2713 done'); });
|
|
Object.keys(spinTimers).forEach(k => { clearInterval(spinTimers[k]); delete spinTimers[k]; });
|
|
Object.keys(cardTimers).forEach(k => { clearInterval(cardTimers[k]); delete cardTimers[k]; });
|
|
for (var k in assistantEls) {
|
|
if (assistantEls[k]) {
|
|
var tc = assistantEls[k].querySelector('.text-content');
|
|
if (tc && !tc.textContent) assistantEls[k].remove();
|
|
assistantEls[k] = null;
|
|
}
|
|
}
|
|
var banner = document.createElement('div');
|
|
banner.className = 'result-banner';
|
|
var h3 = document.createElement('h3');
|
|
h3.textContent = 'Pipeline Complete';
|
|
banner.appendChild(h3);
|
|
if (evt.send_results) {
|
|
var report = document.createElement('div');
|
|
report.className = 'report';
|
|
report.textContent = typeof evt.send_results === 'string'
|
|
? evt.send_results : JSON.stringify(evt.send_results, null, 2);
|
|
banner.appendChild(report);
|
|
}
|
|
if (evt.total_tokens) {
|
|
var tok = document.createElement('div');
|
|
tok.className = 'tokens';
|
|
tok.textContent = 'Total tokens: ' + evt.total_tokens.toLocaleString()
|
|
+ ' | Steps: ' + (evt.steps||'?')
|
|
+ ' | Path: ' + (evt.path||[]).join(' \u2192 ');
|
|
banner.appendChild(tok);
|
|
}
|
|
chat.appendChild(banner);
|
|
chat.scrollTop = chat.scrollHeight;
|
|
disableInput('Pipeline complete. Refresh to restart.');
|
|
}
|
|
else if (evt.type === 'error') {
|
|
setStatus('Error');
|
|
stopProgress();
|
|
addMsg('ERROR: ' + (evt.message || ''), 'event feedback');
|
|
enableInput('Error occurred. Try again...');
|
|
}
|
|
}
|
|
|
|
function sendMsg() {
|
|
const text = inputEl.value.trim();
|
|
if (!text || !ws || ws.readyState !== 1 || !inputEnabled) return;
|
|
addMsg(text, 'user');
|
|
|
|
if (!started) {
|
|
started = true;
|
|
setStatus('Starting pipeline...');
|
|
disableInput('Processing...');
|
|
ws.send(JSON.stringify({type: 'start', message: text}));
|
|
} else {
|
|
ws.send(JSON.stringify({type: 'message', message: text}));
|
|
disableInput('Processing...');
|
|
}
|
|
inputEl.value = '';
|
|
}
|
|
|
|
connect();
|
|
</script>
|
|
</body>
|
|
</html>"""
|
|
|
|
|
|
# =========================================================================
|
|
# WebSocket Handler — Pipeline Orchestrator
|
|
# =========================================================================
|
|
|
|
|
|
async def handle_ws(websocket):
|
|
"""Handle WebSocket connections for the outreach pipeline demo.
|
|
|
|
Uses a single recv() for the start message instead of ``async for``
|
|
so the websocket's recv lock is released before _run_pipeline creates
|
|
its own reader task. (Two concurrent recv calls raise ConcurrencyError.)
|
|
"""
|
|
try:
|
|
raw = await websocket.recv()
|
|
try:
|
|
msg = json.loads(raw)
|
|
except Exception:
|
|
return
|
|
|
|
if msg.get("type") != "start":
|
|
return
|
|
|
|
try:
|
|
await _run_pipeline(websocket, msg.get("message", ""))
|
|
except websockets.exceptions.ConnectionClosed:
|
|
logger.info("WebSocket closed during pipeline")
|
|
except Exception as e:
|
|
logger.exception("Pipeline error")
|
|
try:
|
|
await websocket.send(json.dumps({"type": "error", "message": str(e)}))
|
|
except Exception:
|
|
pass
|
|
except websockets.exceptions.ConnectionClosed:
|
|
pass
|
|
|
|
|
|
async def _run_pipeline(websocket, initial_message: str):
|
|
"""Execute the GitHub outreach pipeline through GraphExecutor."""
|
|
bus = EventBus()
|
|
|
|
# State for routing user messages to active client-facing node
|
|
active_node: EventLoopNode | None = None
|
|
pending_messages: list[str] = []
|
|
|
|
# --- Shared pipeline config (updated by Intake, read by judges) ---
|
|
|
|
pipeline_config: dict[str, Any] = {"min_leads": 50} # default
|
|
|
|
# --- Build judges ---
|
|
# Client-facing nodes (intake, review, approval) have client_facing=True
|
|
# in their NodeSpec — EventLoopNode blocks for user input natively.
|
|
# Only nodes that need output schema validation get a judge.
|
|
|
|
# Intake needs schema validation for its structured outputs
|
|
client_judges: dict[str, SchemaJudge] = {
|
|
"intake": SchemaJudge(IntakeOutput),
|
|
}
|
|
# review & approval: no judge — implicit judge checks output keys,
|
|
# and client_facing blocking is handled by the node itself.
|
|
|
|
# Internal: SchemaJudge (validates output structure)
|
|
schema_judges: dict[str, SchemaJudge] = {
|
|
"scanner": SchemaJudge(
|
|
ScannerOutput,
|
|
min_items={"github_users": lambda: pipeline_config["min_leads"]},
|
|
data_dir=_DATA_DIR,
|
|
),
|
|
"profiler": SchemaJudge(ProfilerOutput),
|
|
"scorer": SchemaJudge(ScorerOutput),
|
|
"extractor": SchemaJudge(ExtractorOutput),
|
|
# campaign_builder is now client_facing — implicit judge + native
|
|
# blocking handle termination (same pattern as review/approval).
|
|
}
|
|
|
|
all_judges: dict = {**client_judges, **schema_judges}
|
|
|
|
# --- Build EventLoopNode for each event_loop node ---
|
|
|
|
tool_executor = TOOL_REGISTRY.get_executor()
|
|
all_tools = list(TOOL_REGISTRY.get_tools().values())
|
|
nodes: dict[str, EventLoopNode] = {}
|
|
|
|
for nid, spec in NODE_SPECS.items():
|
|
if spec.node_type != "event_loop":
|
|
continue
|
|
judge = all_judges.get(nid)
|
|
node = EventLoopNode(
|
|
event_bus=bus,
|
|
judge=judge,
|
|
config=LoopConfig(
|
|
max_iterations=30,
|
|
max_tool_calls_per_turn=30,
|
|
max_context_tokens=64000,
|
|
max_tool_result_chars=8_000,
|
|
spillover_dir=str(_DATA_DIR),
|
|
),
|
|
conversation_store=None,
|
|
tool_executor=tool_executor if spec.tools else None,
|
|
)
|
|
nodes[nid] = node
|
|
|
|
# --- Build GraphExecutor and register all nodes ---
|
|
|
|
executor = GraphExecutor(
|
|
runtime=RUNTIME,
|
|
llm=LLM,
|
|
tools=all_tools,
|
|
tool_executor=tool_executor,
|
|
enable_parallel_execution=True,
|
|
)
|
|
for nid, impl in nodes.items():
|
|
executor.register_node(nid, impl)
|
|
executor.register_node("sender", SenderNode())
|
|
|
|
# --- Event forwarding: bus → WebSocket ---
|
|
|
|
async def forward_event(event: AgentEvent):
|
|
try:
|
|
payload = {"type": event.type.value, **event.data}
|
|
if event.node_id:
|
|
payload["node_id"] = event.node_id
|
|
|
|
# Remap CUSTOM events to their custom_type
|
|
if event.type == EventType.CUSTOM and "custom_type" in event.data:
|
|
payload["type"] = event.data["custom_type"]
|
|
|
|
# Remap CLIENT_INPUT_REQUESTED to awaiting_input for JS compatibility
|
|
if event.type == EventType.CLIENT_INPUT_REQUESTED:
|
|
payload["type"] = "awaiting_input"
|
|
|
|
await websocket.send(json.dumps(payload))
|
|
except Exception:
|
|
pass
|
|
|
|
bus.subscribe(
|
|
event_types=[
|
|
EventType.NODE_LOOP_STARTED,
|
|
EventType.NODE_LOOP_ITERATION,
|
|
EventType.NODE_LOOP_COMPLETED,
|
|
EventType.LLM_TEXT_DELTA,
|
|
EventType.TOOL_CALL_STARTED,
|
|
EventType.TOOL_CALL_COMPLETED,
|
|
EventType.CLIENT_OUTPUT_DELTA,
|
|
EventType.CLIENT_INPUT_REQUESTED,
|
|
EventType.NODE_STALLED,
|
|
EventType.CUSTOM,
|
|
],
|
|
handler=forward_event,
|
|
)
|
|
|
|
# --- Track active client-facing node for message routing ---
|
|
# EventLoopNode publishes CLIENT_INPUT_REQUESTED when a client_facing
|
|
# node blocks for user input (native blocking, no judge needed).
|
|
|
|
CLIENT_FACING_NODES = {"intake", "review", "approval"}
|
|
|
|
async def on_awaiting_input(event: AgentEvent):
|
|
nonlocal active_node
|
|
nid = event.node_id or ""
|
|
if nid not in CLIENT_FACING_NODES:
|
|
return
|
|
active_node = nodes.get(nid)
|
|
logger.info("Active HITL node: %s", nid)
|
|
# Deliver any pending messages
|
|
while pending_messages:
|
|
msg_text = pending_messages.pop(0)
|
|
if active_node:
|
|
await active_node.inject_event(msg_text)
|
|
|
|
bus.subscribe(event_types=[EventType.CLIENT_INPUT_REQUESTED], handler=on_awaiting_input)
|
|
|
|
# --- Capture min_leads from Intake's set_output tool call ---
|
|
|
|
async def on_tool_started(event: AgentEvent):
|
|
if event.node_id != "intake":
|
|
return
|
|
if event.data.get("tool_name") != "set_output":
|
|
return
|
|
tool_input = event.data.get("tool_input", {})
|
|
if tool_input.get("key") != "min_leads":
|
|
return
|
|
raw = tool_input.get("value", "")
|
|
try:
|
|
val = int(raw)
|
|
if val > 0:
|
|
pipeline_config["min_leads"] = val
|
|
logger.info("Scanner min_leads set to %d (from user)", val)
|
|
except (ValueError, TypeError):
|
|
logger.info(
|
|
"No valid min_leads from user, using default %d",
|
|
pipeline_config["min_leads"],
|
|
)
|
|
|
|
bus.subscribe(event_types=[EventType.TOOL_CALL_STARTED], handler=on_tool_started)
|
|
|
|
# --- Inject initial user message into intake node ---
|
|
|
|
if initial_message:
|
|
await nodes["intake"].inject_event(initial_message)
|
|
|
|
# --- Run pipeline as background task ---
|
|
|
|
pipeline_task = asyncio.create_task(executor.execute(GRAPH, GOAL, input_data={}))
|
|
|
|
# --- WS message loop: route incoming messages to active node ---
|
|
|
|
async def ws_reader():
|
|
try:
|
|
async for raw in websocket:
|
|
try:
|
|
msg = json.loads(raw)
|
|
except Exception:
|
|
continue
|
|
text = msg.get("message", "")
|
|
if not text:
|
|
continue
|
|
|
|
# Forward to browser as user bubble
|
|
await websocket.send(
|
|
json.dumps(
|
|
{
|
|
"type": "user_message",
|
|
"content": text,
|
|
}
|
|
)
|
|
)
|
|
|
|
if active_node:
|
|
await active_node.inject_event(text)
|
|
else:
|
|
pending_messages.append(text)
|
|
except websockets.exceptions.ConnectionClosed:
|
|
pass
|
|
|
|
reader_task = asyncio.create_task(ws_reader())
|
|
|
|
# --- Wait for pipeline to complete ---
|
|
|
|
try:
|
|
result = await asyncio.wait_for(pipeline_task, timeout=1800)
|
|
except TimeoutError:
|
|
for nid in CLIENT_FACING_NODES:
|
|
if nid in nodes:
|
|
nodes[nid].signal_shutdown()
|
|
reader_task.cancel()
|
|
await websocket.send(
|
|
json.dumps({"type": "error", "message": "Pipeline timed out (10 min)"})
|
|
)
|
|
return
|
|
except Exception as e:
|
|
reader_task.cancel()
|
|
await websocket.send(json.dumps({"type": "error", "message": str(e)}))
|
|
return
|
|
|
|
reader_task.cancel()
|
|
|
|
# --- Send final result ---
|
|
|
|
send_results = result.output.get("send_results", "")
|
|
await websocket.send(
|
|
json.dumps(
|
|
{
|
|
"type": "pipeline_done",
|
|
"success": result.success,
|
|
"send_results": send_results,
|
|
"total_tokens": result.total_tokens,
|
|
"steps": result.steps_executed,
|
|
"path": result.path,
|
|
"node_visit_counts": result.node_visit_counts,
|
|
"error": result.error,
|
|
}
|
|
)
|
|
)
|
|
|
|
logger.info(
|
|
"Pipeline complete: success=%s, steps=%d, tokens=%d, path=%s, error=%s",
|
|
result.success,
|
|
result.steps_executed,
|
|
result.total_tokens,
|
|
" -> ".join(result.path),
|
|
result.error,
|
|
)
|
|
|
|
|
|
# =========================================================================
|
|
# HTTP Handler
|
|
# =========================================================================
|
|
|
|
|
|
async def process_request(connection, request: Request):
|
|
"""Serve HTML on GET /, upgrade to WebSocket on /ws."""
|
|
if request.path == "/ws":
|
|
return None
|
|
return Response(
|
|
HTTPStatus.OK,
|
|
"OK",
|
|
websockets.Headers({"Content-Type": "text/html; charset=utf-8"}),
|
|
HTML_PAGE.encode(),
|
|
)
|
|
|
|
|
|
# =========================================================================
|
|
# Main
|
|
# =========================================================================
|
|
|
|
|
|
async def main():
|
|
port = 8768
|
|
async with websockets.serve(
|
|
handle_ws,
|
|
"0.0.0.0",
|
|
port,
|
|
process_request=process_request,
|
|
):
|
|
logger.info(f"GitHub Outreach Pipeline demo running at http://localhost:{port}")
|
|
logger.info("Open in your browser to start the pipeline.")
|
|
await asyncio.Future()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|