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ccurme
128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
"""Supervisor — Async Subagent Example.
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An interactive REPL that demonstrates the five async subagent operations
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against the FastAPI server in server.py.
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The supervisor delegates research tasks to the server-hosted researcher
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via Agent Protocol (through the LangGraph SDK). Tasks run in the
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background — the supervisor returns a task ID immediately and lets you
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check in when you're ready.
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Run (after starting server.py in another terminal):
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ANTHROPIC_API_KEY=... python supervisor.py
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Try these prompts:
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> research the latest developments in quantum computing
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> check status of <task-id>
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> update <task-id> to focus on commercial applications only
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> cancel <task-id>
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> list all tasks
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"""
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from __future__ import annotations
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import asyncio
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import uuid
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from pathlib import Path
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from dotenv import load_dotenv
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from langchain_anthropic import ChatAnthropic
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from langchain_core.messages import HumanMessage
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from langgraph.checkpoint.memory import MemorySaver
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from deepagents import create_deep_agent
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from deepagents.middleware.async_subagents import AsyncSubAgent
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load_dotenv(Path(__file__).parent / ".env")
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import os # noqa: E402
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RESEARCHER_URL = os.environ.get("RESEARCHER_URL", "http://localhost:2024")
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# ── Agent setup ───────────────────────────────────────────────────────────────
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async_subagents: list[AsyncSubAgent] = [
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{
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"name": "researcher",
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"description": (
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"A research agent that investigates any topic using web search. "
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"Runs in the background and returns a detailed summary."
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),
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"graph_id": "researcher",
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"url": RESEARCHER_URL,
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"headers": {"x-auth-scheme": "custom"},
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},
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]
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checkpointer = MemorySaver()
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thread_id = str(uuid.uuid4())
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supervisor = create_deep_agent(
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model=ChatAnthropic(model="claude-sonnet-4-5"),
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checkpointer=checkpointer,
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system_prompt=(
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"You are a research supervisor coordinating a background researcher agent.\n\n"
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"For general questions, answer directly — do NOT launch a researcher.\n\n"
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'Only launch the researcher when the user says "research", "investigate", "look into", or "find out".\n\n'
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"START: When the user asks to research something:\n"
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' 1. Call start_async_task with subagent_type "researcher" and the topic.\n'
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" 2. Report the task_id and stop. Do NOT immediately check status.\n\n"
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"CHECK: When the user asks for status or results:\n"
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" 1. Call check_async_task with the exact task_id.\n"
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" 2. Report what the tool returns. If still running, say so and stop.\n\n"
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"UPDATE: When the user asks to change what the researcher is working on:\n"
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" 1. Call update_async_task with the task_id and new instructions.\n"
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" 2. Confirm the update.\n\n"
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"CANCEL: When the user asks to cancel a task:\n"
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" 1. Call cancel_async_task with the exact task_id.\n"
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" 2. Confirm the cancellation.\n\n"
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"LIST: When the user asks to list tasks or check all statuses:\n"
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" 1. Call list_async_tasks.\n"
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" 2. Present the live statuses.\n\n"
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"Rules:\n"
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"- Never report a stale status from memory. Always call a tool.\n"
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"- Never poll in a loop. One tool call per user request.\n"
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"- Always show the full task_id — never truncate it."
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),
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subagents=async_subagents,
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)
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# ── REPL ──────────────────────────────────────────────────────────────────────
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async def chat(user_input: str) -> None:
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"""Send a message to the supervisor and print the response."""
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result = await supervisor.ainvoke(
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{"messages": [HumanMessage(user_input)]},
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config={"configurable": {"thread_id": thread_id}},
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)
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last = result["messages"][-1]
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content = last.content
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print(
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"\n"
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+ (content if isinstance(content, str) else __import__("json").dumps(content, indent=2))
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+ "\n"
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)
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async def main() -> None:
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"""Run the interactive REPL."""
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print(f"Supervisor connected to researcher at {RESEARCHER_URL}")
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print("Type a message and press Enter. Ctrl+C or Ctrl+D to exit.\n")
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while True:
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try:
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user_input = input("> ").strip()
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except (EOFError, KeyboardInterrupt):
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print("\nGoodbye.")
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break
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if not user_input:
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continue
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try:
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await chat(user_input)
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except Exception as exc: # noqa: BLE001
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print(f"Error: {exc}")
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if __name__ == "__main__":
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asyncio.run(main())
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