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97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
"""Deep-agent variant exercising v3 `thread.subgraphs` properly.
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`create_deep_agent` builds a graph whose `task` tool dispatches to one
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of its configured `SubAgent`s. When the supervisor's model issues a
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`task(subagent_type="researcher", description=...)` tool call, the
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sub-agent runs as a nested invocation and the v3 streaming server
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emits the subagent's lifecycle, messages, and tool events under a
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scoped namespace. That namespace is what `thread.subgraphs` surfaces
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as a direct-child `ScopedStreamHandle`.
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Both the supervisor and the researcher use `FakeMessagesListChatModel`
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with pre-scripted responses so this graph is hermetic. No LLM API keys
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are required, and the test is deterministic.
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"""
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from __future__ import annotations
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from typing import Any
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from deepagents import create_deep_agent
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from deepagents.middleware.subagents import SubAgent
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from langchain_core.language_models.fake_chat_models import FakeMessagesListChatModel
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from langchain_core.messages import AIMessage
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class _FakeChatModelWithTools(FakeMessagesListChatModel):
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"""`FakeMessagesListChatModel` that accepts `bind_tools(...)` as a no-op.
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`create_deep_agent` calls `model.bind_tools(tools)` to expose the `task`
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tool to the supervisor. The base `BaseChatModel.bind_tools` raises
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`NotImplementedError`. Pre-baked responses in `responses` already carry
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the desired `tool_calls`, so we ignore the tools list and return self.
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"""
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def bind_tools(self, tools: Any, **kwargs: Any) -> _FakeChatModelWithTools:
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return self
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# Supervisor turn 1: dispatch to the researcher via the `task` tool.
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# Supervisor turn 2: emit a final assistant message (no more tool calls),
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# which closes the agent loop.
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_supervisor_model = _FakeChatModelWithTools(
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responses=[
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AIMessage(
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content="",
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id="sup-1",
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tool_calls=[
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{
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"id": "tc-task-1",
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"name": "task",
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"args": {
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"subagent_type": "researcher",
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"description": "research v3 streaming",
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},
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}
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],
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),
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AIMessage(content="Research complete.", id="sup-2"),
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]
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)
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# Researcher turn 1: final message, no tool calls. Closes the subagent loop.
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_researcher_model = _FakeChatModelWithTools(
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responses=[
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AIMessage(
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content="v3 streaming is event-typed and thread-centric.", id="res-1"
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),
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]
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)
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_researcher: SubAgent = {
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"name": "researcher",
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"description": (
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"Looks up notes on a topic and returns a short summary. "
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"Use this when the user wants to research something."
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),
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"system_prompt": (
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"You are a research assistant. Reply with one or two sentences "
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"summarising what the user asked about. Do not call any tools."
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),
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"model": _researcher_model,
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}
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graph = create_deep_agent(
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model=_supervisor_model,
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system_prompt=(
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"You are a supervisor coordinating a researcher subagent. "
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"When the user asks to research anything, call the `task` tool "
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"with subagent_type='researcher'."
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),
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subagents=[_researcher],
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name="v3_deep_agent",
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)
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