Sub-Agents

from __future__ import annotationsimport loggingimport uuidfrom typing import Any, Literalfrom pydantic import BaseModel, Fieldfrom pydantic_ai import Agent, RunContextfrom pydantic_ai.ag_ui import StateDepsfrom pydantic_ai.models.openai import OpenAIResponsesModellogger = logging.getLogger(__name__)SubAgentName = Literal["research_agent", "writing_agent", "critique_agent"]DelegationStatus = Literal["running", "completed", "failed"]# ── Shared state ────────────────────────────────────────────────────class Delegation(BaseModel):    """One sub-agent invocation, surfaced in the UI's delegation log."""    id: str    sub_agent: str  # SubAgentName at runtime; widened so model_dump round-trips    task: str    status: str  # DelegationStatus at runtime    result: str = ""class SubagentsState(BaseModel):    """Shared state. ``delegations`` is rendered as a live log in the UI."""    delegations: list[Delegation] = Field(default_factory=list)# ── Sub-agents (real PydanticAI Agents) ─────────────────────────────# Each sub-agent is a full-fledged ``Agent(model=..., system_prompt=...)``# with its own system prompt. They don't share memory or tools with the# supervisor — the supervisor only sees their return value._SUB_MODEL = OpenAIResponsesModel("gpt-4o-mini")_research_agent: Agent[None, str] = Agent(    model=_SUB_MODEL,    system_prompt=(        "You are a research sub-agent. Given a topic, produce a concise "        "bulleted list of 3-5 key facts. No preamble, no closing."    ),)_writing_agent: Agent[None, str] = Agent(    model=_SUB_MODEL,    system_prompt=(        "You are a writing sub-agent. Given a brief and optional source "        "facts, produce a polished 1-paragraph draft. Be clear and "        "concrete. No preamble."    ),)_critique_agent: Agent[None, str] = Agent(    model=_SUB_MODEL,    system_prompt=(        "You are an editorial critique sub-agent. Given a draft, give "        "2-3 crisp, actionable critiques. No preamble."    ),)

from __future__ import annotationsimport loggingimport uuidfrom typing import Any, Literalfrom pydantic import BaseModel, Fieldfrom pydantic_ai import Agent, RunContextfrom pydantic_ai.ag_ui import StateDepsfrom pydantic_ai.models.openai import OpenAIResponsesModellogger = logging.getLogger(__name__)SubAgentName = Literal["research_agent", "writing_agent", "critique_agent"]DelegationStatus = Literal["running", "completed", "failed"]# ── Shared state ────────────────────────────────────────────────────class Delegation(BaseModel):    """One sub-agent invocation, surfaced in the UI's delegation log."""    id: str    sub_agent: str  # SubAgentName at runtime; widened so model_dump round-trips    task: str    status: str  # DelegationStatus at runtime    result: str = ""class SubagentsState(BaseModel):    """Shared state. ``delegations`` is rendered as a live log in the UI."""    delegations: list[Delegation] = Field(default_factory=list)# ── Sub-agents (real PydanticAI Agents) ─────────────────────────────# Each sub-agent is a full-fledged ``Agent(model=..., system_prompt=...)``# with its own system prompt. They don't share memory or tools with the# supervisor — the supervisor only sees their return value._SUB_MODEL = OpenAIResponsesModel("gpt-4o-mini")_research_agent: Agent[None, str] = Agent(    model=_SUB_MODEL,    system_prompt=(        "You are a research sub-agent. Given a topic, produce a concise "        "bulleted list of 3-5 key facts. No preamble, no closing."    ),)_writing_agent: Agent[None, str] = Agent(    model=_SUB_MODEL,    system_prompt=(        "You are a writing sub-agent. Given a brief and optional source "        "facts, produce a polished 1-paragraph draft. Be clear and "        "concrete. No preamble."    ),)_critique_agent: Agent[None, str] = Agent(    model=_SUB_MODEL,    system_prompt=(        "You are an editorial critique sub-agent. Given a draft, give "        "2-3 crisp, actionable critiques. No preamble."    ),)async def _invoke_sub_agent(sub_agent: Agent[None, str], task: str) -> str:    """Run a sub-agent on ``task`` and return its final text output.    Uses the async ``Agent.run`` API rather than ``run_sync`` because the    supervisor itself executes inside a running event loop (AG-UI awaits    ``agent.run``); ``run_sync`` from inside a running loop raises    ``RuntimeError: This event loop is already running``.    """    result = await sub_agent.run(task)    output: Any = result.output    return str(output) if output is not None else ""# ── Supervisor ──────────────────────────────────────────────────────_SUPERVISOR_PROMPT = (    "You are a supervisor agent that coordinates three specialized "    "sub-agents to produce high-quality deliverables.\n\n"    "Available sub-agents (call them as tools):\n"    "  - research_agent: gathers facts on a topic.\n"    "  - writing_agent: turns facts + a brief into a polished draft.\n"    "  - critique_agent: reviews a draft and suggests improvements.\n\n"    "For most non-trivial user requests, delegate in sequence: research -> "    "write -> critique. Pass the relevant facts/draft through the `task` "    "argument of each tool. Each tool returns the sub-agent's output as a "    "string. Keep your own messages short — explain the plan once, "    "delegate, then return a concise summary once done. The UI shows the "    "user a live log of every sub-agent delegation, including the "    "in-flight 'running' state.")agent = Agent(    model=OpenAIResponsesModel("gpt-4o-mini"),    deps_type=StateDeps[SubagentsState],    system_prompt=_SUPERVISOR_PROMPT,)def _append_running(    ctx: RunContext[StateDeps[SubagentsState]],    *,    sub_agent: SubAgentName,    task: str,) -> str:    """Append a ``running`` delegation entry and return its id."""    entry = Delegation(        id=str(uuid.uuid4()),        sub_agent=sub_agent,        task=task,        status="running",        result="",    )    ctx.deps.state.delegations = [*ctx.deps.state.delegations, entry]    return entry.iddef _finalise(    ctx: RunContext[StateDeps[SubagentsState]],    *,    entry_id: str,    status: DelegationStatus,    result: str,) -> None:    """Mutate the delegation entry with ``entry_id`` to its terminal state.    If the entry has gone missing (e.g. another part of the system replaced    ``state.delegations`` mid-turn) we log a warning and skip rather than    appending a synthetic entry — same defensive choice as google-adk's    ``_update_delegation``.    """    delegations = list(ctx.deps.state.delegations)    for idx, entry in enumerate(delegations):        if entry.id == entry_id:            delegations[idx] = entry.model_copy(                update={"status": status, "result": result}            )            ctx.deps.state.delegations = delegations            return    logger.warning(        "subagents: delegation entry %s missing on update — final %s state "        "(result_length=%d) will not be rendered",        entry_id,        status,        len(result),    )# ── Delegation tools ────────────────────────────────────────────────async def _delegate(    ctx: RunContext[StateDeps[SubagentsState]],    *,    sub_agent: SubAgentName,    sub_agent_obj: Agent[None, str],    task: str,) -> str:    """Common delegation flow: append running → invoke → finalise.    Returns the sub-agent's output text so the supervisor LLM can read it    on its next step. State mutations (running entry + final entry) are    written through ``ctx.deps.state``; PydanticAI's AG-UI bridge syncs    those back to the frontend at end-of-turn so the delegation log    re-renders automatically.    """    entry_id = _append_running(ctx, sub_agent=sub_agent, task=task)    try:        result = await _invoke_sub_agent(sub_agent_obj, task)    except Exception as exc:  # noqa: BLE001 — surface failure to supervisor        logger.exception("subagents: %s failed", sub_agent)        message = (            f"sub-agent {sub_agent} failed: {exc.__class__.__name__} "            "(see server logs for details)"        )        _finalise(ctx, entry_id=entry_id, status="failed", result=message)        return message    _finalise(ctx, entry_id=entry_id, status="completed", result=result)    return result# Each ``@agent.tool`` wraps a sub-agent invocation. The supervisor LLM# "calls" these tools to delegate work; each call asynchronously runs the# matching sub-agent, records the delegation into shared state, and# returns the sub-agent's output as a string the supervisor can read on# its next step.@agent.toolasync def research_agent(    ctx: RunContext[StateDeps[SubagentsState]],    task: str,) -> str:    """Delegate a research task to the research sub-agent.    Use for: gathering facts, background, definitions, statistics.    Returns a bulleted list of key facts.    """    return await _delegate(        ctx,        sub_agent="research_agent",        sub_agent_obj=_research_agent,        task=task,    )@agent.toolasync def writing_agent(    ctx: RunContext[StateDeps[SubagentsState]],    task: str,) -> str:    """Delegate a drafting task to the writing sub-agent.    Use for: producing a polished paragraph, draft, or summary. Pass    relevant facts from prior research inside ``task``.    """    return await _delegate(        ctx,        sub_agent="writing_agent",        sub_agent_obj=_writing_agent,        task=task,    )@agent.toolasync def critique_agent(    ctx: RunContext[StateDeps[SubagentsState]],    task: str,) -> str:    """Delegate a critique task to the critique sub-agent.    Use for: reviewing a draft and suggesting concrete improvements.    """    return await _delegate(        ctx,        sub_agent="critique_agent",        sub_agent_obj=_critique_agent,        task=task,    )

  private readonly agentStore = injectAgentStore(this.agentId);  protected readonly delegations = computed(() =>    readDelegations(this.agentStore().state()),  );  constructor() {    if (this.feature === "subagents") {      this.registerSubAgent("research_agent");      this.registerSubAgent("writing_agent");      this.registerSubAgent("critique_agent");    }  }  private registerSubAgent(name: SubAgentName): void {    registerRenderToolCall(subAgentRendererConfig(name));  }