ag-ui-crewai

Implementation of the AG-UI protocol for CrewAI.

Provides a complete Python integration for CrewAI flows and crews with the AG-UI protocol, including FastAPI endpoint creation and comprehensive event streaming.

Installation

pip install ag-ui-crewai

Usage

from crewai.flow.flow import Flow, start
from litellm import acompletion
from ag_ui_crewai import (
    add_crewai_flow_fastapi_endpoint,
    copilotkit_stream,
    CopilotKitState
)
from fastapi import FastAPI

class MyFlow(Flow[CopilotKitState]):
    @start()
    async def chat(self):
        response = await copilotkit_stream(
            await acompletion(
                model="openai/gpt-4o",
                messages=[
                    {"role": "system", "content": "You are a helpful assistant."},
                    *self.state.messages
                ],
                tools=self.state.copilotkit.actions,
                stream=True
            )
        )
        self.state.messages.append(response.choices[0].message)

# Add to FastAPI
app = FastAPI()
add_crewai_flow_fastapi_endpoint(app, MyFlow(), "/flow")

Features

• Native CrewAI integration – Direct support for CrewAI flows, crews, and multi-agent systems
• FastAPI endpoint creation – Automatic HTTP endpoint generation with proper event streaming
• Predictive state updates – Real-time state synchronization between backend and frontend
• Streaming tool calls – Live streaming of LLM responses and tool execution to the UI

Tuning knobs

The CrewAI integration exposes three environment variables for tuning timeouts and teardown behaviour. Sensible defaults ship with the package; override these only if your deployment has specific needs (long-running crews, disconnect-heavy workloads, flaky LLM providers).

AGUI_CREWAI_LLM_TIMEOUT_SECONDS

Per-read timeout forwarded to litellm.acompletion in ChatWithCrewFlow.chat. It applies to all three completion sites: the initial call, the post-crew-run follow-up (tool-choice="none") that lets the assistant speak about the crew result, and the post-crew_exit (tool-choice="none") call.

Limitation — no tool chaining after a crew run. The post-crew-run follow-up uses tool_choice="none", so the assistant summarizes the crew result as text but cannot call a frontend action in the same turn. A flow like "run the crew, then update the UI" is not reachable on this path today; allowing bounded tool re-entry there is future work.

• Default: 120 seconds.
• Non-positive (e.g. 0, -1): disables the per-read timeout — the underlying HTTP client's default applies instead.
• Non-finite (nan, inf): falls back to the default.
• Note: LiteLLM forwards this as a per-read timeout to the underlying HTTP client, not a session-level ceiling. A trickle-feeding server can keep the coroutine alive indefinitely at this layer; use AGUI_CREWAI_FLOW_TIMEOUT_SECONDS for the session-level cap.

AGUI_CREWAI_FLOW_TIMEOUT_SECONDS

Hard wall-clock ceiling on a single flow run. Guards against a runaway flow (hung LiteLLM stream, infinite loop in a user task) pinning the process indefinitely.

• Default: 600 seconds (10 minutes).
• Non-positive: disables the ceiling. Only use this for deployments with legitimately long-running crews where the wall-clock ceiling is handled at a higher layer.
• Non-finite (nan, inf): falls back to the default.
• When the ceiling fires, the stream yields a RUN_ERROR event with code AGUI_CREWAI_FLOW_TIMEOUT and a message carrying the configured ceiling plus thread/run correlation IDs.

AGUI_CREWAI_CANCEL_JOIN_TIMEOUT_SECONDS

Teardown ceiling: the total wall-clock budget for _cancel_and_join to unwind the kickoff task after a client disconnect, timeout, or error. Covers the grace window, force-cancel join, AND outer-cancel recovery — one shared monotonic deadline, not three.

• Default: 10 seconds.
• Non-positive or non-finite: falls back to the default (deliberately not disable-able — a cancel that cannot be bounded is a resource leak).
• Tune upward if your deployment sees disconnect-heavy load and a consistently-stuck cancel warning is logged.

To run the dojo examples

cd integrations/crew-ai/python
uv sync
uv run dev