mcp-run-python

# Agent-User Interaction (AG-UI) Protocol The [Agent-User Interaction (AG-UI) Protocol](https://docs.ag-ui.com/introduction) is an open standard introduced by the [CopilotKit](https://webflow.copilotkit.ai/blog/introducing-ag-ui-the-protocol-where-agents-meet-users) team that standardises how frontend applications communicate with AI agents, with support for streaming, frontend tools, shared state, and custom events. !!! note The AG-UI integration was originally built by the team at [Rocket Science](https://www.rocketscience.gg/) and contributed in collaboration with the Pydantic AI and CopilotKit teams. Thanks Rocket Science! ## Installation The only dependencies are: - [ag-ui-protocol](https://docs.ag-ui.com/introduction): to provide the AG-UI types and encoder. - [starlette](https://www.starlette.io): to handle [ASGI](https://asgi.readthedocs.io/en/latest/) requests from a framework like FastAPI. You can install Pydantic AI with the `ag-ui` extra to ensure you have all the required AG-UI dependencies: ```bash pip/uv-add 'pydantic-ai-slim[ag-ui]' ``` To run the examples you'll also need: - [uvicorn](https://www.uvicorn.org/) or another ASGI compatible server ```bash pip/uv-add uvicorn ``` ## Usage There are three ways to run a Pydantic AI agent based on AG-UI run input with streamed AG-UI events as output, from most to least flexible. If you're using a Starlette-based web framework like FastAPI, you'll typically want to use the second method. 1. The [`AGUIAdapter.run_stream()`][pydantic_ai.ui.ag_ui.AGUIAdapter.run_stream] method, when called on an [`AGUIAdapter`][pydantic_ai.ui.ag_ui.AGUIAdapter] instantiated with an agent and an AG-UI [`RunAgentInput`](https://docs.ag-ui.com/sdk/python/core/types#runagentinput) object, will run the agent and return a stream of AG-UI events. It also takes optional [`Agent.iter()`][pydantic_ai.Agent.iter] arguments including `deps`. Use this if you're using a web framework not based on Starlette (e.g. Django or Flask) or want to modify the input or output some way. 2. The [`AGUIAdapter.dispatch_request()`][pydantic_ai.ui.ag_ui.AGUIAdapter.dispatch_request] class method takes an agent and a Starlette request (e.g. from FastAPI) coming from an AG-UI frontend, and returns a streaming Starlette response of AG-UI events that you can return directly from your endpoint. It also takes optional [`Agent.iter()`][pydantic_ai.Agent.iter] arguments including `deps`, that you can vary for each request (e.g. based on the authenticated user). This is a convenience method that combines [`AGUIAdapter.from_request()`][pydantic_ai.ui.ag_ui.AGUIAdapter.from_request], [`AGUIAdapter.run_stream()`][pydantic_ai.ui.ag_ui.AGUIAdapter.run_stream], and [`AGUIAdapter.streaming_response()`][pydantic_ai.ui.ag_ui.AGUIAdapter.streaming_response]. 3. [`AGUIApp`][pydantic_ai.ui.ag_ui.app.AGUIApp] represents an ASGI application that handles every AG-UI request by running the agent. It also takes optional [`Agent.iter()`][pydantic_ai.Agent.iter] arguments including `deps`, but these will be the same for each request, with the exception of the AG-UI state that's injected as described under [state management](#state-management). This ASGI app can be [mounted](https://fastapi.tiangolo.com/advanced/sub-applications/) at a given path in an existing FastAPI app. ### Handle run input and output directly This example uses [`AGUIAdapter.run_stream()`][pydantic_ai.ui.ag_ui.AGUIAdapter.run_stream] and performs its own request parsing and response generation. This can be modified to work with any web framework. ```py {title="run_ag_ui.py"} import json from http import HTTPStatus from fastapi import FastAPI from fastapi.requests import Request from fastapi.responses import Response, StreamingResponse from pydantic import ValidationError from pydantic_ai import Agent from pydantic_ai.ui import SSE_CONTENT_TYPE from pydantic_ai.ui.ag_ui import AGUIAdapter agent = Agent('openai:gpt-5', instructions='Be fun!') app = FastAPI() @app.post('/') async def run_agent(request: Request) -> Response: accept = request.headers.get('accept', SSE_CONTENT_TYPE) try: run_input = AGUIAdapter.build_run_input(await request.body()) # (1) except ValidationError as e: return Response( content=json.dumps(e.json()), media_type='application/json', status_code=HTTPStatus.UNPROCESSABLE_ENTITY, ) adapter = AGUIAdapter(agent=agent, run_input=run_input, accept=accept) event_stream = adapter.run_stream() # (2) sse_event_stream = adapter.encode_stream(event_stream) return StreamingResponse(sse_event_stream, media_type=accept) # (3) ``` 1. [`AGUIAdapter.build_run_input()`][pydantic_ai.ui.ag_ui.AGUIAdapter.build_run_input] takes the request body as bytes and returns an AG-UI [`RunAgentInput`](https://docs.ag-ui.com/sdk/python/core/types#runagentinput) object. You can also use the [`AGUIAdapter.from_request()`][pydantic_ai.ui.ag_ui.AGUIAdapter.from_request] class method to build an adapter directly from a request. 2. [`AGUIAdapter.run_stream()`][pydantic_ai.ui.ag_ui.AGUIAdapter.run_stream] runs the agent and returns a stream of AG-UI events. It supports the same optional arguments as [`Agent.run_stream_events()`](../agents.md#running-agents), including `deps`. You can also use [`AGUIAdapter.run_stream_native()`][pydantic_ai.ui.ag_ui.AGUIAdapter.run_stream_native] to run the agent and return a stream of Pydantic AI events instead, which can then be transformed into AG-UI events using [`AGUIAdapter.transform_stream()`][pydantic_ai.ui.ag_ui.AGUIAdapter.transform_stream]. 3. [`AGUIAdapter.encode_stream()`][pydantic_ai.ui.ag_ui.AGUIAdapter.encode_stream] encodes the stream of AG-UI events as strings according to the accept header value. You can also use [`AGUIAdapter.streaming_response()`][pydantic_ai.ui.ag_ui.AGUIAdapter.streaming_response] to generate a streaming response directly from the AG-UI event stream returned by `run_stream()`. Since `app` is an ASGI application, it can be used with any ASGI server: ```shell uvicorn run_ag_ui:app ``` This will expose the agent as an AG-UI server, and your frontend can start sending requests to it. ### Handle a Starlette request This example uses [`AGUIAdapter.dispatch_request()`][pydantic_ai.ui.ag_ui.AGUIAdapter.dispatch_request] to directly handle a FastAPI request and return a response. Something analogous to this will work with any Starlette-based web framework. ```py {title="handle_ag_ui_request.py"} from fastapi import FastAPI from starlette.requests import Request from starlette.responses import Response from pydantic_ai import Agent from pydantic_ai.ui.ag_ui import AGUIAdapter agent = Agent('openai:gpt-5', instructions='Be fun!') app = FastAPI() @app.post('/') async def run_agent(request: Request) -> Response: return await AGUIAdapter.dispatch_request(request, agent=agent) # (1) ``` 1. This method essentially does the same as the previous example, but it's more convenient to use when you're already using a Starlette/FastAPI app. Since `app` is an ASGI application, it can be used with any ASGI server: ```shell uvicorn handle_ag_ui_request:app ``` This will expose the agent as an AG-UI server, and your frontend can start sending requests to it. ### Stand-alone ASGI app This example uses [`AGUIApp`][pydantic_ai.ui.ag_ui.app.AGUIApp] to turn the agent into a stand-alone ASGI application: ```py {title="ag_ui_app.py" hl_lines="4"} from pydantic_ai import Agent from pydantic_ai.ui.ag_ui.app import AGUIApp agent = Agent('openai:gpt-5', instructions='Be fun!') app = AGUIApp(agent) ``` Since `app` is an ASGI application, it can be used with any ASGI server: ```shell uvicorn ag_ui_app:app ``` This will expose the agent as an AG-UI server, and your frontend can start sending requests to it. ## Design The Pydantic AI AG-UI integration supports all features of the spec: - [Events](https://docs.ag-ui.com/concepts/events) - [Messages](https://docs.ag-ui.com/concepts/messages) - [State Management](https://docs.ag-ui.com/concepts/state) - [Tools](https://docs.ag-ui.com/concepts/tools) The integration receives messages in the form of a [`RunAgentInput`](https://docs.ag-ui.com/sdk/python/core/types#runagentinput) object that describes the details of the requested agent run including message history, state, and available tools. These are converted to Pydantic AI types and passed to the agent's run method. Events from the agent, including tool calls, are converted to AG-UI events and streamed back to the caller as Server-Sent Events (SSE). A user request may require multiple round trips between client UI and Pydantic AI server, depending on the tools and events needed. ## Features ### State management The integration provides full support for [AG-UI state management](https://docs.ag-ui.com/concepts/state), which enables real-time synchronization between agents and frontend applications. In the example below we have document state which is shared between the UI and server using the [`StateDeps`][pydantic_ai.ag_ui.StateDeps] [dependencies type](../dependencies.md) that can be used to automatically validate state contained in [`RunAgentInput.state`](https://docs.ag-ui.com/sdk/js/core/types#runagentinput) using a Pydantic `BaseModel` specified as a generic parameter. !!! note "Custom dependencies type with AG-UI state" If you want to use your own dependencies type to hold AG-UI state as well as other things, it needs to implements the [`StateHandler`][pydantic_ai.ag_ui.StateHandler] protocol, meaning it needs to be a [dataclass](https://docs.python.org/3/library/dataclasses.html) with a non-optional `state` field. This lets Pydantic AI ensure that state is properly isolated between requests by building a new dependencies object each time. If the `state` field's type is a Pydantic `BaseModel` subclass, the raw state dictionary on the request is automatically validated. If not, you can validate the raw value yourself in your dependencies dataclass's `__post_init__` method. ```python {title="ag_ui_state.py"} from pydantic import BaseModel from pydantic_ai import Agent from pydantic_ai.ui import StateDeps from pydantic_ai.ui.ag_ui.app import AGUIApp class DocumentState(BaseModel): """State for the document being written.""" document: str = '' agent = Agent( 'openai:gpt-5', instructions='Be fun!', deps_type=StateDeps[DocumentState], ) app = AGUIApp(agent, deps=StateDeps(DocumentState())) ``` Since `app` is an ASGI application, it can be used with any ASGI server: ```bash uvicorn ag_ui_state:app --host 0.0.0.0 --port 9000 ``` ### Tools AG-UI frontend tools are seamlessly provided to the Pydantic AI agent, enabling rich user experiences with frontend user interfaces. ### Events Pydantic AI tools can send [AG-UI events](https://docs.ag-ui.com/concepts/events) simply by returning a [`ToolReturn`](../tools-advanced.md#advanced-tool-returns) object with a [`BaseEvent`](https://docs.ag-ui.com/sdk/python/core/events#baseevent) (or a list of events) as `metadata`, which allows for custom events and state updates. ```python {title="ag_ui_tool_events.py"} from ag_ui.core import CustomEvent, EventType, StateSnapshotEvent from pydantic import BaseModel from pydantic_ai import Agent, RunContext, ToolReturn from pydantic_ai.ui import StateDeps from pydantic_ai.ui.ag_ui.app import AGUIApp class DocumentState(BaseModel): """State for the document being written.""" document: str = '' agent = Agent( 'openai:gpt-5', instructions='Be fun!', deps_type=StateDeps[DocumentState], ) app = AGUIApp(agent, deps=StateDeps(DocumentState())) @agent.tool async def update_state(ctx: RunContext[StateDeps[DocumentState]]) -> ToolReturn: return ToolReturn( return_value='State updated', metadata=[ StateSnapshotEvent( type=EventType.STATE_SNAPSHOT, snapshot=ctx.deps.state, ), ], ) @agent.tool_plain async def custom_events() -> ToolReturn: return ToolReturn( return_value='Count events sent', metadata=[ CustomEvent( type=EventType.CUSTOM, name='count', value=1, ), CustomEvent( type=EventType.CUSTOM, name='count', value=2, ), ] ) ``` Since `app` is an ASGI application, it can be used with any ASGI server: ```bash uvicorn ag_ui_tool_events:app --host 0.0.0.0 --port 9000 ``` ## Examples For more examples of how to use [`AGUIApp`][pydantic_ai.ui.ag_ui.app.AGUIApp] see [`pydantic_ai_examples.ag_ui`](https://github.com/pydantic/pydantic-ai/tree/main/examples/pydantic_ai_examples/ag_ui), which includes a server for use with the [AG-UI Dojo](https://docs.ag-ui.com/tutorials/debugging#the-ag-ui-dojo).