FastA2A is an agentic framework agnostic implementation of the A2A protocol in Python. The library is designed to be used with any agentic framework, and is not exclusive to Pydantic AI.
FastA2A is available on PyPI as fasta2a so installation is as simple as:
pip install fasta2a # or `uv add fasta2a`The only dependencies are:
- starlette: to expose the A2A server as an ASGI application
- pydantic: to validate the request/response messages
- opentelemetry-api: to provide tracing capabilities
To use FastA2A, you need to bring the Storage, Broker and Worker components.
FastA2A was designed with the mindset that the worker could, and should live outside the web server. i.e. you can have a worker that runs on a different machine, or even in a different process.
You can use the InMemoryStorage and InMemoryBroker to get started, but you'll need to implement the Worker
to be able to execute the tasks with your agentic framework. Let's see an example:
import uuid
from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
from typing import Any
from fasta2a import FastA2A, Worker
from fasta2a.broker import InMemoryBroker
from fasta2a.schema import Artifact, Message, TaskIdParams, TaskSendParams, TextPart
from fasta2a.storage import InMemoryStorage
Context = list[Message]
"""The shape of the context you store in the storage."""
class InMemoryWorker(Worker[Context]):
async def run_task(self, params: TaskSendParams) -> None:
task = await self.storage.load_task(params['id'])
assert task is not None
await self.storage.update_task(task['id'], state='working')
context = await self.storage.load_context(task['context_id']) or []
context.extend(task.get('history', []))
# Call your agent here...
message = Message(
role='agent',
parts=[TextPart(text=f'Your context is {len(context) + 1} messages long.', kind='text')],
kind='message',
message_id=str(uuid.uuid4()),
)
# Update the new message to the context.
context.append(message)
artifacts = self.build_artifacts(123)
await self.storage.update_context(task['context_id'], context)
await self.storage.update_task(task['id'], state='completed', new_messages=[message], new_artifacts=artifacts)
async def cancel_task(self, params: TaskIdParams) -> None: ...
def build_message_history(self, history: list[Message]) -> list[Any]: ...
def build_artifacts(self, result: Any) -> list[Artifact]: ...
storage = InMemoryStorage()
broker = InMemoryBroker()
worker = InMemoryWorker(storage=storage, broker=broker)
@asynccontextmanager
async def lifespan(app: FastA2A) -> AsyncIterator[None]:
async with app.task_manager:
async with worker.run():
yield
app = FastA2A(storage=storage, broker=broker, lifespan=lifespan)You can run this example as is with uvicorn main:app --reload.
Initially, this FastA2A lived under Pydantic AI repository, but since we received community feedback, we've decided to move it to a separate repository.
Note
Other agentic frameworks are welcome to implement the Worker component, and we'll be happy add the reference here.
Install the integration with the pydantic-ai extra:
pip install 'fasta2a[pydantic-ai]' # or `uv add 'fasta2a[pydantic-ai]'`Then turn any pydantic_ai.Agent into an A2A-compatible ASGI app:
from pydantic_ai import Agent
from fasta2a.pydantic_ai import agent_to_a2a
agent = Agent('openai:gpt-5.5')
app = agent_to_a2a(agent)You can run this example as is with uvicorn main:app --reload.
As you see, it's pretty easy from the point of view of the developer using your agentic framework.
Note
The bridge requires Pydantic AI 2. Agent.to_a2a() was removed there; agent_to_a2a is its replacement.
message/stream answers with server-sent events: the task first, then whatever the Worker
publishes while it runs — publish_status for a change of state, publish_artifact for a result,
whole or chunk by chunk with append=True — and the stream ends when the task reaches a final
state. The worker does not have to publish that end: once run_task or cancel_task returns, the
task's state is read back from storage and, if the task is over (completed, canceled, failed,
rejected) or waiting on the client (input-required, auth-required), published and the stream
closed — failed when the run raised — so a worker that only writes storage still ends its stream.
tasks/resubscribe on a task in one of those states returns the task and ends at once.
The Pydantic AI bridge publishes working when it starts, the model's text as chunks of the
answer's artifact while it is written, and the whole artifact as the last chunk.
An extension is a capability negotiated by URI on top of the
core protocol. The agent declares the ones it supports in its card, a client asks for some of them in the
A2A-Extensions request header, and the agent answers on the same header with the ones it activated.
Declare them on the application (or pass extensions= to agent_to_a2a):
from fasta2a import AgentExtension, FastA2A
app = FastA2A(
storage=storage,
broker=broker,
extensions=[
AgentExtension(uri='https://example.com/ext/citations/v1', description='Cites its sources'),
AgentExtension(uri='https://example.com/ext/trace/v1', required=True),
],
)For every request, FastA2A activates the requested extensions it supports — a URI it never declared is
ignored and not echoed back, which is how the client learns that — and puts the activated list in the message
metadata, so a Worker can read what was agreed for the task it is running:
from fasta2a import activated_extensions
class MyWorker(Worker[Context]):
async def run_task(self, params: TaskSendParams) -> None:
if 'https://example.com/ext/citations/v1' in activated_extensions(params):
... # add citation parts to the artifactsA message/send or message/stream that leaves a required extension inactive is refused with a
-32600 error whose data.missing_required_extensions names it, before any task is created.
FastA2A is built on top of Starlette, which means it's fully compatible with any ASGI server.
Given the nature of the A2A protocol, it's important to understand the design before using it, as a developer you'll need to provide some components:
- Storage: to save and load tasks and the conversation context
- Broker: to schedule tasks
- Worker: to execute tasks
Let's have a look at how those components fit together:
flowchart TB
Server["HTTP Server"] <--> |Sends Requests/<br>Receives Results| TM
subgraph CC[Core Components]
direction RL
TM["TaskManager<br>(coordinates)"] --> |Schedules Tasks| Broker
TM <--> Storage
Broker["Broker<br>(queues & schedules)"] <--> Storage["Storage<br>(persistence)"]
Broker --> |Delegates Execution| Worker
end
Worker["Worker<br>(implementation)"]
FastA2A allows you to bring your own Storage, Broker and Worker.
You can also leverage the in-memory implementations of Storage and Broker by using
the InMemoryStorage and InMemoryBroker:
from fasta2a import InMemoryStorage, InMemoryBroker
storage = InMemoryStorage()
broker = InMemoryBroker()FastA2A is designed to be opinionated regarding the A2A protocol. When the server receives a message, according to the specification, the server can decide between:
- Send a stateless message back to the client
- Create a stateful Task and run it on the background
FastA2A will always create a Task and run it on the background (on the Worker).
Note
You can read more about it here.
-
Task: Represents one complete execution of an agent. When a client sends a message to the agent, a new task is created. The agent runs until completion (or failure), and this entire execution is considered one task. The final output should be stored as a task artifact.
-
Context: Represents a conversation thread that can span multiple tasks. The A2A protocol uses a
context_idto maintain conversation continuity:- When a new message is sent without a
context_id, the server generates a new one - Subsequent messages can include the same
context_idto continue the conversation - All tasks sharing the same
context_idhave access to the complete message history
- When a new message is sent without a
The Storage component serves two purposes:
- Task Storage: Stores tasks in A2A protocol format, including their status, artifacts, and message history
- Context Storage: Stores conversation context in a format optimized for the specific agent implementation
This design allows for agents to store rich internal state (e.g., tool calls, reasoning traces) as well as store task-specific A2A-formatted messages and artifacts.
This project is licensed under the MIT License - see the LICENSE file for details.