---
id: fasta2a
version: "0.6.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# fasta2a — Convert an AI Agent into a A2A server! ✨

License: permissive · Maintenance: active · Downloads: 344.1K/mo

## What it is and what it does

FastA2A is a framework-agnostic implementation of the A2A (Agent-to-Agent) protocol that exposes any AI agent as an ASGI web server. It coordinates three pluggable components—Storage (for tasks and conversation context), Broker (for task scheduling), and Worker (for task execution)—allowing agents to run asynchronously, potentially on different machines or processes. The library ships with in-memory implementations of Storage and Broker to lower the barrier to entry, but you provide the Worker to integrate your specific agentic framework.

The design enforces a stateful, task-based model: every incoming message creates a background task, and the server maintains conversation context across multiple tasks using a context_id. This architecture decouples the HTTP server from agent execution, enabling horizontal scaling and fault isolation. FastA2A includes a Pydantic AI integration that converts any pydantic_ai.Agent into an A2A-compatible app with a single function call.

Use it for:

- Expose a Pydantic AI agent as a distributed A2A server without writing boilerplate protocol code
- Build multi-agent systems where agents communicate via the A2A protocol across network boundaries
- Run agent workloads asynchronously with task persistence and conversation history tracking
- Integrate custom agentic frameworks by implementing the Worker interface and plugging into FastA2A
- Scale agent execution by running workers on separate machines while the HTTP server remains stateless

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

FastA2A converts an AI agent into an A2A-protocol-compliant ASGI server, managing task execution, conversation context, and message routing across distributed workers.

Yes, if you are building agent-based services and need A2A protocol compliance. The low install friction, active maintenance, MIT license, and permissive design (framework-agnostic Worker interface) make it a solid foundation. The in-memory implementations let you prototype quickly; production deployments will require custom Storage and Broker implementations. No known vulnerabilities.

## Install

pip install fasta2a
uv add fasta2a
poetry add fasta2a

## Installing fasta2a

Before you install: Low friction: pure Python wheel with four runtime dependencies (starlette, pydantic, opentelemetry-api, eval-type-backport). Active maintenance with last commit on 2026-07-27 and 214 repository stars.

License in practice: MIT license (permissive) allows commercial and private use with minimal restrictions—suitable for production deployment.

Quickstart:

pip install fasta2a

from fasta2a import FastA2A, Worker
from fasta2a.storage import InMemoryStorage
from fasta2a.broker import InMemoryBroker

storage = InMemoryStorage()
broker = InMemoryBroker()
app = FastA2A(storage=storage, broker=broker)

Requires Python 3.9 or later; you must implement a Worker subclass to execute tasks with your agentic framework.

Verify before relying:

- Performance characteristics under high concurrency or large message volumes
- Production-readiness of in-memory storage/broker implementations vs. persistent alternatives
- Compatibility matrix with specific agentic frameworks beyond Pydantic AI

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 344.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

A2A protocol server framework, agentic framework ASGI wrapper, agent to A2A conversion, distributed agent task queue, agent message routing, multi-agent communication protocol, agent task management, agent-framework, asgi-server, distributed-agents

[View on SkillFed](https://skillfed.io/packages/fasta2a) · [View on PyPI](https://pypi.org/project/fasta2a/)