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        * [ Design  ](https://ai.pydantic.dev/a2a/#design)
          * [ Understanding Tasks and Context  ](https://ai.pydantic.dev/a2a/#understanding-tasks-and-context)
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        * [ Installation  ](https://ai.pydantic.dev/a2a/#installation)
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      * [ Agent User Interaction (AG-UI)  ](https://ai.pydantic.dev/examples/ag-ui/)
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    * pydantic_ai
      * [ pydantic_ai.ag_ui  ](https://ai.pydantic.dev/api/ag_ui/)
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      * [ pydantic_ai.models.cohere  ](https://ai.pydantic.dev/api/models/cohere/)
      * [ pydantic_ai.models.fallback  ](https://ai.pydantic.dev/api/models/fallback/)
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      * [ pydantic_ai.models.groq  ](https://ai.pydantic.dev/api/models/groq/)
      * [ pydantic_ai.models.huggingface  ](https://ai.pydantic.dev/api/models/huggingface/)
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      * [ pydantic_ai.models.mcp_sampling  ](https://ai.pydantic.dev/api/models/mcp-sampling/)
      * [ pydantic_ai.models.mistral  ](https://ai.pydantic.dev/api/models/mistral/)
      * [ pydantic_ai.models.openai  ](https://ai.pydantic.dev/api/models/openai/)
      * [ pydantic_ai.models.openrouter  ](https://ai.pydantic.dev/api/models/openrouter/)
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    * [ Upgrade Guide  ](https://ai.pydantic.dev/changelog/)
    * [ Version policy  ](https://ai.pydantic.dev/version-policy/)


  * [ FastA2A  ](https://ai.pydantic.dev/a2a/#fasta2a)
    * [ Design  ](https://ai.pydantic.dev/a2a/#design)
      * [ Understanding Tasks and Context  ](https://ai.pydantic.dev/a2a/#understanding-tasks-and-context)
      * [ Storage Architecture  ](https://ai.pydantic.dev/a2a/#storage-architecture)
    * [ Installation  ](https://ai.pydantic.dev/a2a/#installation)
    * [ Pydantic AI Agent to A2A Server  ](https://ai.pydantic.dev/a2a/#pydantic-ai-agent-to-a2a-server)


  1. [ Pydantic AI  ](https://ai.pydantic.dev/)
  2. [ Integrations  ](https://ai.pydantic.dev/logfire/)


# Agent2Agent (A2A) Protocol
The [Agent2Agent (A2A) Protocol](https://google.github.io/A2A/) is an open standard introduced by Google that enables communication and interoperability between AI agents, regardless of the framework or vendor they are built on.
At Pydantic, we built the [FastA2A](https://ai.pydantic.dev/a2a/#fasta2a) library to make it easier to implement the A2A protocol in Python.
We also built a convenience method that expose Pydantic AI agents as A2A servers - let's have a quick look at how to use it:
[With Pydantic AI Gateway](https://ai.pydantic.dev/a2a/#__tabbed_1_1)[Directly to Provider API](https://ai.pydantic.dev/a2a/#__tabbed_1_2)
[Learn about Gateway](https://ai.pydantic.dev/gateway) agent_to_a2a.py```
from pydantic_ai import Agent

agent = Agent('gateway/openai:gpt-5.2', instructions='Be fun!')
app = agent.to_a2a()

```

agent_to_a2a.py```
from pydantic_ai import Agent

agent = Agent('openai:gpt-5.2', instructions='Be fun!')
app = agent.to_a2a()

```

_You can run the example with`uvicorn agent_to_a2a:app --host 0.0.0.0 --port 8000`_
This will expose the agent as an A2A server, and you can start sending requests to it.
See more about [exposing Pydantic AI agents as A2A servers](https://ai.pydantic.dev/a2a/#pydantic-ai-agent-to-a2a-server).
## FastA2A
**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**.
### Design
**FastA2A** is built on top of [Starlette](https://www.starlette.io), 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`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Storage "Storage"): to save and load tasks, as well as store context for conversations
  * [`Broker`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Broker "Broker



      dataclass
  "): to schedule tasks
  * [`Worker`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Worker "Worker



      dataclass
  "): to execute tasks


Let's have a look at how those components fit together:
FastA2A allows you to bring your own [`Storage`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Storage "Storage"), [`Broker`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Broker "Broker



      dataclass
  ") and [`Worker`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Worker "Worker



      dataclass
  ").
#### Understanding Tasks and Context
In the A2A protocol:
  * **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 is stored as a task artifact.
  * **Context** : Represents a conversation thread that can span multiple tasks. The A2A protocol uses a `context_id` to 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_id` to continue the conversation
  * All tasks sharing the same `context_id` have access to the complete message history


#### Storage Architecture
The [`Storage`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.Storage "Storage") component serves two purposes:
  1. **Task Storage** : Stores tasks in A2A protocol format, including their status, artifacts, and message history
  2. **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.
For example, a Pydantic AI agent might store its complete internal message format (including tool calls and responses) in the context storage, while storing only the A2A-compliant messages in the task history.
### Installation
FastA2A is available on PyPI as [`fasta2a`](https://pypi.org/project/fasta2a/) so installation is as simple as:
[pip](https://ai.pydantic.dev/a2a/#__tabbed_2_1)[uv](https://ai.pydantic.dev/a2a/#__tabbed_2_2)
```
pip install fasta2a

```

```
uv add fasta2a

```

The only dependencies are:
  * [starlette](https://www.starlette.io): to expose the A2A server as an [ASGI application](https://asgi.readthedocs.io/en/latest/)
  * [pydantic](https://pydantic.dev): to validate the request/response messages
  * [opentelemetry-api](https://opentelemetry-python.readthedocs.io/en/latest): to provide tracing capabilities


You can install Pydantic AI with the `a2a` extra to include **FastA2A** :
[pip](https://ai.pydantic.dev/a2a/#__tabbed_3_1)[uv](https://ai.pydantic.dev/a2a/#__tabbed_3_2)
```
pip install 'pydantic-ai-slim[a2a]'

```

```
uv add 'pydantic-ai-slim[a2a]'

```

### Pydantic AI Agent to A2A Server
To expose a Pydantic AI agent as an A2A server, you can use the `to_a2a` method:
[With Pydantic AI Gateway](https://ai.pydantic.dev/a2a/#__tabbed_4_1)[Directly to Provider API](https://ai.pydantic.dev/a2a/#__tabbed_4_2)
[Learn about Gateway](https://ai.pydantic.dev/gateway) agent_to_a2a.py```
from pydantic_ai import Agent

agent = Agent('gateway/openai:gpt-5.2', instructions='Be fun!')
app = agent.to_a2a()

```

agent_to_a2a.py```
from pydantic_ai import Agent

agent = Agent('openai:gpt-5.2', instructions='Be fun!')
app = agent.to_a2a()

```

Since `app` is an ASGI application, it can be used with any ASGI server.
```
uvicorn agent_to_a2a:app --host 0.0.0.0 --port 8000

```

Since the goal of `to_a2a` is to be a convenience method, it accepts the same arguments as the [`FastA2A`](https://ai.pydantic.dev/api/fasta2a/#fasta2a.FastA2A "FastA2A") constructor.
When using `to_a2a()`, Pydantic AI automatically:
  * Stores the complete conversation history (including tool calls and responses) in the context storage
  * Ensures that subsequent messages with the same `context_id` have access to the full conversation history
  * Persists agent results as A2A artifacts:
  * String results become `TextPart` artifacts and also appear in the message history
  * Structured data (Pydantic models, dataclasses, tuples, etc.) become `DataPart` artifacts with the data wrapped as `{"result": <your_data>}`
  * Artifacts include metadata with type information and JSON schema when available


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