> ## Documentation Index
> Fetch the complete documentation index at: https://docs.crewai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent-to-Agent (A2A) Protocol

> Agents delegate tasks to remote A2A agents and/or operate as A2A-compliant server agents.

## A2A Agent Delegation

CrewAI treats [A2A protocol](https://a2a-protocol.org/latest/) as a first-class delegation primitive, enabling agents to delegate tasks, request information, and collaborate with remote agents, as well as act as A2A-compliant server agents.
In client mode, agents autonomously choose between local execution and remote delegation based on task requirements.

## How It Works

When an agent is configured with A2A capabilities:

1. The Agent analyzes each task
2. It decides to either:
   * Handle the task directly using its own capabilities
   * Delegate to a remote A2A agent for specialized handling
3. If delegating, the agent communicates with the remote A2A agent through the protocol
4. Results are returned to the CrewAI workflow

<Note>
  A2A delegation requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
</Note>

## Basic Configuration

<Warning>
  `crewai.a2a.config.A2AConfig` is deprecated and will be removed in v2.0.0. Use `A2AClientConfig` for connecting to remote agents and/or `A2AServerConfig` for exposing agents as servers.
</Warning>

Configure an agent for A2A delegation by setting the `a2a` parameter:

```python Code theme={null}
from crewai import Agent, Crew, Task
from crewai.a2a import A2AClientConfig

agent = Agent(
    role="Research Coordinator",
    goal="Coordinate research tasks efficiently",
    backstory="Expert at delegating to specialized research agents",
    llm="gpt-4o",
    a2a=A2AClientConfig(
        endpoint="https://example.com/.well-known/agent-card.json",
        timeout=120,
        max_turns=10
    )
)

task = Task(
    description="Research the latest developments in quantum computing",
    expected_output="A comprehensive research report",
    agent=agent
)

crew = Crew(agents=[agent], tasks=[task], verbose=True)
result = crew.kickoff()
```

## Client Configuration Options

The `A2AClientConfig` class accepts the following parameters:

<ParamField path="endpoint" type="str" required>
  The A2A agent endpoint URL (typically points to `.well-known/agent-card.json`)
</ParamField>

<ParamField path="auth" type="AuthScheme" default="None">
  Authentication scheme for the A2A agent. Supports Bearer tokens, OAuth2, API keys, and HTTP authentication.
</ParamField>

<ParamField path="timeout" type="int" default="120">
  Request timeout in seconds
</ParamField>

<ParamField path="max_turns" type="int" default="10">
  Maximum number of conversation turns with the A2A agent
</ParamField>

<ParamField path="response_model" type="type[BaseModel]" default="None">
  Optional Pydantic model for requesting structured output from an A2A agent. A2A protocol does not
  enforce this, so an A2A agent does not need to honor this request.
</ParamField>

<ParamField path="fail_fast" type="bool" default="True">
  Whether to raise an error immediately if agent connection fails. When `False`, the agent continues with available agents and informs the LLM about unavailable ones.
</ParamField>

<ParamField path="trust_remote_completion_status" type="bool" default="False">
  When `True`, returns the A2A agent's result directly when it signals completion. When `False`, allows the server agent to review the result and potentially continue the conversation.
</ParamField>

<ParamField path="updates" type="UpdateConfig" default="StreamingConfig()">
  Update mechanism for receiving task status. Options: `StreamingConfig`, `PollingConfig`, or `PushNotificationConfig`.
</ParamField>

<ParamField path="transport_protocol" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="JSONRPC">
  Transport protocol for A2A communication. Options: `JSONRPC` (default), `GRPC`, or `HTTP+JSON`.
</ParamField>

<ParamField path="accepted_output_modes" type="list[str]" default="[&#x22;application/json&#x22;]">
  Media types the client can accept in responses.
</ParamField>

<ParamField path="supported_transports" type="list[str]" default="[&#x22;JSONRPC&#x22;]">
  Ordered list of transport protocols the client supports.
</ParamField>

<ParamField path="use_client_preference" type="bool" default="False">
  Whether to prioritize client transport preferences over server.
</ParamField>

<ParamField path="extensions" type="list[str]" default="[]">
  Extension URIs the client supports.
</ParamField>

## Authentication

For A2A agents that require authentication, use one of the provided auth schemes:

<Tabs>
  <Tab title="Bearer Token">
    ```python bearer_token_auth.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.auth import BearerTokenAuth

    agent = Agent(
        role="Secure Coordinator",
        goal="Coordinate tasks with secured agents",
        backstory="Manages secure agent communications",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://secure-agent.example.com/.well-known/agent-card.json",
            auth=BearerTokenAuth(token="your-bearer-token"),
            timeout=120
        )
    )
    ```
  </Tab>

  <Tab title="API Key">
    ```python api_key_auth.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.auth import APIKeyAuth

    agent = Agent(
        role="API Coordinator",
        goal="Coordinate with API-based agents",
        backstory="Manages API-authenticated communications",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://api-agent.example.com/.well-known/agent-card.json",
            auth=APIKeyAuth(
                api_key="your-api-key",
                location="header",  # or "query" or "cookie"
                name="X-API-Key"
            ),
            timeout=120
        )
    )
    ```
  </Tab>

  <Tab title="OAuth2">
    ```python oauth2_auth.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.auth import OAuth2ClientCredentials

    agent = Agent(
        role="OAuth Coordinator",
        goal="Coordinate with OAuth-secured agents",
        backstory="Manages OAuth-authenticated communications",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://oauth-agent.example.com/.well-known/agent-card.json",
            auth=OAuth2ClientCredentials(
                token_url="https://auth.example.com/oauth/token",
                client_id="your-client-id",
                client_secret="your-client-secret",
                scopes=["read", "write"]
            ),
            timeout=120
        )
    )
    ```
  </Tab>

  <Tab title="HTTP Basic">
    ```python http_basic_auth.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.auth import HTTPBasicAuth

    agent = Agent(
        role="Basic Auth Coordinator",
        goal="Coordinate with basic auth agents",
        backstory="Manages basic authentication communications",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://basic-agent.example.com/.well-known/agent-card.json",
            auth=HTTPBasicAuth(
                username="your-username",
                password="your-password"
            ),
            timeout=120
        )
    )
    ```
  </Tab>
</Tabs>

## Multiple A2A Agents

Configure multiple A2A agents for delegation by passing a list:

```python Code theme={null}
from crewai.a2a import A2AClientConfig
from crewai.a2a.auth import BearerTokenAuth

agent = Agent(
    role="Multi-Agent Coordinator",
    goal="Coordinate with multiple specialized agents",
    backstory="Expert at delegating to the right specialist",
    llm="gpt-4o",
    a2a=[
        A2AClientConfig(
            endpoint="https://research.example.com/.well-known/agent-card.json",
            timeout=120
        ),
        A2AClientConfig(
            endpoint="https://data.example.com/.well-known/agent-card.json",
            auth=BearerTokenAuth(token="data-token"),
            timeout=90
        )
    ]
)
```

The LLM will automatically choose which A2A agent to delegate to based on the task requirements.

## Error Handling

Control how agent connection failures are handled using the `fail_fast` parameter:

```python Code theme={null}
from crewai.a2a import A2AClientConfig

# Fail immediately on connection errors (default)
agent = Agent(
    role="Research Coordinator",
    goal="Coordinate research tasks",
    backstory="Expert at delegation",
    llm="gpt-4o",
    a2a=A2AClientConfig(
        endpoint="https://research.example.com/.well-known/agent-card.json",
        fail_fast=True
    )
)

# Continue with available agents
agent = Agent(
    role="Multi-Agent Coordinator",
    goal="Coordinate with multiple agents",
    backstory="Expert at working with available resources",
    llm="gpt-4o",
    a2a=[
        A2AClientConfig(
            endpoint="https://primary.example.com/.well-known/agent-card.json",
            fail_fast=False
        ),
        A2AClientConfig(
            endpoint="https://backup.example.com/.well-known/agent-card.json",
            fail_fast=False
        )
    ]
)
```

When `fail_fast=False`:

* If some agents fail, the LLM is informed which agents are unavailable and can delegate to working agents
* If all agents fail, the LLM receives a notice about unavailable agents and handles the task directly
* Connection errors are captured and included in the context for better decision-making

## Update Mechanisms

Control how your agent receives task status updates from remote A2A agents:

<Tabs>
  <Tab title="Streaming (Default)">
    ```python streaming_config.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.updates import StreamingConfig

    agent = Agent(
        role="Research Coordinator",
        goal="Coordinate research tasks",
        backstory="Expert at delegation",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://research.example.com/.well-known/agent-card.json",
            updates=StreamingConfig()
        )
    )
    ```
  </Tab>

  <Tab title="Polling">
    ```python polling_config.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.updates import PollingConfig

    agent = Agent(
        role="Research Coordinator",
        goal="Coordinate research tasks",
        backstory="Expert at delegation",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://research.example.com/.well-known/agent-card.json",
            updates=PollingConfig(
                interval=2.0,
                timeout=300.0,
                max_polls=100
            )
        )
    )
    ```
  </Tab>

  <Tab title="Push Notifications">
    ```python push_notifications_config.py lines theme={null}
    from crewai.a2a import A2AClientConfig
    from crewai.a2a.updates import PushNotificationConfig

    agent = Agent(
        role="Research Coordinator",
        goal="Coordinate research tasks",
        backstory="Expert at delegation",
        llm="gpt-4o",
        a2a=A2AClientConfig(
            endpoint="https://research.example.com/.well-known/agent-card.json",
            updates=PushNotificationConfig(
                url="{base_url}/a2a/callback",
                token="your-validation-token",
                timeout=300.0
            )
        )
    )
    ```
  </Tab>
</Tabs>

## Exposing Agents as A2A Servers

You can expose your CrewAI agents as A2A-compliant servers, allowing other A2A clients to delegate tasks to them.

### Server Configuration

Add an `A2AServerConfig` to your agent to enable server capabilities:

```python a2a_server_agent.py lines theme={null}
from crewai import Agent
from crewai.a2a import A2AServerConfig

agent = Agent(
    role="Data Analyst",
    goal="Analyze datasets and provide insights",
    backstory="Expert data scientist with statistical analysis skills",
    llm="gpt-4o",
    a2a=A2AServerConfig(url="https://your-server.com")
)
```

### Server Configuration Options

<ParamField path="name" type="str" default="None">
  Human-readable name for the agent. Defaults to the agent's role if not provided.
</ParamField>

<ParamField path="description" type="str" default="None">
  Human-readable description. Defaults to the agent's goal and backstory if not provided.
</ParamField>

<ParamField path="version" type="str" default="1.0.0">
  Version string for the agent card.
</ParamField>

<ParamField path="skills" type="list[AgentSkill]" default="[]">
  List of agent skills. Auto-generated from agent tools if not provided.
</ParamField>

<ParamField path="capabilities" type="AgentCapabilities" default="AgentCapabilities(streaming=True, push_notifications=False)">
  Declaration of optional capabilities supported by the agent.
</ParamField>

<ParamField path="default_input_modes" type="list[str]" default="[&#x22;text/plain&#x22;, &#x22;application/json&#x22;]">
  Supported input MIME types.
</ParamField>

<ParamField path="default_output_modes" type="list[str]" default="[&#x22;text/plain&#x22;, &#x22;application/json&#x22;]">
  Supported output MIME types.
</ParamField>

<ParamField path="url" type="str" default="None">
  Preferred endpoint URL. If set, overrides the URL passed to `to_agent_card()`.
</ParamField>

<ParamField path="preferred_transport" type="Literal['JSONRPC', 'GRPC', 'HTTP+JSON']" default="JSONRPC">
  Transport protocol for the preferred endpoint.
</ParamField>

<ParamField path="protocol_version" type="str" default="0.3">
  A2A protocol version this agent supports.
</ParamField>

<ParamField path="provider" type="AgentProvider" default="None">
  Information about the agent's service provider.
</ParamField>

<ParamField path="documentation_url" type="str" default="None">
  URL to the agent's documentation.
</ParamField>

<ParamField path="icon_url" type="str" default="None">
  URL to an icon for the agent.
</ParamField>

<ParamField path="additional_interfaces" type="list[AgentInterface]" default="[]">
  Additional supported interfaces (transport and URL combinations).
</ParamField>

<ParamField path="security" type="list[dict[str, list[str]]]" default="[]">
  Security requirement objects for all agent interactions.
</ParamField>

<ParamField path="security_schemes" type="dict[str, SecurityScheme]" default="{}">
  Security schemes available to authorize requests.
</ParamField>

<ParamField path="supports_authenticated_extended_card" type="bool" default="False">
  Whether agent provides extended card to authenticated users.
</ParamField>

<ParamField path="signatures" type="list[AgentCardSignature]" default="[]">
  JSON Web Signatures for the AgentCard.
</ParamField>

### Combined Client and Server

An agent can act as both client and server by providing both configurations:

```python Code theme={null}
from crewai import Agent
from crewai.a2a import A2AClientConfig, A2AServerConfig

agent = Agent(
    role="Research Coordinator",
    goal="Coordinate research and serve analysis requests",
    backstory="Expert at delegation and analysis",
    llm="gpt-4o",
    a2a=[
        A2AClientConfig(
            endpoint="https://specialist.example.com/.well-known/agent-card.json",
            timeout=120
        ),
        A2AServerConfig(url="https://your-server.com")
    ]
)
```

## Best Practices

<CardGroup cols={2}>
  <Card title="Set Appropriate Timeouts" icon="clock">
    Configure timeouts based on expected A2A agent response times. Longer-running tasks may need higher timeout values.
  </Card>

  <Card title="Limit Conversation Turns" icon="comments">
    Use `max_turns` to prevent excessive back-and-forth. The agent will automatically conclude conversations before hitting the limit.
  </Card>

  <Card title="Use Resilient Error Handling" icon="shield-check">
    Set `fail_fast=False` for production environments with multiple agents to gracefully handle connection failures and maintain workflow continuity.
  </Card>

  <Card title="Secure Your Credentials" icon="lock">
    Store authentication tokens and credentials as environment variables, not in code.
  </Card>

  <Card title="Monitor Delegation Decisions" icon="eye">
    Use verbose mode to observe when the LLM chooses to delegate versus handle tasks directly.
  </Card>
</CardGroup>

## Supported Authentication Methods

* **Bearer Token** - Simple token-based authentication
* **OAuth2 Client Credentials** - OAuth2 flow for machine-to-machine communication
* **OAuth2 Authorization Code** - OAuth2 flow requiring user authorization
* **API Key** - Key-based authentication (header, query param, or cookie)
* **HTTP Basic** - Username/password authentication
* **HTTP Digest** - Digest authentication (requires `httpx-auth` package)

## Learn More

For more information about the A2A protocol and reference implementations:

* [A2A Protocol Documentation](https://a2a-protocol.org)
* [A2A Sample Implementations](https://github.com/a2aproject/a2a-samples)
* [A2A Python SDK](https://github.com/a2aproject/a2a-python)