A2a Framework Integration

A2A Framework Integration

Before writing code

Fetch live docs:

1. Fetch https://a2a-protocol.org/latest/specification/ for the protocol spec
2. Web-search site:github.com a2aproject a2a-samples for framework integration examples
3. Web-search the specific framework's docs for A2A support:
   • site:google.github.io adk a2a agent-to-agent for Google ADK
   • site:langchain-ai.github.io langgraph a2a for LangGraph
   • site:docs.crewai.com a2a for CrewAI
   • site:docs.aws.amazon.com bedrock agentcore a2a for AWS Bedrock
   • site:learn.microsoft.com azure ai-foundry a2a for Microsoft Azure AI Foundry
4. Fetch SDK docs for framework adapter classes

Conceptual Architecture

Why Framework Integration

Most production agents are built using frameworks (LangGraph, CrewAI, ADK, etc.). A2A framework integration allows these agents to:

• Expose themselves as A2A servers (other agents can discover and call them)
• Call other agents as A2A clients (delegate work to specialist agents)
• Participate in multi-agent systems without rewriting framework-specific code

Google ADK (Agent Development Kit)

Google ADK has native A2A support since it was co-developed alongside A2A:

• ADK agents can be directly exposed as A2A servers
• ADK provides A2A client utilities for calling other agents
• Agent Cards are generated from ADK agent configuration
• Supports streaming, multi-turn, and push notifications

Integration approach: Use ADK's built-in A2A server/client utilities.

LangGraph / LangChain

LangGraph agents can be wrapped as A2A servers:

• The graph's invoke/stream methods map to message/send and message/stream
• Graph state maps to A2A task state
• Interrupts in LangGraph map to input-required in A2A

Integration approach: Use A2A SDK adapters or build a thin wrapper that translates between LangGraph's interface and A2A's JSON-RPC protocol.

CrewAI

CrewAI's crew-based multi-agent model can integrate with A2A:

• Individual crew members can be exposed as A2A agents
• Entire crews can be wrapped as a single A2A agent
• A2A enables cross-crew communication

Integration approach: Wrap CrewAI agents/crews with an A2A server that translates tasks to crew kickoffs.

AutoGen

Microsoft's AutoGen multi-agent framework:

• AutoGen agents can be wrapped as A2A servers
• AutoGen's conversation patterns map to A2A multi-turn
• Group chat can be exposed as a single A2A agent

Integration approach: Build adapters between AutoGen's message protocol and A2A.

AWS Bedrock AgentCore

AWS Bedrock has added A2A support:

• Bedrock agents can communicate via A2A
• AgentCore provides infrastructure for hosting A2A-compatible agents
• Integration with AWS IAM for authentication

Integration approach: Use Bedrock's A2A-compatible agent hosting and client utilities.

Microsoft Azure AI Foundry

Azure AI Foundry includes A2A support as part of its multi-agent orchestration stack:

• Foundry-hosted agents can be exposed as A2A servers and consumed as A2A clients
• Connected Agents and Multi-Agent Workflows interoperate with external A2A endpoints
• Authentication integrates with Microsoft Entra ID
• A2A is positioned alongside MCP for cross-vendor agent interoperability

Integration approach: Use Foundry's built-in A2A bindings on agent definitions, and the Foundry SDK's A2A client utilities for outbound calls. Verify the current binding shape against Microsoft Learn before implementing — the surface is evolving.

General Integration Pattern

Regardless of framework, the pattern is:

Exposing a framework agent as A2A server:

1. Create an Agent Card describing the framework agent's capabilities
2. Implement a thin A2A server that wraps the framework agent
3. Map incoming A2A messages to framework input format
4. Map framework output to A2A task results/artifacts
5. Handle state mapping (framework states → A2A task states)

Using A2A agents from a framework:

1. Fetch the target agent's Agent Card
2. Create an A2A client in the framework's tool/action layer
3. Map the framework's delegation pattern to A2A message/send or message/stream
4. Handle A2A responses within the framework's processing flow

State Mapping Challenges

Different frameworks manage state differently:

• LangGraph: Graph state with checkpoints → A2A task state
• CrewAI: Crew execution state → A2A task state
• AutoGen: Conversation history → A2A message history
• ADK: Native A2A alignment (minimal mapping needed)
• Azure AI Foundry: Connected Agents / Multi-Agent Workflow state → A2A task state

Best Practices

• Use the simplest integration pattern that works — don't over-abstract
• Test framework-specific edge cases (timeouts, retries, error handling)
• Document the state mapping between the framework and A2A
• Keep the adapter layer as thin as possible
• Check for official A2A adapters before building custom ones
• Handle framework-specific features that don't map cleanly to A2A (e.g., LangGraph branching)
• Version your adapters alongside framework version upgrades

Fetch the latest framework documentation and A2A SDK adapters before implementing integrations.