Feature Request: A2A Protocol Integration

[flefevre]

Summary
Add support for the A2A (Agent‑to‑Agent) protocol into Langflow, enabling:

• Agent discovery via JSON Agent Card
• Structured JSON-RPC task invocation & monitoring
• Real-time streaming updates
• Multi-modal payload handling
• Secure, interoperable multi-agent workflows

Why This Matters
A2A is rapidly emerging as the standard for peer-to-peer AI agent orchestration.
By supporting A2A, Langflow would:

• Let other A2A agents (e.g. LangGraph, LangChain-based systems) call and collaborate with flows in Langflow.
• Unite agents across ecosystems via Python/FastAPI-compatible RPC.
• Complement existing MCP support by aligning with multi-agent patterns and tooling.

Proposed Features

1. Agent Card Endpoint (/.well-known/agent.json)
   Dynamically publish capabilities: tools, modalities, endpoints, version info.

2. JSON-RPC 2.0 Endpoints

   • tasks.send: launch flows with params
   • tasks.get: retrieve status and artifacts
   • tasks.cancel / tasks.update: optional lifecycle control

3. Streaming Updates

   • SSE or WebSocket for tasks.stream: enables GUI/UI to capture logs and intermediate results.

4. Multi-Modal Support
   Accept / emit parts: application/json, text/plain, file/*.

5. Task State Tracking
   Map flow runs to A2A task states: working, completed, failed, input-required, etc.

6. Authentication Layer
   Use FastAPI’s auth backend (API key, JWT, OAuth2).
   Optionally integrate with existing authorization systems (e.g. Permit.io) 33.

7. UI Enhancements
   Add a “Task Monitor” panel within the Langflow UI for visibility into A2A tasks and streams.

8. Interoperation with MCP
   Support both protocols, leveraging Project/Flow primitives already exposed via MCP 34.

Benefits

• Makes Langflow a first-class node in multi-agent A2A ecosystems (LangGraph, CrewAI, custom agents).
• Enables cross-platform task delegation (e.g. "execute this workflow in Langflow").
• Builds on existing flow and UI capabilities with minimal invasive changes.
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Next Steps

• Discuss API structure and endpoint placement
• Define Agent Card schema and capabilities format
• Prototype JSON-RPC + SSE backend
• Integrate with frontend (Task Monitor)
• Pilot interop with existing A2A agents (e.g. LangGraph adapter)

Happy to iterate on the feature a2a in Langflow

References

• A2A Specification: https://github.com/a2aproject/A2A
• Overview: https://a2aprotocol.ai/
• Dev Blog: https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
• Sample Agent: https://github.com/google-a2a/sample-agent