Next Steps

Congratulations on completing the A2A Python SDK Tutorial! You've learned how to:

• Set up your environment for A2A development.
• Define Agent Skills and Agent Cards using the SDK's types.
• Implement a basic HelloWorld A2A server and client.
• Understand and implement streaming capabilities.
• Integrate a more complex agent using LangGraph, demonstrating task state management and tool use.

You now have a solid foundation for building and integrating your own A2A-compliant agents.

Where to Go From Here?

Here are some ideas and resources to continue your A2A journey:

• Explore Other Examples:
  • Check out the other examples in the a2a-samples/samples/ directory in the A2A GitHub repository for more complex agent integrations and features.
  • The main A2A repository also has samples for other languages and frameworks.
• Deepen Your Protocol Understanding:
  • 📚 Read the complete A2A Protocol Documentation site for a comprehensive overview.
  • 📝 Review the detailed A2A Protocol Specification to understand the nuances of all data structures and RPC methods.
• Review Key A2A Topics:
  • A2A and MCP: Understand how A2A complements the Model Context Protocol for tool usage.
  • Enterprise-Ready Features: Learn about security, observability, and other enterprise considerations.
  • Streaming & Asynchronous Operations: Get more details on SSE and push notifications.
  • Agent Discovery: Explore different ways agents can find each other.
• Build Your Own Agent:
  • Try creating a new A2A agent using your favorite Python agent framework (like LangChain, CrewAI, AutoGen, Semantic Kernel, or a custom solution).
  • Implement the a2a.server.AgentExecutor interface to bridge your agent's logic with the A2A protocol.
  • Think about what unique skills your agent could offer and how its Agent Card would represent them.
• Experiment with Advanced Features:
  • Implement robust task management with a persistent TaskStore if your agent handles long-running or multi-session tasks.
  • Explore implementing push notifications if your agent's tasks are very long-lived.
  • Consider more complex input and output modalities (e.g., handling file uploads/downloads, or structured data via DataPart).
• Contribute to the A2A Community:
  • Join the discussions on the A2A GitHub Discussions page.
  • Report issues or suggest improvements via GitHub Issues.
  • Consider contributing code, examples, or documentation. See the CONTRIBUTING.md guide.

The A2A protocol aims to foster an ecosystem of interoperable AI agents. By building and sharing A2A-compliant agents, you can be a part of this exciting development!