Explore tutorials, guides, and examples for integrating A2A Protocol with LangGraph to build powerful multi-agent systems.
Complete guide to integrating A2A Protocol with LangGraph for building multi-agent systems.
Read guideThe A2A (Agent2Agent) Traceability Extension is a powerful distributed tracing system specifically designed to provide complete call chain tracing for agent-to-agent communication in the A2A framework. This extension implements functionality similar to distributed tracing systems (such as Jaeger, Zipkin), but is optimized for the specific needs of multi-agent systems.
This project demonstrates an adversarial multi-agent simulation system based on the A2A protocol. The system contains two competing agents: an attacker (red team) and a defender (blue team), engaging in strategic intellectual confrontation.
A2A protocol hands-on tutorial: Three lightweight agents collaborating to complete a number guessing game. Features evaluator, CLI frontend, and visualizer roles, demonstrating inter-agent communication, task management, and multi-turn dialogue mechanisms.
AgentMaster is a next-generation multi-agent conversational framework jointly developed by Stanford University and George Mason University, pioneering the integration of A2A and MCP protocols in a single system. It supports multimodal inputs including text, images, and audio, automatically decomposes complex tasks through coordinator agents, and implements various functions such as SQL queries, information retrieval, and image analysis with excellent performance and user-friendliness.
A2A (Agent2Agent Protocol) and ACP (Agent Communication Protocol) represent two mainstream technical approaches in AI multi-agent system communication: 'cross-platform interoperability' and 'local/edge autonomy' respectively. A2A, with its powerful cross-vendor interconnection capabilities and rich task collaboration mechanisms, has become the preferred choice for cloud-based and distributed multi-agent scenarios; while ACP, with its low-latency, local-first, cloud-independent characteristics, is suitable for privacy-sensitive, bandwidth-constrained, or edge computing environments. Both protocols have their own focus in protocol design, ecosystem construction, and standardization governance, and are expected to further converge in openness in the future. Developers are advised to choose the most suitable protocol stack based on actual business needs.
This guide provides a detailed explanation of how to build an A2A-compliant agent using LangGraph and the Google Gemini model. We'll walk through the Currency Agent example from the A2A Python SDK, explaining each component, the flow of data, and how the A2A protocol facilitates agent interactions.
Master the Python A2A protocol for building interoperable AI agents. Learn how to create, connect, and orchestrate AI services with standardized communication, from basic echo agents to complex multi-agent workflows.