A2a Langgraph Boilerplate

A2A LangGraph Boilerplate

This project is a boilerplate for developers who want to start building an AI agent cluster with A2A and MCP servers integration faster and more efficiently.

• Project Overview
• Agent Instructions

Features

• Create & manage AI crews
• Create & manage AI agents
• Create & manage MCP servers
• Create & manage Tools
• Integrate MCP servers for tool usage
• Monitor activity logs
• Create & manage conversations with AI crews / AI agents
• Expose Swagger API Docs for frontend integration instructions

Concept

• Each AI agent cluster can have multiple AI agent crews (AI Crews)
• Each AI crew can have multiple AI agent, leaded by a superviser (a default AI agent of an AI crew)
• Each AI agent can call tools via MCP servers integration

How it works

• A supervisor agent will receive input (prompt) from a user via API call, then create a detailed plan with its current capabilities (AI agents underneat and their tools)
• Then request the AI agents to perform tasks via A2A protocol
• Wait for all AI agents finish given tasks
• Grab all the results, analyze and decide to assign more tasks to AI agents, or finish the plan
• Synthesize the results and respond to user based on the original input prompt.

Example workflow:

• Case 1: Simple Direct Response - User asks "hello" and supervisor decides to answer directly with a simple response. This requires no agent delegation and completes in a single workflow step.
• Case 2: Multi-Agent Collaboration with Termination Control - User asks for travel advice about Nha Trang beach (Vietnam):
  1. Supervisor receives query and creates a task plan with clear termination conditions
  2. Supervisor delegates to agent 1 (connected to Search API MCP server) to find top attractions
  3. Supervisor delegates to agent 2 (connected to Search API MCP server) to research local cuisine
  4. Each agent responds with its findings in a sin