A2AMCP

# What Sets A2AMCP Apart from Agentic Frameworks ## Understanding the Fundamental Difference A2AMCP and agentic frameworks like CrewAI, AutoGen, or LangGraph solve different problems in the AI agent ecosystem. While they may seem similar on the surface, they operate at completely different layers of the stack. ## The Key Distinction ### Agentic Frameworks: Orchestration Layer **What they do**: Define, create, and orchestrate AI agents - Create agent personalities and roles - Define workflows and task delegation - Manage agent interactions through code - Control the flow of information between agents ### A2AMCP: Communication Infrastructure **What it does**: Provides communication channels for already-running agents - Real-time messaging between independent agents - Shared state management across processes - Conflict prevention for parallel work - Discovery of other active agents ## A Simple Analogy Think of it this way: - **Agentic Frameworks** = The company that hires employees and assigns them roles - **A2AMCP** = The office communication system (Slack/Teams) they use to talk You can have employees (agents) without Slack, but they work in isolation. You can have Slack without employees, but there's no one to use it. **They're complementary, not competing solutions.** ## Detailed Comparison ### 1. **Agent Creation and Management** **CrewAI/AutoGen/LangGraph:** ```python # Define agents in code from crewai import Agent, Crew researcher = Agent( role='Researcher', goal='Find information', backstory='Expert researcher' ) writer = Agent( role='Writer', goal='Write content', backstory='Professional writer' ) crew = Crew( agents=[researcher, writer], tasks=[research_task, writing_task] ) ``` **A2AMCP:** ```python # Agents already exist (Claude Code in tmux) # A2AMCP helps them communicate register_agent("project-1", "task-001", "001", "feature/auth", "Building auth") query_agent("project-1", "task-001", "task-002", "interface", "What's the User schema?") ``` ### 2. **Where Agents Run** **Agentic Frameworks:** - Agents run in the same Python process - Share memory and variables - Synchronous or async within one runtime - If the process crashes, all agents stop **A2AMCP:** - Agents run in separate processes (tmux sessions) - Different git worktrees - Can be on different machines - One agent crashing doesn't affect others - Persistent state in Redis survives restarts ### 3. **Communication Method** **Agentic Frameworks:** ```python # Direct method calls or shared memory result = researcher.execute(task) writer.receive_context(result) ``` **A2AMCP:** ```python # Network-based communication through MCP tools query_agent(project_id, from_session, to_session, query_type, question) # Message goes through Redis, persistent and observable ``` ### 4. **Use Cases** **Agentic Frameworks Excel At:** - Automated research and writing - Data analysis pipelines - Customer service bots - Decision-making workflows - Sequential task processing **A2AMCP Excels At:** - Parallel software development - Multi-agent code generation - Distributed team coordination - Conflict prevention in shared codebases - Real-time progress visibility ## Why Not Just Use CrewAI for Multi-Agent Coding? ### 1. **Process Isolation Requirements** When multiple AI agents modify code simultaneously, they need: - Separate git worktrees - Independent file systems - Isolated execution environments CrewAI agents share the same process and file system, making parallel file modifications dangerous. ### 2. **Tool Integration** A2AMCP is built specifically for Claude Code (and similar MCP-compatible agents): - Native MCP tool integration - No need to wrap Claude in Python code - Direct tool calls from the AI's perspective ### 3. **Persistence and Recovery** ```python # CrewAI - If this crashes, you lose everything crew = Crew(agents=[...]) crew.kickoff() # Single point of failure # A2AMCP - Distributed and persistent # Agent 1 crashes? Others continue # Redis persists all state # New agents can join mid-project ``` ### 4. **Real-World Scaling** **CrewAI Scaling Challenge:** ```python # 20 agents in one process? agents = [Agent(...) for i in range(20)] # Memory issues, coordination complexity, single failure point ``` **A2AMCP Scaling:** ```bash # 20 agents in separate tmux sessions # Each with its own git worktree # Communicating through Redis # Natural horizontal scaling ``` ## When to Use Each ### Use Agentic Frameworks When: - Building a multi-agent system from scratch - Agents need tight coupling and shared memory - You control the entire agent lifecycle - Tasks are sequential or have clear dependencies - You need complex reasoning chains ### Use A2AMCP When: - Agents already exist (Claude Code, GitHub Copilot Workspace) - Agents need to work on the same codebase in parallel - You need persistent communication across sessions - Agents might restart or fail independently - You want observable, debuggable communication ## The Power of Combination The real power comes from combining both approaches: ```python # Use CrewAI to orchestrate high-level planning planning_crew = Crew( agents=[architect, designer], tasks=[design_system_task] ) # Use A2AMCP for parallel implementation # Each CrewAI decision spawns multiple Claude Code agents for feature in planned_features: spawn_claude_agent(feature) # These use A2AMCP to coordinate ``` ## Real-World Example: Building an E-commerce Platform ### Traditional Agentic Framework Approach: ```python # Everything in one process crew = Crew( agents=[ Agent(role="Backend Developer"), Agent(role="Frontend Developer"), Agent(role="Database Designer") ] ) # They take turns, can't work simultaneously on files # If it crashes, all work stops ``` ### A2AMCP Approach: ```bash # 5 Claude Code agents working in parallel tmux new-session -s task-auth # Building authentication tmux new-session -s task-products # Product catalog tmux new-session -s task-cart # Shopping cart tmux new-session -s task-payment # Payment integration tmux new-session -s task-frontend # React UI # All communicating through A2AMCP # Real-time coordination, no conflicts # Each in its own git worktree ``` ## Summary: Apples and Oranges Comparing A2AMCP to CrewAI/AutoGen is like comparing Slack to a company's HR department: - **HR (CrewAI)**: Defines roles, assigns tasks, manages workflows - **Slack (A2AMCP)**: Lets employees communicate while doing their work A2AMCP isn't trying to replace agentic frameworks—it's solving a different problem entirely. It's infrastructure for agent communication, not agent orchestration. The future likely involves both: agentic frameworks for high-level orchestration and planning, with A2AMCP enabling real-time communication between the deployed agents as they work. --- *A2AMCP: Communication infrastructure for the multi-agent coding era.*