Agent-MCP is a framework for creating multi-agent systems that enables coordinated, efficient AI collaboration through the Model Context Protocol (MCP). The system is designed for developers building AI applications that benefit from multiple specialized agents working in parallel on different aspects of a project.
暂未识别到可直接复制的 MCP 配置，请查看 GitHub README。后台管理员可以补充配置。
# Agent-MCP [](https://deepwiki.com/rinadelph/Agent-MCP) > 🚀 **Advanced Tool Notice**: This framework is designed for experienced AI developers who need sophisticated multi-agent orchestration capabilities. Agent-MCP requires familiarity with AI coding workflows, MCP protocols, and distributed systems concepts. We're actively working to improve documentation and ease of use. If you're new to AI-assisted development, consider starting with simpler tools and returning when you need advanced multi-agent capabilities. > > 💬 **Join the Community**: Connect with us on [Discord](https://discord.gg/7Jm7nrhjGn) to get help, share experiences, and collaborate with other developers building multi-agent systems. Multi-Agent Collaboration Protocol for coordinated AI software development. Think **Obsidian for your AI agents** - a living knowledge graph where multiple AI agents collaborate through shared context, intelligent task management, and real-time visualization. Watch your codebase evolve as specialized agents work in parallel, never losing context or stepping on each other's work. ## Why Multiple Agents? Beyond the philosophical issues, traditional AI coding assistants hit practical limitations: - **Context windows overflow** on large codebases - **Knowledge gets lost** between conversations - **Single-threaded execution** creates bottlenecks - **No specialization** - one agent tries to do everything - **Constant rework** from lost context and confusion ## The Multi-Agent Solution Agent-MCP transforms AI development from a single assistant to a coordinated team: **Real-time visualization** shows your AI team at work - purple nodes represent context entries, blue nodes are agents, and connections show active collaborations. It's like having a mission control center for your development team. ### Core Capabilities **Parallel Execution** Multiple specialized agents work simultaneously on different parts of your codebase. Backend agents handle APIs while frontend agents build UI components, all coordinated through shared memory. **Persistent Knowledge Graph** Your project's entire context lives in a searchable, persistent memory bank. Agents query this shared knowledge to understand requirements, architectural decisions, and implementation details. Nothing gets lost between sessions. **Intelligent Task Management** Monitor every agent's status, assigned tasks, and recent activity. The system automatically manages task dependencies, prevents conflicts, and ensures work flows smoothly from planning to implementation. ## Quick Start ### Python Implementation (Recommended) ```bash # Clone and setup git clone https://github.com/rinadelph/Agent-MCP.git cd Agent-MCP # Check version requirements python --version # Should be >=3.10 node --version # Should be >=18.0.0 npm --version # Should be >=9.0.0 # If using nvm for Node.js version management nvm use # Uses the version specified in .nvmrc # Configure environment cp .env.example .env # Add your OpenAI API key uv venv uv install # Start the server uv run -m agent_mcp.cli --port 8080 --project-dir path-to-directory # Launch dashboard (recommended for full experience) cd agent_mcp/dashboard && npm install && npm run dev ``` ### Node.js/TypeScript Implementation (Alternative) ```bash # Clone and setup git cl...
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