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Basic Information

Agent-MCP is a developer-focused framework that implements a Multi-Agent Collaboration Protocol to coordinate multiple specialized AI agents for software development. It provides a persistent, searchable project knowledge graph (RAG) where agents share context, a dashboard for real-time visualization of agent activity and task progress, and an MCP server that exposes tools and resources to MCP-compatible clients. The project is intended for experienced AI developers who need to run parallel, specialized agents that operate with limited context and strict lifecycles. It includes instructions to run the server, launch a frontend dashboard, configure MCP transports, and connect clients using Python or JavaScript. System requirements and modes are documented and the repository emphasizes reproducible project blueprints called MCDs to seed the shared memory.

Links

Stars
784
App URL
https://github.com/rinadelph/Agent-MCP
Github Repository
https://github.com/rinadelph/Agent-MCP/blob/main/README.md

App Details

Features
Parallel execution of specialized agents with task orchestration and automatic dependency management. A persistent knowledge graph for project context, semantic queries, versioned memory, tagging, and garbage collection. Real-time dashboard with network visualization, activity timeline, memory health, and task status. MCP server implementation with tools such as create_agent, assign_task, ask_project_rag and agent management, plus examples for Python and JavaScript MCP clients. Agent modes that enforce behavioral contracts (worker, frontend/playwright, research, memory manager). File-level locking, conflict resolution, cleanup protocol with agent lifecycle limits, and environment-based configuration for transport, ports, and logging. Integration examples for editor, CI/CD, and Claude Desktop are included.
Use Cases
Agent-MCP addresses common limits of single-agent assistants by decomposing development work into linear, atomic tasks executed by short-lived, specialized agents to avoid context bloat and reduce hallucination. It preserves institutional knowledge in a shared, versioned memory so new agents or humans can pick up work without long conversation histories. The framework improves development speed through parallelism, prevents merge conflicts via locking, and enforces reproducible behaviors with agent modes. It also enhances security by minimizing each agent"s accessible context, provides audit trails and rollback capability, and supports integration into developer workflows such as editors and CI/CD pipelines. The system is useful for solo developers, small teams, and large projects that require coordinated AI-assisted development.

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