PraisonAI is a production-ready multi-AI agent framework with self-reflection capabilities, designed to create AI agents to automate the solution of various problems from simple tasks to complex challenges. It simplifies the construction and management of multi-agent LLM systems by integrating PraisonAI agents, AG2, and CrewAI into a low-code solution, emphasizing simplicity, customization, and effective human-machine collaboration.

An advanced sequential thinking processing tool based on the Multi-Agent System (MAS), enabling in-depth analysis and problem decomposition by coordinating multiple specialized agents.

The Swarms API is a REST API for building, deploying, and managing multi-agent systems. It supports multiple AI models and swarm architectures, providing functions such as real-time monitoring, batch processing, and task scheduling.

A multi - agent system laboratory project for educational purposes, simulating e - commerce and health consultation scenarios, demonstrating multi - agent collaboration, the use of the MCP protocol, and the integration of different LLM models.

Gaia-X is an enterprise-level AI application platform based on a new generation of AI paradigms. It solves the core pain points of enterprise AI products through an innovative technical architecture and provides core functions such as multi-agent collaboration, natural language RPA, and MCP protocol support.

An OpenAI agent server based on the MCP protocol, providing various professional agents (such as web search, file search, and computer operation) and a multi-agent coordinator, which can interact with clients (such as the Claude desktop application) through the MCP protocol.

Beads Village is a multi-agent MCP server for task coordination and file locking between AI agents. It combines Beads issue tracking and a built-in mail system, enabling multiple agents to collaborate in the same codebase without conflicts.

ThinkingCap is a multi-agent research server based on the model context protocol that can run multiple LLM providers in parallel and synthesize their responses to achieve comprehensive multi-angle analysis.

A multi-agent deep research system based on MCP, integrating LinkUp search, CrewAI coordination, and the Gemini large model, providing an interactive interface through Streamlit.

A multi-agent debate framework based on the MCP protocol for code review and debate planning. It conducts code reviews by executing multiple AI agents (such as Claude and Codex) in parallel, uses a deterministic scoring system (P0/P1/P2 severity classification) and platform-specific rules, and finally merges the results to generate a review report.

A multi-model AI code review and analysis server based on the Model Context Protocol. It supports the parallel execution of code quality checks, security analysis, and multi-agent consensus reviews by multiple AI models such as OpenAI, Anthropic, and Google Gemini.

This is an MCP (Multi-Agent Conversation Protocol) server project based on the OpenAPI specification, supporting the Google Drive API and providing a full set of toolchains for development, testing, and publishing.

Intellistant is a high-performance multi-agent AI framework built on C++23, supporting MCP protocol tool calls and agent collaboration, designed for software development automation.

Enterprise-level multi-agent orchestration MCP server for coordinating professional agents in architecture, quality, cloud, and prompt to achieve autonomous software development, testing, and deployment, with self-healing, authentication, and analysis capabilities.

AgentForgeMCP is a multi-agent collaboration platform that focuses on the collaborative work and task allocation among agents.

This project is the documentation repository for the Multi-agent Control Platform (MCP). It uses GitHub Actions to achieve automated deployment and includes documentation structure, local testing methods, and deployment architecture descriptions.

This is an OpenAPI-based MCP (Multi-Agent Conversation Protocol) server project that supports multiple transmission modes and provides a complete development, testing, and release process.

A local multi-agent research system based on Ollama and BraveSearch, supporting tool calls and collaboration, suitable for AI research tasks.

AI Tutor is an AI tutoring system for higher education based on MCP client/server and multi-agent collaboration. It supports Claude and OpenAI models and provides a flexible server configuration method.

MeshSeeks is an AI task-solving platform based on multi-agent parallel processing technology. By creating a specialized AI agent network, it enables the rapid decomposition and collaborative resolution of complex coding problems. The project offers 4 times the context capacity, a real-time status panel, and intelligent task coordination functions, significantly improving development efficiency (benchmark tests show a 3.64-fold speed increase).

The official website of A2A-MCP showcases the technical vision, core advantages, and application scenarios of the multi-agent collaboration protocol, and is built using a modern front-end technology stack.

MCP-TEAMATE is an AI agent communication server based on SSE, providing an interaction environment similar to a corporate team for AI agents, supporting real-time communication, document management, and multi-agent collaboration.

AgentMCP is a universal AI agent collaboration system. Through a simple decorator, any AI agent can be connected to the global collaboration network (MACNet) to achieve automatic communication and task coordination across frameworks and protocols.

This project showcases a lightweight multi-agent AI system that combines the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) for business analysis. The system provides statistical analysis of business data and knowledge retrieval functions for natural language queries by coordinating multiple dedicated tool servers. It features a modular design that facilitates expansion and the switching of LLM backends.

This project builds a cryptocurrency price query service based on the ALPACA API, specifically designed for MCP (Multi-Agent Control Protocol) integration, providing real-time cryptocurrency prices and market data.