Cipher is an open-source memory layer framework designed for programming AI agents. It integrates with various IDEs and AI coding assistants through the MCP protocol, providing core functions such as automatic memory generation, team memory sharing, and dual-system memory management.

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.

The Stripe Agent Toolkit is a toolkit that supports the integration of multiple AI agent frameworks (such as OpenAI, LangChain, CrewAI, etc.) with the Stripe API. It provides support for Python and TypeScript, simplifying payment-related operations.

MCPAdapt is an open - source project aiming to seamlessly integrate the tools of over 650 MCP servers into various agent frameworks, enabling developers to easily use these tools in their agent workflows.

automcp is a tool that can easily convert existing AI agent frameworks (such as CrewAI, LangGraph, etc.) into MCP servers to achieve standardized interface access. It supports the adaptation and deployment of multiple frameworks and provides functions for quick startup and configuration.

An intelligent task management system based on the MCP protocol, providing an efficient programming workflow framework for AI agents, with functions such as task planning, decomposition, execution, and memory.

MCPHub is an embeddable Model Context Protocol (MCP) solution for AI service integration. It provides a unified way of configuration and management, supports multiple AI frameworks (such as OpenAI Agents, LangChain, Autogen), and simplifies the connection between MCP servers and AI applications.

Nucleus is a locally - prioritized and sovereignty - prioritized operating system designed for AI agents, providing persistent memory, governance frameworks, compliance audits, and decision - tracking functions. It is particularly suitable for regulated industries.

The project involves documentation, sample code repositories, and community resources for the LangChain framework, including technical content such as Python programming, AI agent development, FastAPI integration, and LLM fine-tuning.

An AI agent and task management server based on the CrewAI framework, providing functions such as agent creation, task assignment, and workflow execution.

CrewAI Crew is a multi - agent AI system project based on the crewAI framework, aiming to complete complex tasks through configuration and collaboration.

An MCP server based on the CrewAI framework, providing AI agent and task management functions, supporting the creation of agents, tasks, and workflows, and running automatically.

Digimon Engine is an open - source multi - agent, multi - player game framework focusing on AI - native game and intelligent metaverse development. It supports social and financial AI agents, provides an immersive game experience, and is committed to building an AI agent environment similar to that in 'Westworld'.

Pinchwork is an AI agent task marketplace where agents can post and undertake tasks from each other, pay through a credit system, and support real-time communication, credit escrow, and integration with multiple frameworks.

Graphiti MCP Server is an enhanced knowledge graph framework specifically designed for AI agents to build and query time - aware knowledge graphs in dynamic environments. It exposes core functions through the MCP protocol, supports continuous integration of user interactions, enterprise data, and external information, and provides capabilities for incremental updates, efficient retrieval, and precise historical queries.

AgentCraft is an enterprise-level AI agent framework that provides secure and scalable intelligent agent services.

DomainKits MCP is a domain name intelligent agent framework designed for AI assistants. It integrates tools such as domain name search, analysis, monitoring, trend discovery, and batch processing, and can transform general - purpose AI assistants into professional domain name agents with industry - specific knowledge.

An intelligent task management system based on the Model Context Protocol (MCP), providing an efficient programming workflow framework for AI agents, with functions such as task planning, decomposition, dependency management, verification, and memory.

The AutoGen MCP server is a multi - agent dialogue service integrating the Microsoft AutoGen framework, providing standardized interfaces for AI agent management and collaboration.

Distri is a Rust-based composable AI agent framework that supports building and combining intelligent agent capabilities through the MCP protocol, offering modular design and high efficiency.

The MCP Dual Cycle Reasoner is a metacognitive enhancement tool designed for autonomous AI agents. Through a dual-cycle framework (sentinel monitoring and arbiter management), it enables anomaly detection and experience learning to enhance the self-awareness and reliability of agents.

Azure MCP Hub is a platform that helps developers quickly build, run, or reuse Model Context Protocol (MCP) servers. It supports multiple programming languages and AI frameworks, enabling AI agents to call real APIs.

Minion Works is a modular AI agent framework that connects to browsers to autonomously perform complex tasks, suitable for developers, researchers, and creative builders.

A2AMCP is a real-time collaboration protocol between AI agents based on Redis, which solves the code conflict problem during parallel development of multiple AI agents, provides functions such as file locking, interface sharing, and task coordination, and supports Docker deployment and integration with mainstream AI frameworks.

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.

The OWL x WhatsApp MCP Server project is an application that integrates WhatsApp messaging functionality with a multi - agent collaboration framework. It enables AI agents to access, search, and send messages to WhatsApp data through the Streamlit interface and supports real - time web searches.

Agentify Components is a framework for adding semantic metadata to React components, enabling AI systems and automation tools to understand component functions. It adds standardized descriptions to components through decorators and generates MCP server configurations to enable interaction between components and AI models.

This project demonstrates how to use the Model Context Protocol (MCP) to integrate local and remote tools in the AutoGen framework, including a local mathematical calculation server and a remote Apify web browsing tool. It enables flexible interaction between AI agents and tools through a standardized protocol.

automcp is a tool that can convert existing AI agent frameworks (such as CrewAI and LangGraph) into MCP protocol servers, enabling standardized interface access. It supports rapid deployment, multi - framework adaptation, and provides two transmission modes: SSE and STDIO.

Vibehacker Metasploit MCP is a FastMCP - based interface for connecting to the Metasploit framework, enabling AI agents to invoke Metasploit's functions, including exploitation, payload generation, target scanning, and session management.

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.

The MCP protocol enables standardized interaction between LLMs and the openGauss database, and builds an AI Agent application framework

MemoraMCP is an AI agent storage project based on the Next.js framework. It uses IPFS technology to ensure data security and verifiability, and supports self-deployment.

AutoGPT is an open - source AI agent toolkit that provides a modular and extensible framework, supporting users to build, test, and monitor AI agents. The project includes the Forge Innovation Lab, a Benchmark testing environment, and a user - friendly front - end interface, and uses the Agent Protocol for standardized communication. The community is active, regularly hosting hackathons and other activities to drive innovation.

BRAINS OS is an AI operating system based on modern cloud-native technologies, designed specifically for managing large language models and dedicated AI agents, providing visual workflow editing, a unified command system, and a secure deployment framework.

The ROS 2 MCP Server is a model context protocol server designed for the ROS 2 system, enabling AI agents such as GitHub Copilot to monitor, debug, and manage ROS 2 nodes, topics, services, and the TF2 framework, providing real-time interaction and system visualization tools.

This project demonstrates how to integrate a Model Context Protocol (MCP) server with multiple LLM agent frameworks, including Google ADK, LangGraph, OpenAI Agents, and Pydantic - AI. It connects different LLM providers through a standardized interface and uses Logfire for tracking and visualization.

An MCP server that provides best practices for AI agent access development, security guidelines, and coding standards. It supports multiple programming languages and frameworks, includes over 284 standards, and features a plug-in architecture and fast memory storage.

Lex is a TypeScript framework that provides situational memory and architecture awareness for AI agents, solving the problem of context loss in long - process development. Through memory snapshots, module dependency navigation, and architecture policy execution, it enables AI assistants to remember work progress and understand code structure.

This is a list of multiple open - source AI projects, covering multiple fields such as automated agents, large - language models, image generation, and AI development frameworks. These projects aim to help developers make money using AI technology, including building intelligent assistants, automating workflows, and content generation applications.