Repo of skills for autogen studio using model context protocol (mcp)

Open-source protocol suite standardizing LLM, Vector, Graph, and Embedding infrastructure across LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, and MCP. 3,330+ conformance tests. One protocol. Any framework. Any provider.

支持查询主流agent框架技术文档的MCP server（支持stdio和sse两种传输协议）, 支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai

Static security scanner for LLM agents — prompt injection, MCP config auditing, taint analysis. 49 rules mapped to OWASP Agentic Top 10 (2026). Works with LangChain, CrewAI, AutoGen.

MCPHub is an embeddable Model Context Protocol (MCP) solution for AI services. Seamlessly integrate MCP servers with OpenAI Agents, LangChain, and Autogen frameworks through a unified interface. Simplifies configuration, setup, and management of MCP tools across different AI applications.

Turns Model Context Protocol server tools available in AutoGen >= v0.4

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

a simple example demonstrating MCP + ag2 (autogen) integration

This repository demonstrates how to use AutoGen to integrate local and remote MCP (Model Context Protocol) servers. It showcases a local math tool (math_server.py) using Stdio and a remote Apify tool (RAG Web Browser Actor) via SSE for tasks like arithmetic and web browsing.

Experimental Seat Booking Agent implementation using OpenAI Agents SDK and Autogen memory and MCP tools support

This project demonstrates how to leverage the Model Context Protocol (MCP) to connect AutoGen agents with Notion, enabling natural language control of Notion workspace operations. You'll build a Flask API, expose it via ngrok, and create an end-to-end workflow for AI agents to create pages, search content, and manage data within Notion.

A flexible and powerful framework for creating AI persona agents using AutoGen with Model Context Protocol (MCP) integration.

Diving into agentic frameworks like Openai SDK, Crewai, langgraph, autogen, MCP and every Agentic Patterns.

Runtime security proxy for MCP: lockfile enforcement, drift detection, artifact pinning, Sigstore/Ed25519 signing, CEL policy, OpenTelemetry tracing. Works with Claude Desktop, LangChain, AutoGen, CrewAI.

🧠 Concept-focused notes for The Complete Agentic AI Engineering Course by Ed Donner — covering key ideas, frameworks (CrewAI, LangGraph, Autogen, MCP), and agentic design patterns.

complete project structure for a Claude-inspired MCP (Multi-Client Protocol) system using AutoGen for orchestration, FastAPI for the backend, and Streamlit for the UI

AI-powered multi-agent QA automation using Autogen, OpenAI, Jira MCP, and Playwright MCP. Features round-robin orchestration to analyze Jira bugs, generate smoke tests, convert them to Playwright scripts, and execute end-to-end UI automation with logs, screenshots, and structured outputs fully automated, scalable, and extensible.

Project repository featuring multi-agent systems, LLM workflows, and AI automation using CrewAI, LangGraph, AutoGen, OpenAI SDK, and MCP

Comprehensive AI Agent study project with 31 examples across 12 frameworks (LangChain, CrewAI, AutoGen, LangGraph, Pydantic AI, OpenAI Agents SDK, LlamaIndex, Microsoft Agent Framework, MCP, A2A)

⭐ Curated list of AI Agent frameworks: AutoGen, CrewAI, MetaGPT, LangGraph, Swarms. Multi-Agent Systems, Swarm Intelligence, A2A Protocol, MCP Protocol. LLM Agents, Agentic AI, Agent Workflows.

ArXiv MCP server built for multi-agent systems like Magentic-UI and AutoGen

🤖 AI-Powered Backend Builder Multi-agent + multi-tool system using AutoGen, Gemini & Groq to automate production-ready backend development. Features 4 specialized AI agents, MCP integration, and end-to-end automation. Perfect for rapid API development with built-in security & testing. 🔧 Tech: Python, AutoGen, Gemini, Groq, MCP, FastAPI, Docker

autogen_mcp

Give an AI agent access to a repo of MCP servers with the goal of self-teaching.

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This repo contains the code for the AutoGen + MCP tutorial

This is a very basic implementation of a multi-agent system that connects an Autogen LLM client to one or more MCP (Model Context Protocol) tool servers. You can use it as blueprint for making your MCP workflow from scratch. 🚀

A Model Context Protocol (MCP) server that provides AI assistants with the ability to search and retrieve Microsoft AutoGen documentation.

MCP (Model Context Protocol) is a research and development project aimed at integrating LangGraph and AutoGen Agent with the Model Context Protocol (MCP) framework. This project focuses on exploring innovative ways to leverage advanced AI models and tools to streamline workflows, enhance automation, and enable intelligent decision-making.

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