Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth!

结合知识库管理的 Agent Harness 平台。 An agent harness that integrates a LightRAG knowledge base and knowledge graphs. Build with LangChain v1 + Vue + FastAPI, support DeepAgents、MinerU PDF、Neo4j 、MCP.

AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

天枢 - 企业级 AI 一站式数据预处理平台 | PDF/Office转Markdown | 支持MCP协议AI助手集成 | Vue3+FastAPI全栈方案 | 文档解析 | 多模态信息提取

A modern template for agentic orchestration — built for rapid iteration and scalable deployment using highly customizable, community-supported tools like MCP, LangGraph, and more.

这是一个基于 FastAPI-MCP 的股票分析服务，旨在通过 MCP 工具函数接口提供股票相关的综合数据和分析能力，包括价格、评分、技术报告和 AI 分析。

Semantic level code search/indexer with tree-sitter parsing, Qdrant vector store, and Typer/FastAPI interfaces. Supports calling via MCP.

FastAPI server implementing MCP protocol Browser automation via browser-use library.

🚀 SuperMCP - Create multiple isolated MCP servers using a single connector. Build powerful Model Context Protocol integrations for databases (PostgreSQL, MSSQL) with FastAPI backend, React dashboard, and token-based auth. Perfect for multi-tenant apps and AI assistants.

A working example to create a FastAPI server with SSE-based MCP support

Production-ready Python template for building MCP servers with FastMCP, FastAPI, OAuth, and OpenShift deployment.

MCP-enabled AI conversation engine with MCTS analysis, FastAPI backend, and async operations for building advanced LLM applications

这是一个基于FastAPI的微信文章搜索MCP服务，提供了通过关键词搜索微信公众号文章的功能。

Awesome MCP template for awesome FastAPI builders

PyRASP is a Runtime Application Self Protection package for Python-based Web Servers (Flask, FastAPI and Django), Serverless Functions (AWS Lambda, Azure and Google Cloud Functions) and MCP Servers (FastMCP)

The best way to deploy mcp server. A high-performance WebSocket/SSE transport layer & gateway for Anthropic's MCP (Model Context Protocol) — powered by Nginx, Nchan, and FastAPI.

A simple MCP ODBC server using FastAPI, ODBC and SQLAlchemy.

一个基于FastAPI和React的智能体系统，支持多智能体管理、mcp管理、知识库、聊天对话等功能。An intelligent agent system based on FastAPI and React, supporting multi-agent management, mcp management, knowledge base, chat dialogue, and other functions.

OmniData 是一个基于 Playwright 和 FastAPI 构建的企业级可扩展网页爬虫框架，插件化扩展支持接入多种数据源，自由组合多个mcp，平替akshare，为个人金融量化提供接口数据支持

Autonomous multi-agent system for intelligent HDB resale search — combining geospatial analytics, MRT proximity, and price intelligence using DeepAgents, LangGraph, FastAPI, Gradio UI, and MCP Toolbox. Fully Dockerized with reproducible data ingestion pipelines.

A FastAPI application that integrates with Telegram using webhooks and OpenAI Agents SDK for AI-powered stock trading assistance, utilizing MCPHub for multiple MCP server management.

⚡ Open-source AI toolkit: PyTorch/TensorFlow implementations, FastAPI deployment, Docker configs, MCP protocol. Learn & build AI applications.

基于 FastAPI 的生产级 AI Agent 框架，支持 100+ LLM 提供商、MCP 工具集成、多 Agent 协作、RAG 知识库和沙箱隔离执行

A MCP server built with Python, FastMCP, Ollama, Open-webUI, FastAPI, and Docker following a microservice architecture..

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A scalable AI chatbot platform built with FastAPI and LangGraph, featuring multi-agent orchestration, multi-tenant vector storage, cross-chat memory, and voice call capabilities through LiveKit integration.

企业级多智能体AI管理系统 | 企业级智慧管理解决方案 | 基于MCP架构的财务分析、成本预测、知识管理、效能评估一体化平台|- 多智能体系统 (Multi-agent System) - MCP协议 (Model Context Protocol) - - AI决策支持系统 - 企业级AI应用 - Python + FastAPI + Streamlit

Demo of MCP Auth in FastAPI with OAuth 2.1 and ScaleKit

这是我开发的一个基于大模型的小红书内容运营智能 Agent 系统。 该系统可以实现从数据采集到内容生成，再到自动发布的完整流程，主要功能包括： 使用 Playwright 爬取小红书热门内容 分析爆款规律（关键词、标签、标题结构） 基于分析结果自动生成选题 使用大模型生成完整文案（标题、正文、标签、互动语） 调用图像模型生成配图 支持通过 API 或 MCP 一键发布内容 技术栈包括： FastAPI、LangChain、OpenAI（GPT + 图像模型）、Playwright、Pydantic 等。 该项目的核心思路是将数据分析与大模型生成结合，实现内容生产的自动化与智能化。 适用于： 内容运营、自媒体自动化、AI Agent 应用开发等场景。

A FastAPI application that demonstrates Server-Sent Events (SSE) integration with the Model Context Protocol