# AI agent

The Dumpling AI MCP Server is a model context protocol service integrating multiple functions, providing capabilities such as data scraping, content processing, knowledge management, AI agents, and code execution, and supporting a rich set of API interfaces and tools.

Demonstrate connecting the Keboola Registry API to the MCP server through the CrewAI wrapper, enabling AI agents to call registered skills.

The Graphiti MCP Server is a server based on multi - project knowledge graphs, supporting the rapid construction and management of temporary knowledge graphs for AI agents to query through the model context protocol. This project extends the official getzep/graphiti MCP Server, adds support for multiple servers and a single database, and provides a CLI tool with an optimized developer experience.

MCP speech synthesis server based on Edge-TTS

AI code review and issue repair agent for analyzing codebases, detecting security vulnerabilities, reviewing code quality, and providing repair suggestions based on Sentry error logs.

PG-MCP is a model context protocol server designed for PostgreSQL databases. It provides APIs for AI agents to connect to, query, and understand databases, and supports multiple database connections, rich metadata extraction, and query analysis functions.

The Azure MCP Server is a server that implements the MCP protocol, providing seamless connection between AI agents and Azure services (such as Storage and Cosmos DB), and supporting the conversion of natural language commands into Azure operations.

The Agentic MCP Client is an independent AI agent runner that interacts with models such as Claude, BedRock, and OpenAI through MCP protocol tools, supporting autonomous cloud operation and secure system interaction.

This project demonstrates how to integrate OpenAI, Azure OpenAI, and GitHub models through the MCP protocol to build a terminal application for a TODO list agent. The project includes three major components: MCP host, client, and server, supports HTTP and SSE protocols, and provides tool invocation and database storage functions.

The ThingsBoard MCP Server is a service that provides a natural language interface for LLMs and AI agents, allowing interaction with the ThingsBoard IoT platform through natural language commands to achieve functions such as device management, data query, and analysis.

trackio-mcp is a Python package that provides MCP (Model Context Protocol) server support for the trackio experiment tracking tool, enabling AI agents to observe and interact with trackio experiments through the MCP protocol. It can automatically enable the MCP server functionality by a simple import without modifying existing code, and supports local and Hugging Face Spaces deployment.

Engram is a hosted MCP service that provides reliable memory for AI agents, enabling AI to remember past conversations, facts, and decisions, making it more like a real team member.

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.

MemCP is an extensible memory MCP server designed for AI programming agents. It is extended based on Zep AI's Graphiti project, supports building, maintaining, and querying temporal knowledge graphs, and provides entity extraction, persistent storage, and support for multiple transport protocols.

An AI-driven browser automation server based on the MCP protocol, supporting natural language control of web page operations and in-depth web research.

AI agent robot project based on multi-MCP servers

The MCP iCal Server is a macOS calendar management tool based on an AI agent. It enables natural language interaction through a single interface and supports functions such as event creation, schedule query, and intelligent update.

An AI agent based on Pydantic and Chainlit, supporting web browsing and multi-command protocol interaction

This project demonstrates how to integrate an AI agent powered by Azure OpenAI with the Microsoft Fabric data warehouse through the Model Context Protocol (MCP), using GraphQL as a common data connection layer to achieve bidirectional access to enterprise data.

This project is an interaction server for the Open eClass platform based on the MCP protocol, supporting the SSO authentication system of the University of Athens. It provides functions such as course management and session maintenance, enabling AI agents to securely interact with the eClass platform.

Agent X is an autonomous AI agent based on Google Gemini Flash and Twitter API, capable of intelligently generating and posting tweets.

Intelligent product search system based on MCP service and OpenAI Agent SDK

The Ultimate MCP Server is an AI agent operating system based on the Model Context Protocol (MCP), providing a rich toolset and intelligent task delegation function, supporting multi-LLM provider integration, optimizing cost and performance, and automating complex workflows.

MCPML is a Python framework for building servers compliant with the Model Context Protocol (MCP), supporting CLI tools and OpenAI agent functions, and providing features such as dynamic loading and structured output.

An MCP server implementation for AdGuard Home, supporting querying and managing DNS records, filtering rules, etc. through an AI agent.

PG-MCP is a model context protocol server designed for PostgreSQL databases, providing APIs for AI agents to discover, connect, query, and understand databases. It supports multi - database connection, rich catalog information, and query explanation functions.

Sequential Thinking MCP is an MCP server that supports AI agents in advanced metacognition and dynamic reflective problem-solving, guiding the step-by-step execution of complex tasks through virtual thought recording and planning.

A Django application that implements a Firebase Model Context Protocol (MCP) server, enabling AI agents to interact with Firebase services through a standardized protocol. It provides 14 Firebase tools covering authentication, database, and storage functions, supporting quick testing and full integration.

BoostSecurity MCP is a security tool for protecting the AI agent development workflow. It prevents supply chain attacks by verifying the security of third - party software packages and supports multiple programming languages and package ecosystems.

TEN Agent is a multi-functional AI agent framework that integrates real-time vision, voice recognition, and screen sharing detection capabilities, and supports rapid expansion and development.