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.

A Bilibili video search server based on the Model Context Protocol (MCP), providing API interfaces to support video content search, paginated queries, and video information return, including LangChain call examples and test scripts.

This project provides support for Model Context Protocol (MCP) tool calls for LangChain, enabling interaction with AI models through the MCP toolkit, such as file reading and summary generation.

This project is an MCP client based on LangChain, used to demonstrate how to call MCP server tools through the LangChain ReAct Agent. It supports the LLMs of Anthropic, OpenAI, and Groq and provides the function of converting MCP tools to LangChain tools.

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.

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.

This is a Model Context Protocol server for accessing the KEGG biological database. It provides 30 bioinformatics analysis tools, supports HTTP transmission and LangChain integration, and facilitates LLM to call KEGG data for bioinformatics analysis.

The Higress OPS MCP Server is a service implementation based on the Model Context Protocol (MCP) for configuring and managing the Higress gateway. The project also provides an MCP client based on LangGraph and LangChain MCP Adapters, which interacts with the Higress MCP Server through a proxy flow architecture.

A chat platform integrating LangChain, a custom MCP server, and Google Gemini AI model, providing enhanced conversation functions.

Hippycampus is a CLI and MCP server based on LangChain that supports dynamic loading of OpenAPI specifications and integration with Langflow, providing visualization workflow creation capabilities.

This project implements a document retrieval server based on the Model Context Protocol (MCP), which can dynamically obtain the latest official documentation content of Python libraries for AI assistants. It supports libraries such as LangChain, LlamaIndex, and OpenAI, conducts efficient searches through the SERPER API, and uses BeautifulSoup to parse HTML content. The project is designed to be extensible, facilitating the addition of support for more libraries.

The LangChain MCP client is a project that demonstrates how to use MCP server tools through the LangChain ReAct agent. It supports flexible selection of LLM models and enables dynamic interaction via the CLI.

The OpenAPITools SDK is a multi - AI platform tool management library that provides a unified interface to support the integration and execution of tools for models such as Claude, GPT, and LangChain, and supports both local and API usage modes.

An MCP server project that enables Claude to directly access the documentation of popular libraries such as LangChain, LlamaIndex, and OpenAI, search through the Serper API and parse content using BeautifulSoup to enhance the AI's context understanding ability.

A project demonstrating the MCP protocol, including a server, client, and debugging tool, supporting SSE communication and JWT authentication, and integrating LangChain/LangGraph technology.

This project is a TypeScript library that converts MCP services into LangChain tools, simplifying the process of using MCP server tools in LangChain.

A documentation search server based on the MCP protocol that can retrieve the latest documentation of popular libraries such as Langchain in real-time, solving the problem of outdated knowledge in LLM models.

This project demonstrates the implementation of a microservice architecture based on the NestJS Model Context Protocol (MCP), including an MCP server that provides time context functions and a client that integrates LangChain.js, supporting multiple server connections.

A TypeScript client library that integrates LangChain.js with the Model Context Protocol

A simple MCP server that provides the function of obtaining the latest documentation for tools such as LangChain and OpenAI.