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LangGraph-powered ReAct agent with Model Context Protocol (MCP) integration. A Streamlit web interface for dynamically configuring, deploying, and interacting with AI agents capable of accessing various data sources and APIs through MCP tools.
| Date | Stars |
|---|---|
| 2026-07-31 | 714 |
| 2026-08-05 | 714 |
| 2026-08-06 | 714 |
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# LangGraph Agents + MCP [](README.md) [](README_KOR.md) [](https://github.com/teddylee777/langgraph-mcp-agents) [](https://opensource.org/licenses/MIT) [](https://www.python.org/) [](https://github.com/teddylee777/langgraph-mcp-agents)  ## Project Overview  `LangChain-MCP-Adapters` is a toolkit provided by **LangChain AI** that enables AI agents to interact with external tools and data sources through the Model Context Protocol (MCP). This project provides a user-friendly interface for deploying ReAct agents that can access various data sources and APIs through MCP tools. ### Features - **Streamlit Interface**: A user-friendly web interface for interacting with LangGraph `ReAct Agent` with MCP tools - **Tool Management**: Add, remove, and configure MCP tools through the UI (Smithery JSON format supported). This is done dynamically without restarting the application - **Streaming Responses**: View agent responses and tool calls in real-time - **Conversation History**: Track and manage conversations with the agent ## MCP Architecture The Model Context Protocol (MCP) consists of three main components: 1. **MCP Host**: Programs seeking to access data through MCP, such as Claude Desktop, IDEs, or LangChain/LangGraph. 2. **MCP Client**: A protocol client that maintains a 1:1 connection with the server, acting as an intermediary between the host and server. 3. **MCP Server**: A lightweight program that exposes specific functionalities through a standardized model context protocol, serving as the primary data source. ## Quick Start with Docker You can easily run this project using Docker without setting up a local Python environment. ### Requirements (Docker Desktop) Install Docker Desktop from the link below: - [Install Docker Desktop](https://www.docker.com/products/docker-desktop/) ### Run with Docker Compose 1. Navigate to the `dockers` directory ```bash cd dockers ``` 2. Create a `.env` file with your API keys in the project root directory. ```bash cp .env.example .env ``` Enter your obtained API keys in the `.env` file. (Note) Not all API keys are required. Only enter the ones you need. - `ANTHROPIC_API_KEY`: If you enter an Anthropic API key, you can use "claude-3-7-sonnet-latest", "claude-3-5-sonnet-latest", "claude-3-haiku-latest" models. - `OPENAI_API_KEY`: If you enter an OpenAI API key, you can use "gpt-4o", "gpt-4o-mini" models. - `LANGSMITH_API_KEY`: If you enter a LangSmith API key, you can use LangSmith tracing. ```bash ANTHROPIC_API_KEY=your_anthropic_api_key OPENAI_API_KEY=your_openai_api_key LANGSMITH_API_KEY=your_langsmith_api_key LANGSMITH_TRACING=true LANGSMITH_ENDPOINT=https://api.smith.langchain.com LANGSMITH_PROJECT=LangGraph-MCP-Agents ``` When using the login feature, set `USE_LOGIN` to `true` and enter `USER_ID` and `USER_PASSWORD`. ```bash USE_LOGIN=true USER_ID=admin USER_PASSWORD=admin123 ``` If you don't want to use the login feature, set `USE_LOGIN` to `false`. ```bash USE_LOGIN=false ``` 3. Select the Docker Compose file that matches your system architecture. **AMD64/x86_64 Architecture (Intel/AMD Processors)** ```bash # Run container docker compose -f docker-compose.yaml up -d ``` **ARM64 Architecture (Apple Silicon M1/M2/M3/M4)** ```bash # Run container docker compose -f docker-compose-mac.yaml up -d ``` 4. Access the application in your browser at http://localhost:8585 (Note) - If you need to modify ports or other settings, edit the docker-compose.yaml file before b
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f33ca0d9b3d30a67, desc:ai agents, desc:react agent