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.

Production-ready Next.js template for building AI agents with LangGraph.js. Features MCP integration for dynamic tool loading, human-in-the-loop tool approval, persistent conversation memory with PostgreSQL, and real-time streaming responses. Built with TypeScript, React, Prisma, and Tailwind CSS.

LangGraph ReAct Agents with an ability to use MCP Tools dynamically

Multi-agent AI chatbot with dynamic configuration using LangGraph workflows, RAG search, MCP tools, and LaunchDarkly AI Configs. Control AI behavior, swap models (Claude/GPT/Mistral), enable research tools, and run A/B experiments without code deploys.

Framework for building and managing multi-agent systems with Model Context Protocol (MCP) and LangGraph support. Features a modern React UI, JWT-secured agent communication, and dynamic LLM provider integration (OpenAI, Azure, Google). Easily create, configure, and monitor agents that connect to external tools—all from a single interface.

A conversational AI agent built with LangGraph and Ollama that dynamically routes queries to multiple Model Context Protocol (MCP) servers, including Context7 and the Met Museum.

Multi-AI agent framework with MCP for automating Notion workflows using LangGraph. Built for extensibility, reliability, and intelligent orchestration via specialized agents and a central supervisor, featuring robust API handling and modular design for dynamic task execution.

Tutorial: Orchestrating LangGraph agents with OpenAI and MCP. Features dynamic server routing, JIT SSE connections, and automated tool schema mapping.

Model Context Protocol (MCP) template with a LangGraph Agent, allowing it to dynamically access external tools, data sources, and APIs.

Python-based AI agent leveraging Model Context Protocol (MCP) to dynamically connect to external services and tools through LangChain and LangGraph.

This project showcases how to turn a LangGraph agent into a tool that other LLMs or agents can discover and use dynamically through the Model Context Protocol (MCP)

Agentic Weather Chatbot (LangGraph + MCP + FastAPI) An advanced AI-powered Weather Chatbot built using LangGraph, FastAPI, and a dynamic tool-calling architecture. The system intelligently decides when to call an external weather API and exposes the agent as an MCP-compatible tool for third-party integration.

Production-ready Next.js template for building AI agents with LangGraph.js. Features MCP integration for dynamic tool loading, human-in-the-loop tool approval, persistent conversation memory with

Multi-AI agent framework with MCP for automating Notion workflows using LangGraph. Built for extensibility, reliability, and intelligent orchestration via specialized agents and a central supervisor, featuring robust API handling and modular design for dynamic task execution.

An AI agent system powered by DeepSeek , integrated with LangGraph and Multi-Agent Control Protocol (MCP) tools like Firecrawl. This project enables dynamic tool-calling, web scraping, and structured multi-step reasoning through a graph-based agent workflow.

Built lightweight MCP servers (Math & Weather) Created modular AI tools using FastMCP, exposing functions over stdio and HTTP for reuse. Integrated multi-server ReAct agent Combined MCP servers in a LangGraph agent with Groq LLMs, enabling dynamic tool selection and contextual reasoning.

A stateful multi-agent production studio built with LangGraph that transforms ideas into structured screenplays and character assets using MCP-based dynamic tooling, vector memory, and human-in-the-loop control.

This project demonstrates an intelligent AI agent built using LangGraph that can dynamically select and call multiple tools via the MCP (Multi-Call Protocol). The agent integrates asynchronous execution, external APIs, and large language models (Groq) to process user queries efficiently.

Langie is a LangGraph Agent that models customer support workflows as graph-based stages. Each stage represents a step in the workflow, with state persistence and dynamic ability orchestration through MCP (Model Context Protocol) clients.

Built lightweight Math & Weather MCP servers using FastMCP, exposing reusable tools over stdio/HTTP. Integrated them into a ReAct-driven LangGraph agent powered by Groq LLMs for dynamic tool selection, contextual reasoning, and multi-server orchestration.

Built a multi-agent research assistant with LangChain and LangGraph, integrating ScholarAI MCP, MongoDB Atlas, and Pinecone to enable dynamic retrieval, semantic search, and RAG from sources like arXiv and PubMed; automated ingestion, citation checks, and summarization for efficient research.

Hub-and-spoke multi-agent AI pipeline that converts YouTube videos into newsletters and social posts - built with LangGraph, MCP, and Ollama (Llama 3.2). Features dynamic LLM-based routing, tool isolation via MCP stdio, local inference to bypass API rate limits, and a Human-in-the-Loop revision cycle before dispatch.

A LangGraph-based ReAct research agent that can reason step-by-step, call tools dynamically, and combine internal RAG search (LangChain) with external web search (Tavily MCP). It generates structured research briefs and includes clear docs on how to import your own knowledge base and extend tools.

An intelligent AI agent built using LangGraph and Gemini LLM that can scrape websites, crawl pages, and extract structured data using Firecrawl tools. Powered by the Model Context Protocol (MCP), this CLI-based assistant dynamically reasons with user queries and decides when to trigger tools like URL scrapers and content extractors.