A project-based course repository for developing AI agents using LangChain v1+ and LangGraph: search agents, RAG systems, reflection agents, and code interpreters.

Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, Ollama, LLMs, RAGs, LangChain, LangGraph, Fine-tuning, DSPy & AI Agents (CrewAI), (Using ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini)

Demo of a customer service agent (Cymbal Air) using LangGraph, Tools, and RAG to interact with Google Cloud Databases via MCP Toolbox.

How to build a Multi-Agentic Systems for RAG using LangGraph - Full project

Local RAG researcher agent built using Langgraph, DeepSeek R1 and Ollama

Multi-Agent Retrieval-Augmented Generation (RAG) Customer Support System using Python, LangChain, and LangGraph.

Multi-agent AI research system — finds academic papers via semantic search & citation snowballing, then answers questions over them using agentic RAG with self-reflection. Built with LangGraph, FastAPI, Celery, and Qdrant.

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

Agentic RAG implementation using LangGraph

DeepResearch Agent with LangGraph, using any LLM models, search engine, RAG retrieval. 基于LangGraph构建的DeepResearch Agent，可以搭配任意搜索引擎、OpenAI接口兼容的模型。

Advanced multi-agent Medical AI Assistant powered by LangGraph that delivers empathetic, doctor-like responses using a hybrid pipeline of LLM reasoning, RAG from medical PDFs, and intelligent fallback tools. Features Long-term memory with SQLite, dynamic tool routing, and state reasoning for reliable, context-aware consultation.

A LangGraph-based conversational RAG system: Multi-turn retrieval and adaptive query optimization

It shows advanced RAG and 4 design patterns of agentic workflow based on LangChain and LangGraph using Amazon Nova Pro.

In this story, I have a super quick tutorial showing you how to create a multi-agent chatbot using LangGraph, RAG, and long-term memory to build a powerful agent chatbot for your business or personal use.

Agentic RAG chatbot using Langchain and Langgraph

Template for Retreival-Augmented Generation (RAG) application using Milvus for vector storage, LangGraph for ReAct agentic workflows, and Streamlit for a front end. Runs locally on Ollama with a pipeline to ingest markdown documentation.

An advanced Multi-Agent RAG system using Python, LangChain, and LangGraph to query diverse databases like SQLite, MongoDB, Neo4j, and MeiliSearch.

First learning project on Multi-Agentic RAG using Langchain and LangGraph. You could check it out on the link below 😁

Python LLM-RAG deep agent using LangChain, LangGraph and LangSmith built on Quart web microframework and served using Hypercorn ASGI and WSGI web server.

An agentic application that uses langgraph, chromadb for RAG and api function calling

An intelligent multi-agent AI assistant powered by Gemini LLM using the ReAct pattern to orchestrate five specialized agents (crypto, stocks, portfolio, RAG, web search) through MCP. Features a LangGraph supervisor with streaming Gradio UI.

🚀 Build a production-ready Agentic RAG system with LangGraph using minimal code and streamline your AI development process.

An Agentic RAG System for Academic Research Automatically fetches, indexes, and reasons over academic papers from arXiv using hybrid search and LangGraph-powered agents

Innovative AI agent implementations using LangGraph—featuring ReAct, RAG (Corrective, Self, Agentic), chatbots, microagents, and more, with multi-AI agent systems on the horizon! 🤖🚀

Intelligent cooking companion that finds recipes based on your ingredients using advanced AI. Built for Google's Gen AI Intensive Course 2025Q1, showcasing 8 AI capabilities: LangGraph agents, RAG with ChromaDB, voice commands, nutrition API integration, Google Search grounding, and more.

Enterprise-grade Agentic RAG for ITSM automation using LangGraph, FastAPI, and ChromaDB

Multi-agent AI system for automated financial research using LangGraph, Pinecone RAG, Redis caching, and Alpha Vantage data.

The goal of this project is to develop a RAG system using Agent from LangGraph to improve the travelling experience of tourists.

This repository contains various implementations of AI agents using LangGraph, demonstrating different patterns and use cases for building intelligent, tool-enabled conversational systems. Each project explores different aspects of agentic AI development, from basic chatbots to sophisticated RAG (Retrieval-Augmented Generation) systems.

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.