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.

A multi-agent RAG (Retrieval-Augmented Generation) system powered by Google Gemini, Docling, and LangGraph for intelligent document Q&A with built-in fact-checking and hallucination prevention.

Hands-on LangChain and LangGraph study guide covering RAG, LangGraph workflows, multi-agent systems, and advanced agentic AI patterns, with HTML ebook chapters and runnable Python examples.

A high-performance agentic RAG system combining Graphiti's temporal knowledge graphs with LangGraph's multi-agent orchestration to achieve 100x faster retrieval speeds than traditional RAG through intelligent graph-based indexing and parallel agent processing.

🚀 Comprehensive LangGraph learning repository with hands-on examples, and practical implementations. Master stateful multi-agent applications, RAG systems, SQL agents, custom tools, and debugging techniques. From basics to advanced workflows with real-world examples.

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

A multi-agent RAG system built with LangGraph and FastAPI, featuring a hierarchical architecture with a Supervisor and parallel-processing specialist agents for grounded, conversational AI.

An agentic graph-based Retrieval-Augmented Generation (RAG) system for querying PDFs and the web, built with LangGraph, Gemini LLM, ChromaDB, and Streamlit. Features intelligent multi-node workflows for ingestion, retrieval, web search, and reasoning.

AI Research Assistant is a multi-agent system built with LangChain, LangGraph, and LangSmith. It lets users ask research questions and returns search results, summaries, and synthesized reports in a ChatGPT-like Streamlit UI. Easily extendable with real APIs or RAG.

Autonomous multi-agent AI system for intelligent GitHub PR review and bug detection. Built with LangGraph, RAG + tree-sitter, and GitHub MCP for deep codebase understanding. Automatically raises issues, drafts fixes, and opens PRs as smart-pr-review-bot[bot]. Supports Review Only, Human-in-the-Loop, and Auto-Pilot modes with full LangSmith tracing.

An AI-powered research and content generation assistant built with LangGraph/LangChain and Streamlit. The system orchestrates multiple specialized agents (Planner -> Researcher -> Prompt Augmentor -> Generator) to plan searches, gather context from the web, refine your request, and produce a final result.

Multi-agent RAG system with automated response evaluation and conditional scheduling using LangGraph, OpenAI , Firecrawl, Qdrant, LangFuse

Agentic lead-qualification system built with LangGraph, featuring intent detection, deterministic RAG, multi-turn state management, and gated tool execution.

Production-ready multi-agent RAG system with LangGraph orchestration, real-time token optimization, GPU monitoring, semantic caching, and comprehensive performance analytics. Built for scale.

ISC-CodeConnect is a sophisticated multi-agent Retrieval-Augmented Generation (RAG) system specifically designed for Salesforce development. Built with LangGraph and powered by IBM WatsonX.ai Granite models, it orchestrates a network of specialized AI agents .

Multi-agent AI movie recommendation system - 6 LangGraph agents (profile analysis, content intelligence, context-aware, serendipity, adversarial critic, explainability) with RAG, ChromaDB vector search, multi-modal embeddings, and a React + FastAPI stack. Supports group recommendations, Letterboxd import, and real-time cinema news.

Agentic digital twin system enabling conversational interviews with personas, combining tool-based reasoning over multi-year health datasets, RAG over academic theses, and external developer profile retrieval using LangChain and LangGraph. Built with React, FastAPI, and deployed on Railway.

Multi-agent AI system with RAG + Coding agents using LangGraph and ChromaDB

Full-stack clinic management system with RAG-powered clinical intelligence and LangGraph multi-agent workflows

Multi-agent AI research system with LangGraph, FastAPI & RAG. Parallel research orchestration inspired by OpenAI Deep Research.

A production-ready Agentic RAG system built with LangGraph's native features, implementing Ed Donner's patterns for multi-agent orchestration.

A production-grade multi-agent clinical orchestration system built with LangGraph, RAG, and self-correcting logic for autonomous medical workflows.

Experimental AI agent playground with LangChain, LangGraph, RAG pipelines, multi-agent orchestration & OpenAI GPT-4 Turbo. Build intelligent agents, retrieval-augmented generation systems & autonomous AI workflows.

Multi-agent RAG system for Oil & Gas compliance gap analysis using LangGraph supervisor orchestration. Features specialized agents (retriever, analyzer, reporter) with Supabase pgvector, Gemini 2.5, and production deployment on GCP Cloud Run. Includes LangGraph Studio visualization.

Multi-agent RAG system built with LangGraph for complex query handling. Features agent orchestration, dynamic tool utilization, hallucination detection, and error correction. Processes documents with vector database indexing for accurate retrieval-augmented responses.

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.

A hands-on learning repository covering LangGraph fundamentals — from basic chatbots and human-in-the-loop workflows to multimodal RAG, multi-agent systems, and MCP server integration with LangChain.

LangGraph Models is a curated collection of modular LLM workflows built with LangGraph. It showcases a range of step-by-step examples—from foundational agents to advanced multi-agent and RAG systems—purposefully designed for stateful, memory-aware, and tool-augmented applications.

A multi-agent Retrieval-Augmented Generation (RAG) system for customer support. Built with LangGraph for agent orchestration, domain-specific assistants, tool calling, conversation memory, and vector retrieval grounding to deliver accurate, context-aware responses.