Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams, powered by LangGraph, Langchain, FastAPI, and NextJS.（Flock 是一个基于workflow工作流的低代码平台，用于快速构建聊天机器人、RAG、Agent和Muti-Agent应用，采用 LangGraph、Langchain、FastAPI 和 NextJS 构建。）

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

本项目实现了一个基于多智能体（Multi-Agent）和检索增强生成（Retrieval-Augmented Generation, RAG）技术的客户支持系统。它利用 Python、LangChain 和 LangGraph 构建了一个能够处理各种旅行相关查询的对话式 AI，包括航班预订、租车、酒店预订和行程推荐。还有对接了woocommerce商城进行商品查询，文章查询，表单提交，订单查询等商城功能。

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.

Sample for context-aware Agentic RaG, Q&A with multi-source verification, and self-curating knowledge base. Powered by Azure AI Foundry Agent Service, Azure AI Search with agentic retrieval and query rewrite, Semantic Kernel and LangGraph agents running in Azure Container Apps, and ready for Copilot Studio

📚 AI-powered research reading workbench. Project-based paper Q&A with Hybrid RAG, multi-agent workflows (ReAct/Plan-Act/RePlan), long-term memory, and traceable evidence. Built with LangChain + LangGraph + Streamlit.

Production-grade multi-agent orchestration engine. Compose agentic workflows from a pluggable catalog of Agents, LLMs, tools, and retrievers. Execute locally with LangGraph or distributed with Temporal. Built-in RAG pipeline for enterprise knowledge retrieval. A2A and MCP protocol support. Visual drag-and-drop blueprint builder.

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.

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.

Production-ready RAG framework for Python — multi-tenant chatbots with streaming, tool calling, agent mode (LangGraph), vector search (FAISS), and persistent MongoDB memory. Built on LangChain.

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.

No description available

Open-source multi-agent AI assistant powered by LangGraph, FastAPI & Next.js — 16+ agents, Human-in-the-Loop, MCP integration, voice TTS, RAG, 500+ metrics, 6 languages.

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.

AI-powered autonomous equity research agent. Multi-source analysis (Yahoo Finance, SEC 10-K, Reddit, news) with LangGraph orchestration, hybrid RAG, and real-time market data. Generates professional research reports via Telegram bot or REST API. Deployed on Azure Container Apps.

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

一个面向鞋服零售行业的智能导购系统，为企业导购提供 AI 驱动的功能，包括： - 商品智能文案生成（支持流式输出 SSE） - 基于用户行为的意图分析（Intent Engine） - 智能促单话术生成（Hybrid Rule + LLM） - 商品知识增强检索（RAG） - 多智能体（Multi-Agent）销售流程自动化（LangGraph） 系统旨在补充线下导购能力，使导购在分享商品、小程序引流、促单转化过程中具备“虚拟销售助手”的支持。

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.

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

🚀 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.

In this Blog we will build a multi AI agent with RAG using Langraph and AstraDB with integration with the Llama 3.1 open source model using Groq API.

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.

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.

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

A self-paced bootcamp for engineers building AI into production systems. Starts from fundamentals (tokenization, embeddings, prompting) and builds to RAG, Agents (LangGraph, tool design, memory, multi-agent), and LLMOps. Depth over breadth.

A Multi-Modal Agentic RAG pipeline designed to handle unstructured documents containing tables, charts, and images. It integrates Docling and ElasticSearch for structured indexing, and leverages LangGraph for agent-based reasoning and dynamic query reformulation.