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

🤖 An open-source, AI agent-native research canvas application that performs real-time search with HITL (Human in The Loop) capabilities, powered by CopilotKit, Tavily and LangGraph

Multi-agent autonomous research system using LangGraph and LangChain. Generates citation-backed reports with credibility scoring and web search

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.

Master the art of building and enhancing AI agents. Learn to develop flow-based applications, implement agentic search, and incorporate human-in-the-loop systems using LangGraph's powerful components.

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

The Retail Shopping Assistant is an AI-powered blueprint that provides a comprehensive interface for an intelligent retail shopping advisor. Built with LangGraph for agent orchestration, it features multi-agent architecture, real-time streaming responses, image-based search, and intelligent shopping cart management.

This project is a comprehensive recruitment and interview assistance system developed based on large models and agents, leveraging the langgraph and llamaindex agent frameworks. It aims to enhance job search efficiency and interview performance by automating and intelligentizing the entire interview process. it is China Software Cup A3 competition

A complete LangGraph multi-agent system demo using SQL tools, Tavily search, MCP Toolbox, and OpenRouter models — with reproducible notebooks and a full supervisor-led agent workflow.

A LangGraph-based research assistant that helps find, analyze, and summarize scientific papers. Built with Mistral LLM (via Ollama) and Flask, this agent can search papers through CORE API, process PDFs, and provide structured research summaries.

Multi-agent chatbot with integrated search engine, utilizing a plan-execute-replan approach for complex queries, built with LangGraph and Tavily.

OpenProbe is an advanced agent-based search system that performs deep web searches to answer complex questions. It uses a graph-based approach with LangGraph to orchestrate different components and deliver comprehensive answers.

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。

Agentic search web search using LangGraph and OpenAI GPT-4o, powered by Tavily and DuckDuckGo search.

Autonomous multi-agent system for intelligent HDB resale search — combining geospatial analytics, MRT proximity, and price intelligence using DeepAgents, LangGraph, FastAPI, Gradio UI, and MCP Toolbox. Fully Dockerized with reproducible data ingestion pipelines.

🌍 AI Travel Agent - Intelligent multi-agent system powered by LangGraph that coordinates specialized AI agents (flight search, hotel booking, activity planning) to provide personalized travel recommendations and assistance

A Streamlit webapp deployed on Hugging Face Spaces featuring multiple AI agents built using LangGraph, LangChain, and LLMs. Includes a Startup Idea Validator, Travel Planner, and Agentic AI Chatbot with tools like web search and code execution etc. A modular platform for real-world, graph-driven multi-agent AI interactions.

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.

Full-stack AI fitness planner using LangGraph multi-agent workflows to generate personalized meal and workout plans from real USDA nutrition data. Features coordinated AI agents, semantic food search, and production-ready architecture with Docker deployment.

Multi-step AI agents powered by Gemini 2.0 and the LangGraph framework. These agents orchestrate complex workflows and enhance their reasoning with grounded responses, drawing on real-world data from Google Search, BigQuery, and third-party APIs.

A go-to repository for building web search agents and multi-agent systems using LangChain, LangGraph, Phidata/Agno, Pydantic, and CrewAI. It includes a multi-agent email response agent, LLaMA OCR, and a multi-agent travel planner, enabling autonomous and intelligent workflows. 🚀

A template implementation of a deep research agent using LangGraph and GPT-4. This agent performs comprehensive research on any given topic by: Generating an initial research plan and outline, breaking down the topic into logical sections and performing targeted web searches using Tavily.

A2A protocol demo: Orchestrated AI agents for retail using Google ADK, LangGraph, and Vertex AI Search

Example of LangGraph agent with Tavily Search API tool with Bielik LLM model.

Real-world AI agents that think, search, write, and build - powered by LangGraph and modular LLM

A state-of-the-art multi-agent travel planning system built with LangGraph, Google Gemini Flash-2.0, and DuckDuckGo Search. The system offers three different planning approaches, from traditional single-agent to cutting-edge multi-agent collaboration using modern industry frameworks.

Iterative retrieval+search agent template powered by LangGraph, pgvector & WebSearch.

Taro.ai - SurrealDB Agentic Search Harness. LangGraph ReAct agent with 8 search tools (vector, BM25, graph, hybrid RRF). Built at LangChain x SurrealDB Hackathon 2026.

The LangGraph project implements a "Reflection Agent" designed to iteratively refine answers to user queries using a Large Language Model (LLM) and web search. It simulates a research process where an initial answer is generated, critiqued, and revised based on information gathered from web searches, all managed by a LangGraph workflow.

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.