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。）

instinct.cpp provides ready to use alternatives to OpenAI Assistant API and built-in utilities for developing AI Agent applications (RAG, Chatbot, Code interpreter) powered by language models. Call it langchain.cpp if you like.

Redistributable ChatBot APIs with ChatGPT + RAG(Agents) with metadata, Nest.js + Langchain

Knowledge chatbot using Agentic Retrieval Augmented Generation (RAG) techniques. Full-stack proof of concept built on langchain, llama-index, django, pgvector, with multiple advanced RAG techniques used.

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.

Agentic RAG chatbot using Langchain and Langgraph

An Agentic RAG implementation using Langchain and a telegram client to send/receive messages from the chatbot

A WhatsApp chatbot for real estate inquiries, built using Twilio API, Flask, and a BERT intent classifier. It uses LangChain and RAG for managing context and delivering accurate property information, automating client interactions while easing the workload for agents.

A project-based tutorial showing how to build a Retrieval-Augmented Generation (RAG) chatbot with LankChain, complete with agents, memory, and chat interface integration.

Showcasing my AI journey — GenAI, Agentic AI, LangGraph, LangChain, CrewAI, RAG, ChatBot, FastAPI, Docker, Azure and more.

AI chatbot, using LangChain and the 8-bit quantised Falcon-7B LLM. Crafted a conversational agent with Retrieval Augmented Generation (RAG) pipeline.

RAGent Chatbot is a smart AI assistant that combines Retrieval-Augmented Generation (RAG) with a tool-using LLM agent. It can answer user queries using uploaded documents, perform web searches, summarize text, solve math expressions, check weather, and more — all powered by LangChain, Qdrant, and Gemini.

The LangChain Crash Course repository serves as a comprehensive resource for beginners who are ready to learn LangChain, a programming framework designed for creating AI agents, building RAG (Retrieval-Augmented Generation) chatbots, and automating tasks using artificial intelligence.

Agentic AI Chatbot (LangGraph + RAG) An agent-based AI chatbot built using **LangChain**, **LangGraph**, and **Streamlit** with tool usage and document-based question answering.

Simple AI agent RAG chatbot based on Python, Ollama, Langchain, Gradio. Included tools: real time weather, calculate, local documents RAG, local SQL database .

AI-powered rural healthcare system built with Django using an Agentic RAG architecture (LangGraph + LangChain). Combines local document retrieval with SerpAPI web search fallback. Includes chatbot, appointments, awareness campaigns, and document management.

Built real-world LLM apps using LangChain, including an AI Code Tutor, CV Assistant, and smart chatbots powered by RAG and agentic workflows. Hands-on projects showcasing practical use of Generative AI.

Hands-on training (July 2025) at Sathyabama Univ on GenAI & AI Agents. Learn chatbot creation, RAG from text/PDF/web/image/vector DBs, and build agents using LangChain, LangGraph, LangFlow, Google ADK & n8n. Python best practices and API credentials included.

Designed and developed a chatbot for hospital management systems using LangChain. The chatbot incorporated RAG with Neo4j graph database for knowledge retrieval, memory chains for context retention, and ChatGPT models to handle dynamic patient interactions. It also employed agents and tools for enhanced conversational capabilities

GraphRAG, Outperforms traditional RAG ( Retrieval-Augmented Generation ) for Query Focused Summarization. Opensource research of Knowledge Graph to improving the accuracy of data discovery, solving RAG pain points, solving Black-box hallucination, and to enhance LLMs. Knowledge Graph to GenAI. PDF AI Agent/Chatbot with Python, Neo4j and LangChain

SmartQuery is an AI agent chatbot built with LangChain, Gemini API, RAG, and a custom knowledge base. It features session-based memory, Google/GitHub auth, web search tools, and dynamic tool use. Delivers a ChatGPT-like experience for intelligent, context-aware conversations with secure login.

Tutorial on builduing customer chatbot for coffee shop with Agent LLM and RAG

RAG chatbot agent with open ai GPT model, pinecone vector db and langchain in javascript

Hybrid LLM and Agentic RAG Powered E-commerce Customer Support Chatbot built with Langchain, Pinecone Vector DB, Rasa chatbot framework and, integrated with DeepSeek-r1

Production-ready AI agents & chatbots using Google Gemini, Groq, LangChain, CrewAI & LangGraph | 10+ projects covering RAG, document analysis, code exploration & more

Knowledge chatbot using Agentic Retrieval Augmented Generation (RAG) techniques. Full-stack proof of concept built on langchain, llama-index, django, pgvector, with multiple advanced RAG techniques used.

A collection of LangChain-based implementations covering agents, APIs, chatbots, RAG pipelines, and Groq integrations, created to explore and experiment with modern LLM application patterns

Hi i'm Bhavjeet singh |🎓 IIT Madras | 🤖 GenAI & Agentic Systems Building RAG pipelines, LangChain agents, and scalable AI tools. Projects: PDF comparison portal, Huggingface chatbot, LangGraph workflows. 📌 Aiming for GenAI internships

This Streamlit-based RAG chatbot vectorizes PDF documents and stores them in Qdrant, performs semantic search using OpenAI and Tavily, and leverages CrewAI agents to analyze, rewrite query, route, retrieve, and generate responses. Built with Crewai, LangChain, OpenAI, Qdrant, and Tavily technologies.

Developed a stateful, multi-agent RAG chatbot using LangGraph and LangChain to manage roles like retriever, planner, and summarizer. Enabled memory-aware chat with AstraDB as a vector store, improving user query handling and response quality by 40% in long, multi-turn conversations.