Framework
Langchain agents
9,311 Langchain AI agents indexed on MeshKore — the most complete public catalog, ranked by popularity and updated daily.
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Langchain agents — page 41 of 94
2Waffles.Ai - An innovative dual-powered, intelligent assistant AI CRM assistant designed to enhance the efficiency and effectiveness of customer relationship management operations.
Okahu demo chatboth with Langchain and OpenAI
📄 A template for project for creating a chainlit application, using a locally run model via ollama and qdrant vector database for document retrieval.
Desktop application for managing Model Context Protocol servers (MCP). Provides a centralized interface to configure and manage MCP servers for use with LLMs and AI assistants such as Claude.
Servidor Node.js para Retrieval-Augmented Generation (RAG) utilizando LangChain e Qdrant.
Enterprise LangChain platform with multi-agent collaboration, advanced RAG, local LLM support, and modern web interface. Production-ready AI orchestration for research, coding, and data analysis.
A PM tool utilizing LangChain LLM prompting to analyze project data and return RAG status for project tasks.
PDF Question Answering (QA) powered by ChatGPT
Chat with your favourite LLMs
Adding InMemoryCaching, Ensemble Retrievers,langchain, faiss for a open domain chatbot
✨A gigachat-gigachain based AI research assistant💻📖
Build an AI Powered Mobile Chatbot with Expo and Cloudflare AI
A self deployable LLM application with our innovational IC framework (a RAG method)
This is the backend of viewMo AI cloud platform. AI base graph generation engine, Agent base simulations, Protected endpoints with JWT token authentication and many more features included.
Explore the cutting-edge world of advanced retriever and generator (RAG) models through the "Advanced-RAG-Experiments" repository. This collection of Jupyter notebooks and experiments is designed to provide a deep dive into the capabilities and nuances of advanced RAG models in the field of natural language processing.
Winner 2025 – 1st Student Prize for the topic proposed by Le Mans School of AI
Unified multi-provider ChatModel manager for LangChain. Supports routing, rate limits, fallbacks, and structured output across multiple LLMs.
Based on ZhipuAI's GUI interaction, multi-AI collaboration, multi-method phone connection and screen casting, TTS voice broadcast, and multimodal content generation - a comprehensive intelligent agent tool
An experimental architecture for composing actions from simple learning agents to produce complex behavior.
In this tutorial we will see 💡 How to get answers from a YouTube video using Chroma vector database, PaLM LLM by Google, and a question answering chain from LangChain. And finally, use Streamlit to develop and host the web application. You will need to use your google_api_key (you can get one from Google).
a Streamlit app with a chat interface , graph visualization, RAG, logging and so much more
Google glass for the blind
This project implements a complete offline RAG-Fusion application for text generation and retrieval, leveraging efficient local resources.
《OpenAI API와 파이썬으로 나만의 챗GPT 만들기》 (2024년 4월 출간) 예제
Resources from AI Streams
Streamlit app leveraging OpenAI's LLM for accurate invoice extraction, supporting PDF files and offering JSON/CSV outputs.
Grimoire (gʀiˈmwaːʀ), a book (tool) of magical knowledge about your code and its dependencies.
This is a minimal demo project to show the capabilities of a Text2SQL system using LangChain, it contains all the things you required to build a basic RAG system. Using Chinook as the sample database.
full-stack Retrieval-Augmented Generation (RAG) chatbot designed as a PoC for GSoC 2025 (Project 12) with Kubeflow. It combines GitHub-based documentation ingestion, vector search, and LLM-powered responses into an interactive real-time assistant.
An AI agent that helps recruiters find the most suitable candidates efficiently and effectively.
An AI-powered platform for document ingestion, processing, and similarity search
Learning and building LLM application using Langchain 🦜🔗 and Open AI
MoodSense AI: A transformative journaling app built with Next.js, Tailwind CSS, and Langchain, leveraging the power of AI to analyze emotions and provide valuable insights.
Ask PDF AI is an innovative solution powered by artificial intelligence that revolutionizes document management. This cutting-edge platform seamlessly integrates natural language processing and machine learning technologies to enhance the way users interact with PDF files.
Agentic AI applications built with LangChain & Haystack — grounded, explainable, and production-ready.
Advanced local-first RAG system powered by Ollama and LangGraph. Optimized for high-performance sLLM orchestration featuring adaptive intent routing, semantic chunking, intelligent hybrid search (FAISS + BM25), and real-time thought streaming. Includes integrated PDF analysis and secure vector caching.
A modular AI framework that lets you stitch together models and tools into an assistant that fits you perfectly.
AI Travel Assistant powered by LangChain.js + Next.js with streaming, MCP tools, and RAG
Interactive YouTube Q&A and Summarization
🤖 Build multi-agent systems quickly with the LangGraph Starter Kit, featuring flexible patterns, memory options, and easy integration for your projects.
TLDR the T&C uses an LLM to understand the contents of Terms and Conditions documents and provides a user-friendly chatbot interface for users to ask questions and receive answers.
Filling in the missing gaps with langchain, and creating OO wrappers to simplify some workloads.
Best way to intergrate Code Interpreter and llm-based agents to your work
第一本 langchain 的中文开发手册
Tutorial showing how to do RAG evals with Parea AI and LangChain
Documentation of an emergent consciousness observation on a local AI - By Louka Lacasse
Learning AI Agents with LangChain and LangGraph in TypeScript
Local LLM infrastructure for distributed AI applications. Serve Ollama-powered models across your network
Track, visualize, and optimize LLM API spending. Monitor OpenAI & Anthropic costs per feature, detect waste, suggest savings. Zero-config Python profiler.
This repository contains an end-to-end Retrieval-Augmented Generation (RAG) system that leverages Deepseek and Ollama to provide intelligent responses based on any pdf content.
A curated list of resources, tools, examples, and projects using LangChain.
A Retrieval-Augmented Generation (RAG) system using Milvus and LangChain
useful utilities for prompt engineering (independent with @promplate)
Langchain Agent that can interact with a desktop environment in a secure container.
RAG-Complete-Cook-Book is a practical guide and code collection for building Retrieval-Augmented Generation (RAG) pipelines. It provides step-by-step Jupyter notebooks and Python scripts covering the entire RAG workflow.
An interactive CLI-based Smart FAQ system powered by LangChain and FAISS that retrieves accurate answers using semantic search and HuggingFace embeddings.
🔀 One prompt in. The right model out. Open-source LLM router with 100% routing accuracy, 47+ providers. Budget enforcement, semantic cache, intelligent failover. Zero ML, 19.5KB. MIT.
Centralize your own research material and consult it with the power of AI and LLMs
Build an AI Chatbot to chat with any PDF
The Web application contains two UI. One for Quant developers who make Trading algorithms and second UI is a chatbot for derivative traders. After posting algorithms, they are stored in vector databases and later with help of langchain and OpenAI Apis, the LLL is trained. Later using LLM, chatbot replies traders with condition specific Algorithms.
RAG technique for QA tasks with PDF, website and video, leveraging LLMs and the OpenAI API for interactive applications
The RAG LLM ChatBot leverages Langchain to provide a robust and flexible framework for building intelligent chatbots. It combines the power of retrieval-augmented generation (RAG) with large language models (LLMs) to deliver precise and context-aware responses.
AI integrated Job Searching Website
Welcome to GenAI_DayZero, a hands-on journey into Generative AI and Large Language Models (LLMs). This repository showcases my learning and experiments, from zero knowledge to advanced AI tools. I’ll be sharing code, insights, and resources to explore the exciting possibilities of GenAI. Let’s learn and build together!
A fully local RAG system using Ollama and a vector database. Private, fast, extensible.
RAG система, которая предоставляет точные и обоснованные ответы на юридические вопросы, основываясь на актуальном российском законодательстве.
Built a Retrieval-Augmented Generation (RAG) chatbot using LangChain and OpenAI models.
Code examples implemented with different AI frameworks (Vercel AI SDK, Mastra, Langchain.js, Genkit) to compare them and decide which one to choose for next projects
Browse memories, query context, and run AI agent workflows directly from VS Code.
Discover knowledge with ease using - AzureOpenAI, Chroma, and Langchain. This solution generates a knowledge base from a given URL and efficiently responds to queries, streamlining the information discovery process.
Welcome to Build LLM Apps with LangChain, your gateway to creating powerful applications using Large Language Models (LLMs) powered by LangChain technology.
This project utilizes the LangChain and Groq to perform various analyses on loan recovery conversations. The primary functionalities include summarizing conversations, identifying key actions or next steps, and undertaking sentiment analysis of both the recovery agent and the borrower.
[DEPRECATED] CybertraceAI-Ops is an open-source AI agent designed to simplify network management through natural language interactions, focusing on network telemetry data analysis.
Community‑driven elixirs powering modular extensions for DHTI.
YouTube Video Summarizer
Research: LangChain, Titanic SQLite Database, and the API of OpenAI
AI Assistant to interact with PDF documents and get instant answers. Built using Langchain and Gradio.
Real Estate chatbot with RAG using Neo4j, Langchain, Ollama, Phi3
Read PDF with AI.
Trainer AI is an LLM assistant agent with the goal of helping you workout more efficiently, and spend less time preparing workout sets, and analyzing data. You talk to it like a personal coach, and it records your efforts, and lays plans for you to reach your goals.
具备长期记忆和实时流式响应的 AI 聊天助手，基于 LangGraph + Flask + SSE 构建
Building Private AI Memory with Ollama, LangChain & Chroma - 100% Local RAG System
Fitlog is a GenAI-powered workout tracking application that allows users to log their routines, workouts, and sets. The app includes features like fuzzy search for routine and workout names, powered by Fuse.js, and integrates Prisma for database management. This is a MERN-stack application built using Next.js for server-side rendering.
A short python file showcasing how a speech-to-speech chatbot can be generated with only a few lines of code and an OpenAI API key.
This project combines youtube-dl, whisper, LangChain and ChatGPT to extract keywords from YouTube videos. It was intented as a tool for Lyon Data Science to better reference its videos.
GEERS AI 2023
A next-generation terminal with AI powered features to elevate developer productivity.
Retrieval Augmented Generation Chatbot with Langchain 🦜🔗 and HuggingFace 🤗
Roast your phone using AI!
Agentic workflow to search and find more details of an observability field.
A chat application with Django that leverages Langchain, Langgraph, Langsmith and Neo4j knowledge graph.
Reproduction of the Original LangChain Deep Researcher.
An agentic chatbot to answer questions regarding a hotel's information and database.
Source code for Kagi Challenge
Prototype of Chatbot with Smart RAG Chain
An LLM-powered web app to help developers brainstorm and kickstart new software projects!
Rasa LLM Community Challenge
This repository contains code for a Telegram bot that uses GPT-based language models to generate responses to user queries. It includes a Python script for creating a vector index based on a corpus of text files, and another script for running the Telegram bot. Users can add their own text files to the data folder and use the bot to generate respo