A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.

AI-powered StartUp Accelerator Engine built with Next.js, LangChain, PostgreSQL + pgvector. Upload, organize, and chat with documents. Includes predictive missing-document detection, role-based workflows, and page-level insight extraction.

A sophisticated LangGraph-based agent that automates financial options analysis with real-time data from Polygon.io, smart caching, persistent memory, and professional-grade analysis. Built for traders, analysts, and developers who need intelligent options data processing

One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.

Open-source, self-hosted alternative to NotebookLM. Chat with your documents, generate audio summaries, and ground AI in your own sources—built with Supabase and N8N on a React frontend.

A RAG pipeline implementation built on the 'Epstein Files 20K' dataset from Hugging Face (Teyler).

Agentic RAG for any scenario. Customize sources, depth, and width

Prototype SDK for RAG development.

Open-source, fully private and local alternative to NotebookLM. Chat with your documents, generate audio summaries, and ground AI in your own sources—built with Supabase, N8N on a React frontend using Ollama for local inference

HiveMind Protocol - A Local-First, Privacy-Preserving Architecture for Agentic RAG

pdfLLM is a completely open source, proof of concept RAG app.

AnythingLLM Embed widget submodule for the main AnythingLLM application

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Open-source toolkit to extract structured knowledge graphs from documents and tables — power analytics, digital twins, and AI-driven assistants.

A RAG agent using Google's ADK & Vertex AI that lets set up semantic search across documents in under 2 minutes. Features GCS integration and natural language querying

CrawlAI RAG is an AI-powered website intelligence platform that allows users to crawl entire websites, index their content, and ask natural-language questions using Retrieval-Augmented Generation (RAG). It transforms static websites into queryable knowledge bases.

A Terminal User Interface for AI collaboration on code, using a Retrieval-Augmented Generation (RAG) pipeline designed specifically for Rust code generation and refactoring.

Django BM25 full-text search using PostgreSQL - a lightweight Elasticsearch alternative

On premise enterprise-grade RAG-powered agentic workflow chatbot platform with multi-provider support

Template for AI chatbots & document management using Retrieval-Augmented Generation with vector search and FastAPI.

An open‑source desktop RAG application that enables semantic search across your Zotero library. Easily discover conceptually related papers and ideas within your PDF collection using local or cloud‑based LLMs. The app provides source attribution, metadata filtering, and seamless integration with Zotero to support deeper, more efficient research.

FinchBot is an AI Agent framework that empowers agents with true autonomy, built on LangChain v1.2 and LangGraph v1.0. With fully async architecture, agents gain the ability to self-decide, self-extend, and self-evolve

Build and deploy a full-stack RAG app on AWS with Terraform, using free tier Gemini Pro, real-time web search using Remote MCP server and Streamlit UI with token based authentication.

Multi-Agent Systems with Google's Agent Development Kit + A2A + MCP

Supacrawler's ultralight engine for scraping and crawling the web. Written in go for maximum performance and concurrency.

ChatBot, show how to implement a RAG based on OceanBase or OceanBase seekdb AI capabilities escpecailly hybrid search and AI embedding.

A complete Retrieval-Augmented Generation (RAG) application that demonstrates modern AI capabilities for answering questions about Ultimate Frisbee rules and strategies. This project showcases how to build a production-ready RAG system using cutting-edge technologies.

MediNotes: SOAP Note Generation through Ambient Listening, Large Language Model Fine-Tuning, and RAG

Crawl any website with Tavily, embed the content, and deploy the RAG on MongoDB Atlas vector search.