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.

Lightening fast RAG on Apple Silicon. On-Device. No Server. No API. One File. Pure Swift

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

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

Move from idea to production in hours with policy-driven autonomous AI agents. Unified Control Plane: Centralised tools, MCPs, models, data, and policies with consistent observability and governance.

PDFStract - The Extraction and Chunking Layer in Your RAG Pipeline - Available as CLI - WEBUI - API

A scalable RAG platform combining LangGraph agents, hybrid retrieval (Vector+Graph), and Ray orchestration on Kubernetes.

A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.

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.

We believe that every SOTA result is only valid on its own dataset. RAGView provides a unified evaluation platform to benchmark different RAG methods on your specific data.

RAG boilerplate with semantic/propositional chunking, hybrid search (BM25 + dense), LLM reranking, query enhancement agents, CrewAI orchestration, Qdrant vector search, Redis/Mongo sessioning, Celery ingestion pipeline, Gradio UI, and an evaluation suite (Hit-Rate, MRR, hybrid configs).

An in-memory Vector Database & AI Gateway written in Go. Supports HNSW, Hybrid Search (BM25), GraphRAG context, a built-in RAG Pipeline, and can be embedded directly into your apps.

Agent Fusion is a local RAG semantic search engine that gives AI agents instant access to your code, documentation (Markdown, Word, PDF). Query your codebase from code agents without hallucinations. Runs 100% locally, includes a lightweight embedding model, and optional multi-agent task orchestration. Deploy with a single JAR

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

Generate & Ship UI with minimal effort - Open Source Generative UI with natural language

Quickest way to production grade RAG UI.

The first database built to let AI agents think their way to the right answer using structural reasoning, rather than guessing based on vector similarity.

Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.

Your private AI companion that lives on your wrist. Complete local AI assistant with emotional intelligence.

AI-powered mock interview platform using Next.js, Gemini AI, JSON, Drizzle, NeonDB, API routes and Clerk for dynamic questions, feedback & session recording, plus Dockerized & deployed microservices.

A python module library that simplifies RAG through abstraction

Agentic Resume Search Engine

AI travel planner with 7 specialized agents, RAG, and tool-calling. Built with CrewAI & LangChain. Generates personalized itineraries with flights, hotels, activities, and cultural tips. Production-ready Python codebase.

A local-first RAG engine for web archival and semantic search. Crawl, embed, and query your own knowledge base entirely offline.

The implementation of Test Time Diffusion paper by Google with some tweaks to run on 24gb gpu

Create private, local RAG libraries that work offline—no API keys, no cloud services. Share them as single files your whole team can use.

RAG-powered code search via simple REST API

The Audited Context Generation (ACG) Protocol prevents AI hallucinations with a dual-layer system. The UGVP layer links every fact to a precise source for verification. The RSVP layer audits the AI's logical reasoning when combining facts. This creates a fully transparent, machine-auditable trail for both source and logical integrity.

A multi-provider AI coding agent with the persona of a Tech-Priest