A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
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Updated
Aug 30, 2026 - Jupyter Notebook
Retrieval-augmented generation (RAG) is a technique that improves large language models by retrieving relevant information from external sources and using it to generate more accurate and context-aware responses.
A RAG system combines information retrieval with a language model. It is commonly used in AI assistants, search systems, document question answering, and applications that need access to private or frequently updated information.
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
Knowhere extracts, parses, and outputs structured chunks ready for AI Agents and RAG.
Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.
Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift
A RAG pipeline implementation built on the 'Epstein Files 20K' dataset from Hugging Face (Teyler).
Local RAG MCP server for Claude Code — hybrid search (semantic + BM25), cross-encoder reranking, 13 MCP tools, 20 format parsers. Zero external servers, zero API keys.
Unified Agentic AI and Data Platform
KSoR (Knowledge System of Record) is an open-source SDK for building governed, authoritative knowledge systems for humans and AI agents. It is a foundation of an AI-native knowledge platform.
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.
HiveMind Protocol - A Local-First, Privacy-Preserving Architecture for Agentic RAG
Open-source toolkit for reliable RAG pipelines: convert PDFs to Markdown, clean documents, inspect chunks, compare chunking strategies, and enrich metadata for LLM applications.
A scalable RAG platform combining LangGraph agents, hybrid retrieval (Vector+Graph), and Ray orchestration on Kubernetes.
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.
PDFStract - Extract, Chunking and Embedding Layer in Your RAG Pipeline - Available as CLI - WEBUI - API
see live demo of chatbot. follow the link
A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.
Declarative AI pipelines in one YAML file. Tokens, audio chunks, and video frames flow between isolated components. Compose 100+ components for models, agents, speech, vision, and live broadcast. Run local models, cloud APIs, or both. Inspired by docker-compose.
Self-hostable RAG platform - document ingestion, embedding, and vector search behind a simple REST API
Vectorless, Reasoning-Based Retrieval-Augmented Generation (RAG)
AI memory system combining vector search with temporal knowledge graph. Built-in cognitive engine for agents. Supports memory decay, contradiction detection, and MCP integration.