agentic rag for dummies
GiovanniPasqA modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
Benchmarks we run. Prices we verify daily. Field evidence we curate from postmortems, dev posts, and vendor retros ; terse, dated, honest.
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.
Open-source context retrieval layer for AI agents
The all-in-one AI productivity accelerator. On device and privacy first with no annoying setup or configuration.
Documentation of AnythingLLM by Mintplex Labs Inc.
A Model Context Protocol server for searching and analyzing arXiv papers
Incremental engine for long horizon agents 🌟 Star if you like it!
Build Real-Time Knowledge Graphs for AI Agents
A modular graph-based Retrieval-Augmented Generation (RAG) system
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.
An open-source RAG-based tool for chatting with your documents.
Langchain-Chatchat(原Langchain-ChatGLM)基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
[ICLR 2026] LightMem: Lightweight and Efficient Memory-Augmented Generation
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
LlamaIndex is the leading document agent and OCR platform
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
Open Source Implementation of Karpathy's LLM Wiki. Upload documents, connect your Claude account via MCP, and have it write your wiki !
AI Data Vault - A query engine for AI Agents to securely query data from any datasource
Open Source AI Platform - AI Chat with advanced features that works with every LLM
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Semantica 🧠 — AI-native knowledge graph intelligence framework for semantic retrieval, ontology reasoning, context graphs, and explainable AI systems.
A filesystem designed for agents, with SOTA retrieval, automatic memory profiles, sync engine. Drop any file type (pdf, images, videos), and grep through them.
Stash — persistent memory layer for AI agents. Episodes, facts, and working context stored in Postgres. MCP server included. Self-hosted, single binary, no cloud required.
The local-first LLM Wiki: open-source knowledge graph builder, RAG knowledge base, and agent memory store. Built on Andrej Karpathy's pattern. An Obsidian alternative for personal knowledge management, AI second brain, and durable Claude Code / Codex / OpenClaw memory.
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.
Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift