#Agentic-rag
Showing 60 of 65 repositories tagged #agentic-rag, ranked by stars
12 Lessons to Get Started Building AI Agents
🌊 The leading agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
Code search MCP for Claude Code. Make entire codebase the context for any coding agent.
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
Everything you need to know to build your own RAG application
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
企业级 Agentic RAG 智能体 - 全链路覆盖文档解析、多路检索、意图识别、问题重写、会话记忆、MCP 工具调用与深度思考。面向真实业务场景，从 0 到 1 完整工程实现。
The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.
Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.
Nuwax Agent OS - An enterprise-grade AI Agent Development and Operation Platform - Providing a complete solution for agent creation and distribution, knowledge base management, model proxy, memory system, and plugin ecosystem.
[ACL 2026 KnowFM] Awesome Agentic Deep Research Resources
RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.
The .NET library to build AI agents with 30+ built-in connectors.
Healthy Diet AI Agent is a Bun + TypeScript backend for nutrition chat, food-image analysis, RAG document ingestion, and knowledge-grounded diet guidance.
YT Navigator: AI-powered YouTube content explorer that lets you search and chat with channel videos using AI agents. Extract insights from hours of content in seconds with semantic search and precise timestamps.
NextPlaid, ColGREP: Multi-vector search, from database to coding agents.
[EMNLP 2025] Awesome RAG Reasoning Resources
Chat2Graph: Graph Native Agentic System.
Llama Agents + Workflows are an event-driven, async-first, step-based way to control the execution flow of AI applications like agents.
Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)
Save thousands of lines of code by building universal, domain-agnostic Multi-Agent Systems (MAS) through high-level semantic orchestration. This repository provides a production-ready blueprint for the Agentic Era, allowing you to replace rigid, hard-coded workflows with a dynamic transparent Context Engine that provides 100% transparency.
企业级 AI 智能体 Agent 平台，覆盖智能对话、文档知识问答、联网搜索、RAG 检索、MCP 工具协议、Skills 扩展等完整能力。三层执行器体系、双通道混合检索、组合式切块引擎、会话记忆管理、全链路可观测，每个环节都经过深 度设计和工程化打磨。
Source code of LogicRAG at AAAI'26.
Agentic RAG to achieve human like reasoning
📁 This repository hosts a growing collection of AI blueprint projects that run end-to-end using Jupyter notebooks, MLflow deployments, and Streamlit web apps.🛠️ All projects are built using HP AI Studio with ❤️ If you find this useful, please don’t forget to star the repository ⭐ and support our work 🚀
This repository contains resources and materials for courses and presentations related to AI Agents and Agentic Systems for Cybersecurity Operations by Omar Santos.
Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agnostic, event-driven, with multi-agent workflows, MCP tool discovery, and production-grade observability.
Hands-on tutorials for building AI agents from scratch. Learn LLM APIs, prompt engineering, tool calling, and the agent loop through practical examples.
A python library for creating AI assistants with Vectara, using Agentic RAG
A comprehensive collection of LangGraph implementations, tutorials, and advanced AI workflows covering Agentic RAG systems, MCP (Model Context Protocol) development, and practical AI application patterns.
Multi-agent AI research system — finds academic papers via semantic search & citation snowballing, then answers questions over them using agentic RAG with self-reflection. Built with LangGraph, FastAPI, Celery, and Qdrant.
Context-Aware RAG library for Knowledge Graph ingestion and retrieval functions.
Repositorio-Tutorial para desarrollo de chatbots, aplicaciones con LLMs y Agentes IA
This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.
A complete collection of RAG interview questions, answers (418 questions & 29 RAG types), system design scenarios, architecture patterns, and production-ready concepts.
Awesome Agentic AI Learning Resource by DevKay is a curated roadmap for mastering Agentic AI—from ML foundations to production-ready agents. Features a 0–12+ month path, hands-on projects, top resources, agent architectures, and tools like LangChain and AutoGen.
Persistent, inspectable memory that grows and refines over time. Built on files. Owned by you.
Enterprise RAG ecosystem managing 15,000+ semantic chunks. Features hybrid parsing (LlamaParse/PyMuPDF) and 256-dim MRL embeddings for 512MB RAM environments
The Python Harness for Production AI Multi-Agent Systems
[DeepRead] This is the official implementation of the DeepRead paper.
Agentic RAG to help you build a startup🚀
Multi-Agent Deep RAG
Agentic AI framework built using LangGraph and Multi-Agent Control Plane (MCP) for building structured, goal-driven multi-agent systems.
[ACL'26 Main Conference] Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning
Hierarchical RAG architecture scaling to 693K chunks on consumer hardware (4GB VRAM). Features 3-address routing, hybrid vector+graph fusion, and SetFit classification.
Building production-ready Retrieval-Augmented Generation (RAG) systems with LangGraph orchestration and local Ollama models for privacy-preserving AI applications.
Turn scattered knowledge, operational data, and history into source-linked context that your agents can inspect, explain, and reuse.
HSD AGÜ From Model to Agent Mini Bootcamp Proje ve Eğitim Dokümanları
Interactive LLM Chatbot that constructs direct and transitive software dependencies as a knowledge graph and answers user's questions leveraging RAG and critic-agent approach
🎯Awesome-AgenticRAG_DeepResearch: A curated list of resources on Agentic RAG & DeepResearch. 学习参考关于AgenticRAG、DeepResearch的发展相关论文
Turn your Bilibili favorites and cloud documents into a chat-ready personal knowledge base. Agentic RAG with ASR transcription, Milvus vector search, multi-provider LLM support (OpenAI / Anthropic / DeepSeek), and full source citation.
It shows how to realize agentic RAG.
AgenticRAG is an advanced AI-powered retrieval-augmented generation (RAG) Agent designed to provide users with an interactive and intelligent conversational experience
This repo contains my coursework, assignments for IBM RAG and Agentic AI Professional Certificate on Coursera
An advanced Self-Centered Intelligence (SCI) prototype that represents a new paradigm in AI-human interaction.