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

Multi AI agents for customer support email automation built with Langchain & Langgraph

Local RAG researcher agent built using Langgraph, DeepSeek R1 and Ollama

152 open-source tools to run LLMs 100% locally – no cloud, no API keys, no censorship

Innovative AI agent implementations using LangGraph—featuring ReAct, RAG (Corrective, Self, Agentic), chatbots, microagents, and more, with multi-AI agent systems on the horizon! 🤖🚀

Educational toolkit for all things RAG (Retrieval Augmented Generation)

SYNAPSE: AI-Driven Adaptive Software Engineering. A 2025 research prototype of an autonomous agent that adapts its own success criteria.

A full-stack, microservices-based application for secure document management, enabling users to upload, parse, index, and query various file types through advanced NLP and RAG agents. Built with Docker and Kubernetes for scalability, and integrates Elasticsearch, Redis, and AWS S3 for efficient storage and search capabilities.

QueryPilot is an advanced document intelligence platform that combines Large Language Models (LLMs) with vector embeddings to enable natural language querying of your documents. The application processes various file formats (PDFs, DOCXs, TXT files, and images), extracting and embedding content for semantic search and AI-powered analysis.

This python powered AI based RAG Scraper allows you to ask question based on PDF/URL provided to the software using local Ollama powered LLMs

This repository contains the implementation of a Retrieval-Augmented Generation (RAG) agent using Large Language Models (LLMs). RAG agents combine the power of information retrieval with text generation, enabling applications such as intelligent question-answering systems, and more.

System design for the AI era — LLM infra, RAG, agents, and the classic patterns updated for 2026

This project implements a Retrieval-Augmented Generation (RAG) based chatbot designed to handle university-related queries using natural language understanding. It combines semantic search with generative AI to provide precise, context-aware answers to students, faculty, and visitors.

Learn system design for the AI era with clear patterns, diagrams, and production-ready guidance beyond the Primer

🤖 Explore and utilize top open-source tools for running, fine-tuning, and building LLMs entirely locally, without cloud dependencies or API keys.

AI driven Content Creation Automation.

A fully local RAG agent that parses PDFs and enables contextual Q&A using LLaMA 3.2 via Ollama, ChromaDB, and Gradio — no internet or API keys required, with faster response time.

A multilingual Multi RAG Agricultural Intelligence System for Indian farmers. Powered by FastAPI, React, and Groq LLMs. Features 5 specialized AI agents, real time market trends, weather advisories, voice search, and multimodal leaf disease scanning.

This project is a complete local RAG system for answering questions over document collections such as PDFs. It indexes documents, builds vector search indexes, routes queries to the right retrieval strategy, grades candidate results, and generates final answers with an LLM.

a RAG (retrieval-augmented generation) agent specialized in cinema