[EMNLP 2025] ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents

Agent-native knowledge engine with MCP tools for document indexing, wiki organization, fast retrieval and deep reading across PDF/DOCX/PPTX/Markdown

RAG Agent System - Spring AI + React + Multi-modal Document Parsing + Hybrid Vector Retrieval

A multi-agent RAG (Retrieval-Augmented Generation) system powered by Google Gemini, Docling, and LangGraph for intelligent document Q&A with built-in fact-checking and hallucination prevention.

A beginner-friendly and extensible Agentic RAG project that demonstrates the full pipeline of document parsing, retrieval, reranking, workflow orchestration, tool calling, and answer generation, designed for both learning and secondary development.

🖼️ Workshop: Build a multimodal AI agent with Haystack & GPT-4o — featuring image understanding, document retrieval, conversational memory, and human-in-the-loop safety controls

Agent-WebVoyager autonomously navigates the web like a human, performing tasks without specific APIs. It uses visual cues and intelligent decisions for web scraping and information retrieval, documenting each step visually. This innovative approach showcases AI's versatility in dynamic web exploration.

An Agentic AI that assists with academic and scientific research tasks—summarizing papers, organizing references, drafting sections, and managing citations. This is ideal because: It aligns well with IBM watsonx.ai’s strengths in text summarization, RAG (Retrieval-Augmented Generation), and document ingestion.

LLM-powered research knowledge base — compile raw documents into a living wiki with persistent agent memory and RAG retrieval.

AI-Powered PDF Query: LangChain ReAct agents with Qdrant and Groq's llama3 for intelligent document retrieval.

This project aims to develop a Retrieval-Augmented Generation (RAG) based conversational agent that enables users to interact with a corpus of PDF documents through a natural language interface

AI document intelligence with agentic retrieval and workflow orchestration. Extract, search, and answer at scale.

Advanced AI agent system for ERPNext with reinforcement learning-based retrieval, multi-agent orchestration, and intelligent document processing.

Ragnar is a pure Ruby command-line RAG (Retrieval-Augmented Generation) tool with zero external dependencies. It provides local document indexing, semantic search, and LLM-powered query processing. Built to be hackable, it lets Ruby developers experiment with agentic workflows and RAG pipelines natively in Ruby.

This repository demonstrates a simple OpenAI Swarm-based system for multi-agent orchestration with Retrieval-Augmented Generation (RAG). It handles tasks like summarization, sentiment analysis, keyword extraction, and document search using FAISS and OpenAI models, showcasing the power of collaborative agents.

A production-ready AI Agent API with advanced RAG (Retrieval-Augmented Generation) capabilities, built with Node.js, TypeScript, MongoDB Atlas, Redis, and Google Gemini integration. This API provides intelligent question-answering based on trained knowledge from various sources including documents, websites, YouTube videos, audio, and video files.

A 100% local memory layer for chatbots and agents with an MCP server for Claude, GPT, Gemini, and local models. It auto-saves conversations, ingests documents and markdown vaults, and provides hybrid retrieval (vector + keyword + graph) plus enterprise security (OAuth2, API keys, rate limiting, audit logs) and integrations (Slack import, Notion/GDr

Agentic RAG is a next-generation AI architecture that combines the precision of structured agent-based reasoning with the power of retrieval-augmented generation. Unlike traditional RAG pipelines that blindly retrieve documents for every query

An Agentic RAG (Retrieval-Augmented Generation) system powered by LangChain, enabling multi-step reasoning over documents using LLMs, ChromaDB, and Google Drive as a document source.

In this we implements a Retrieval-Augmented Generation (RAG) based conversational AI agent designed for intelligent knowledge extraction from PDF documents. Leveraging LangChain and Google’s Gemini LLM

ARLC 2026 Agentic RAG Legal Challenge — Legal QA pipeline for DIFC court documents. Hybrid BM25 + vector retrieval, cross-encoder reranking, answer-grounded page verification.

Chat With Multiple PDF Documents using Conversational RAG on CPU with LLAMA2, Langchain ChromaDB

Recursive Document Agents for Dynamic Retrieval

Toolchain for LLMs built on LangChain, providing a flexible framework for document processing, retrieval-augmented generation (RAG), structured Data Retrieval (SDR), agents, fine-tuning and more...

AI-powered agent leveraging RAG (Retrieval-Augmented Generation) with tool integration capabilities. Built with langchain, OpenAI, FastAPI, React frontend, it combines document-based knowledge with real-time data access and calculation tools to provide context-aware responses.

Legal Agent is an AI-powered package for legal research, document analysis, and grounded question answering. It combines retrieval and reasoning workflows to help teams produce faster, more reliable legal outputs. Built for practical use, it emphasizes clear traceability and audit-friendly results.

AI Lawyer Chatbot App uses RAG to answer legal questions from uploaded PDFs like contracts or policies. It combines FAISS-powered retrieval, Groq LLMs, and a Streamlit UI for fast, accurate document-grounded Q&A. Built with modular agents for scalability and real-world use.

Multi-Agent Framework that enhances document creation with three agents: Semantics Identification, Document Retrieval, and Content Generation, ensuring accuracy and user alignment.

Extended Groq SDK with RAG (Retrieval-Augmented Generation), web browsing, and AI agent capabilities. Features include document retrieval, web search, URL parsing, and ReAct-style agents with tool use.

RAGent Chatbot is a smart AI assistant that combines Retrieval-Augmented Generation (RAG) with a tool-using LLM agent. It can answer user queries using uploaded documents, perform web searches, summarize text, solve math expressions, check weather, and more — all powered by LangChain, Qdrant, and Gemini.