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

Multi Generative AI agents for customer support email automation built with Golang, Google-GenAi and Customgraph solution

A curated collection of LLM-powered Flutter apps built using RAG, AI Agents, Multi-Agent Systems, MCP, and Voice Agents.

Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python

Chat with your Obsidian notes; entirely locally

RAG-API: A production-ready Retrieval Augmented Generation API leveraging LLMs, vector databases, and hybrid search for accurate, context-aware responses with citation support.

🛡️ Web3 Guardian is a comprehensive security suite for Web3 that combines browser extension and backend services to provide real-time transaction analysis, smart contract auditing, and risk assessment for decentralized applications (dApps).

📄 Transform your PDF documents into actionable insights with this RAG-based Question-Answering App for efficient and accurate responses.

🤖 NoCapGenAI is a Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, Ollama, MongoDB, and ChromaDB. It features a clean, modern UI and persistent vector memory for context-aware conversations. Easily integrates with Ollama-supported models like phi3:mini, llama3, mistral, and more. Designed to support customizable assistant modes

📧 Streamline your inbox with Email Agent AI; it automatically sorts, classifies, and archives emails across multiple accounts for effortless management.

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 workflow assistant is a fast and easy way to convert natural-language user requests into valid workflow configuration snippets by using retrieval (from existing real configs) + an LLM prompt.

RAG-powered PDF QA system with self-reflection and multiple retrieval strategies (Stuff/Map Reduce/Refine). Includes monitoring via Langfuse & LangSmith and containerization with Docker

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.

A comprehensive Retrieval Augmented Generation (RAG) application built with Next.js, featuring document processing, website scraping, and AI-powered chat functionality.

Agentic RAG chatbot for exploring the Quran & Hadith — local FAISS retrieval, multi-query expansion, cross-encoder re-ranking, FLAN-T5 generation, live verse enrichment (Arabic + audio), and web-validated Hadith authenticity. Built with Streamlit.

pdfKotha.AI - Interact with PDFs using AI! Upload, ask questions, and get instant answers from Google's Gemini model. Streamline your research and information retrieval tasks effortlessly

BetterRAG: Powerful RAG evaluation toolkit for LLMs. Measure, analyze, and optimize how your AI processes text chunks with precision metrics. Perfect for RAG systems, document processing, and embedding quality assessment.

A Customizable RAG (Retrieval Augmented Generation) App

A basic RAG application for inventory management. Provides real-time stock updates, checks availability, suggests similar products, and generates responses to both customer and manager queries .