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

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.

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

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.

A Customizable RAG (Retrieval Augmented Generation) App

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.

Local rag app example

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 .