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

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

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

Terminal-first local search and AI chat over your documents, code, and crawled websites. Semantic + hybrid search, vision OCR, auto-built wiki, browsable GGUF model catalog. Works as CLI, TUI, MCP server, REST API, or Python library. Offline by default, no sidecar services.

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

Chat with your Obsidian notes; entirely locally

🛡️ 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).

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

Obsidian-native search and AI chat over your vault — notes, PDFs, code, and 150+ formats. Hybrid search, vision OCR, auto-built wiki, browsable GGUF model catalog. Click any citation to verify the answer at the source. Runs offline with local LLMs, or against frontier API providers.

🤖 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

📧 Send personalized mass emails securely with Mailflow, a CLI tool built in Python using only standard libraries and no external dependencies.

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.

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

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

🧠 The Enterprise "Neural Architecture" for Agents. Build self-evolving, privacy-first reasoning engines with Gemini 2.5 & LangGraph. ⚡ Zero-Latency.

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 .

Local rag app example