Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.

An agent based LLM assistant that extends RAG with batch entity extraction and SQL querying to improve performance on multi-step and analytical questions.

A structured prompt framework for building software products with AI coding assistants. This toolkit guides you through product specification, technical design, and implementation planning—producing documents that AI agents can execute against.

Campus Docs Assistant – Built to solve a common challenge in universities, this AI-powered chatbot uses LLMs, intelligent agents and RAG to make academic documents and institutional information easier to access and understand.

This project leverages autogen multi agent framework along with Azure OpenAI Assistants API to automate data analysis and report generation. It uses multiple agents to process data, generate insights, and compile a final report document

A collection of intelligent AI agents built using Ollama, LangChain, and local LLMs — including chatbots, voice assistants, web scrapers, document readers, and custom tools. No APIs required. Fully offline.

A multi agent healthcare assistant system implemented using Python and Langgraph. The agents include icd10 code extractor, SOAP document generator and medical image report generator. This project uses one of the latest LLMs, MedGemma model.

The AI Assistant uses OpenAI's GPT models and Langchain for agent management and memory handling. With a Streamlit interface, it offers interactive responses and supports efficient document search with FAISS. Users can upload and search pdf, docx, and txt files, making it a versatile tool for answering questions and retrieving content.

A curated collection of modular n8n workflows built for real-world automation, featuring AI-powered agents, social media analyzers, resume screeners, lead generation systems, AI Personal Assistant , and others. Each workflow is documented for clarity, scalability, and community learning.

Build a useful assistant agent that answers question from a provided database, a provided document and bash commands.

🤖 Agentic RAG - An intelligent document assistant with semantic chunking, hybrid search (vector + BM25), multi-format support (PDF, Word, CSV, Excel), and multi-channel bots (Telegram, WhatsApp). Built with FastAPI, React, PostgreSQL/pgvector, and LangChain.

A virtual assistant is an independent contractor who provides administrative services to clients while operating outside of the client's office. A virtual assistant typically operates from a home office but can access the necessary planning documents, such as shared calendars, remotely. People employed as virtual assistants often have several years of experience as an administrative assistant or office manager. New opportunities are opening up for virtual assistants who are skilled in social media, content management, blog post writing, graphic design, and Internet marketing. As working from home has become more accepted for both workers and employers, the demand for skilled virtual assistants is expected to grow. In today’s era almost all tasks are digitalized. We have Smartphone in hands and it is nothing less than having world at your fingertips. These days we aren’t even using fingers. We just speak of the task and it is done. There exist systems where we can say Text Friend, “I will Call you Letter.” And the text is sent or any one may make call by saying “Call To Mr. Roy” That is the task of a Virtual Assistant. It also supports specialized task such as booking a flight, or finding cheapest book online from various e-commerce sites and then providing an interface to book an order are helping automate search, discovery and online order operations. An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term "chatbot" is sometimes used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal (spoken?) commands. A similar concept, however with differences, lays under the dialogue systems. The project aims to develop a personal-assistant for Linux-based / Windows systems using Python, Jupitar Notebook, PyCharm. Voice Assistant using Python draws its inspiration from virtual assistants like Cortana for Windows, and Siri for iOS. It has been designed to provide a user-friendly interface for carrying out a variety of tasks by employing certain well-defined commands. Users can interact with the assistant either through voice commands or using sound or Voice input input. As a personal assistant, this system assists the end-user with day-to-day activities like general human conversation, searching queries in google, Bing or yahoo, searching for videos, retrieving images, live weather conditions, word meanings, searching for medicine details, health recommendations based on symptoms and reminding the user about the scheduled events and tasks. The user statements/commands are analyzed with the help of machine learning to give an optimal solution.

Cyber Vision AI is an award-winning, open-source AI assistant for cybersecurity, document analysis, and knowledge management. Built with advanced RAG, MindMap, and multi-agent AI, it empowers security professionals and researchers with unrestricted, ethical, and insightful tools.

Personal, private & local AI assistant — 9 specialized agents (code, web, debug, security…), 1M token vector memory, RLHF feedback, document processing (PDF/DOCX/Excel), real-time web search and modern GUI. 100% local with Ollama. Your data never leaves your machine.

A collection of intelligent AI agents built using Ollama, LangChain, and local LLMs, including chatbots, voice assistants, web scrapers, document readers, and specialized tools. No API required. Fully autonomous and privacy-preserving.

An AI-powered personal assistant that manages emails, schedules, to-do lists, Slack messages, web research, and document queries—all through your preferred messaging apps (Telegram, Slack, WhatsApp). Built with a hierarchical multi-agent system, it automates workflow, enhances productivity, and provides seamless AI-driven task management.

AgenticRAG-ResearchAssistant is an advanced research assistant that uses an agentic RAG approach to answer research queries. It integrates a large language model with external tools for document retrieval, summarization, comparison, and citation analysis in a conversational interface.

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.

ReAct Agent built upon LangChain and LangGraph to make custom and complex multi-step questions by combining retrieval and analyitical tools. Do SQL querying and/or RAG approaches to get the best answer

Agentic AI research assistant that autonomously processes domain-specific documents and generates structured insights through multi-agent collaboration.

This directory configures how issues, instructions, and AI assistants (Copilot/agents) behave in the open-verilog-workbench repository. The repo is a shared workbench for documenting, testing, and fixing Verilog/SystemVerilog designs via GitHub Issues.

🤖 Enhance your research efficiency with an AI-powered assistant that analyzes documents and provides insights through a smart multi-agent system.

🧠 Personal POC demonstrating agentic workflows with LangGraph and real-time legal research via Tavily. ⚖️📚 An autonomous assistant that helps users find applicable laws 📜, case precedents 🏛️, and document templates ✍️ based on jurisdiction 🌍, legal topic 📂, and specific requirements ✅.

A simple yet powerful local AI Agent assistant that runs entirely on your machine. Option for remote LLM. Built for learning and deploy, Little MCP combines the power of open-source LLMs with advanced RAG that work with your personal documents. Included tools: real time weather, local documents RAG, local SQL database. Easy deploy your own agent.

AI assistant backend for document-based question answering using RAG (LangChain, OpenAI, FastAPI, ChromaDB). Features modular architecture, multi-tool agents, conversational memory, semantic search, PDF/Docx/Markdown processing, and production-ready deployment with Docker.

Voice Assistant with RAG, LiveKit, and OpenAI Chat or talk with your own documents using Retrieval-Augmented Generation (RAG), Pinecone, OpenAI, and LiveKit. Includes a Streamlit chat UI and a real-time voice agent for LiveKit rooms.

This project focuses on developing a real-time, intelligent healthcare assistant powered by Agentic RAG (Retrieval-Augmented Generation) and the LangChain framework. Designed to provide multimodal support—text, image, and document-based inputs—it aims to assist medical personnel and individuals in remote areas.

A multi-agent AI legal assistant for Malaysian Civil Cases, developed as a Final Year Project (FYP). Features intelligent document analysis, legal research, summarization, and prediction using advanced LLMs, vector search, and a Gradio web interface. Supports PDF/image uploads, semantic memory, and comprehensive evaluation with LLM-as-a-Judge.

superior AI agent breaking down the "Intelligent Document Summarizer and Research Assistant" project

An advanced multi-agent research assistant and RAG platform built with LangGraph, Next.js, and Ollama, capable of deep web analysis and intelligent document interaction.