An AI-native design team for VS Code — one orchestrator routes your requests to specialist agents (UX, UI, research, content) that collaborate autonomously. Design Lead reviews every output and drives next steps until the project is complete. Memory persists decisions across sessions. Just talk to @Design Jarvis and it handles the rest.

AI productivity workspace with 27+ tools in one interface. Chat with documents (RAG), convert media formats, extract data, manage links/QR codes, and deploy autonomous agents for research or meeting prep across Gmail/Calendar/Slack/Drive. All through natural language. Open source student project.

AI-Powered Development — A documentation-structured workflow where agents drive software projects.

This Project is for HackSecure Hackathon. This is a n8n based Data Leak Prevention AI Agent Project. This scans files from Discord, Slack, GitHub and Drive and checks for Data Leaks.

This project is about obstacle detection for autonomous self driven AI cars. We used Python 3 to implement this project. It uses Python3, Pygame, Pymunk, Keras and Theanos. It employes a Q-learning (unsupervised) algorithm to learn how to move an AI agent around in the environment (drive itself) without running into obstacles.

🤖 Your one-stop shop for Agentic AI projects, tools, and workflows — because smart automation drives real-world impact!

This project uses AI agents, Playwright, and the Xperience by Kentico Virtual Inbox integration's MCP server to drive E2E automated tests for membership experiences.

NEXUS CLI is an intelligent project scaffolding tool that generates production-ready, AI-optimized project structures with complete documentation templates. It's the first development tool designed for the AI era—where documentation drives development and AI agents are first-class citizens in the development process.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. Agentic AI Drive Project leverages autonomous AI agents to manage digital files, automate workflows, and improve productivity through intelligent data organization and decision-making.

Demo project: AI agent interacting with Google Drive via MCP

AI agent to autonomously drive the web. A 24-hour hackathon project.

Sirúami is a project that combines AI agents with Arduino projects to drive new technology and applications.

AI agent to organize Google Drive, built as a project for the Google PM Certificate.

Whatsapp Google drive automated message projected powered by n8n, is used to automate the agentic AI tasks by seemlessly integrating the AI, Automation into a workflow without any code but through node management system.

MCP-enabled agent service using OpenAI’s Agents SDK. Provides a reusable HTTP API that mounts multiple MCP servers (filesystem, Google Drive, Blender, etc.) for cross-project AI integration.

A Python project implementing an autonomous AI-coding agent: orchestrates prompts, code generation, tests and workflow pipelines to drive development tasks end-to-end.

OpsAgent is an agentic AI system built to autonomously analyze, forecast, and respond to flight disruptions using real-world aviation data. Inspired by Southwest Airlines’ Integrated Operations, this project demonstrates how multi-agent AI workflows can drive smarter, faster decisions in airline operations.

Final Project for Deep Learning. AI Agents custom trained for image-based or drive-data-based competition in Tux Kart - an open-source Mario Kart.

# AI Agent: LogicLens (Frontend Logic & Aesthetic Architect) ### Submission for DeepThought Frontend Internship Role Test Drive ## 🚀 Project Overview **Use Case:** An AI Agent designed to help developers bridge the gap between Figma designs and functional Vanilla JavaScript logic.

Sovereign AI‑governance system and document controller for RPR COMMUNICATIONS, LLC, implementing the SENTINEL PROTOCOL for forensic project tracking, agent performance audits, and mirrored Google Drive/Notion records

his project implements an intelligent companionship ecosystem within a Mini-Program environment. It leverages cloud-hosted LLMs to drive specialized AI Agents capable of participating in multi-user dialogues.

Commando AI: AI-powered project management using Monte Carlo simulation to predict delivery dates with confidence intervals. Multi-agent AI optimizes resource allocation. Automates workflows across Slack, Discord, Google Drive, Notion. Features real-time meeting transcription and intelligent task automation.

The project focuses on developing an AI Agent that integrates with multiple platforms, including SharePoint, relational databases (e.g., PostgreSQL), and Google Drive. The agent will interpret user queries, determine the appropriate data source(s), securely retrieve relevant information, process it using an AI reasoning layer.

A reinforcement learning project where an AI agent learns to drive an underpowered car up a steep mountain by building momentum. Built with Double DQN algorithm using PyTorch, featuring experience replay and custom rewards for efficient training.

This project implements a Retrieval-Augmented Generation (RAG) pipeline in n8n . It connects a chat interface with company/contract documents stored in Google Drive, allowing an AI agent to answer user queries using both memory and retrieved knowledge.

This project evaluates agentic AI workflows for NVIDIA DRIVE OS to reduce the manual burden of triaging and fixing static analysis findings. We define metrics and a scoring rubric, curate historical test cases, and build an automated benchmark pipeline to compare workflows guiding a unified solution that improves compliance and safety.

AI-powered image editing and generation system built with n8n, featuring an intelligent Photoshop-style agent. The project supports image generation, editing, renaming, and combining workflows using Nano Banana models, integrated with Telegram, Google Drive, and cloud image hosting services.

This project implements a Retrieval-Augmented Generation (RAG) AI chatbot that can answer questions based on documents stored in Google Drive. The workflow automatically processes uploaded files, converts them into embeddings, stores them in a vector database (Qdrant), and enables an AI agent to retrieve relevant information and generate accurate .

This project implements a Retrieval-Augmented Generation (RAG) system using n8n. The system: Downloads company documents from Google Drive Converts them into embeddings Stores them in a Supabase Vector Store Uses an AI Agent to answer user queries based only on company documents

This project implements a Deep Q-Network (DQN) using PyTorch to solve the MountainCar-v0 environment from OpenAI Gym. The objective is to train an AI agent that learns to drive a car up a steep hill by maximizing cumulative rewards through reinforcement learning.