Project Proposal: Virtual Me (vme)

Hackathon: All Things Agentic Hackathon (Google)

https://virtual-me-vme-workspace-609435322201.us-west1.run.app/

https://github.com/shengliangsong-ai/vme-workspace

1. Elevator Pitch

Virtual Me (vme) is a cloud-native, agentic developer workspace where an autonomous "digital twin" assists with daily engineering tasks, issue management, and workflow automation. Designed to be scalable and cost-effective, it deploys seamlessly to Cloud Run and leverages a suite of modern AI frameworks to deliver powerful, stateful AI orchestration.

2. The Problem

Developers suffer from constant context switching between task trackers, documentation, and terminal windows. While AI agents are becoming powerful productivity multipliers, they often lack long-term persistent memory and the ability to autonomously execute asynchronous background tasks. Developers need an intelligent digital twin that remembers their specific architectural choices and can handle long-running workflows without constant hand-holding.

3. The Solution (Virtual Me)

Virtual Me is a full-stack Single Page Application (SPA) backed by powerful Google Cloud agentic services:

• Persistent Agent Memory: Uses Firestore as a simple NoSQL datastore to manage the agent's long-term memory, ensuring that proprietary code, daily standups, and architectural decisions are retained across sessions.
• Agentic Task Queue: Developers can queue up complex, event-driven workflows that are processed by agents built with the Agent Development Kit (ADK) and the Antigravity SDK.
• Scale-to-Zero Infrastructure: Deployed on Cloud Run, the agentic backend scales out to handle compute-heavy background tasks and scales to zero when idle, making it highly cost-efficient.

4. Hackathon Alignment

We are primarily targeting The Collaborative Partner track, with strong underlying elements of The Taskmaster.

• The Collaborative Partner (Primary): Virtual Me is fundamentally designed as a digital twin with persistent memory. By storing daily standups, architectural blog posts, and skill maps in Firestore, the agent has deep, real-time context retrieval. It doesn't start from scratch; it adapts to the developer's specific tech stack via the Antigravity SDK, acting as a highly personalized pairing partner.
• The Taskmaster (Secondary): Through the job queue, the agent coordinates workflows autonomously using Genkit. A developer can queue a high-level task, and the agent watches the queue, executes multi-step operations using the Agent Development Kit (ADK), and reports the results back to the workspace.

5. Key Features

• Cost-Efficient Scaling: Deploys via Cloud Run with a single URL, automatically scaling to zero when not actively running background workflows.
• Long-Running Persistent Workflows: A background runner built on the ADK that executes long-running agentic workflows. It supports crash recovery, idempotency, and human-in-the-loop approval pauses before executing sensitive actions.
• Digital Twin Memory Bank: Integrated issue tracking, daily standups, and session reports stored in Firestore. Utilizes Vector Search and Managed Cloud Memory to semantically recall relevant past architectural decisions.
• Self-Evolving Intelligence (Personalized Fine-Tuning): Virtual Me analyzes your daily activity and execution logs to build personalized skills and memories. Through continuous self-evaluation, the context gets smarter and better over time—effectively fine-tuning the model to each individual developer.
• Open-Source Intelligence: Powered by Genkit to create structured, AI-powered app features and maintain high code quality.

6. Tech Stack

• Frontend: React, Vite, Tailwind CSS
• AI Models: Gemini API & Google AI Studio (for core reasoning, multimodal features, and quickstarts)
• Agent Orchestration: Agent Development Kit (ADK) (Python) for building and deploying the agents, alongside the Antigravity SDK for a pre-packaged runtime tightly integrated with Gemini.
• AI Framework: Genkit (open-source framework for building AI-powered apps).
• Database: Firestore (simple NoSQL datastore for agent state/memory and vector search).
• Deployment: Cloud Run (deploy the agent with a URL; scales to zero when idle).

7. Future Roadmap

• The Global Standard for "Smart Context": Virtual Me aims to become an integral architectural feature of top-tier AI models (Gemini, ChatGPT, Claude) and AI infrastructure companies. While models act as the "brain" with 1M or 2M context windows, utilizing them fully is highly expensive. Virtual Me dynamically routes only the necessary data into a much cheaper 200K "hot token" window per task. Furthermore, because 1 Million tokens equates to roughly 4MB of data, attaching just 10GB of storage grants Virtual Me 2.5 Billion tokens of persistent memory—transforming an expensive, limited window into virtually limitless, cost-effective smart context.
• Multi-Agent Orchestration Teams: Transition from a single agent to a multi-agent system utilizing ADK 2 orchestration patterns (e.g., Planner, Executor, and QA Reviewer sub-agents) to tackle complex enterprise epics.
• Real-time Output Streaming: Stream terminal output from agent-executed shell commands directly to the React frontend.

8. Tags

agentic-ai, developer-tools, productivity, digital-twin, workspace, automation, react, firestore, cloud-run, genkit, antigravity-sdk, adk, gemini-api, google-ai-studio, job-queue, task-management, autonomous-agents, workflow-automation, ai-assistant, full-stack, hackathon, developer-experience, knowledge-management

Built With

• agentic-ai
• ai-assistant
• automation
• autonomous-agents
• developer-experience
• developer-tools
• digital-twin
• enterprise-software
• express.js
• full-stack
• google-ai
• hackathon
• job-queue
• local-first
• node.js
• offline-first
• privacy-first
• productivity
• react
• sqlite
• tailwind-css
• task-management
• workflow-automation
• workspace