Virtual AI Manager (VAM)

🌟 Vision

The Virtual AI Manager (VAM) is an autonomous managerial system designed to operate continuously alongside human teams. Unlike request-response chatbots, VAM proactively plans, monitors, and executes management tasks—scheduling meetings, approving leave, tracking deadlines, forecasting risks, and reporting progress—while keeping humans in the loop via a dedicated control plane.

🏗 System Architecture

VAM operates on a multi-agent architecture using LangGraph for orchestration and MCP (Model Context Protocol) for tool integration.

graph TD
    User[Control Plane UI] <--> API[FastAPI Backend]
    API <--> Orchestrator[Manager Orchestrator]
    
    subgraph "Agent Core (8 Agents)"
        Orchestrator --> Planning[Planning Agent]
        Orchestrator --> People[People Ops Agent]
        Orchestrator --> Growth[Growth & Scaling Agent]
        Orchestrator --> Exec[Execution Agent]
        Orchestrator --> Comm[Communication Agent]
        Orchestrator --> Managerial[Managerial Agent]
        Orchestrator --> Analytics[Analytics Agent]
        Orchestrator --> Platform[Platform Agent]
    end
    
    subgraph "Memory & Tools"
        Planning --> VectorDB[(Vector Memory)]
        People --> Cal[Calendar MCP]
        Growth --> KB[Knowledge Base]
        Exec --> Mon[Task Monitors]
        Comm --> Slack[Slack/Email MCP]
        Analytics --> Forecast[Risk & Velocity]
        Platform --> RBAC[Security & Audit]
    end

✨ Key Features (Phases 1-6 Complete)

Phase 1-2: Core Foundation & Communication

• 🧠 Autonomous Planning: Decomposes goals into actionable DAGs with dependency tracking
• 📋 Task & Project Management: Full lifecycle management with milestones and goals
• 🐙 GitHub Integration: Bi-directional sync with GitHub Issues, OAuth login, and webhook automation
• 📅 Google Calendar: Real-time calendar sync, free slot detection, focus block scheduling
• 💬 Slack Integration: Socket Mode bot, DM handling, proactive standups
• ☀️ Morning Standups: Automated 09:00 check-ins with GitHub issue context
• 👁️ Active Monitoring: Proactive risk detection and deadline tracking
• 📊 Managerial Intelligence: Strategic risk analysis, goal refinement, automated reporting

Phase 3: Human-Centric Intelligence

• 🤝 People Operations: Leave management, burnout detection, skill matrices
• 📅 Calendar Integration: Working hours, time zones, meeting scheduling
• ⚖️ Capacity Planning: get_available_hours(), check_overload() functions
• 🔥 Burnout Watchdog: Sustained overload and deadline pressure monitoring

Phase 4: Cognitive Persistence (New)

• 🧠 Long-Term Memory: Vector embeddings (OpenAI text-embedding-3-small) to recall past decisions
• 📚 Context Injection: Semantic search retrieves relevant history into agent prompts
• 📌 Semantic Search: pgvector integration for finding related tasks, plans, and meetings
• 🔄 Auto-Memory triggers: Daily standup focus and task completions are automatically memorized

Phase 5: Growth Intelligence

• 📈 Hiring Pipeline: Candidate tracking with automated resume scoring
• ✅ Interview Management: Scheduling, feedback collection, offer workflows
• 🎯 Onboarding: 30-60-90 day plans with auto-generated tasks
• 📚 Knowledge Base: Internal documentation with role-based curation

Phase 6: Analytics & Automation

• 📉 Velocity Tracking: Task completion trends and projected dates
• ⚠️ Risk Scoring: Weighted algorithm (overdue × 5, blocked × 3, load × 10)
• 📊 Executive Dashboards: Goal + project + risk summaries
• 🤖 Automation Rules: IFTTT triggers for proactive interventions
• 📸 Project Snapshots: Historical metrics for trend analysis
• 🔮 AI Forecasting: Completion predictions with confidence scores

Phase 7: Safety & Governance (Implemented)

• 🛡️ Risk Gate: Intercepts high-risk actions (score > 50) for human approval
• ✋ Intervention UI: Modal to review, approve, or reject agent actions
• 🔐 RBAC: Role-based access (Admin, Manager, Contributor, Viewer)
• 📝 Immutable Audit Logs: Automatic @log_activity capture for all sensitive ops
• ✅ Approval Workflow: Async approval requests with risk scoring (Critical/High/Medium)
• 🔌 MCP Tool Safety: Circuit breakers and permissions for tool execution

🚀 Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• Git
• GitHub OAuth App (Client ID & Secret)
• Google OAuth App (for Calendar - optional)
• Slack App (for Slack integration - optional)

Installation

1. Clone the repository

   git clone https://github.com/agusain2001/Virtual-manager.git
   cd Virtual-manager
   

2. Backend Setup

   cd backend
   python -m venv venv
   # Windows
   ./venv/Scripts/Activate.ps1
   # Linux/Mac
   # source venv/bin/activate
   
   pip install -r requirements.txt
   
   # Configure Environment
   cp .env.example .env
   # Edit .env with your GitHub Client ID/Secret and DB settings
   

3. Frontend Setup

   cd ../frontend
   npm install
   

Running the System

1. Start the Brain (Backend)

cd backend
uvicorn backend.app.main:app --reload

API runs on: http://localhost:8000

2. Start the Control Plane (Frontend)

cd frontend
npm run dev

Dashboard runs on: http://localhost:3000

📂 Project Structure

Virtual-manager/
├── backend/                     # Python/FastAPI Agent Core
│   ├── app/
│   │   ├── agents/              # 8 Specialized Agents
│   │   │   ├── orchestrator.py      # Manager Orchestrator (LangGraph)
│   │   │   ├── planning.py          # Task decomposition & DAGs
│   │   │   ├── execution.py         # Monitoring & escalation
│   │   │   ├── people_ops.py        # HR & capacity (1400+ lines)
│   │   │   ├── growth_scaling.py    # Hiring & onboarding (800+ lines)
│   │   │   ├── analytics_automation.py  # Forecasting & insights (720+ lines)
│   │   │   ├── platform_enterprise.py   # Security & RBAC (1000+ lines)
│   │   │   └── advanced_capabilities.py # Rules, plugins, voice
│   │   ├── core/                # Core Logic Modules
│   │   │   ├── availability.py      # Capacity calculations
│   │   │   ├── analytics.py         # Velocity & risk scoring
│   │   │   ├── growth_logic.py      # Candidate scoring
│   │   │   ├── security.py          # RBAC middleware
│   │   │   └── scheduler.py         # Cron jobs
│   │   ├── services/            # Service Layer
│   │   │   ├── people_service.py    # Leave & calendar CRUD
│   │   │   ├── growth_service.py    # Applications & onboarding
│   │   │   ├── analytics_service.py # Dashboard & rules
│   │   │   ├── platform_service.py  # Tenants & audit export
│   │   │   ├── github_service.py    # GitHub API & Sync
│   │   │   ├── google_calendar_service.py  # Google Calendar API
│   │   │   └── slack_service.py     # Slack Socket Mode
│   │   ├── routers/             # API Routers (auth, webhooks, etc)
│   │   ├── mcp/                 # MCP Tool Servers
│   │   ├── models.py            # 30+ SQLAlchemy models
│   │   └── main.py              # FastAPI entry point
│   └── requirements.txt
│
├── frontend/                    # Next.js Control Plane
│   ├── src/
│   │   ├── app/                 # Pages & Layouts
│   │   └── components/          # 20+ UI Components
│   └── package.json
│
├── AGENTS.md                    # Detailed Agent Documentation
└── README.md                    # Project Overview

📊 API Endpoints (100+)

🤝 Contributing

We welcome contributions to expand agent capabilities!

1. Fork the repo.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes.
4. Push to the branch.
5. Open a Pull Request.

📄 License

Distributed under the MIT License.