README.md

metadata

title: Aubm
sdk: docker
app_port: 7860
license: mit
short_description: Automated Business Machines

🤖 Aubm

Enterprise-Grade AI Agent Orchestration & Collaboration Platform

Aubm (Automated Unified Business Machines) is a sophisticated platform designed to orchestrate multiple autonomous AI agents to complete complex projects. Featuring Human-in-the-Loop supervision, Dynamic DAG task execution, and Semantic RAG context injection.

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🚀 Key Features

• Multi-Provider Support: Seamless integration with OpenAI, AMD (inference.do-ai.run), Groq, Gemini, Qwen, Ollama, and OpenRouter.
• Autonomous Orchestration: Intelligent task prioritization and execution based on dependencies (DAG).
• Human-in-the-Loop: Approval-based workflow ensuring quality and safety.
• Semantic Backpropagation: Context from completed tasks is automatically injected into subsequent tasks.
• Real-time Monitoring: SSE-powered live logs and progress tracking.
• Project Wizard: AI-driven project creation and task decomposition.
• Operational Safety: Automatic recovery of stale runs and comprehensive health monitoring.

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🛠️ Tech Stack

• Frontend: React + Vite + TypeScript (Styled with Vanilla CSS for maximum performance)
• Backend: FastAPI (Python 3.10+)
• Database: Supabase (Postgres + Auth + Real-time)
• Deployment: Optimized for Vercel (Serverless Backend + Static Frontend)

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🏗️ Project Structure

aubm/
├── backend/            # FastAPI Application & AI Core
│   ├── agents/         # LLM Provider Implementations
│   ├── routers/        # API Endpoints (Runner, Orchestrator)
│   ├── services/       # Business Logic (Queue, RAG, Guards)
│   └── main.py         # App Entrypoint
├── frontend/           # React Application
│   ├── src/            # Components, Hooks, Context, Services
│   └── vite.config.ts  # Vite Configuration
└── database/           # Supabase Schema & Migrations

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⚙️ Getting Started

1. Database Setup (Supabase)

1. Create a new project in Supabase.
2. Go to the SQL Editor and execute the content of backend/schema.sql.
3. Enable Auth with your preferred providers (Email/Password by default).

2. Backend Installation

cd backend
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

Create a .env file in /backend:

SUPABASE_URL=your_project_url
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key
OPENAI_API_KEY=optional_key
GROQ_API_KEY=optional_key
TAVILY_API_KEY=optional_key
# See SPEC.md for all available providers

Run the server:

uvicorn main:app --reload --port 8000

3. Frontend Installation

cd frontend
npm install

Create a .env file in /frontend:

VITE_API_URL=http://localhost:8000
VITE_SUPABASE_URL=your_project_url
VITE_SUPABASE_ANON_KEY=your_anon_key

Run the development server:

npm run dev

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📈 Operational Modes

• Embedded Worker: Runs the task queue within the FastAPI process (set TASK_QUEUE_EMBEDDED_WORKER=true).
• Standalone Worker: For high-load environments, run the worker in a separate process:

  cd backend
  python worker.py
  

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Hugging Face Spaces

This repository is ready to deploy as a Docker Space. Create a Hugging Face Space with SDK Docker, then push this repo to the Space remote.

Configure these Space secrets or variables:

SUPABASE_URL=your_project_url
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key
SUPABASE_ANON_KEY=your_anon_key
GROQ_API_KEY=optional_key
OPENAI_API_KEY=optional_key
GEMINI_API_KEY=optional_key
AMD_API_KEY=optional_key
TAVILY_API_KEY=optional_key
TASK_QUEUE_EMBEDDED_WORKER=true

VITE_API_URL can stay empty on Spaces because the frontend calls the FastAPI backend on the same origin.

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📄 Documentation

For detailed technical architecture, refer to:

• SPEC.md - Deep technical specifications.
• ROADMAP.md - Future development goals.
• docs/ - Extended guides and manuals.