Advanced AI Agent Management Platform for Enterprise Automation

Automatos AI is a powerful, enterprise-grade platform for creating, managing, and orchestrating AI agents across your organization. Built with modern technologies and designed for scalability, security, and performance.

• Multi-type AI Agents: Code architects, security experts, performance optimizers, data analysts
• Dynamic Agent Orchestration: Auto-scaling and load balancing
• Real-time Status Monitoring: Live agent health and performance metrics
• Bulk Operations: Create and manage multiple agents efficiently

• RAG (Retrieval Augmented Generation): Advanced document processing and retrieval
• Vector Embeddings: Semantic search and knowledge extraction
• Document Processing: PDF, DOCX, and text analysis
• Intelligent Chunking: Optimized content segmentation

• FastAPI Backend: High-performance async API
• PostgreSQL + pgvector: Vector database for AI operations
• Redis: High-speed caching and session management
• Docker: Containerized deployment
• Next.js Frontend: Modern, responsive web interface

• OpenAPI Documentation: Auto-generated API docs
• Type Safety: Full TypeScript/Python type coverage
• Database Migrations: Alembic-powered schema management
• Testing Framework: Comprehensive test suite
• Code Quality: Black, isort, pytest integration

Get Automatos AI running in 3 simple steps:

• Docker & Docker Compose (latest versions)
• Git
• Optional: OpenAI API key (get one here)

git clone https://github.com/AutomatosAI/automatos-ai.git cd automatos-ai cp .env.example .env # Optional: Edit .env and add your OPENAI_API_KEY

docker-compose up

⏳ First startup takes 2-3 minutes (building images, loading seed data)

• 🌐 Frontend: http://localhost:3000
• 📚 API Docs: http://localhost:8000/docs
• ❤️ Health Check: http://localhost:8000/health

That's it! 🎉 The platform is ready to use.

# Add monitoring (Prometheus + Grafana) docker-compose --profile monitoring up # Add everything (includes Adminer for database management) docker-compose --profile all up

See Quick Start Guide for detailed setup instructions and troubleshooting.

automatos-ai/ ├── orchestrator/ # Backend API & Services │ ├── src/ # Source code │ │ ├── api/ # FastAPI routes │ │ ├── database/ # Models & database │ │ └── services/ # Business logic │ ├── alembic/ # Database migrations │ ├── tests/ # Test suite │ └── main.py # Application entry point ├── frontend/ # Next.js web interface ├── docs/ # Documentation └── docker-compose.yml # Container orchestration 

• GET /health - System health check
• GET /api/agents - List all agents
• POST /api/agents - Create new agent
• GET /api/agents/{id}/status - Agent status
• POST /api/agents/{id}/execute - Execute agent

• GET /api/agents/types - Available agent types
• GET /api/agents/stats - System statistics
• POST /api/agents/bulk - Bulk operations

• GET /api/context/stats - RAG system metrics
• POST /api/documents - Upload documents
• GET /api/skills - Available skills

# Run all tests cd orchestrator python -m pytest # Run with coverage python -m pytest --cov=orchestrator # Async tests python -m pytest tests/test_agents.py -v

# Build optimized images docker compose -f docker-compose.prod.yml build # Deploy with environment config docker compose -f docker-compose.prod.yml up -d

# Database DATABASE_URL=postgresql://user:pass@localhost:5432/automatos_ai POSTGRES_DB=automatos_ai POSTGRES_USER=automatos_user POSTGRES_PASSWORD=your_secure_password # API Keys OPENAI_API_KEY=your_openai_key ANTHROPIC_API_KEY=your_anthropic_key # Security SECRET_KEY=your_jwt_secret API_KEY=your_internal_api_key

• Quick Start Guide: Get started in 5 minutes
• Developer Guide: Development setup and workflows
• Deployment Guide: Production deployment

• Agent System Guide: Agent creation, multi-model support, LLM-driven orchestration
• Workflow Orchestration Guide: 9-stage intelligent workflow pipeline
• Context Engineering Guide: RAG, token optimization, mathematical foundations
• Tools & Integration Guide: 400+ MCP integrations, credential management
• Memory & Knowledge Guide: Hierarchical memory, knowledge graphs, multimodal KB

• Agent Communication Guide: Inter-agent messaging and shared context
• Playbooks Guide: Automated pattern discovery and learning
• CodeGraph Guide: Code understanding and semantic analysis
• Credential System Guide: Secure credential management

• API Documentation: Interactive OpenAPI docs
• Comprehensive Guide: Complete platform overview
• Architecture: System design and components

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Run the test suite: python -m pytest
5. Commit your changes: git commit -m 'Add amazing feature'
6. Push to the branch: git push origin feature/amazing-feature
7. Open a Pull Request

Copyright (c) 2025 Automatos AI

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0 

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

For enterprise licensing, commercial support, and custom development:

• Website: https://automatos.ai
• Email: enterprise@automatos.ai
• Documentation: https://docs.automatos.ai

Built with ❤️ by the Automatos AI Team