2.9 KiB
2.9 KiB
AI Agent Project Specification
Executive Summary
Develop a modular, multi-modal AI agent system capable of handling personal assistance, home automation, DevOps tasks, and intelligent information retrieval through an extensible plugin architecture.
Core Objectives
- Implement foundational modular architecture with clear role-based access control
- Establish MCP (Multi-Context Provider) integration framework
- Deliver multi-modal interaction capabilities (CLI/Web/REST)
- Create persistent memory system with SQLite backend
- Enable proactive task execution capabilities
Functional Requirements
Core System
- Dynamic role management (roles.d)
- Tool/module registry (tools.d)
- MCP runtime integration (mcps.d)
- Configuration management (conf.d)
Interfaces
- CLI interface with Typer integration
- FastAPI-based web interface
- REST API with OpenAPI documentation
- WebSocket support for real-time updates
Operational Requirements
- Systemd service integration
- Structured logging with rotation
- Health monitoring endpoints
- Automated testing framework
Non-Functional Requirements
Performance
- <500ms response time for local commands
- <2s response time for cloud-integrated tasks
- Support 100 concurrent API connections
Security
- Role-based access control
- Secrets encryption at rest
- Audit logging of privileged operations
Scalability
- SQLite → PostgreSQL migration path
- Horizontal scaling support for MCPs
- Load-balanced API endpoints
Technology Stack
| Component | Technology Choices |
|---|---|
| Core Language | Python 3.11+ |
| Web Framework | FastAPI + Uvicorn |
| CLI Framework | Typer |
| Database | SQLite (initial), PostgreSQL (future) |
| Task Queue | Celery + Redis |
| NLP Integration | LangChain + Local LLMs |
| Monitoring | Prometheus + Grafana |
Integration Points
- Home Automation (Home Assistant API)
- Calendar Services (Google Calendar API)
- Infrastructure Management (Docker API)
- External AI Services (OpenAI/Anthropic)
- MCP Service Discovery Protocol
Success Criteria
- Demonstrate core assistant capabilities within local environment
- Show MCP integration with 3 sample providers
- Achieve 90% test coverage on core modules
- Document full API surface with examples
Constraints
- Initial deployment targets Linux systems
- Must maintain compatibility with Python 3.11+
- All external integrations must support offline operation
- Core system memory footprint <512MB RAM
Assumptions
- Primary users are technical operators
- Initial deployment environment has Python 3.11+ installed
- Networking connectivity available for cloud integrations