# 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
1. Implement foundational modular architecture with clear role-based access control
2. Establish MCP (Multi-Context Provider) integration framework
3. Deliver multi-modal interaction capabilities (CLI/Web/REST)
4. Create persistent memory system with SQLite backend
5. 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
1. Home Automation (Home Assistant API)
2. Calendar Services (Google Calendar API)
3. Infrastructure Management (Docker API)
4. External AI Services (OpenAI/Anthropic)
5. 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