# OpenClaw - Functional Specification

**Version**: v1.0.0 | **Status**: Active | **Last Updated**: February 2026

## Purpose

The `openclaw` module integrates the OpenClaw autonomous AI agent framework into Codomyrmex's agent subsystem. OpenClaw runs as a personal AI assistant on local devices, supporting 15+ messaging channels via a Gateway/WebSocket architecture.

## Design Principles

### Modularity

- **CLI Pattern**: Extends `CLIAgentBase` following the established CLI agent pattern.
- **Config Driven**: All behavior configurable via `openclaw_command`, `openclaw_timeout`, `openclaw_working_dir`, `openclaw_thinking_level`.

### Functionality

- **Agent Invocation**: `openclaw agent --message "prompt"` with optional `--thinking` levels.
- **Multi-Channel Messaging**: `openclaw message send --to <target> --message "text"`.
- **Gateway Management**: Start, stop, and status of the WebSocket control plane.
- **Health Diagnostics**: `openclaw doctor` for config and connectivity checks.

## Functional Requirements

### Core Capabilities

1. **Execute**: Run agent queries via `_execute_impl`.
2. **Stream**: Stream agent output via `_stream_impl`.
3. **Message Send**: Route messages to channels via `send_message`.
4. **Gateway**: Manage control plane via `start_gateway` / `get_gateway_status`.
5. **Doctor**: Health check via `run_doctor`.
6. 
## Interface Contracts

### Public API

- `OpenClawClient(config: dict)`: Constructor.
- `OpenClawClient.execute(request: AgentRequest) -> AgentResponse`: Execute agent query.
- `OpenClawClient.stream(request: AgentRequest) -> Iterator[str]`: Stream output.
- `OpenClawIntegrationAdapter(agent)`: Integration adapter constructor.

### Dependencies

- `codomyrmex.agents.core`
- `codomyrmex.agents.generic`

## Navigation

- **Human Documentation**: [README.md](README.md)
- **Technical Documentation**: [AGENTS.md](AGENTS.md)
- **Parent**: [../SPEC.md](../SPEC.md)

## Detailed Architecture and Implementation

### Design Principles

1. **Strict Modularity**: Each component is isolated and communicates via well-defined APIs.
2. **Performance Optimization**: Implementation leverages lazy loading and intelligent caching to minimize resource overhead.
3. **Error Resilience**: Robust exception handling ensures system stability even under unexpected conditions.
4. **Extensibility**: The architecture is designed to accommodate future enhancements without breaking existing contracts.

### Technical Implementation

The codebase utilizes modern Python features (version 3.10+) to provide a clean, type-safe API. Interaction patterns are documented in the corresponding `AGENTS.md` and `SPEC.md` files, ensuring that both human developers and automated agents can effectively utilize these capabilities.
