## Phase 5 Strategic Decisions

### **Research Timeline & User Needs**
**Priority**: CLI over Python API - enables broader research community participation. Focus CLI on existing 8 approaches; they address core research questions adequately.

### **CLI Design Philosophy**
**Recommendation**: Researcher-focused with `ml-agents` CLI commands. Academic workflows prioritize reproducibility over general ML tooling.

```bash
ml-agents run --approach ChainOfThought --samples 50
ml-agents compare --approaches "ChainOfThought,AsPlanning" --config experiment.yaml
```

### **P3 Approaches Priority**
**Defer all P3 approaches** until post-CLI. Existing 8 approaches cover fundamental reasoning patterns. Implement P3 only if specific research questions emerge requiring them.

### **Documentation Scope**
**Target**: Academic researchers. Include research methodology guidance alongside technical usage. Focus on experimental design, result interpretation, and reproducibility practices.

### **Resource Allocation**
**Preference**: Complete CLI + basic docs (12h CLI + 3h docs). Polished researcher experience more valuable than comprehensive documentation initially.

### **Community & Open Source**
**Timeline**: Prepare for Q1 2025 release. Include contribution guidelines and Discord integration hooks. Design CLI for community extensibility (plugin architecture for new reasoning approaches).

**Phase 5 Budget Allocation**:
- CLI (12h): Full-featured with config files, progress display, result summaries
- Documentation (3h): Quick start + methodology guide + troubleshooting
- Polish (3-5h): Performance testing, error recovery, examples

## Additional Phase 5 Considerations

### **CLI Tech Stack: Rich + Typer**
Excellent choice. Rich provides beautiful terminal output, progress bars, and tables. Typer offers type-safe CLI with automatic help generation.

```python
import typer
from rich.console import Console
from rich.progress import Progress
from rich.table import Table

app = typer.Typer()
console = Console()
```

### **Key Technical Considerations**

**Configuration Hierarchy**: CLI args → config file → environment → defaults. Use Pydantic for validation.

**Progress Display**: Rich progress bars for experiment execution, parallel approach tracking, and cost monitoring.

**Result Visualization**: Rich tables for comparison summaries, colored success/failure indicators, cost breakdowns.

**Error Handling**: Rich-formatted error messages with actionable suggestions and troubleshooting links.

### **Integration Points**

**Logging Strategy**: Separate CLI logging (Rich console) from programmatic logging (file-based). Use different loggers.

**Config Validation**: Extend ExperimentConfig validation for CLI-specific requirements (file paths, output directories).

**Graceful Interruption**: Handle Ctrl+C cleanly, save partial results, display progress summary.

### **Distribution & Installation**

**Entry Point**: Add CLI entry point to pyproject.toml for `pip install` support.

**Dependencies**: Rich/Typer add ~2MB. Consider optional CLI dependencies for minimal installations.

**Cross-platform**: Test path handling, terminal width detection, color support across Windows/Mac/Linux.

### **Testing Strategy**

**CLI Testing**: Use Typer's TestClient, mock Rich output, test argument parsing and config loading.

**Snapshot Testing**: Capture CLI output for regression testing of help text and error messages.
