# Framework Configuration

This file contains global framework settings that apply across all projects.

## VRAM Configuration

Settings for task decomposition based on available VRAM.

- **Auto-detect**: Yes  # Detect GPU VRAM, RAM, and model context window automatically
- **Target context**: 16k tokens  # Override auto-detect if needed
- **Headroom**: 25%  # Leave headroom for code, context, and reasoning
- **Max peak context per sub-task**: 12k tokens  # Max context for any single sub-task

### Auto-detection

When `Auto-detect: Yes`, the framework probes your system to detect:
- GPU VRAM (via `nvidia-smi` or `lspci`)
- System RAM (via `free`)
- Model context window (via API config or model name lookup)
- Framework overhead (by reading all loaded prompt files)

To disable auto-detection and use manual values:

```
## VRAM Configuration
- **Auto-detect**: No
- **Target context**: 8k
- **Headroom**: 30%
- **Max peak context per sub-task**: 5.6k
```

## Model Configuration

Settings for the LLM model being used.

- **Model**: auto  # Use auto-detection from API config, or specify explicitly (e.g., gpt-4o, claude-3-5-sonnet)
- **Override context window**: auto  # Override auto-detection, or specify (e.g., 128k, 200k)

### Auto-detection

When `Model: auto`, the framework detects the model name from:
1. `.agent.md` in the project (if specified there)
2. API config files (`.env`, `config.yaml`, `config.json`, etc.)
3. Model name lookup by name (e.g., gpt-4o → 128k, claude-3-5-sonnet → 200k)

To disable auto-detection and use manual values:

```
## Model Configuration
- **Model**: gpt-4o
- **Override context window**: 128k
```

## System Requirements

Requirements for the environment the framework runs in.

- **nvidia-smi**: Required if NVIDIA GPU (for VRAM detection)
- **lspci**: Fallback if NVIDIA GPU not available (for AMD GPU VRAM detection)
- **/proc/meminfo**: Required for RAM detection (Linux)
- **sysctl**: Fallback for RAM detection (macOS)