automaton/config.md

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)