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 /proc/meminfo or sysctl)
• 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: omlx/Ornith-1.0-35B-4bit-mlx # Local LLM (opencode provider); used as the Implement role
• Override context window: 32768 # Matches opencode.json limit.context for ornith

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

Available Models

Models available for model-divergence enforcement. This file is managed by scripts/detect_models.py. In single-LLM mode (0-1 models), no hard blocks are enforced. In multi-LLM mode (2+ models), the conflict matrix enforces role-model separation.

• Default: omlx/Ornith-1.0-35B-4bit-mlx # Used when no role-specific binding is set
• Advised: true # Recommend a second model in single-LLM mode

No additional models are configured in the manifest. To add models:

1. Run python3 ~/.automaton/scripts/detect_models.py --write to auto-detect from opencode.json and localhost endpoints.
2. Or manually create ~/.automaton/models.json (see design/framework/technical.md §2 for schema).

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)

Loop Role Models

Loop ticks run three session roles. Roles are sessions, not models — a single model can fill multiple roles. Configure each loop's role-to-prompt binding in its loop.json; this section documents the framework's expectations only.

• Implement: — produces the artifact for this tick. Bound to prompts/loop-implement.md by default.
• Verify: — grades the artifact and emits the JSON verdict {pass, score, reasons, next_hint}. Bound to prompts/loop-verifier.md. The framework never inspects this role's model (D8); only its session.
• Orchestrate: — applies the verdict, calls exactly one status.py operation per tick, enforces brakes. Bound to prompts/loop-orchestrate.md.

Conflict-of-interest rule (D12): Verify: and Implement: must never be the same session. When two distinct sessions are infeasible (single-session harness), the runner falls back to session-only divergence — still safe.

Role context tiers are set per-loop in loop.json, not globally. The 16k floor (D13) applies regardless of tier.

Framework Version

• Version: 2.0
• State enforcement: enabled (.state file + status.py)