# 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`)