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OpenCode model integration

dmn-wanderer never talks to a vendor API. It talks to the opencode CLI and therefore uses the model currently selected in the user's OpenCode /models setting. This keeps the research reproducible for the user while requiring no API key and no model-name coupling in this repository.

How it works

OpenCodeLanguageModel (in app/llm.py) invokes:

opencode run --format json "<user prompt>"

with --no-spinner where supported, reading NDJSON on stdout. It never passes -m/--model, so the CLI's active model selection applies. The system prompt is provided inline.

NDJSON shape parsed

Emitting lines like:

{"type":"text","part":{"type":"text","text":"...","tokens":{...}}}

The parser:

  • collects every type == "text" event's part.text;
  • reads token accounting from the step_finish part when present (text tokens + tool tokens), using it only for cost/latency bookkeeping — parse failures there fall back to None and never crash the pipeline;
  • treats the joined text as the model response through the same plain-text parser as the mock, so the offline and live paths are identical.

Malformed or unexpected output is handled by app/parsing.py (JSON and free-form fallbacks), and the provider returns an explicit error message instead of fabricating a result.

Commands

# pick a model in OpenCode first, then:
MODEL_PROVIDER=opencode python3 -m app.cli ask "What should we invest in?"
MODEL_PROVIDER=opencode python3 -m app.cli wander --strategy serendipitous
MODEL_PROVIDER=opencode python3 experiments/run_benchmark.py --provider opencode

Caveats (mirrored in limitations.md)

  • Portability: results depend on the user's /models selection; the app cannot and will not pin a model.
  • CLI-shape sensitivity: mitigations documented; fails safe when parsing cannot recover.
  • Latency: pipe overhead per call.
  • Credentials: none — relies on the user's existing OpenCode configuration.