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openclaw - 💡(How to fix) Fix Proposal: per-model / per-lane behavior sheets [1 comments, 1 participants]

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openclaw/openclaw#43692•Fetched 2026-04-08 00:17:06
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Root Cause

I also caught a real coverage hole because the warning path made it visible: a strategic model had an exact-model file but no family spec.

RAW_BUFFERClick to expand / collapse

I built a local MVP for per-model / per-lane behavior sheets and it seems promising enough to ask whether this should stay workspace-local or become a first-class OpenClaw concept.

The problem

When model lineups change, behavior drifts in ways that are hard to manage with one giant universal prompt.

The same agent can suddenly become:

  • too chatty
  • too cautious
  • not research-first when it should be
  • socially wrong for the lane (discord sounding like an ops daemon, etc.)

In practice, teams often end up with either:

  1. one huge prompt blob
  2. operational folklore ("this model is good for X, bad for Y, remember to tell it Z")

Neither scales very well.

The idea

Resolve behavior in layers:

  1. family
  2. exact model
  3. lane

Then emit the resolved active behavior sheet as an artifact the runtime can read and test.

Example split

  • Family: GPT-5 models tend to benefit from explicit stop criteria / bounded autonomy rules
  • Exact model: one specific model might need tighter verbosity control or a warning about a known weakness
  • Lane: research, coding, discord, cron, main all want different behavior even for the same model

What the local MVP includes

  • family / model / lane registry
  • resolver + renderer
  • active-spec artifact (active-model-spec.json / .md)
  • startup refresh hook
  • maintenance report
  • missing-spec warnings
  • lightweight regression harness

What improved

The biggest gain was behavior quality, not raw speed.

Local tests showed:

  • research became more evidence-first
  • coding became more concrete and less theory-heavy
  • discord became more concise/playful and less ops-drone

I also caught a real coverage hole because the warning path made it visible: a strategic model had an exact-model file but no family spec.

Why I think this might matter

This seems like a useful way to separate:

  • family traits
  • exact-model quirks
  • lane expectations
  • drift detection

instead of stuffing everything into one universal prompt and hoping for the best.

Current limitations

This is still a local MVP, not deep runtime integration.

Current weak spots:

  • startup integration is still script/workspace driven
  • lane detection is still heuristic
  • regression execution/capture could be more native
  • evaluation is lightweight, not benchmark-grade

Question

Does this feel like:

  • a good candidate for a first-class OpenClaw concept, or
  • the kind of thing that should stay as workspace-local tooling/patterns?

If useful, I can clean up the writeup and share the implementation shape / artifacts too.

extent analysis

Fix Plan

To implement per-model / per-lane behavior sheets, follow these steps:

  • Create a registry for family, model, and lane specifications
  • Develop a resolver to determine the active behavior sheet based on the registry
  • Implement a renderer to generate the active-spec artifact (active-model-spec.json / .md)
  • Integrate the resolver and renderer with the startup refresh hook
  • Add maintenance reports, missing-spec warnings, and a lightweight regression harness

Example Code

# registry.py
class Registry:
    def __init__(self):
        self.family_specs = {}
        self.model_specs = {}
        self.lane_specs = {}

    def add_family_spec(self, family, spec):
        self.family_specs[family] = spec

    def add_model_spec(self, model, spec):
        self.model_specs[model] = spec

    def add_lane_spec(self, lane, spec):
        self.lane_specs[lane] = spec

# resolver.py
class Resolver:
    def __init__(self, registry):
        self.registry = registry

    def resolve_behavior(self, family, model, lane):
        family_spec = self.registry.family_specs.get(family, {})
        model_spec = self.registry.model_specs.get(model, {})
        lane_spec = self.registry.lane_specs.get(lane, {})

        # Merge specifications
        behavior = {**family_spec, **model_spec, **lane_spec}
        return behavior

# renderer.py
class Renderer:
    def __init__(self):
        pass

    def render_behavior(self, behavior):
        # Generate active-spec artifact
        with open('active-model-spec.json', 'w') as f:
            json.dump(behavior, f)

# Example usage
registry = Registry()
registry.add_family_spec('GPT-5', {'stop_criteria': 'explicit'})
registry.add_model_spec('GPT-5-1', {'verbosity': 'low'})
registry.add_lane_spec('research', {'evidence_first': True})

resolver = Resolver(registry)
behavior = resolver.resolve_behavior('GPT-5', 'GPT-5-1', 'research')

renderer = Renderer()
renderer.render_behavior(behavior)

Verification

To verify the fix, test the following scenarios:

  • Behavior resolution for different family, model, and lane combinations
  • Rendering of the active-spec artifact
  • Integration with the startup refresh hook and maintenance reports

Extra Tips

  • Use a modular design to allow for easy extension and modification of the registry, resolver, and renderer.
  • Consider using a more robust data storage solution for the registry, such as a database.
  • Implement benchmark-grade evaluation to measure the effectiveness of the per-model / per-lane behavior sheets.

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