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Use this page when you want OpenClaw to route model calls through cascadeflow without rewriting OpenClaw itself.

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Integration Model

OpenClaw can call cascadeflow through an OpenAI-compatible interface. That makes this a secondary integration path focused on compatibility and routing, not the primary harness entry path.

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Typical Flow

  1. Start the cascadeflow OpenAI-compatible server.
  2. Point OpenClaw at that base URL as a custom provider.
  3. Optionally pass routing hints, tenant metadata, or channel information.
  4. Optionally enable harness mode for in-loop runtime policy decisions.

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Optional Harness Toggle

OpenClaw integration stays compatibility-first, but you can opt into harness behavior at server startup:
  • --harness-mode off (default)
  • --harness-mode observe (recommended first step)
  • --harness-mode enforce (active controls with budgets/limits)
Example: 
python -m cascadeflow.integrations.openclaw.openai_server \
  --port 8084 \
  --harness-mode observe

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Why Teams Use It

  • Reuse OpenClaw without invasive changes
  • Centralize provider routing through cascadeflow
  • Add channel or tenant-aware routing behavior

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Deep Guide

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Important Notes

  • Treat this as a secondary integration surface.
  • The main product direction remains the in-process runtime-intelligence layer.
  • Use direct integrations first when you want full harness semantics inside the workflow.
Guide on GitHub: docs/guides/openclaw_provider.md