---
title: OpenClaw
description: Secondary OpenClaw integration path using cascadeflow as an OpenAI-compatible provider.
---

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

## 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.

## 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.

## 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:

```bash
python -m cascadeflow.integrations.openclaw.openai_server \
  --port 8084 \
  --harness-mode observe
```

## Why Teams Use It

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

## Deep Guide

- [openclaw_provider.md](https://github.com/lemony-ai/cascadeflow/blob/main/docs/guides/openclaw_provider.md)

## 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.

<Tip>
**Guide on GitHub:** [docs/guides/openclaw_provider.md](https://github.com/lemony-ai/cascadeflow/blob/main/docs/guides/openclaw_provider.md)
</Tip>
