@headroom-ai/openclaw

Context compression plugin for OpenClaw. Compresses tool outputs, code, logs, and structured data — 70-90% token savings with zero LLM calls.

Install

pip install "headroom-ai[proxy]"
openclaw plugins install @headroom-ai/openclaw

Configure

{
  "plugins": {
    "slots": {
      "contextEngine": "headroom"
    }
  }
}

That's it. The plugin auto-starts the Headroom proxy if it's not already running.

How It Works

Every time OpenClaw assembles context for the model, the plugin compresses tool outputs and large messages:

• JSON arrays (tool outputs, search results) — statistical selection keeps anomalies, errors, boundaries
• Code — AST-aware compression via tree-sitter
• Logs — pattern deduplication, keeps errors and boundaries
• Text — ML-based token compression

Compression is lossless via CCR (Compress-Cache-Retrieve): originals are stored and the agent gets a headroom_retrieve tool to access full details when needed.

Configuration Options

| Option | Default | Description | |--------|---------|-------------| | proxyUrl | auto-detected | URL of the Headroom proxy | | autoStart | true | Start proxy automatically if not running | | pythonPath | auto-detected | Path to Python binary | | proxyPort | 8787 | Port for auto-started proxy |

Comparison with lossless-claw

| | lossless-claw | headroom | |---|---|---| | Compaction method | LLM summarization (DAG) | Content-aware compression (zero LLM) | | Cost of compaction | Tokens (LLM calls) | Zero | | Best for | Long conversations | Tool-heavy agents with large outputs | | Retrieval | lcm_grep, lcm_expand | headroom_retrieve (instant) |

License

Apache-2.0