headroom/plugins/openclaw

@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