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headroomlabs-ai/headroom

hook
token optimization

Headroom is a context compression layer that reduces token usage for AI agents by 15–95% depending on content type (JSON data sees the highest savings). It works as a library, proxy, MCP server, or agent wrapper, compressing prompts, tool outputs, logs, and RAG results before they reach the LLM while preserving accuracy and offering reversible compression for retrieval.

  • Import as a library (Python/TypeScript) to compress messages inline in your app
  • Run as a local proxy (zero code changes) to compress traffic from any language or agent
  • Wrap coding agents (Claude, Cursor, Cline, etc.) with a single command for automatic compression
  • Deploy as an MCP server to integrate compression into any MCP-compatible client
  • Trim model output tokens with verbosity steering and effort routing, reducing costs on expensive output-heavy models
65
REVIEW
Risk score 65/100
✓ CRITICAL/HIGH reflect AI-verified findings (false positives excluded) · MEDIUM/LOW/INFO are unverified scanner output
AI-verified (CRITICAL/HIGH):
Findings by checker · 4 high-signal, 2 mostly false-positive (hidden by default)
CHK-08311 findings
10 likely1 false positive
9% FP
CHK-0475 findings
4 likely1 false positive
20% FP
CHK-0421 finding
1 likely
0% FP
CHK-0731 finding
1 likely
0% FP
120 findingsclick to expand
+20 more findings — view full report in dashboard →
Last scanned: Jul 12, 2026
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