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HEADROOM
Compress tool outputs, logs, and files by 60-95% before they reach the LLM
Apache 2.0
ABOUT
AI agents routinely consume massive context windows filled with verbose tool outputs, logs, files, and RAG results, leading to high API costs, slow response times, and context window exhaustion. Headroom compresses agentic context by 60-95% using content-aware compressors (AST-aware code compression, JSON compression, and an ML model trained on agentic traces), while preserving answer quality through reversible compression — originals are stored locally and retrieved on demand when the LLM needs full details.
INSTALL
pip install "headroom-ai[all]"
npm install headroom-ai
INTEGRATION GUIDE
1. Wrap Claude Code, Cursor, Codex, or Aider to compress tool outputs by 60-95% and reduce API costs
2. Compress RAG chunks before injecting them into LLM context to fit 4x more information
3. Run as a transparent proxy to compress all LLM-bound traffic without any code changes
4. Use as an MCP server for shared cross-agent memory with automatic deduplication
5. Compress verbose SRE and monitoring logs before sending them to the LLM for analysis
TAGS
token-compressioncontext-optimizationllmagentmcpproxypythontypescriptrag