headroom
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Li
Star Growth
Overview
Headroom is a library, proxy, and MCP server that compresses agent inputs like tool outputs, logs, RAG chunks, and files to reduce token usage. It runs locally, offers multiple integration methods (library, proxy, agent wrap), and maintains reversible compression with local caching. It supports cross-agent memory and can learn from failed sessions.
Deep Analysis
Provides lossless compression for diverse agent inputs (JSON, code, text) with local execution and reversible retrieval.
⚡ Capabilities
- • Token reduction via compression
- • Local processing (no data sent externally)
- • Multiple integration modes (library, proxy, wrap, MCP)
- • Cross-agent memory with dedup
- • Reversible compression with local retrieval
- • Output token reduction
- • Learning from failed sessions
🔗 Integrations
✓ Best For
- ✓ Reducing LLM token costs in agent workflows
- ✓ Optimizing tool output, log, and RAG chunk ingestion
- ✓ Developers building or running AI agents
✗ Not Ideal For
- ✗ End-user AI applications like chatbots or image generators
- ✗ Non-AI products
⚠ Known Limitations
- ⚠ Requires local deployment/integration
- ⚠ Primarily focused on input optimization
Alternatives
Works with headroom
Tools that integrate with headroom, often used together in the same stack.
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