Overview

Headroom is your AI agent's intelligent context compression layer, dramatically reducing token counts for tool outputs, logs, RAG chunks, and files before they reach your Large Language Model (LLM). It achieves significant token reduction (60-95%) without sacrificing answer quality, offering a reversible compression mechanism that keeps original data accessible.

Key Features

• Massive Token Reduction: Shrink your context by 60-95%, leading to substantial cost and latency savings.
• Versatile Integration: Available as a Python/TypeScript library, a drop-in proxy (zero code changes), an agent wrapper, or an MCP server.
• Multi-Algorithm Compression: Employs specialized compressors (SmartCrusher for JSON, CodeCompressor for AST, and Kompress-base for text) tailored to content type.
• Reversible Compression (CCR): Original data is never lost and can be retrieved on demand by the LLM.
• Cross-Agent Memory: Enables shared, deduplicated memory storage across different AI agents.
• Local-First Operation: Your data stays local, ensuring privacy and security.
• Intelligent Context Routing: Automatically detects content type and applies the most effective compression algorithm.
• Failure Learning: Mines failed sessions to automatically write corrections and improve agent performance.

Typical Use Cases

• Coding Agents: Compress tool outputs, logs, and RAG results for agents like Claude Code, Codex, and Cursor, reducing their context window usage.
• LLM Proxy: Integrate Headroom as a proxy to transparently compress context for any LLM or agent without modifying existing code.
• Multi-Agent Systems: Facilitate efficient communication and shared memory across multiple autonomous agents.
• RAG Optimization: Significantly reduce the token size of retrieved documents for retrieval-augmented generation pipelines.
• Log and Output Summarization: Compress lengthy logs or tool outputs before they are fed into LLM reasoning steps.