Headroom is a context compression layer designed for AI agents and LLMs, significantly reducing token usage (60-95% fewer tokens) by compressing tool outputs, logs, RAG chunks, files, and conversation history. It operates locally and reversibly, ensuring data privacy and the ability to retrieve original content on demand.

Is headroom open source?

Yes, licensed under Apache-2.0.

headroom: A context compression layer designed for AI agents and LLMs,… (★ 2.1k, Apache-2.0)

Features

01High Token Savings: Reduces token usage by 60-95% for various agent workloads, including code search, SRE debugging, and GitHub issue triage.

02Multi-Modal Compression: Employs specialized algorithms like SmartCrusher (JSON), CodeCompressor (AST), and Kompress-base (text) to efficiently compress different content types.

03Local-First & Reversible (CCR): Processes data locally to maintain privacy and offers Reversible Compression (CCR) where original content is never deleted and can be retrieved on demand by the LLM.

04Flexible Integration: Can be used as an inline library (Python/TypeScript), a zero-code proxy, or an agent wrapper for popular tools like Claude Code, Codex, and Cursor.

05Cross-Agent Memory & Learning: Provides shared memory across different agents (Claude, Codex, Gemini) with auto-deduplication, and includes `headroom learn` to mine failed sessions and suggest corrections.

Compatibility

Python

Runtime

Verified via docs

Node.js

Runtime

Verified via docs

Docker

Deployment

Verified via docs

Anthropic

LLM Provider

Verified via docs

OpenAI

LLM Provider

Verified via docs

LangChain

Framework

Verified via docs

Quick start

$ pip install "headroom-ai[all]"

Use cases

↳Optimizing AI Coding Agent Workflows: Significantly reduce token costs and improve efficiency when using agents like Claude Code, Cursor, or Aider for daily coding tasks.

↳Enhancing Multi-Agent Collaboration: Enable shared context and memory across different AI agents, fostering more cohesive and efficient multi-agent systems.

↳Efficient Debugging and Incident Response: Compress large volumes of logs and incident data to fit within LLM context windows, facilitating quicker analysis by AI.

↳Cost-Effective Codebase Exploration: Explore extensive codebases with LLMs without incurring high token costs, by compressing code, documentation, and RAG chunks.

↳Maintaining Data Privacy in AI Applications: Utilize local-first context compression to ensure sensitive data remains on-premises, rather than being sent to external APIs for processing.