TL;DR: LLM costs eating your budget? Headroom is a free, open-source context compression layer that reduces token consumption by 60-95% without sacrificing output quality.
The Problem: LLMs Are Expensive
Every time your AI agent reads a log file, searches code, or processes RAG chunks, it burns through tokens. Those tokens add up fast.
I found Headroom.
What Is Headroom?
Headroom is an AI-native context compression layer. It sits between your agent/application and the LLM, compressing everything before it reaches the model.
Key numbers:
- 60-95% token reduction
- Zero quality loss on GSM8K (0.870 \u2192 0.870)
- +0.03 improvement on TruthfulQA (0.530 \u2192 0.560)
5 Ways to Use It
1. Library Mode (1 line of code)
from headroom import compress
compressed = compress(messages)
2. Proxy Mode (zero code)
headroom proxy --port 8787
3. Agent Wrap (one command)
headroom wrap claude
4. MCP Server - Compress, retrieve, stats tools
5. Cross-Agent Memory - Shared store across Claude, Codex, Gemini, Cursor
The Numbers
| Scenario | Before | After |
|---|---|---|
| Code search (17K tokens) | 17,765 | 1,408 |
| SRE incident (65K tokens) | 65,694 | 5,118 |
| Issue triage (54K tokens) | 54,174 | 14,761 |
Verdict
One of those rare tools that immediately saves you money while being free and open source. Open source (Apache 2.0), runs locally, actively maintained.
Top comments (0)