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Home/Categories/AI & Machine Learning/headroom
icon of headroom

headroom

Open source alternative to The Token Company and Kong AI Gateway

  • 76.5K stars
  • Apache-2.0
  • Active this week
  • Last commit 2 days ago

Last verified Oct 20, 2026

Optimize LLM context by compressing tool outputs, logs, files, and RAG chunks before they reach the model.

Visit websiteGitHub76.5K stars1-click deploy on Hostinger
TL;DR

headroom compresses inputs for LLM applications, reducing token usage while maintaining accuracy. It supports various content types and integrates seamlessly with multiple frameworks. Ideal for developers and teams using AI coding agents.

Read the full breakdownHide the full breakdownWho it is for, how it works, trade-offs, the quick start and the FAQ
who it's for

Who headroom is for#

Daily AI Coding Agent Users

Great for developers using AI coding agents who want to save on token costs.

Skip if:

Skip if you only use a single provider's native compaction.

Teams Using Multiple Agents

Ideal for teams that work across various AI agents needing shared memory.

Skip if:

Skip if you work in a sandboxed environment where local processes can't run.

the problem

The problem it solves#

The project helps solve the problem of high token usage in LLM applications by compressing inputs without losing essential information.

how headroom solves it

How it solves it#

Lossless Compression

Compresses data aggressively while retaining originals for retrieval.

Smart Content Detection

Automatically detects and routes content types to the best compressor.

Cache Optimization

Enhances cache hits by stabilizing prefixes, leading to cost savings.

Image Compression

Reduces token usage by 40-90% through intelligent image processing.

Persistent Memory

Maintains memory across sessions using SQLite and HNSW backends.

Failure Learning

Learns from past failures to improve future performance.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • High Token SavingsAchieves 60-95% reduction in token usage while maintaining accuracy.
  • Seamless IntegrationWorks with various frameworks and requires no code changes.
  • Reversible CompressionAllows retrieval of original data after compression.
  • Multi-Agent SupportFacilitates shared memory across different AI agents.

Trade-offs

  • -Not for Single Provider UseLess beneficial for users relying solely on one provider's native compaction.
  • -Local Process RequirementRequires local processes to run, which may not suit all environments.
versus alternatives

headroom vs alternatives#

Headroom runs locally, covers every content type, works with every major framework, and is reversible. Unlike other tools, it does not require sending data to an external API, ensuring data privacy and control.

install · quick start

Quick start#

bash
Install the Python package with all extras, or add the Node package for TypeScript apps:

pip install "headroom-ai[all]"
npm install headroom-ai
tech stack · detected from GitHub

What it's built on#

Languages
JavaScriptPythonRustTypeScript
Frameworks
FastAPILangChainNext.jsReact
frequently asked

FAQ#

What types of content can Headroom compress?

Headroom can compress tool outputs, database results, file reads, RAG results, and more.

How does Headroom ensure data is not lost during compression?

It uses lossless compression techniques that retain original data for retrieval.

Can I integrate Headroom with existing applications?

Yes, Headroom integrates seamlessly with various frameworks and requires minimal changes.

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Hostinger

Run headroom on your own server

Hostinger sets headroom up as a one-click app on a VPS.

Deploy on Hostinger

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