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Headroom

Headroom is an open-source context-compression library and self-hostable tooling layer for AI applications. It compresses agent messages, tool outputs, logs, files and RAG chunks before they reach a language model, with interfaces for Python, TypeScript, a proxy, CLI wrappers and an MCP server.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Headroom — OpenViST project profile in Agent Infrastructure.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Consider Headroom when an AI application or agent sends large tool results, logs, files, RAG passages or conversation history to a language model and you want a local compression layer.

Who it is for

  • Developers adding context compression to Python or TypeScript AI applications.
  • Teams routing agent traffic through a local proxy.
  • Users of MCP-compatible agent tooling seeking compression, retrieval and statistics tools.

Documented uses

  • Reduce the token volume of messages and tool outputs sent to an LLM.
  • Compress JSON, source code and prose using content-specific components.
  • Keep original text locally available for retrieval when an agent needs the full content.
  • Expose compression functionality through a library, proxy, CLI wrapper or MCP workflow.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. The repository documents Python and TypeScript packages, a CLI, and a locally run proxy.

  • Select the integration path that fits the application: library, proxy, CLI wrapper or MCP server.
  • Headroom's content router assigns JSON, source code and prose to SmartCrusher, CodeCompressor and Kompress-v2-base, respectively.
  • CCR keeps originals locally for on-demand retrieval, so compressed context does not have to be the only form available to the agent.
  • The repository describes token-reduction results, but performance will depend on the content and workflow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Add inline compression to an application

Use the documented library entry point in Python or TypeScript before sending messages to a model.

Before you start

  • A Python or TypeScript application
  • The corresponding Headroom library package

Steps

  1. Install headroom-ai[all] with pip for Python, or install headroom-ai with npm for the TypeScript SDK.

  2. Use the documented compress(messages) library entry point in the application.

Expected outcome

The application can pass messages through Headroom's inline context-compression layer.

Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
30 September 2026
Original website title
headroomlabs-ai/headroom · Official homepage title · retained in its source language
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