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What you can do with Headroom

Headroom is an open-source tool that compresses tool outputs, logs, files, RAG chunks and chat history on your machine before they reach an LLM, available as a Python or TypeScript library, a local proxy, and an MCP server.

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headroomlabs-ai/headroom on GitHub

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What do you want to do?

4 use cases

  • Cut Claude Code token use with one wrap command

    Run Claude Code through a local Headroom proxy to shrink tool outputs before each request.

    View prompt
    Using the repo headroomlabs-ai/headroom, install the CLI, run `headroom doctor`, then start a session with `headroom wrap claude` in the project at <project-path>. Report the output of `headroom savings` after a short session. Verify that doctor reports routing as working before you start.

  • Shrink messages in a Python app before each LLM call

    Add a compress() call to existing Python code so the model receives fewer tokens.

    View prompt
    Using the repo headroomlabs-ai/headroom, add a compress() call to the LLM request in <file-path>, passing the message list through headroom's compress function before the provider call. Expected output: the same answer with a printed token saving. Verify that the request still succeeds and that tokens_saved is greater than zero for a payload with repeated JSON.

  • Route any OpenAI-compatible client through a local proxy

    Start Headroom as a local proxy so existing tools shrink traffic without code changes.

    View prompt
    Using the repo headroomlabs-ai/headroom, start `headroom proxy --port 8787` and configure the OpenAI-compatible client in <client-config> to use http://localhost:8787 as its base URL. Expected output: normal responses and savings shown in the dashboard. Verify that a test request returns a valid answer.

  • Give an MCP client on-demand access to full originals

    Install the Headroom MCP server so the model can fetch uncompressed text when it needs it.

    View prompt
    Using the repo headroomlabs-ai/headroom, run `headroom mcp install` and connect the MCP client to the headroom server. Compress the file <file-path> with headroom_compress, then call headroom_retrieve to get the original back. Expected output: the original text matches the source file. Verify that the retrieved text equals the source.

How to use Headroom

  1. Check you have what it needs: Python 3.13 with uv, or pip, to install the headroom-ai package; An account with an LLM provider such as Anthropic or OpenAI; A coding agent or app that sends prompts through an API.
  2. Open headroomlabs-ai/headroom on GitHub and read the README and license.
  3. Install the CLI with uv tool install --python 3.13 "headroom-ai[all]"
  4. Run headroom doctor to confirm routing works
  5. Start a wrapped session with headroom wrap claude
  6. Work as usual, then check results with headroom savings

These steps come from our top use case, Cut Claude Code token use with one wrap command, which also has a copy-paste agent prompt.

Headroom alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Python 3.13 with uv, or pip, to install the headroom-ai package
  • An account with an LLM provider such as Anthropic or OpenAI
  • A coding agent or app that sends prompts through an API
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Questions about Headroom

What is Headroom?

Headroom is an open-source tool that compresses tool outputs, logs, files, RAG chunks and chat history on your machine before they reach an LLM, available as a Python or TypeScript library, a local proxy, and an MCP server. It is an open-source project on GitHub: https://github.com/headroomlabs-ai/headroom.

Who is Headroom for?

Developers and platform teams who run coding agents or LLM apps and want fewer tokens sent and billed per request.

What can I do with Headroom?

4 use cases so far, for example: Cut Claude Code token use with one wrap command; Shrink messages in a Python app before each LLM call; Route any OpenAI-compatible client through a local proxy. Each has steps and a copy-paste agent prompt.

What do I need to run Headroom?

Python 3.13 with uv, or pip, to install the headroom-ai package; An account with an LLM provider such as Anthropic or OpenAI; A coding agent or app that sends prompts through an API.

What are alternatives to Headroom?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Headroom safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.