# AI use cases for Headroom (headroomlabs-ai/headroom) Repo: [headroomlabs-ai/headroom](https://github.com/headroomlabs-ai/headroom) | Use cases: 4 | Page: https://dedupedai.com/use-cases/headroomlabs-ai-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. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Cut Claude Code token use with one wrap command: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/cut-token-spend-in-claude-code/ - Shrink messages in a Python app before each LLM call: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/compress-llm-calls-in-python-app/ - Route any OpenAI-compatible client through a local proxy: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/route-any-openai-client-through-proxy/ - Give an MCP client on-demand access to full originals: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/let-agents-retrieve-full-compressed-text/ ## How to cut Claude Code token use with one wrap command with Headroom Repo: [headroomlabs-ai/headroom](https://github.com/headroomlabs-ai/headroom) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Run Claude Code through a local Headroom proxy to shrink tool outputs before each request. **Who it is for:** A developer using Claude Code daily on a large codebase who sees token costs climb during long sessions. **The problem:** Tool outputs such as search results, logs and file reads fill the context window and add cost on every turn. **The outcome:** Claude Code runs routed through Headroom, and you can see how many tokens were saved on your own traffic. ### Steps 1. Install the CLI with `uv tool install --python 3.13 "headroom-ai[all]"` 2. Run `headroom doctor` to confirm routing works 3. Start a wrapped session with `headroom wrap claude` 4. Work as usual, then check results with `headroom savings` 5. Remove the wrapping later with `headroom unwrap claude` (inferred, check the README) ### Prompt to paste into your agent ```text Using the repo headroomlabs-ai/headroom, install the CLI, run `headroom doctor`, then start a session with `headroom wrap claude` in the project at . Report the output of `headroom savings` after a short session. Verify that doctor reports routing as working before you start. ``` ### Caveat Savings depend on how repetitive the payload is, and the README notes prose and already dense output compress very little. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/cut-token-spend-in-claude-code/ ## How to shrink messages in a Python app before each LLM call with Headroom Repo: [headroomlabs-ai/headroom](https://github.com/headroomlabs-ai/headroom) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Add a compress() call to existing Python code so the model receives fewer tokens. **Who it is for:** A Python developer building an LLM feature who wants lower per-request cost without rewriting the app. **The problem:** Tool results, logs and retrieved chunks are sent to the model in full, even when most of the text is repetition. **The outcome:** Your app passes compressed messages to the provider and prints how many tokens were saved. ### Steps 1. Install the package with `pip install "headroom-ai[all]"` 2. Import the library with `from headroom import compress` 3. Pass your message list to `compress(messages, model="gpt-4o")` and send `result.messages` to the provider 4. Print `result.tokens_saved` and `result.compression_ratio` to check the effect ### Prompt to paste into your agent ```text Using the repo headroomlabs-ai/headroom, add a compress() call to the LLM request in , 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. ``` ### Caveat The README gives a benchmark table for one seeded scenario set, so measure your own payloads with Headroom savings or the printed result values before you rely on a percentage. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/compress-llm-calls-in-python-app/ ## How to route any OpenAI-compatible client through a local proxy with Headroom Repo: [headroomlabs-ai/headroom](https://github.com/headroomlabs-ai/headroom) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Start Headroom as a local proxy so existing tools shrink traffic without code changes. **Who it is for:** A team lead with several internal tools or scripts that call an OpenAI-compatible API and no time to edit each one. **The problem:** Each tool sends full prompts and tool outputs, and changing every codebase to add compression is slow. **The outcome:** Clients point at a local port, and Headroom compresses their requests while you watch live savings. ### Steps 1. Install with `pip install "headroom-ai[all]"` 2. Start the proxy with `headroom proxy --port 8787` 3. Point your client's base URL at the local port 8787 4. Open the live view with `headroom dashboard` while the proxy is running ### Prompt to paste into your agent ```text Using the repo headroomlabs-ai/headroom, start `headroom proxy --port 8787` and configure the OpenAI-compatible client in 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. ``` ### Caveat The README notes that third-party OpenAI-compatible providers need an explicit upstream URL, otherwise the provider key goes to OpenAI and is rejected. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/route-any-openai-client-through-proxy/ ## How to give an MCP client on-demand access to full originals with Headroom Repo: [headroomlabs-ai/headroom](https://github.com/headroomlabs-ai/headroom) | Difficulty: Advanced | Time: 45 minutes | Basis: From the README Install the Headroom MCP server so the model can fetch uncompressed text when it needs it. **Who it is for:** A developer using an MCP-capable client who sometimes needs the full text of a compressed log or file. **The problem:** Compressed content may hide a detail the model needs, and there is no simple way to recover the original. **The outcome:** The client gets headroom_compress, headroom_retrieve and headroom_stats tools and can pull originals back on request. ### Steps 1. Install the CLI with `pip install "headroom-ai[all]"` 2. Register the MCP server with `headroom mcp install` 3. In your MCP client, call headroom_compress on a large payload 4. Call headroom_retrieve when the model needs the full original text 5. Check usage with headroom_stats ### Prompt to paste into your agent ```text Using the repo headroomlabs-ai/headroom, run `headroom mcp install` and connect the MCP client to the headroom server. Compress the file 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. ``` ### Caveat The README says registry authors should use the canonical server.json rather than prose, so verify the exact MCP registration your client expects before relying on it. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/headroomlabs-ai-headroom/let-agents-retrieve-full-compressed-text/