# Headroom

> Context optimization layer for LLM applications. Compress tool outputs, logs, files, and RAG chunks before they reach the model. Same answers, 60–95% fewer tokens. Library, proxy, and MCP server. Apache 2.0, local-first.

Headroom is shipped as a Python package (`headroom-ai`), a TypeScript package (`headroom-ai`), an OpenAI + Anthropic-compatible HTTP proxy (`headroom proxy`), and an MCP server (`headroom_compress`, `headroom_retrieve`, `headroom_stats` tools). All four modes use the same compression pipeline: per-content-type compressors (JSON, code, logs, diffs, text) feed into a Compress-Cache-Retrieve (CCR) store so compression stays reversible — the LLM can ask for the original whenever it wants.

The canonical, always-current documentation index lives at the docs site below. If you can fetch one URL, fetch that one; the entries here are a hand-curated subset.

## Canonical docs (start here)

- [Live llms.txt (full doc index)](https://headroom-docs.vercel.app/llms.txt): Auto-generated index of every doc page with descriptions.
- [Live llms-full.txt (every doc page concatenated)](https://headroom-docs.vercel.app/llms-full.txt): One Markdown blob containing every doc page. Use when you can spend the tokens for full context.
- [Docs site](https://headroom-docs.vercel.app/docs): Human-browsable docs with search.
- [GitHub repo](https://github.com/chopratejas/headroom): Source, issues, releases.
- [PyPI package](https://pypi.org/project/headroom-ai/): Python install.
- [npm package](https://www.npmjs.com/package/headroom-ai): TypeScript install.

## Install (copy-paste-runnable)

- Python: `pip install headroom-ai` (add `[all]` for every optional extra)
- TypeScript / Node: `npm install headroom-ai` (or `pnpm add headroom-ai`, `bun add headroom-ai`)
- Docker: `docker run -p 8787:8787 ghcr.io/chopratejas/headroom:latest`
- Run the proxy: `headroom proxy --port 8787` then point any client at `http://127.0.0.1:8787`
- Wrap an agent in one command: `headroom wrap claude` (also: `codex`, `cursor`, `aider`, `copilot`, `opencode`)

## Entry points

- [Quickstart](https://headroom-docs.vercel.app/docs/quickstart): 5-minute end-to-end (install → compress → call the model).
- [Installation](https://headroom-docs.vercel.app/docs/installation): All install paths, extras, Docker tags, env vars.
- [Proxy server](https://headroom-docs.vercel.app/docs/proxy): Run as a local HTTP proxy in front of OpenAI / Anthropic / Gemini.
- [MCP server](https://headroom-docs.vercel.app/docs/mcp): `headroom_compress`, `headroom_retrieve`, `headroom_stats` for Claude Code / Cursor / any MCP host.
- [API reference](https://headroom-docs.vercel.app/docs/api-reference): Python + TypeScript `compress()` API.

## How it works

- [How compression works](https://headroom-docs.vercel.app/docs/how-compression-works): Three-stage pipeline + automatic content routing.
- [SmartCrusher](https://headroom-docs.vercel.app/docs/smart-crusher): Statistical JSON / array compression (70–90% on tool outputs).
- [Code compression](https://headroom-docs.vercel.app/docs/code-compression): AST-aware via tree-sitter (preserves imports, signatures, types).
- [Text & log compression](https://headroom-docs.vercel.app/docs/text-and-logs): Search results, build logs, diffs.
- [CCR (reversible)](https://headroom-docs.vercel.app/docs/ccr): Compress-Cache-Retrieve — originals never deleted; LLM retrieves on demand.

## SDK / framework integrations

- [Anthropic SDK](https://headroom-docs.vercel.app/docs/anthropic-sdk): `withHeadroom(anthropic)` wrapper.
- [OpenAI SDK](https://headroom-docs.vercel.app/docs/openai-sdk): `withHeadroom(openai)` wrapper.
- [Vercel AI SDK](https://headroom-docs.vercel.app/docs/vercel-ai-sdk): Middleware + `withHeadroom()`.
- [LangChain](https://headroom-docs.vercel.app/docs/langchain): Chat models, memory, retrievers, agents.
- [Agno](https://headroom-docs.vercel.app/docs/agno): Model wrapping + observability hooks.
- [Strands](https://headroom-docs.vercel.app/docs/strands): Model wrapping + hook-based tool output compression.
- [LiteLLM](https://headroom-docs.vercel.app/docs/litellm): Single callback; works with all 100+ LiteLLM providers.

## Memory & cross-agent state

- [Persistent memory](https://headroom-docs.vercel.app/docs/memory): Per-project SQLite + HNSW vector store. No cross-project bleed (GH #462).
- [SharedContext](https://headroom-docs.vercel.app/docs/shared-context): Compressed inter-agent context handoffs.
- [Failure learning](https://headroom-docs.vercel.app/docs/failure-learning): Offline analysis writes corrections to `CLAUDE.md` / `AGENTS.md`.

## Operations

- [Configuration](https://headroom-docs.vercel.app/docs/configuration): Env vars, config file, per-call overrides.
- [Benchmarks](https://headroom-docs.vercel.app/docs/benchmarks): Token-savings numbers across content types.
- [Troubleshooting](https://headroom-docs.vercel.app/docs/troubleshooting): Common failure modes and fixes.
- [Limitations](https://headroom-docs.vercel.app/docs/limitations): What Headroom won't do well today.

## Licensing

Apache 2.0. Use commercially, modify, redistribute. Data stays on the user's machine when running the library, proxy, or MCP server locally. Anonymous telemetry enabled by default; set `HEADROOM_TELEMETRY=off` to disable.
