Headroom

Quickstart · Headroom for Teams · Install · Proof · Agents · Docs · Discord · llms.txt

AI agents / LLMs: read /llms.txt here, or fetch the live index · full docs blob.

Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM. Same answers, fraction of the tokens. Compression runs on your machine; no prompt or file content is sent anywhere to be compressed.

10,144 → 1,260 tokens. The same FATAL found.

What it does

• Library — compress(messages) in Python or TypeScript, inline in any app.
• Proxy — headroom proxy --port 8787, zero code changes, any language.
• Agent wrap — headroom wrap claude|codex|grok|copilot|cursor|aider|opencode|cline|continue|goose|openhands|openclaw|vibe|omp|zcode in one command; undo with headroom unwrap <tool>.
• MCP server — headroom_compress, headroom_retrieve, headroom_stats for any MCP client.
• Cross-agent memory — one shared store across Claude, Codex, Gemini and Grok, with automatic dedup.
• headroom learn — mines failed sessions and writes corrections to CLAUDE.local.md (default, gitignored), CLAUDE.md, AGENTS.md, GEMINI.md or GROK.md.
• Output token reduction — trims what the model writes back, not only what you send. See below.
• Reversible (CCR) — originals are cached locally and retrieved on demand.

How it works

 Your agent / app
   (Claude Code, Cursor, Codex, LangChain, Agno, Strands, your own code…)
        │   prompts · tool outputs · logs · RAG results · files
        ▼
    ┌────────────────────────────────────────────────────┐
    │  Headroom   (runs locally — your data stays here)  │
    │  ────────────────────────────────────────────────  │
    │  CacheAligner  →  ContentRouter  →  CCR            │
    │                    ├─ SmartCrusher   (JSON)        │
    │                    ├─ CodeCompressor (AST)         │
    │                    └─ Kompress-v2-base (text, HF)  │
    │                                                    │
    │  Cross-agent memory  ·  headroom learn  ·  MCP     │
    └────────────────────────────────────────────────────┘
        │   compressed prompt  +  retrieval tool
        ▼
 LLM provider  (Anthropic · OpenAI · Bedrock · …)

• ContentRouter detects the content type and selects a compressor for it.
• SmartCrusher / CodeCompressor / Kompress-v2-base handle JSON, source code and prose respectively.
• CacheAligner flags volatile content that would bust a provider KV-cache prefix. It never rewrites prompts.
• CCR stores originals locally so the model can call headroom_retrieve when it needs the full text.

→ Architecture · CCR · Kompress-v2-base model card

Get started (60 seconds)

# 1 — Install
uv tool install --python 3.13 "headroom-ai[all]"  # CLI in a self-contained env
pip install "headroom-ai[all]"                    # Python — ships the `headroom` CLI
npm install headroom-ai                           # TypeScript SDK only — no CLI