headroom

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                  The context compression layer for AI agents

60–95% fewer tokens · library · proxy · MCP · 6 algorithms · local-first · reversible

Docs · Install · Proof · Agents · Discord · llms.txt · Enterprise

_{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.

_{Live: 10,144 → 1,260 tokens — 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|cursor|aider|copilot in one command
• MCP server — headroom_compress, headroom_retrieve, headroom_stats for any MCP client
• Cross-agent memory — shared store across Claude, Codex, Gemini, auto-dedup
• headroom learn — mines failed sessions, writes corrections to CLAUDE.md / AGENTS.md
• Reversible (CCR) — originals never deleted; LLM retrieves on demand

How it works (30 seconds)

 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-base  (text, HF)    │
    │                                                    │
    │  Cross-agent memory  ·  headroom learn  ·  MCP     │
    └────────────────────────────────────────────────────┘
        │   compressed prompt  +  retrieval tool
        ▼
 LLM provider  (Anthropic · OpenAI · Bedrock · …)

• ContentRouter — detects content type, selects the right compressor
• SmartCrusher / CodeCompressor / Kompress-base — compress JSON, AST, or prose
• CacheAligner — stabilizes prefixes so provider KV caches actually hit
• CCR — stores originals locally; LLM calls headroom_retrieve if it needs them

→ Architecture · CCR reversible compression · Kompress-base model card

Get started (60 seconds)

# 1 — Install
pip install "headroom-ai[all]"          # Python
npm install headroom-ai                 # Node / TypeScript

# 2 — Pick your mode
headroom wrap claude                    # wrap a coding agent
headroom proxy --port 8787              # drop-in proxy, zero code changes
# or: from headroom import compress      # inline library

# 3 — See the savings
headroom perf

Granular extras: [proxy], [mcp], [ml], [code], [memory], [relevance], [image], [agno], [langchain], [evals]. Requires Python 3.10+.

Proof

Savings on real agent workloads:

Accuracy preserved on standard benchmarks:

Reproduce: python -m headroom.evals suite --tier 1 · Full benchmarks & methodology

Agent compatibility matrix

Any OpenAI-compatible client works via headroom proxy. MCP-native: headroom mcp install.

GitHub Copilot CLI subscription mode

Headroom can route GitHub Copilot CLI subscription traffic through the local proxy:

headroom wrap copilot --subscription -- --model gpt-4o

This lets Headroom intercept OpenAI-compatible Copilot CLI requests and apply the same proxy compression pipeline before forwarding to GitHub Copilot's hosted API. The wrapper resolves the account-specific Copilot API endpoint and prints it as COPILOT_PROVIDER_API_URL=... during launch.

Platform support note: macOS auth reuse via Copilot CLI Keychain storage has been smoke-tested. Windows Credential Manager, Linux Secret Service / secret-tool, and Docker/CI token-injection paths are implemented or planned as auth-discovery paths, but still need real OS validation before they should be considered fully vetted. For Docker and CI, prefer passing an explicit GITHUB_COPILOT_TOKEN or GITHUB_COPILOT_GITHUB_TOKEN rather than relying on host keychain access.

When to use · When to skip

Great fit if you…

• run AI coding agents daily and want savings without changing your code
• work across multiple agents and want shared memory
• need reversible compression — originals always retrievable via CCR

Skip it if you…

• only use a single provider's native compaction and don't need cross-agent memory
• work in a sandboxed environment where local processes can't run

Install

pip install "headroom-ai[all]"          # Python, everything
npm install headroom-ai                 # TypeScript / Node
docker pull ghcr.io/chopratejas/headroom:latest

Granular extras: [proxy], [mcp], [ml] (Kompress-base), [code], [memory], [relevance], [image], [agno], [langchain], [evals]. Requires Python 3.10+.

Using pipx? Choose a supported interpreter explicitly:

pipx install --python python3.13 "headroom-ai[all]"

→ Installation guide — Docker tags, persistent service, PowerShell, devcontainers.

headroom learn

headroom learn — mines failed sessions, writes corrections to CLAUDE.md / AGENTS.md / GEMINI.md.

Documentation

Compared to

Headroom runs locally, covers every content type, works with every major framework, and is reversible.

Attribution. Headroom ships with the excellent RTK binary for shell-output rewriting — git show --short, scoped ls, summarized installers. Huge thanks to the RTK team; their tool is a first-class part of our stack, and Headroom compresses everything downstream of it. Headroom can also use lean-ctx as the selected CLI context tool; set HEADROOM_CONTEXT_TOOL=lean-ctx before running headroom wrap ....

Contributing

git clone https://github.com/chopratejas/headroom.git && cd headroom
pip install -e ".[dev]" && pytest

Devcontainers in .devcontainer/ (default + memory-stack with Qdrant & Neo4j). See CONTRIBUTING.md.

Community

• Discord — questions, feedback, war stories.
• Kompress-base on HuggingFace — the model behind our text compression.

License

Apache 2.0 — see LICENSE.