headroomlabs-ai/headroomPublic

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              The context compression layer for AI agents

60–95% fewer tokens (for JSON data), 15-20% fewer tokens (for coding agents) · library · proxy · MCP · content-aware compressors · local-first · reversible

Docs · Install · Proof · Agents · 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.

_{Live: 10,144 → 1,260 tokens — same FATAL found.}

• 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 — shared store across Claude, Codex, Gemini, Grok, auto-dedup
• headroom learn — mines failed sessions, writes corrections to CLAUDE.local.md (default, gitignored) or CLAUDE.md / AGENTS.md / GEMINI.md / GROK.md
• Output token reduction — trims what the model writes back (not just what you send): drops ceremony/restated code and skips deep "thinking" on routine steps. See Output token reduction.
• Reversible (CCR) — originals are cached for retrieval on demand

 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 content type, selects the right compressor
• SmartCrusher / CodeCompressor / Kompress-v2-base — compress JSON, AST, or prose
• CacheAligner - detects and warns about volatile content that can bust provider KV cache prefixes; never rewrites prompts
• CCR — stores originals locally; LLM calls headroom_retrieve if it needs them

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

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

# 2 — Pick your mode  (the `headroom` commands below come from the uv or pip install)
headroom deploy                         # turnkey local deployment + agent config
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 — Verify setup and see the savings
headroom doctor                         # health check — confirms routing is working
headroom perf
headroom dashboard                      # live savings dashboard (proxy must be running)

To use headroom, it is recommended you launch a wrapped agent session each time so that all necessary setup is completed. When wrapping a coding agent, headroom starts a local proxy, installs Serena for semantic code navigation, and launches a coding agent session configured to proxy requests through headroom.

The headroom CLI ships only via the PyPI package. The npm headroom-ai is the TypeScript SDK — a library you import (import { compress } from 'headroom-ai'), not a CLI, so it provides no headroom command.

Granular extras: [proxy], [mcp], [ml], [code], [memory], [vector] (optional HNSW backend — needs a C++ toolchain, not in [all]), [relevance], [image], [agno], [langchain], [evals], [pytorch-mps] (Apple-GPU memory-embedder offload — set HEADROOM_EMBEDDER_RUNTIME=pytorch_mps). Requires Python 3.10+.

If Codex or another MCP client cannot inherit a shell PATH reliably, install Headroom as a persistent uv tool and point the client at the absolute binary path:

uv tool install "headroom-ai[all]"
command -v headroom

Then use the returned path in MCP config:

[mcp_servers.headroom]
command = "/absolute/path/from/command-v/headroom"
args = ["mcp", "serve"]

command = "headroom" only works when the client starts with a PATH that already includes the uv tool directory.

Savings on real agent workloads:

Accuracy preserved on standard benchmarks:

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

Everything above shrinks the prompt you send. But you also pay for every token the model writes back — and on Opus-class models output costs 5× input. A lot of that output is waste: "Great, let me…" preambles, re-printing code you just showed it, and deep "thinking" on routine steps like reading a file.

Headroom can trim that too, from the proxy, without you changing any code:

• Verbosity steering — appends a short "be terse, don't restate context" note to the end of the system prompt (so your prompt cache still hits).
• Effort routing — when a turn is just the model resuming after a tool result (a file read, a passing test), it dials the model's thinking effort down. New questions and errors keep full effort.

Applies to Anthropic /v1/messages and OpenAI-compatible endpoints (/v1/chat/completions, /v1/responses). Effort routing uses reasoning_effort on OpenAI, thinking.budget_tokens / output_config.effort on Anthropic — same clamp-only invariant on both paths, same output_shaper:* label vocabulary.

Turn it on:

export HEADROOM_OUTPUT_SHAPER=1     # off by default
headroom proxy --port 8787

Already running a proxy? These switches are read live on every request, so a proxy that headroom wrap reused (rather than started) would not see a value you export afterwards — its environment was snapshotted at launch. headroom wrap now hot-syncs your current settings to the running proxy via a loopback POST /admin/runtime-env, so they take effect immediately with no restart (no cold start, no dropped requests, no lost caches). Set them before you wrap. On a shared proxy these overrides are global — the last explicit setting wins.

Learn the right terseness for you. People don't say how terse they want answers — they show it (they interrupt long replies, or move on before they could have read them). headroom learn --verbosity reads your past sessions and picks the level automatically:

headroom learn --verbosity            # preview what it found (dry run)
headroom learn --verbosity --apply    # save it; the proxy uses it from now on

See how many output tokens you saved. Output savings are counterfactual — we never see what the model would have written — so Headroom reports an honest estimate with a confidence range, never a made-up number:

headroom output-savings
# Reduction: 31.7%  (95% CI 27.7% … 35.7%)   [estimated]

Want a measured number instead of an estimate? Leave 10% of conversations unshaped as a control group: export HEADROOM_OUTPUT_HOLDOUT=0.1. The dashboard shows an Output Tokens Saved card next to input compression, labelled measured or estimated with the confidence band.

→ Full write-up incl. the measurement methodology: Output token reduction

Any OpenAI-compatible client works via headroom proxy. MCP-native: headroom mcp install. Undo durable wrapping with headroom unwrap <tool> (supports: claude, copilot, codex, grok, kimi, omp, opencode, openclaw, zcode). Registry authors can use the canonical server.json in the repo root instead of reconstructing the headroom mcp serve contract from prose.

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

headroom copilot-auth login
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 exchanges Headroom's reusable GitHub OAuth token for Copilot's short-lived API token and prints the upstream endpoint as COPILOT_PROVIDER_API_URL=... during launch.

headroom copilot-auth login stores a Headroom-specific Copilot OAuth token. This avoids relying on generic GitHub or Copilot CLI tokens that can read Copilot account metadata but may still be rejected by Copilot's token-exchange endpoint.

For GitHub Enterprise Server or custom-domain Copilot deployments, set one of these before launching:

export GITHUB_COPILOT_ENTERPRISE_DOMAIN=ghe.example.com
# or
export GITHUB_COPILOT_ENTERPRISE_URL=https://ghe.example.com

Both variables are supported. If both are set, GITHUB_COPILOT_ENTERPRISE_URL takes precedence.

For GitHub.com Enterprise Cloud URLs such as github.com/enterprises/your-enterprise, do not set an enterprise-domain override. Headroom uses GitHub's normal token-exchange endpoint and the Copilot API endpoint advertised for the signed-in account.

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.

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 are retrievable via CCR within the configured TTL

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

Headroom OSS is built for individual developers: run headroom proxy or headroom wrap on your laptop and start cutting tokens in minutes — free, local-first, your data never leaves your machine.

Running it across a whole engineering org is a different job: a shared, always-on deployment; centralized config and version rollout; org-wide savings dashboards; SSO and access controls; air-gapped / VPC installs; and someone to call when it matters. That's what we help companies with — self-hosted with support, or fully managed.

If your team is spending real money on LLM tokens — Claude Code, Codex, Cursor, or agents running in CI — and you want those savings across everyone, not just one laptop:

→ Email hello@headroomlabs.ai with your stack and rough monthly LLM spend, and we'll help you roll Headroom out across your organization.

Everything in this repo stays open source (Apache 2.0). The managed offering is simply for teams that would rather have it deployed, supported, and scaled for them.

uv tool install --python 3.13 "headroom-ai[all]"  # CLI, isolated app env
pip install "headroom-ai[all]"                    # Python, everything — includes the `headroom` CLI
npm install headroom-ai                           # TypeScript SDK (library only — no `headroom` CLI)
docker pull ghcr.io/chopratejas/headroom:latest

Granular extras: [proxy], [mcp], [ml] (Kompress-v2-base), [code], [memory], [vector] (optional HNSW backend — needs a C++ toolchain, not in [all]), [relevance], [image], [agno], [langchain], [evals], [pytorch-mps] (Apple-GPU memory-embedder offload — set HEADROOM_EMBEDDER_RUNTIME=pytorch_mps). Requires Python 3.10+.

Note: [all] covers the core stack but excludes framework adapters. Install them separately: pip install "headroom-ai[langchain]" (also [agno], [strands], [anyllm], [bedrock]).

Using uv for the headroom CLI? Prefer uv tool install so the command lives in an isolated app environment. On macOS, pass --python 3.13 if your default python3 is newer than the current wheel set:

brew install python@3.13  # if Python 3.13 is not already available
uv tool install --python 3.13 "headroom-ai[all]"
uv tool update-shell      # if ~/.local/bin is not already on PATH
headroom --version

For MCP clients such as Codex that do not inherit your interactive shell PATH, configure the absolute executable path returned by command -v headroom:

[mcp_servers.headroom]
command = "/Users/you/.local/bin/headroom"
args = ["mcp", "serve"]

Current native wheels cover macOS Apple Silicon and Linux. On Intel macOS, use Docker-native install until native wheel support lands.

Using pipx? Choose a supported interpreter explicitly:

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

Pick 3.13 if you want dollar savings. The dashboard's Proxy $ Saved tile prices compression with LiteLLM, and LiteLLM can't be installed on Python 3.14+. On 3.14 token savings still track, but the dollar figure stays $0.00. If you already installed on 3.14, switch with pipx reinstall headroom-ai --python python3.13 and restart the proxy.

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

CPU requirement (x86/x86_64): the ONNX-backed features — Magika content detection and embedding relevance — use a precompiled ONNX Runtime that needs AVX2. On x86 hosts without AVX2 (some Docker/QEMU setups and older cloud VMs) Headroom automatically falls back to its non-ONNX paths (BM25 relevance, heuristic detection) rather than crashing. arm64/Apple Silicon needs no AVX2.

headroom update          # detects pip / pipx / uv tool and upgrades in place
headroom update --check  # report the latest release without upgrading
headroom update --pre    # include pre-releases

headroom update figures out how Headroom was installed (pip/venv, pip --user, pipx, uv tool) and runs the matching upgrade across macOS, Linux, and Windows. For git checkouts, editable installs, Docker images, and externally-managed system Pythons (PEP 668) it prints the correct manual step instead of guessing.

The proxy also shows a one-line "update available" notice on startup. It checks PyPI at most once a day, in the background, and never blocks. Opt out with HEADROOM_UPDATE_CHECK=off (also skipped in --stateless mode and CI).

If pip install "headroom-ai[all]" fails with CERTIFICATE_VERIFY_FAILED (unable to get local issuer certificate), your network uses SSL inspection — a MITM proxy presenting a company-issued CA. The build backend (maturin) downloads rustup over a connection your TLS stack doesn't trust. Install Rust first so the build doesn't fetch it:

# macOS / Linux
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && rustup default stable
# Windows
winget install Rustlang.Rustup && rustup default stable

Restart your shell, then pip install "headroom-ai[all]". A prebuilt wheel avoids the Rust build entirely where available: pip install --only-binary headroom-ai headroom-ai. Prebuilt wheels are published for Windows (win_amd64), Linux (x86_64 / aarch64), and macOS (Apple Silicon and Intel), so installs on those platforms never need a local Rust toolchain — the Rust-first dance above is only for the platform-independent sdist fallback when no wheel matches.

Two runtime assets are fetched over TLS; if they are blocked, trust your corporate CA via REQUESTS_CA_BUNDLE / SSL_CERT_FILE / CURL_CA_BUNDLE:

• cdn.pyke.io — the ONNX Runtime for the Rust core. Alternatively pre-provide it with ORT_STRATEGY=system and ORT_LIB_LOCATION=/path/to/onnxruntime.
• huggingface.co — the kompress-base compression model. Pre-download it and run with HF_HUB_OFFLINE=1, or set HF_ENDPOINT to a trusted mirror.

Running with compression disabled (pure gateway) requires neither asset.

ort-sys ships no prebuilt ONNX Runtime binary for Intel macOS, so a source build fails by default even outside a corporate-proxy environment. The same ORT_STRATEGY=system mechanism above fixes it — point it at a system ONNX Runtime instead:

brew install onnxruntime
ORT_STRATEGY=system \
ORT_LIB_LOCATION="$(brew --prefix onnxruntime)/lib" \
ORT_PREFER_DYNAMIC_LINK=1 \
  pip install "headroom-ai[all]"

# ORT is dlopen'd at runtime too:
export ORT_DYLIB_PATH="$(brew --prefix onnxruntime)/lib/libonnxruntime.dylib"

ORT_LIB_LOCATION must point at lib/ (not the bare prefix) and ORT_PREFER_DYNAMIC_LINK=1 is required, or ORT_STRATEGY=system still attempts static linking, which the Homebrew keg doesn't provide.

A different failure from the one above. If TLS fails with:

[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed:
Basic Constraints of CA cert not marked critical

then the corporate CA is found and trusted — adding it to a CA bundle changes nothing. Python 3.13 + OpenSSL 3.x enable VERIFY_X509_STRICT by default, which enforces RFC 5280 §4.2.1.9: a CA cert's basicConstraints must be marked critical. Inspection roots like Zscaler set CA:TRUE without the critical bit, so the chain is rejected.

Set HEADROOM_TLS_STRICT=0 to clear only the strict flag from every TLS context Headroom controls — the proxy's httpx upstream client and the urllib3/huggingface_hub path used for model downloads. Chain validation, signature, expiry, and hostname checks all stay on; this is strictly narrower than disabling verification.

HEADROOM_TLS_STRICT=0 headroom proxy --port 8787

The Rust core's ONNX download (cdn.pyke.io) uses a separate TLS stack (rustls / OS trust store), unaffected by HEADROOM_TLS_STRICT. On Windows the corporate root must be in the machine certificate store (browsers already trust it there); or pre-provision ONNX Runtime with ORT_STRATEGY=system + ORT_LIB_LOCATION=/path/to/onnxruntime to skip the download entirely.

headroom learn — mines failed sessions, writes corrections to CLAUDE.local.md (default, gitignored; use --target CLAUDE.md for the shared team file) / AGENTS.md / GEMINI.md.

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

Stack & integrations. Headroom is the proxy — that's what we build and offer, and it compresses everything flowing through it no matter what sits upstream. Our recommended companion is Serena (installed by default when you wrap an agent) for semantic code navigation — plus Ponytail if you want leaner model output. Everything else is your call: Headroom vendors the third-party RTK and lean-ctx binaries for shell-output rewriting, but we don't own or control either project — swap between them with HEADROOM_CONTEXT_TOOL, or turn them off. You're free to attach your own tooling too — code-memory MCP, Graphify, Caveman, or any MCP server — and Headroom compresses downstream of all of it.

git clone https://github.com/chopratejas/headroom.git && cd headroom
uv sync --extra dev && uv run pytest

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

• Discord — questions, feedback, war stories.
• Kompress-v2-base on HuggingFace — the model behind our text compression.

• Claude Code status-line indicator — a Claude Code plugin that shows live Headroom usage in your status line: idle until headroom_compress fires, then the running total of tokens saved.

Apache 2.0 — see LICENSE.