Quickstart · 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|zcodein one command; undo withheadroom unwrap <tool>. - MCP server —
headroom_compress,headroom_retrieve,headroom_statsfor 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 toCLAUDE.local.md(default, gitignored),CLAUDE.md,AGENTS.md,GEMINI.mdorGROK.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_retrievewhen 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
# 2 — Pick a mode
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 — Check it and watch the savings
headroom doctor # health check — confirms routing works
headroom perf
headroom dashboard # live savings (proxy must be running)Inline, in Python:
from headroom import compress
from openai import OpenAI
messages = [{"role": "user", "content": "Analyze these results"}]
result = compress(messages, model="gpt-4o")
client = OpenAI()
response = client.chat.completions.create(model="gpt-4o", messages=result.messages)
print(f"Saved {result.tokens_saved} tokens ({result.compression_ratio:.0%})")Launch a wrapped agent session each time, so the setup runs. headroom wrap
starts a local proxy, installs Serena for
semantic code navigation, and launches the agent configured to route through
Headroom. Serena is registered at user scope (for Claude Code, in
~/.claude.json), so it stays available in your other projects until you run
headroom unwrap. Skip it with --code-memory none.
The headroom CLI ships only in the PyPI package. The npm headroom-ai package
is the TypeScript SDK — a library you import
(import { compress } from 'headroom-ai') — and provides no headroom command.
Proof
Four scenarios built from real MCP server output formats, measured with the
provider tokenizer and the shipped compress(). Seeded and offline, so you get
the same numbers we did:
uv run python benchmarks/index_proof_table.py --seed 20260902| Scenario | Before | After | Saved |
|---|---|---|---|
| Code search (100 results) | 17,199 | 13,597 | 21% |
| SRE incident debugging | 55,957 | 24,340 | 57% |
| Codebase exploration | 58,801 | 33,895 | 42% |
| GitHub issue triage | 46,067 | 32,429 | 30% |
Savings scale with how repetitive the payload is. Repeated JSON arrays and log
lines clear 90% in benchmarks/bench_latency.py; prose and already-dense output
compress very little. Run headroom savings against your own traffic for the
number that applies to you.
Compression costs well under a millisecond — 0.21 ms p50 on a 10K-token JSON search result, 1.4 ms at 100K tokens — so it does not show up in agent latency.
Accuracy. python -m headroom.evals suite --tier 1:
| Benchmark | Category | N | Baseline | Headroom | Delta |
|---|---|---|---|---|---|
| GSM8K | Math | 100 | 0.870 | 0.870 | ±0.000 |
| TruthfulQA | Factual | 100 | 0.530 | 0.560 | +0.030 |
| SQuAD v2 | QA | 100 | — | 97% | at 19% compression |
| BFCL | Tools | 100 | — | 97% | at 32% compression |
At N=100 a delta of ±0.03 falls inside the confidence interval, so TruthfulQA shows no detectable difference rather than an improvement. Methodology →
Output token reduction
Everything above shrinks the prompt you send. You also pay for every token the model writes back, and on Opus-class models output costs 5× input. Much of that output is ceremony: "Great, let me…" preambles, code re-printed straight back at you, and deep reasoning spent on routine steps like reading a file.
Headroom trims it from the proxy, with no change to your 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 dials thinking effort down when a turn is only the model resuming after a tool result — a file read, a passing test. New questions and errors keep full effort.
Both apply to Anthropic /v1/messages and to OpenAI-compatible
/v1/chat/completions and /v1/responses. Effort routing uses
reasoning_effort on OpenAI and thinking.budget_tokens / output_config.effort
on Anthropic, with the same clamp-only invariant and the same output_shaper:*
labels on both paths.
export HEADROOM_OUTPUT_SHAPER=1 # off by default
headroom proxy --port 8787Already running a proxy? These switches are read live on every request, so a proxy that
headroom wrapreused rather than started would not see a value you export afterwards — its environment was snapshotted at launch.headroom wraphot-syncs your current settings to the running proxy over a loopbackPOST /admin/runtime-env, so they take effect with no restart and no dropped requests. On a shared proxy these overrides are global; the last explicit setting wins.
Terseness you didn't have to configure. People rarely state how terse they
want answers — they show it, by interrupting long replies or moving on before
they could have read them. headroom learn --verbosity reads past sessions and
picks the level:
headroom learn --verbosity # dry run — preview what it found
headroom learn --verbosity --apply # save it; the proxy picks it upMeasuring it. Output savings are counterfactual — we never see what the model would have written — so Headroom reports an estimate with a confidence range and labels it as one:
headroom output-savings
# Reduction: 31.7% (95% CI 27.7% … 35.7%) [estimated]For a measured number instead, hold out 10% of conversations as an unshaped
control: export HEADROOM_OUTPUT_HOLDOUT=0.1. The dashboard's Output Tokens
Saved card then reads measured rather than estimated, with the band.
Agent compatibility
| Agent | headroom wrap |
Notes |
|---|---|---|
| Claude Code | ✅ | --memory · --code-graph · --1m · --tool-search |
| Codex | ✅ | shares memory with Claude |
| Grok CLI | ✅ | routes via GROK_MODELS_BASE_URL |
| Cursor | Manual setup | starts the proxy and prints base URLs for Cursor settings |
| Aider | ✅ | starts proxy + launches |
| Copilot CLI | ✅ | starts proxy + launches |
| VS Code Copilot | ✅ | transparent proxy; keeps the selected model |
| OpenClaw | ✅ | installs as a ContextEngine plugin |
| OpenCode | ✅ | injects config · starts proxy + launches |
| Cline | ✅ | starts proxy + injects config |
| Continue | ✅ | starts proxy + injects config |
| Goose | ✅ | starts proxy + launches |
| OpenHands | ✅ | starts proxy + launches |
| Mistral Vibe | ✅ | starts proxy + launches |
| Oh My Pi | ✅ | injects config · starts proxy + launches |
| Cortex Code | Library only | 60–65% savings in library mode; no wrap |
| Kimi CLI | ✅ | OAuth bearer forwarded — log in once |
| ZCode | ✅ | starts the proxy and prints base URLs for ZCode settings |
Any OpenAI-compatible client works through headroom proxy. MCP-native clients:
headroom mcp install. Undo durable wrapping with headroom unwrap <tool>
(claude, copilot, codex, grok, kimi, omp, opencode, openclaw,
zcode). Registry authors should use the canonical server.json
rather than reconstructing the headroom mcp serve contract from prose.
GitHub Copilot CLI subscription mode
Headroom can route Copilot CLI subscription traffic through the local proxy:
headroom copilot-auth login
headroom wrap copilot --subscription -- --model gpt-4oThe 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=... at launch. headroom copilot-auth login stores a
Headroom-specific Copilot OAuth token, rather than relying on generic GitHub or
Copilot CLI tokens that can read account metadata but are still rejected by
Copilot's token-exchange endpoint.
For GitHub Enterprise Server or a custom-domain Copilot deployment, set one of these before launching. If both are set, the URL wins:
export GITHUB_COPILOT_ENTERPRISE_DOMAIN=ghe.example.com
export GITHUB_COPILOT_ENTERPRISE_URL=https://ghe.example.comFor GitHub.com Enterprise Cloud URLs such as
github.com/enterprises/your-enterprise, set neither — Headroom uses GitHub's
normal token-exchange endpoint and the Copilot API endpoint advertised for the
signed-in account.
Platform support. macOS auth reuse through Copilot CLI Keychain storage and
Windows device authentication are live-tested. Copilot CLI 1.0.81 does not expose
its Windows login through the legacy Credential Manager schema Headroom reads, so
run headroom copilot-auth login on Windows. Linux Secret Service /
secret-tool reuse is implemented but not yet validated on a real desktop. In
Docker and CI, pass an explicit GITHUB_COPILOT_TOKEN or
GITHUB_COPILOT_GITHUB_TOKEN instead of relying on host keychain access.
GitHub Copilot in VS Code
Headroom overrides Copilot's API proxy endpoint, so the VS Code model picker stays authoritative. GPT-5.5, GPT-5.6 Luna/Sol/Terra, Claude Sonnet/Opus and other Copilot models keep their original model IDs while traffic passes through the local compression proxy. Headroom does not patch VS Code or change Codex settings.
headroom copilot-auth login
headroom wrap vscodeKeep the command running and use Copilot normally. The short-lived upstream Copilot token is held only in the proxy process. Full guide →
Claude Code in VS Code
The official Claude Code extension embeds Claude Code and reads the same user settings as the CLI. Install the proxy extra, then run the wrapper from the project you will open in VS Code:
pip install "headroom-ai[proxy]"
headroom wrap vscode-claude
# Optional: persist Claude Code's client-owned 1M model selector
headroom wrap vscode-claude --1mReload the VS Code window on first run. Keep the wrapper terminal running while
you use the Claude Code panel; the dashboard or proxy log printed at startup
shows requests and savings. Your Anthropic authentication and selected model are
preserved. Ctrl+C stops the proxy; headroom unwrap vscode-claude restores the
settings that existed before setup.
With --1m, Headroom writes the resolved [1m] model selector to Claude Code's
top-level user setting and restores the exact prior model on
headroom unwrap vscode-claude or when setup runs again without --1m. Use
headroom wrap vscode-claude --no-configure --1m to print the setting without
writing files.
Claude Code and Anthropic own 1M model support and account eligibility. Local tests prove persisted settings and restoration, not a live entitled VS Code session or the resulting context window. Full guide →
When to use · when to skip
Good fit if you run coding agents daily and want savings without touching your code, work across several agents and want one shared memory, or need compression that is reversible — originals stay retrievable through CCR for the configured TTL.
Skip it if you only use one provider's native compaction and don't need cross-agent memory, or work in a sandbox where local processes can't run.
Headroom pays off on long agent sessions with heavy tool output. Short
conversational exchanges, prose, and already-dense payloads see little or no
reduction, and blocks under min_input_words come back byte-identical.
Limitations has the full list.
Integrations — drop Headroom into any stack
| Your setup | Hook in with |
|---|---|
| Any Python app | compress(messages, model=…) |
| Any TypeScript app | await compress(messages, { model }) |
| Anthropic / OpenAI SDK | withHeadroom(new Anthropic()) · withHeadroom(new OpenAI()) |
| Vercel AI SDK | wrapLanguageModel({ model, middleware: headroomMiddleware() }) |
| LiteLLM | litellm.callbacks = [HeadroomCallback()] |
| LangChain | HeadroomChatModel(your_llm) |
| Agno | HeadroomAgnoModel(your_model) |
| Strands | Strands guide |
| ASGI apps | app.add_middleware(CompressionMiddleware) |
| Multi-agent | SharedContext().put / .get |
| MCP clients | headroom mcp install |
What's inside
- SmartCrusher — universal JSON: arrays of dicts, nested objects, mixed types. It keeps error items, values outside the normal statistical range, and first/last boundaries, selected from field-variance statistics rather than a keyword list.
- CodeCompressor — AST-aware for Python, JS/TS, Go, Rust, Java, C/C++, C#, PHP.
- Kompress-v2-base — our HuggingFace model, trained on agentic traces.
- Image compression — 40–90% reduction through a trained ML router.
- CacheAligner — flags volatile content that would bust a provider KV-cache prefix; never rewrites prompts.
- Live-zone compression — only new bytes are compressed (fresh tool output, the latest turn). The frozen prefix stays byte-identical, so the provider cache survives, and history is never dropped.
- CCR — reversible compression; the model retrieves originals on demand.
- Cross-agent memory — shared store with agent provenance and auto-dedup.
- SharedContext — compressed context passing across multi-agent workflows.
headroom learn— plugin-based failure mining for Claude, Codex and Gemini.
Pipeline internals
One request lifecycle is shared by compress(), the SDKs and the proxy:
Setup → Pre-Start → Post-Start → Input Received → Input Cached →
Input Routed → Input Compressed → Input Remembered → Pre-Send →
Post-Send → Response Received
- Transforms do the work: CacheAligner → ContentRouter → SmartCrusher / CodeCompressor / Kompress-base, live-zone only. IntelligentContext and RollingWindow were retired in PR-B1.
- Pipeline extensions observe or customise lifecycle stages through
on_pipeline_event(...). - Compression hooks sit alongside the lifecycle as an additional extension seam.
- Proxy extensions are the integration seam for ASGI middleware, routes and startup policy.
Provider- and tool-specific behaviour lives under headroom/providers/, so core
orchestration stays focused on lifecycle, sequencing and policy:
- CLI/tool slices —
headroom/providers/claude,copilot,codex,grok,openclaw - Provider runtime slices —
headroom/providers/claude,gemini, with shared backend dispatch inheadroom/providers/registry.py wrap.py,client.py,cli/proxy.pyandproxy/server.pydelegate env shaping, API target normalisation, backend selection and transport dispatch
Install
uv tool install --python 3.13 "headroom-ai[all]" # CLI, isolated app env
pip install "headroom-ai[all]" # Python, everything — includes the CLI
npm install headroom-ai # TypeScript SDK (library only)
docker pull ghcr.io/headroomlabs-ai/headroom:latestGranular 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+.
[all]covers the core stack but not the framework adapters. Install those separately:pip install "headroom-ai[langchain]", and likewise[agno],[strands],[anyllm],[bedrock].
→ Installation guide — Docker tags, persistent service, PowerShell, devcontainers.
uv, pipx, and MCP clients that don't inherit your PATH
Prefer uv tool install for the CLI 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 3.13 is not already available
uv tool install --python 3.13 "headroom-ai[all]"
uv tool update-shell # if ~/.local/bin is not on PATH
headroom --versionCodex and other MCP clients often cannot inherit an interactive shell PATH.
Configure the absolute path returned by command -v headroom:
[mcp_servers.headroom]
command = "/Users/you/.local/bin/headroom"
args = ["mcp", "serve"]command = "headroom" only works when the client starts with a PATH that
already includes the uv tool directory.
With pipx, choose the interpreter explicitly:
pipx install --python python3.13 "headroom-ai[all]"Native wheels currently cover macOS Apple Silicon and Linux. On Intel macOS, use the Docker-native install until native wheel support lands.
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, older cloud VMs)
Headroom falls back to its non-ONNX paths — BM25 relevance, heuristic detection —
rather than crashing. arm64 and Apple Silicon need no AVX2.
Updating
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-releasesheadroom update works out how Headroom was installed (pip/venv, pip --user,
pipx, uv tool) and runs the matching upgrade on 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 prints a one-line "update available" notice at startup. It checks
PyPI at most once a day, in the background, and never blocks. Opt out with
HEADROOM_UPDATE_CHECK=off; it is also skipped in --stateless mode and CI.
Corporate networks and SSL inspection
If pip install "headroom-ai[all]" fails with CERTIFICATE_VERIFY_FAILED
(unable to get local issuer certificate), your network runs SSL inspection — a
MITM proxy presenting a company CA. The build backend (maturin) downloads
rustup over a connection your TLS stack does not trust. Install Rust first so
the build never fetches it:
# macOS / Linux
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && rustup default stable
# Windows
winget install Rustlang.Rustup && rustup default stableRestart your shell, then install. A prebuilt wheel avoids the Rust build
entirely: pip install --only-binary headroom-ai headroom-ai. Wheels are
published for Windows (win_amd64), Linux (x86_64 / aarch64) and macOS
(Apple Silicon and Intel), so those platforms never need a local Rust toolchain —
the Rust-first step above is only for the sdist fallback when no wheel matches.
Two runtime assets are fetched over TLS. If they are blocked, trust your
corporate CA through REQUESTS_CA_BUNDLE / SSL_CERT_FILE / CURL_CA_BUNDLE:
cdn.pyke.io— the ONNX Runtime for the Rust core. Or pre-provide it withORT_STRATEGY=systemandORT_LIB_LOCATION=/path/to/onnxruntime.huggingface.co— thekompress-basemodel. Pre-download it and run withHF_HUB_OFFLINE=1, or pointHF_ENDPOINTat a trusted mirror.
Running with compression disabled (pure gateway) needs neither asset.
Intel macOS: no prebuilt ONNX Runtime (#941).
ort-sys ships no prebuilt binary for x86_64-apple-darwin, so a source build
fails by default even outside a corporate proxy. Point it at a system runtime:
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 — without it ORT_STRATEGY=system still
attempts static linking, which the Homebrew keg does not provide.
"Basic Constraints of CA cert not marked critical" is a different failure. 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, and adding it to a CA bundle
changes nothing. Python 3.13 with OpenSSL 3.x enables VERIFY_X509_STRICT by
default, which enforces RFC 5280 §4.2.1.9: a CA cert's basicConstraints must be
marked critical. Inspection roots such as Zscaler set CA:TRUE without the
critical bit, so the chain is rejected.
HEADROOM_TLS_STRICT=0 clears 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.
HEADROOM_TLS_STRICT=0 headroom proxy --port 8787The Rust core's ONNX download uses a separate TLS stack (rustls / OS trust store)
and is 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 to skip the download.
headroom learn
headroom learn mines failed sessions and writes corrections to
CLAUDE.local.md (default, gitignored; use --target CLAUDE.md for the shared
team file), AGENTS.md or GEMINI.md.
→ Failure learning
Telemetry
An anonymous beacon is on by default. It reports how compression behaved: ratios, counters, provider and model IDs, OS and architecture. It never sends prompts, completions, code or file paths. It exists so we can see when a release regresses a compression ratio across real workloads rather than only our own test corpus.
Turn it off with HEADROOM_BEACON=off, the DO_NOT_TRACK=1 convention, or
--offline. The full field list is in
the proxy docs.
Headroom for teams
Headroom OSS is built for individual developers: run headroom proxy or
headroom wrap on your laptop and start cutting tokens in minutes, free and
local-first.
Running it across an engineering org is a different job — a shared always-on deployment, centralised config and version rollout, org-wide savings dashboards, SSO and access control, air-gapped and VPC installs, and someone to call. We help companies with that, 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 — email hello@headroomlabs.ai with your stack and rough monthly LLM spend.
Everything in this repo stays open source under Apache 2.0. The managed offering is for teams that would rather have it deployed, supported and scaled for them.
Documentation
Compared to
Headroom runs locally, covers every content type, works with every major framework, and is reversible.
| Scope | Deploy | Local | Reversible | |
|---|---|---|---|---|
| Headroom | All context — tools, RAG, logs, files, history | Proxy · library · middleware · MCP | Yes | Yes |
| Compresr, Token Co. | Text sent to their API | Hosted API call | No | No |
| OpenAI Compaction | Conversation history | Provider-native | No | No |
Headroom is the proxy, and it compresses everything flowing through it whatever sits upstream. Our recommended companion is Serena for semantic code navigation, installed by default when you wrap an agent, plus Ponytail if you want leaner model output. Everything else is your call — attach a code-memory MCP, Graphify, Caveman, or any other MCP server, and Headroom compresses downstream of all of it.
Contributing
git clone https://github.com/headroomlabs-ai/headroom.git && cd headroom
uv sync --extra dev && uv run pytestDevcontainers in .devcontainer/ (default, plus memory-stack with Qdrant and
Neo4j). See CONTRIBUTING.md.
Community
- Discord — questions, feedback, war stories.
- Kompress-v2-base on HuggingFace — the model behind text compression.
- Claude Code status-line plugin — live token savings in your status line, by @Ship-Wright.
License
Apache 2.0 — see LICENSE.
