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.

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)

```bash

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:

```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. For Claude Code, Serena is registered for the wrapped project only (as a local-scope MCP server in ~/.claude.json). Use --code-memory-scope user to make it available in every project, or --code-memory none to skip it. headroom unwrap removes either registration.

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:

bash 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.

bash 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 hot-syncs your current settings to the running proxy over a loopback POST /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:

bash headroom learn --verbosity # dry run — preview what it found headroom learn --verbosity --apply # save it and apply it to the running proxy

A proxy in cache mode (the default) never re-reads the learned level, because a level that changes mid-conversation would bust the prompt cache. There --apply pins it with HEADROOM_VERBOSITY_LEVEL; set that variable yourself to keep it after a restart.

Measuring 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:

```bash 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.

→ Output token reduction

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 [^third-party-upstream] | | 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 |

[^third-party-upstream]: headroom wrap routes OpenAI-compatible traffic to the default upstream (https://api.openai.com/v1). For a third-party OpenAI-compatible provider (DeepSeek, Together, OpenRouter, a self-hosted gateway), pass --openai-api-url https://api.deepseek.com/v1 — or set OPENAI_TARGET_API_URL before wrapping — otherwise your provider key is sent to OpenAI and rejected with 401 Incorrect API key provided. See OpenCode + DeepSeek.

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.

For Anthropic /v1/messages, --mode cache skips automatic --memory context injection so the provider prefix remains stable. OpenAI chat/responses and Gemini append memory to the live-zone tail. Use --mode token when you need automatic memory context on the Anthropic path.

GitHub Copilot CLI subscription mode

Headroom can route Copilot CLI subscription traffic through the local proxy: ```bash headroom copilot-auth login headroom wrap copilot --subscription -- --model gpt-4o ``` 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=...` 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: ```bash export GITHUB_COPILOT_ENTERPRISE_DOMAIN=ghe.example.com export GITHUB_COPILOT_ENTERPRISE_URL=https://ghe.example.com ``` For 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. ```bash headroom copilot-auth login headroom wrap vscode ``` Keep the command running and use Copilot normally. The short-lived upstream Copilot token is held only in the proxy process. [Full guide →](https://docs.headroomlabs.ai/docs/vscode-copilot)

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: ```bash pip install "headroom-ai[proxy]" headroom wrap vscode-claude # Optional: persist Claude Code's client-owned 1M model selector headroom wrap vscode-claude --1m ``` Reload 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 →](https://docs.headroomlabs.ai/docs/vscode-claude-code)

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](https://docs.headroomlabs.ai/docs/strands) | | 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 in `headroom/providers/registry.py` - `wrap.py`, `client.py`, `cli/proxy.py` and `proxy/server.py` delegate env shaping, API target normalisation, backend selection and transport dispatch

Install

bash 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: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+.

[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: ```bash 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 --version ``` Codex and other MCP clients often cannot inherit an interactive shell `PATH`. Configure the absolute path returned by `command -v headroom`: ```toml [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: ```bash 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

```bash 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` 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: ```bash # 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 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. **At runtime** Headroom verifies upstream TLS against the operating system's certificate store — where IT installs the Zscaler / Netskope / Palo Alto root — plus certifi's public roots, so a managed machine normally needs no setup. `headroom doctor --network` shows who signed each upstream certificate, whether Headroom trusts it, and whether a gateway block page is in the way. If the root is not in the OS store, export it as PEM and set `HEADROOM_CA_BUNDLE=/path/to/root.pem` (`NODE_EXTRA_CA_CERTS` also works). Full guide and the domain list for IT: [Corporate networks](https://docs.headroomlabs.ai/docs/corporate-networks). Two download hosts can still need attention: - **`cdn.pyke.io`** — the ONNX Runtime for the Rust core, fetched with Mozilla's roots only. Pre-provide it with `ORT_STRATEGY=system` and `ORT_LIB_LOCATION=/path/to/onnxruntime`. - **`huggingface.co`** — the `kompress-base` model (Python, OS store) and the Rust core's embedding model (Mozilla's roots only). If the domain is blocked, pre-download and run with `HF_HUB_OFFLINE=1`, or point `HF_ENDPOINT` at a trusted mirror. Running with compression disabled (pure gateway) needs neither asset. **Intel macOS: no prebuilt ONNX Runtime ([#941](https://github.com/headroomlabs-ai/headroom/issues/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: ```bash 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"** only occurs with `HEADROOM_CERT_STORE=bundled` (the OS store verifies these roots fine). 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. ```bash HEADROOM_TLS_STRICT=0 headroom proxy --port 8787 ``` The Rust core's downloads use rustls with Mozilla's roots and are unaffected by `HEADROOM_TLS_STRICT` and the CA variables; pre-provision ONNX Runtime with `ORT_STRATEGY=system` and models via `HF_ENDPOINT` as above.

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, HEADROOM_TELEMETRY=off, the DO_NOT_TRACK=1 convention, or HEADROOM_OFFLINE=1. The full field list is in the proxy docs.

Headroom for teams

Headroom OSS is built for one developer on one laptop. Headroom Enterprise is built for the whole team.

| | Open source | Enterprise | |---|---|---| | Savings | Compression, plus tool search on Anthropic requests (on by default) | Also: tool search for every model, harness tuning, extra compressors | | Model routing | Rule-based and opt-in: turn it on with HEADROOM_MODEL_ROUTER_ENABLED=1 and write the rules in HEADROOM_MODEL_ROUTES | Automatic: a cheaper model per request when quality allows, with a shadow mode that shows the savings before you turn it on | | Security | — | Detect and strip prompt injection and risky tool calls | | Rollout | Per laptop | Managed or in your VPC, SSO, central config, org-wide savings | | Support | GitHub, Discord | Dedicated engineers, onboarding, priority response |

3 months of Enterprise free. Companies that sign a 12-month Enterprise plan get their first 3 months free. One perk per company.

Claim 3 months free → · or email hello@headroomlabs.ai with your stack and monthly LLM spend.

Everything in this repo stays open source under Apache 2.0.

Documentation

| Start here | Go deeper | |---|---| | Quickstart | Architecture | | Proxy | How compression works | | MCP tools | CCR — reversible compression | | Memory | Cache optimization | | Failure learning | Benchmarks | | Configuration | Limitations | | Persistent installs | Savings analytics |

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

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

Devcontainers 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.