An open-source context compression tool built by Headroom Labs, Inc. The tool output, logs, file reads, and search results that coding agents push into a model are full of repetitive boilerplate. Headroom strips that noise before it reaches the model, shrinking the tokens exchanged without degrading answer quality. A single command such as headroom wrap claude retrofits it onto an existing agent, and running it as a local proxy means not a line of your application code has to change. It is licensed under Apache 2.0 and is free for individuals running it locally. It was released in January 2026.
Key Features
- Compression method chosen automatically per content type: Headroom detects whether it is looking at JSON, source code, logs, search results, plain text, git diffs, or images, and applies a method suited to each. The official documentation cites rough savings of 70–90% for JSON, 40–70% for code (AST-aware compression that preserves structure), 80–95% for logs (keeping failures while thinning out passing entries), and 60–80% for search results (ranking by relevance)
- Four ways to integrate: an agent wrapper (
headroom wraparound Claude Code, Codex, Cursor, Aider, and others), a local compression proxy (no code changes), inline use as a Python or TypeScript library, or an MCP server - Originals can be recovered (CCR): pre-compression content is kept in a Compress-Cache-Retrieve store, and the model is handed a
headroom_retrievetool. When more detail is needed, the model can fetch the original itself, so you are less likely to end up with information silently lost to compression - Works with major agents and frameworks: agents including Claude Code, Cursor, Aider, Codex, Cline, Continue, and OpenHands, plus frameworks such as LangChain, LiteLLM, Vercel AI SDK, Agno, and Strands, along with MCP
- Local processing: compression runs on your own machine and completes before anything reaches the model. It is not an architecture where full text is handed to an external compression service
- Visibility into what was compressed: traces show what shrank and by how much, so you can separate the places it is working from the places it is not
Pricing
| Plan | Price | What’s included |
|---|---|---|
| Open source | $0 | Apache 2.0. Local execution, CLI, proxy, SDKs, and MCP server all available |
| Enterprise | Contact sales | VPC deployment, custom compression pipelines, support with an SLA |
Pricing is as of August 2026. Check the official site for the latest details.
Note that while Headroom itself is free, what compression reduces is the token bill for whichever LLM API you are using. On a flat-rate agent plan, the benefit shows up not as money saved but as more room within a single conversation and more headroom before you hit usage limits.
Pros & Cons
✅ Pros
- Drops in on top of your existing agent — no replacement required, just one command layered over it
- The proxy setup requires no changes to application code
- Because the compression method switches per content type, the effect is large on repetitive, verbose data such as JSON and logs
- CCR lets the model pull the original back when it needs to
- Apache 2.0 open source, so you can inspect exactly what the compression does
⚠️ Cons
- Savings depend heavily on the nature of the data. The official README itself cites a more modest 15–20% for coding-agent use, against 60–95% for JSON data
- Requires Python 3.10 or later, and installing and configuring the CLI takes some familiarity
- Inserting a proxy adds a hop, which adds effort when troubleshooting
- Whether compression ever cuts too deep and loses context has to be verified against your actual workload
- It is still early days since release, so the specification and configuration options may change
Comparison with Similar Services
| Criteria | Headroom | LLMLingua | LiteLLM Proxy | Built-in agent auto-summarization |
|---|---|---|---|---|
| Main role | Context compression layer | Prompt compression library | LLM gateway (routing, caching) | Automatic compaction of conversation history |
| Integration | CLI wrap / proxy / SDK / MCP | Python library | Proxy / SDK | Feature of the agent itself |
| Granularity | Method switches per content type | Compresses the whole prompt with a language model | Compression is not the focus | Summarizes the whole conversation at once |
| Recovering originals | Retrievable from the CCR store | Generally not possible | Not applicable | Not possible |
| License | Apache 2.0 | MIT | Open-source edition available | Depends on the agent |
Headroom is not something you swap in for a model or an agent; it is a middle layer that sits between them and trims waste. It overlaps with gateway products in places, but differs in that its focus is on making the data being sent smaller in the first place.
Who Is It For
- Developers who use coding agents such as Claude Code or Codex daily and worry about context limits or API costs
- Anyone feeding agents large volumes of repetitive, lengthy data such as logs and JSON responses
- Developers who want to add compression to a homegrown agent or workflow with almost no code changes
- Teams that do not want compression to be a black box and prefer open source they can inspect
- Organizations considering in-house operation, VPC deployment, or supported use
Summary
Headroom is a compression layer you slot between a coding agent and the model to cut wasted context. By combining automatic method selection per content type with CCR, which allows originals to be retrieved, it is designed to reduce tokens while limiting the risk of cutting away something that mattered. The effect varies widely with the nature of the data, so the sensible path is to trial it through the proxy on the workloads you actually handle, confirm the savings in the traces, and then commit to it.