Alternatives · Updated 2026-10-21
Best Headroom alternatives in 2026
Headroom is an open-source context-compression layer for coding agents. If you want something hosted, more selective about the question being asked, narrower, or simply different to benchmark against, these are the alternatives worth trying.
The short answer
For hosted, question-aware compression that keeps original lines word for word, SuperCompress. For open-source sandboxing of MCP and tool output, Context Mode. For noisy shell commands only, RTK. For persistent codebase memory instead of compression, NeuralMind. For shorter replies, the Caveman skill. For research-style token pruning you run yourself, LLMLingua-2. For other hosted APIs to compare, Compresr and The Token Company. And for long sessions, your agent's built-in /compact.
What Headroom is
Headroom compresses tool output, logs, files, RAG chunks and conversation history before they reach the model, and keeps the originals so the model can ask for them back. It detects each block's content type and applies a matching compressor. It runs locally as a Python or TypeScript library, a proxy, an MCP server or an agent wrapper (headroom wrap claude), and it is released under the Apache 2.0 licence.
How we picked
We kept tools that compress or avoid the same context Headroom targets, have public documentation, and can be tested on your own agent traffic. Each entry says plainly when it is the better choice.
The alternatives
1. SuperCompress
SuperCompress is the closest like-for-like alternative, with two differences. It is hosted, so there is no proxy to run, and it is query-aware: instead of compressing by content type, it scores each line against the question the agent is answering and keeps the original lines that matter. Coding agents get it through one setup command; apps call the HTTP API.
- Best for: cutting the input tokens an agent or app sends: tool output, file reads, logs, MCP results, retrieved docs
- How it works: a hosted, query-aware model keeps the original lines that answer the current question, word for word, and drops the rest
- Runs: hosted API (
POST /api/v1/compress) plus an MCP tool and hooks for coding agents - Price: 5M free tokens a month, then $0.10 per million, per the site
2. Context Mode
Context Mode is the closest open-source alternative for tool output specifically. Rather than compressing a result in place, it parks the full output in a local sandbox and gives the model a compact version it can search, which suits MCP-heavy agents.
- Best for: agents drowning in large MCP and tool results
- How it works: an open-source MCP server keeps each full tool result in a sandbox outside the conversation, hands the model a compact version, and indexes the original (SQLite FTS5 with BM25) so the agent can pull details back
- Watch out for: its 98% figure comes from the project's own worked examples on supported workflows
3. RTK (Rust Token Killer)
RTK solves a slice of Headroom's job very cheaply. It rewrites shell commands to run through output filters, so git, test and build output arrive smaller. It does not touch file reads, MCP results or history.
- Best for: noisy shell commands such as
git status, test runs and builds - How it works: a hook rewrites Bash commands to run through command-specific output filters
- Watch out for: it only sees Bash calls, so built-in Read, Grep and Glob tools are not filtered; its 60–90% figures are the project's own estimates
4. NeuralMind
NeuralMind takes a different route to the same goal: instead of compressing what the agent reads, it builds a persistent local memory of your codebase so the agent fetches less in the first place. Worth trying on large repositories.
- Best for: large codebases where the agent keeps re-reading the same files
- How it works: persistent, local codebase memory with semantic retrieval, so the agent asks targeted questions instead of loading whole files; it also compresses tool output through a hook
- Watch out for: its 12–50× figures are self-reported, and it is a memory layer more than a compressor
5. Caveman (open-source skill)
Caveman works on the other side of the conversation: it makes agent replies terse. If your bill is dominated by output rather than by what the agent reads, it is worth a test, and it is free.
- Best for: shorter agent replies, at no cost
- How it works: a skill that makes the agent answer in terse, caveman-style prose, plus local shrinking of some tool output
- Watch out for: an independent JetBrains A/B test measured an 8.5% output saving against the advertised 65%, and the skill itself adds input tokens every turn
6. LLMLingua-2
LLMLingua-2 from Microsoft Research is the reference point for learned prompt compression. It prunes individual tokens with a small classifier. It is a good fit for natural-language prompts in a pipeline you control, and a poorer fit for code and logs, where deleting characters can break meaning.
- Best for: research and self-hosted pipelines over natural-language prompts
- How it works: a small token-classification model, distilled from GPT-4, deletes low-value tokens
- Watch out for: token-level deletion can break code, IDs and stack traces, because individual characters disappear rather than whole lines
7. Compresr
Compresr is another hosted, query-aware compression API, reached through a Python SDK. If you are choosing a hosted service, benchmark it against SuperCompress on the same traffic.
- Best for: a hosted compression API from a second vendor, to benchmark against
- How it works: query-aware compression through a Python SDK
- Runs: hosted, with an API key from its dashboard
8. The Token Company (bear-1.1)
The Token Company's bear-1.1 is a hosted compression endpoint aimed at long prompts. Like Compresr, it is worth running against your own traffic if you are comparing hosted services.
- Best for: a hosted compression endpoint for long natural-language prompts
- How it works: you send text to the bear compression API and pass the shorter result to your model
- Watch out for: its 66% LongBench v2 result is the company's own, and its published pricing descriptions differ, so confirm billing before you commit
9. Built-in compaction (/compact)
Every major coding agent can summarize its own history (/compact in Claude Code). It is built in and helps with very long sessions, but writing the summary is an extra model call, and it rewrites rather than extracts, so precise details can be lost.
- Best for: sessions that have simply grown too long
- How it works: the agent asks the model to summarize the conversation so far, then continues from the summary
- Watch out for: writing the summary is itself a model call, and summaries rewrite the original lines, so exact errors, paths and IDs can be lost
Side by side
| Tool | What it shrinks | How | Runs |
|---|---|---|---|
| Headroom | Tool output, files, logs, RAG, history | Content-type compressors | Local |
| SuperCompress | Tool output, files, logs, MCP results, docs | Keeps original lines that answer the question | Hosted |
| Context Mode | MCP and tool output | Sandboxes output, indexed retrieval | Local |
| RTK | Shell command output | Command-specific filters | Local |
| NeuralMind | Repeated code reads | Persistent codebase memory | Local |
| Caveman | Agent replies | Terse-reply skill | Local |
| LLMLingua-2 | Prompt text | Learned token pruning | Self-hosted |
| Compresr | Context | Query-aware compression | Hosted |
| The Token Company | Prompt text | Hosted compression model | Hosted |
| /compact | Session history | Summarization | Inside the agent |
How to pick fairly
Every tool here publishes its own headline numbers, and independent studies have found that fewer tokens do not always mean a smaller bill once prompt caching, retries and extra agent turns are counted (see PointFive's end-to-end study and JetBrains' rtk trial; neither tested SuperCompress). Run the same tasks with and without a tool, check that the tasks still succeed, and compare your provider bill per finished task. That test matters more than any list, including this one.
Disclosure: SuperCompress is our product, so we are not neutral. We describe every other tool from its own documentation and public reporting, link the sources, and say where another tool is the better fit. Plans and prices change; check each vendor before you decide.
Try SuperCompress
One command installs the CLI and wires the hosted compressor into every coding agent it finds (Claude Code, Cursor, Codex, Windsurf, Cline, Gemini CLI, OpenCode and more). supercompress doctor then shows a health check per agent.
npm install -g supercompress-proxy && supercompress setupBuilding your own app instead? Call the hosted API with a key from the dashboard.
FAQ
What is the best Headroom alternative?
For hosted, question-aware compression of the same context Headroom targets, SuperCompress. For free, open-source options, Context Mode (tool output), RTK (shell output), NeuralMind (codebase memory) or Caveman (replies).
Is there a hosted Headroom alternative?
Yes. SuperCompress, Compresr and The Token Company all offer hosted compression APIs, so there is no local proxy to run.
Is Headroom free?
Headroom is open source under the Apache 2.0 licence. Its README also describes a separate offering for organizations.
Can I use Headroom and SuperCompress together?
You can, but they compress the same kinds of context, so test them one at a time first and keep whichever gives the lower bill per finished task.