Headroom Alternatives: 7 Tools Compared (2026)

What Headroom is

An Apache-2.0 compression layer that runs on your machine as a Python or TypeScript library, a local proxy, an MCP server or an agent wrapper (headroom wrap claude). It routes each block to a compressor for its type (JSON, code, logs, prose) and keeps originals retrievable. Source: Headroom.

People look for alternatives when they want a different trade-off: python runtime and a local model to install and maintain; compression is by content type rather than by the current question; more moving parts to undo when uninstalling.

TL;DR: one line per tool

• SuperCompress: Best if your agent keeps pulling big tool results, logs or retrieved docs and only a few lines matter for the question at hand, and you want those lines kept verbatim.
• Tokenade: Best if you want one local binary that bundles several token levers (search, filtering, compaction) across many agents.
• Caveman: Best if your main goal is shorter model replies and you do not mind the terse register.
• LLMLingua: Best if you run your own Python pipeline, can host a small model next to it, and want a well-cited research method.
• rtk: Best if your pain is noisy shell command output and you want a free, local, rule-based filter.
• claude-token-efficient: Best if you want the lightest possible experiment in Claude Code with nothing to install.
• claude-context: Best if retrieval quality is the goal and you already run, or are happy to run, vector infrastructure.

Disclosure: SuperCompress publishes this page and is listed first because it is ours. Every claim about other tools comes from their own docs or repositories as of 2026-10-31; tell us if anything is out of date and we will fix it.

How we compared them

• What it optimizes: input context, shell output, code reads, retrieval or model replies are different problems.
• Fidelity: whether original lines survive verbatim, or tokens are dropped inside lines.
• Where it runs: on your machine or as a hosted service.
• Setup weight and agent coverage.
• Licence.

The alternatives

1. SuperCompress (our product)

Best if your agent keeps pulling big tool results, logs or retrieved docs and only a few lines matter for the question at hand, and you want those lines kept verbatim.

Hosted, query-aware context compression. Your agent or app sends a bulky block (tool output, a log, a diff, a file read, an MCP result, retrieved docs) with the current question, and the hosted Neural Keep model keeps the original lines that answer it, word for word, and drops the rest.

• How it works: Query-aware extraction of original lines
• Runs: Hosted API, plus an MCP tool and hooks for coding agents
• Setup: npm install -g supercompress-proxy && supercompress setup
• Licence: Hosted service

Strengths:

• Keeps original lines verbatim, so error messages, paths, IDs and code survive exactly
• Query-aware: what is kept depends on the current question, not only on the content type
• One setup command wires an MCP tool into each detected coding agent (Claude Code, Cursor, Codex, Windsurf, Cline, Gemini CLI, OpenCode and more), plus hooks where supported
• HTTP API returns original_tokens and kept_tokens so you can measure on your own traffic
• Works with any model provider because the output is plain text

Limitations:

• Hosted: the context you compress is sent to the SuperCompress API
• Compresses input only; it does not change how verbose model replies are
• Not a code index or retrieval system; it shrinks what is already being sent

2. Tokenade

Best if you want one local binary that bundles several token levers (search, filtering, compaction) across many agents.

A local Rust binary that sits between an agent and its tools, combining code search, output filtering, skeleton compression, sandboxed execution and MCP proxying, with a savings dashboard. MIT or Apache-2.0. Its author also maintains the THOL leaderboard it cites, and discloses that.

• How it works: Retrieval plus output filtering plus compression
• Runs: Locally (binary with agent hooks)
• Setup: See the Tokenade install guide
• Licence: MIT or Apache-2.0

Strengths:

• Local and dependency-light
• Bundles several mechanisms in one install
• Publishes agent-facing install briefs

Limitations:

• The benchmark it leads is maintained by its own author (disclosed)
• Many mechanisms means more behaviour to understand and tune
• Not query-aware extraction of a single large result

3. Caveman

Best if your main goal is shorter model replies and you do not mind the terse register.

A skill for coding agents that switches the model's replies to a terse, telegraphic style, cutting output tokens.

• How it works: Terse output style
• Runs: Locally, as an agent skill or prompt
• Setup: Install the skill for your agent
• Licence: Open source

Strengths:

• Near zero setup and footprint
• Works across many agents
• Easy to try and remove

Limitations:

• Only touches output; agent spend is usually dominated by input context
• Terse replies are not to everyone's taste
• Does nothing about big tool results or file reads

4. LLMLingua

Best if you run your own Python pipeline, can host a small model next to it, and want a well-cited research method.

Microsoft Research's MIT-licensed Python library for prompt compression. A small language model scores tokens and drops the ones a larger model can do without. It bundles LLMLingua, LongLLMLingua (question-aware, for long retrieval contexts) and LLMLingua-2 (a faster token classifier).

• How it works: Token-level pruning by a small model
• Runs: Locally, in your Python pipeline
• Setup: pip install llmlingua
• Licence: MIT

Strengths:

• Well-known, peer-reviewed methods
• Runs locally on CPU or GPU
• LangChain and LlamaIndex integrations

Limitations:

• Token pruning can break code, paths and identifiers because it drops tokens inside lines
• Research code; releases have been infrequent, so expect to pin versions
• No coding-agent setup out of the box

5. rtk

Best if your pain is noisy shell command output and you want a free, local, rule-based filter.

A Rust CLI proxy that filters and compresses shell command output (git, test runners, package managers and more) before it reaches the model, with command-specific filters and a transparent hook.

• How it works: Rule-based filters per shell command
• Runs: Locally (CLI proxy and hook)
• Setup: brew, cargo or the install script
• Licence: Open source

Strengths:

• Light, fast, local install
• Deep command-specific filtering
• Per-command savings analytics

Limitations:

• Only shell output; no MCP results, file reads or retrieved docs
• Rules are fixed per command, not aware of the current question
• Unknown commands pass through unfiltered

6. claude-token-efficient

Best if you want the lightest possible experiment in Claude Code with nothing to install.

A drop-in CLAUDE.md with compact behaviour rules that suppress filler, restated questions and over-engineering in Claude Code replies.

• How it works: Instruction file (CLAUDE.md rules)
• Runs: Inside Claude Code, as a project file
• Setup: Copy CLAUDE.md into your project root
• Licence: Open source

Strengths:

• Nothing to install or audit
• Easy to tweak or remove
• Free

Limitations:

• Claude Code only
• The file itself adds input tokens every turn
• Does nothing about file reads, command output or MCP output

7. claude-context

Best if retrieval quality is the goal and you already run, or are happy to run, vector infrastructure.

An MCP server from Zilliz that indexes your codebase for hybrid BM25 plus vector search with AST-aware chunking, so the agent retrieves relevant code instead of reading whole files.

• How it works: Semantic code retrieval (vector plus BM25)
• Runs: MCP server backed by Milvus or Zilliz Cloud
• Setup: Add the MCP server, plus a vector DB and an embedding API key
• Licence: Open source (needs vector DB and embeddings)

Strengths:

• Sends less in the first place by retrieving relevant chunks
• Code-aware chunking
• Mature vector backend

Limitations:

• Needs a Milvus or Zilliz Cloud instance and an embedding key
• Does not compress tool output, logs or MCP results
• Index has to stay fresh as code changes

At a glance

Tool	Best if	What it optimizes	Runs	Licence
SuperCompress	your agent keeps pulling big tool results, logs or retrieved docs and only a few lines matter for the question at hand, and you want those lines kept verbatim.	Input context (tool output, logs, files, MCP results, RAG)	Hosted API, plus an MCP tool and hooks for coding agents	Hosted service
Tokenade	you want one local binary that bundles several token levers (search, filtering, compaction) across many agents.	Tool calls, file reads, command and MCP output	Locally (binary with agent hooks)	MIT or Apache-2.0
Caveman	your main goal is shorter model replies and you do not mind the terse register.	Model output (replies)	Locally, as an agent skill or prompt	Open source
LLMLingua	you run your own Python pipeline, can host a small model next to it, and want a well-cited research method.	Prompts and retrieved context	Locally, in your Python pipeline	MIT
rtk	your pain is noisy shell command output and you want a free, local, rule-based filter.	Shell command output	Locally (CLI proxy and hook)	Open source
claude-token-efficient	you want the lightest possible experiment in Claude Code with nothing to install.	Model output style	Inside Claude Code, as a project file	Open source
claude-context	retrieval quality is the goal and you already run, or are happy to run, vector infrastructure.	What code gets read	MCP server backed by Milvus or Zilliz Cloud	Open source (needs vector DB and embeddings)
Headroom	you want a broad, local, open-source compression layer and are happy to run a Python runtime and proxy.	Input context and history	Locally (library, proxy, MCP, agent wrap)	Apache-2.0

How to test any of these fairly

Every tool here, including ours, publishes its own numbers. Fewer tokens do not always mean a smaller bill once prompt caching, retries and extra agent turns are counted. Run the same tasks with and without a tool, check the tasks still succeed, and compare cost per finished task on your provider bill.

Head-to-head: SuperCompress vs Headroom. More: all comparisons, what context compression is.

FAQ

What is the best Headroom alternative?

It depends on what you want to cut. For bulky tool output, logs and retrieved documents, SuperCompress keeps the original lines that answer the current question. Tokenade is best if you want one local binary that bundles several token levers (search, filtering, compaction) across many agents.

Is there a hosted alternative to Headroom?

Yes. SuperCompress is hosted, query-aware context compression with an MCP tool for coding agents and an HTTP API for apps.

How is SuperCompress different from Headroom?

Headroom: content-type compressors with retrievable originals, locally (library, proxy, mcp, agent wrap). SuperCompress sends a bulky block plus the current question to a hosted model that keeps only the relevant original lines, verbatim.

Can I use Headroom and SuperCompress together?

Often yes, when they work on different tokens. Test both together on the same tasks and compare cost per finished task.

Does SuperCompress work with Claude Code, Cursor and Codex?

Yes. npm install -g supercompress-proxy && supercompress setup installs an MCP tool for each detected coding agent, plus hooks where supported.

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