Alternatives · Updated 2026-10-31
context-mode Alternatives: 7 Tools Compared (2026)
Short answer: the best context-mode alternative depends on which tokens you are trying to cut. For bulky tool output, logs and retrieved docs where only a few lines matter, SuperCompress keeps the original lines that answer the current question. Headroom: best if you want a broad, local, open-source compression layer and are happy to run a Python runtime and proxy. 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.
What context-mode is
An MCP server plus hooks that sandboxes tool output, persists session memory and routes tool use, with Claude Code as its primary home. Licensed under the Elastic License 2.0 (source-available). Source: context-mode.
People look for alternatives when they want a different trade-off: elv2 is source-available, not open source; you may not offer it as a hosted service; deepest support is for claude code; more behaviour to learn than a single tool.
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.
- Headroom: Best if you want a broad, local, open-source compression layer and are happy to run a Python runtime and proxy.
- 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.
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 Every claim about other tools comes from their own docs or repositories as of 2026-10-31; tell us if anything is out of date and we will fix it.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. Headroom
Best if you want a broad, local, open-source compression layer and are happy to run a Python runtime and proxy.
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.
- How it works: Content-type compressors with retrievable originals
- Runs: Locally (library, proxy, MCP, agent wrap)
- Setup:
pip install "headroom-ai[all]" - Licence: Apache-2.0
Strengths:
- Runs locally, so prompts are not sent to a third party for compression
- Covers many content types with specialised compressors
- Library, proxy, MCP and agent-wrapper entry points
Limitations:
- 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
3. 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
4. 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
5. 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
6. 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
7. 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
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 |
| 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 |
| 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 |
| context-mode | you live in Claude Code and want sandboxed tool output with session memory and dashboards. | Tool output and session state | Locally, as a plugin, MCP server and hooks | Elastic License 2.0 (source-available) |
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 context-mode. More: all comparisons, what context compression is.
FAQ
What is the best context-mode 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. Headroom is best if you want a broad, local, open-source compression layer and are happy to run a Python runtime and proxy.
Is there a hosted alternative to context-mode?
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 context-mode?
context-mode: sandboxed tool output plus session memory, locally, as a plugin, mcp server and hooks. SuperCompress sends a bulky block plus the current question to a hosted model that keeps only the relevant original lines, verbatim.
Can I use context-mode 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.