Comparisons · Updated 2026-10-31
Context compression and token tools compared
Short answer: context and token tools attack different problems. Compression of input context (SuperCompress, Headroom, LLMLingua, Compresr, Bear), shell output filters (rtk), code retrieval (claude-context, jCodeMunch), local gateways (LeanCTX, Tokenade, context-mode) and output style (Caveman, claude-token-efficient). Pick by which tokens dominate your bill.
Every tool 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 |
| 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) |
| 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) |
| Compresr | you want a hosted compression API for RAG and Q&A pipelines and are fine with token-level output. | Prompts, documents, RAG context | Hosted API (on-premises option) | Closed hosted service |
| Bear (The Token Company) | you want a one-line wrapper around an OpenAI or Anthropic client that deletes low-signal tokens. | Prompts, chat history, search results | Hosted API (enterprise VPC option) | Closed hosted service |
| LeanCTX (lean-ctx) | you want a local, policy-aware gateway that controls what your agent reads, with search, memory and a code graph. | File reads, shell output, requests | Locally (CLI, MCP, hooks, proxy) | Apache-2.0 |
| jCodeMunch | most of your waste is the agent reading whole files to find one function. | Source code reads | Locally, as an MCP server | Custom dual-use licence |
Alternatives pages
- Headroom alternatives
- Caveman alternatives
- Tokenade alternatives
- LLMLingua alternatives
- rtk alternatives
- claude-token-efficient alternatives
- claude-context alternatives
- context-mode alternatives
- Compresr alternatives
- Bear (The Token Company) alternatives
- LeanCTX (lean-ctx) alternatives
- jCodeMunch alternatives
Head-to-head pages
- SuperCompress vs Headroom
- SuperCompress vs rtk
- SuperCompress vs LLMLingua
- Headroom vs Caveman
- SuperCompress vs summarization
- SuperCompress vs truncation
- SuperCompress vs Tokenade
- SuperCompress vs Caveman
- SuperCompress vs claude-token-efficient
- SuperCompress vs claude-context
- SuperCompress vs context-mode
- SuperCompress vs Compresr
- SuperCompress vs Bear (The Token Company)
- SuperCompress vs LeanCTX (lean-ctx)
- SuperCompress vs jCodeMunch
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.
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.
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.
FAQ
What is the best tool to reduce coding agent token usage?
Find what dominates first. If big tool results, logs and file reads dominate, use query-aware input compression such as SuperCompress. If noisy shell output dominates, a filter like rtk helps. If whole-file reads dominate, a code index such as jCodeMunch or claude-context helps.
Which context compression tools keep original text verbatim?
SuperCompress keeps whole original lines verbatim. Token-pruning tools such as LLMLingua and Bear delete tokens inside lines, which can break code and identifiers.
Are these comparisons independent?
No. SuperCompress publishes them and lists itself. Claims about other tools come from their own docs; we recommend testing on your own tasks.