Comparison · Updated 2026-10-31
SuperCompress vs jCodeMunch
Short answer: jCodeMunch is symbol-level code retrieval (tree-sitter index). Where it runs: Locally, as an MCP server. SuperCompress is hosted, query-aware compression that keeps the original lines answering the current question. Pick jCodeMunch if most of your waste is the agent reading whole files to find one function. Pick SuperCompress 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.
Side by side
| jCodeMunch | SuperCompress | |
|---|---|---|
| What it optimizes | Source code reads | Input context (tool output, logs, files, MCP results, RAG) |
| How it decides | Symbol-level code retrieval (tree-sitter index) | Query-aware extraction of original lines |
| Where it runs | Locally, as an MCP server | Hosted API, plus an MCP tool and hooks for coding agents |
| Setup | uv tool install jcodemunch-mcp && jcodemunch-mcp init | npm install -g supercompress-proxy && supercompress setup |
| Licence | Custom dual-use licence | Hosted service |
jCodeMunch
A. jCodeMunch
Best if most of your waste is the agent reading whole files to find one function.
A local MCP server that indexes a repository with tree-sitter and lets agents fetch exact symbols (functions, classes, methods) instead of whole files. Published under its own dual-use licence.
- How it works: Symbol-level code retrieval (tree-sitter index)
- Runs: Locally, as an MCP server
- Setup:
uv tool install jcodemunch-mcp && jcodemunch-mcp init - Licence: Custom dual-use licence
Strengths:
- Exact symbol retrieval instead of whole-file reads
- Local index with hooks to keep it fresh
- Configures many MCP clients in one command
Limitations:
- Code only; does not shrink logs, test output or MCP results
- Custom licence, so check terms for your use
- Index must be built per repository
SuperCompress
B. 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
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.
See also: jCodeMunch alternatives, all comparisons.
FAQ
Is SuperCompress better than jCodeMunch?
They solve different parts of the problem. Best if most of your waste is the agent reading whole files to find one function. 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.
What is the main difference between SuperCompress and jCodeMunch?
jCodeMunch works on source code reads and runs locally, as an mcp server. SuperCompress works on input context and is hosted.
Can I use both?
Usually, if they act on different tokens. Measure cost per finished task with each on and off.