Comparison · Updated 2026-10-31
SuperCompress vs claude-context
Short answer: claude-context is semantic code retrieval (vector plus BM25). Where it runs: MCP server backed by Milvus or Zilliz Cloud. SuperCompress is hosted, query-aware compression that keeps the original lines answering the current question. Pick claude-context if retrieval quality is the goal and you already run, or are happy to run, vector infrastructure. 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
| claude-context | SuperCompress | |
|---|---|---|
| What it optimizes | What code gets read | Input context (tool output, logs, files, MCP results, RAG) |
| How it decides | Semantic code retrieval (vector plus BM25) | Query-aware extraction of original lines |
| Where it runs | MCP server backed by Milvus or Zilliz Cloud | Hosted API, plus an MCP tool and hooks for coding agents |
| Setup | Add the MCP server, plus a vector DB and an embedding API key | npm install -g supercompress-proxy && supercompress setup |
| Licence | Open source (needs vector DB and embeddings) | Hosted service |
claude-context
A. 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
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: claude-context alternatives, all comparisons.
FAQ
Is SuperCompress better than claude-context?
They solve different parts of the problem. Best if retrieval quality is the goal and you already run, or are happy to run, vector infrastructure. 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 claude-context?
claude-context works on what code gets read and runs mcp server backed by milvus or zilliz cloud. 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.