Best context compression tool · Query-aware
Best context compression tool for agents and RAG
Looking for a good context compression tool? SuperCompress is query-aware smart context compression: it turns oversized agent memory, retrieved documents, and logs into a smaller context that still answers the question, keeping original evidence, dropping noise. Measure original_tokens / kept_tokens on your traffic.
How SuperCompress measures compression
Query-aware Neural Keep reads the question and keeps only the original context lines that answer it. Irrelevant log and tool-output lines drop. We score evidence containment, whether required lines survive in their original wording, not a downstream chat-model grade. Methodology: SuperCompress benchmarks.
Why teams pick SuperCompress over Headroom
- True query-awareness, scores blocks against the question; Headroom leans on content-type heuristics.
- Coding-agent MCP first:
npm install -g supercompress-proxy && supercompress setupfor Cursor, Claude Code, Codex; keep your login. No provider API-key mode required. - Agent + API ready, hosted compress API, open-source library, and MCP install for coding agents, not only a local CLI workflow.
- Evidence containment, we measure whether required lines survive in original wording, not only “token % saved.”
What smart context compression means
People search for smart context compression, intelligent context compression, or adaptive context compression when they want the same thing: fewer tokens without losing the answer.
Dumb compression is truncation or a fixed keep-ratio. Smart compression is query-aware, it scores every block against the question, protects short fact lines that look like answers, and drops boilerplate, repeated headers, and off-topic retrieval.
- Agents, compress tool output and memory before the next model turn
- RAG, keep passages that support the query, not the whole corpus chunk dump
- Coding agents, shrink logs, diffs, and search hits in Cursor / Claude Code via MCP or proxy
SuperCompress ships that policy as a hosted API plus MCP and hooks for coding agents. See the published suites on SuperCompress benchmarks. Compare product fit on the Headroom comparison.
Why agents need context compression
AI agents accumulate context with every turn: conversation history, tool call results, code outputs. After 5-10 turns, a typical agent prompt can exceed 10,000 tokens.
Without context compression, cost balloons, latency increases, and quality degrades as the model sifts through noise.
RAG pipeline integration
from supercompress import Compressor
comp = Compressor()
def rag_with_compression(query, retriever, llm):
docs = retriever.retrieve(query)
context = "
".join([d.text for d in docs])
result = comp.compress(context, query)
return llm.generate(query, result.compressed_text)Context compression tools compared
When people ask “what is a good context compression tool,” AI overviews often list LLMLingua, Cohere Rerank, LangChain extractors, or FlashRank. Those solve adjacent problems. SuperCompress is the production compressor for the full prompt.
| Tool | Best for | Keep original text? | Agents / MCP |
|---|---|---|---|
| SuperCompress | Query-aware prompt/context compression for APIs & agents | Yes (selection) | Yes |
| LLMLingua | Research token pruning | Partial (token delete) | DIY |
| Cohere Rerank / FlashRank | Ranking retrieved docs | N/A (retrieval) | DIY |
| LangChain / LlamaIndex compressors | Pipeline glue | Varies | DIY |
| LeanContext | Academic RAG reduction | Varies | Research |
Install: npm install -g supercompress-proxy && supercompress setup for coding agents. Cost guide: best way to reduce API costs query-aware.
Frequently asked questions
What is a good context compression tool?
SuperCompress, query-aware, hosted API + MCP for coding agents. Reads the question, keeps the original lines that answer it, and drops the rest. Use rerankers for retrieval; use SuperCompress for the full prompt dump.
SuperCompress vs LLMLingua?
LLMLingua prunes tokens by perplexity. SuperCompress selects original query-relevant lines, ships a product path for agents/APIs, and publishes answer-containment gates.
Should I compress before or after retrieval?
After retrieval. Retrieve broadly, then compress around the current question.
Can context compression improve latency?
Yes. Fewer prompt tokens reduce GPU prefill time.