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 (~65% tokens, ≥98% answer keep).
Held-out proof (updated 2026-08-01)
Gates: ≥98% gold-answer containment, ≥55% mean cut, ≥55% token-weighted cut. No restore-to-98% floor.
| Suite | Answer kept | Mean cut | TW cut | Gates |
|---|---|---|---|---|
| Real (seed 4242) | 98.4% (61/62) | 57.8% | 64.7% | pass |
| Fresh4 (seed 9091) | 100% (57/57) | 66.1% | 68.8% | pass |
| Fresh5 (seed 6161) | 100% (62/62) | 57.8% | 61.6% | pass |
| Fresh6 cold (seed 3377) | 98.6% (70/71) | 68.2% | 72.5% | pass |
Primary bundle (real + fresh4 + fresh5): 99.4% pooled answer keep (180/181), 65.4% pooled token-weighted cut. Full tables: 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 —
npx 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.
- Held-out answer gates — we publish answer containment on LongBench + OOD mixes, 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 open-source JS plus a hosted API. Held-out evals target ≥98% answer retention with ~65% token weight cut. See the benchmarks and 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: pip install supercompress or npx 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, open source, hosted API + MCP, held-out ≥98% answer keep. 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.