Why it exists
Every LLM call ships a pile of context: RAG chunks, chat history, tool dumps, logs, JSON. Most of it is irrelevant to the current question — but the model still reads it, and you still pay for it.
| Usual “fix” | What actually happens |
|---|---|
| Truncate | Deletes the middle. The answer is often in the middle. |
| Summarize | Rewrites evidence. IDs, stack traces, and exact errors get soft. |
| Hope | Ship the full dump. Watch the bill climb. |
SuperCompress v2 compresses context against the query. It keeps answer-critical lines in their original wording and drops the rest — on our coding-agent benchmark (B5), 64.1% mean context reduction with 24/24 evidence-retention passes.
Product
SuperCompress is a compression layer in front of inference:
- Takes a long context + the current query
- Segments and scores blocks by relevance to that query
- Keeps entities, errors, definitions, nearby dependencies
- Returns a smaller prompt + token stats
The query is never compressed — only the surrounding context.
Two paths
| Neural v2 (recommended) | Compiler (fast/local) | |
|---|---|---|
| Engine | ~400M query-aware cross-encoder | Lightweight local policy |
| Runtime | Hosted Fly CPU (Neural Keep) | Local CPU · millisecond-class |
| Best for | Highest-quality keep on agent dumps | Local preprocessing / speed |
| Benchmarks | Launch / B5 | Legacy section |
Two products
| Coding-agent plugin | API / Python | |
|---|---|---|
| For | Cursor, Claude Code, Codex, and 40+ agent harnesses | Apps, RAG, agents, backends |
| Install | npm i -g supercompress-proxy && npx supercompress setup | pip install supercompress |
| What you get | MCP compress_context on big dumps | Compress before every model call |
| Login | Keep your normal agent login | API key from the dashboard |
Docs: coding agents (first 5 minutes checklist) · API quickstart
Repo map
| Path | What |
|---|---|
packages/proxy | Coding-agent plugin (npm) |
api/ | Hosted API + billing |
web/ | Site + docs HTML |
supercompress/ | Python package |
docs/REPO_LAYOUT.md | What belongs in OSS vs private |
Private marketing, outreach, and model training stay out of this repo (see .gitignore + docs/REPO_LAYOUT.md).
Benchmarks & stats
We measure whether required evidence survives (containment), not downstream LLM completion.
Neural v2 launch (hosted ~400M) — B5 coding-agent suite
| Metric | Result |
|---|---|
| Mean context cut | 64.1% |
| Evidence passes | 24 / 24 |
| Tokens | 16,647 → 5,148 |
| Max cut @ ≥99% retention | 96.6% |
| B5 latency p50 | ~5.8 s (hosted Neural Keep on Fly CPU) |
| Public cases (full suite) | 390 |
| Downstream LLM eval | Not yet run |
Aggregate mean cut across all 390 cases is ~3.6% — the engine often refuses to over-cut dense needle/QA slices. The 64.1% figure is the coding-agent suite where dumps are noisy.
Raw JSON: launch-benchmark.json · writeup: benchmarks
Legacy compiler (separate product)
CPU / millisecond-class local path. Older held-out compiler numbers (≈58–66% cut, 99.4% gold containment) live under Legacy/compiler on /benchmarks. Do not mix with Neural v2.
Try it
Coding agents (recommended)
npm install -g supercompress-proxy
npx supercompress setup
Links your account, detects agents, installs MCP. Docs: coding agents
Python / HTTP
pip install supercompress
export SUPERCOMPRESS_API_KEY=sc_live_YOUR_KEY
from supercompress.client import SuperCompress
sc = SuperCompress()
result = sc.compress(
context=long_context,
query="What failed and how do we fix it?",
)
print(f"{result.original_tokens} → {result.kept_tokens} tokens")
print(result.compressed_text)
curl -X POST https://www.supercompress.dev/api/v1/compress \
-H "X-API-Key: $SUPERCOMPRESS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"context":"...","query":"What failed?"}'
Or paste a dump into the Arena — no integration required.
How it compares
| Truncate | Summarize | SuperCompress | |
|---|---|---|---|
| Cuts tokens | ✓ | ✓ | ✓ |
| Uses the query | ✗ | weak | ✓ |
| Keeps original evidence | sometimes | ✗ | ✓ |
| Auditable kept lines | partial | ✗ | ✓ |
More: vs truncation · vs summarization · vs alternatives