winnow

Local-first context compression for AI agents. Keep the signal, winnow the chaff.

Agents burn tokens on fat tool outputs — JSON dumps, logs, file reads, RAG chunks, conversation history. winnow compresses that text before it reaches the model, cutting tokens by 40–95% while keeping what matters. It's content-aware, reversible (originals are recoverable on demand), and the core has zero runtime dependencies. Everything runs on your machine — no proxy, no API key, no egress.

your agent / app  →  winnow (local)  →  LLM provider

Why

Compression that silently drops the wrong line is worse than no compression. winnow is built around three ideas:

1. Content-aware, lossy-but-reversible. Different compressors for JSON, logs, code, and binary. Every original is stashed locally under a content id, so the model can retrieve the full text the moment it needs detail. Lossy inline, lossless on demand.
2. Delivery is backbone-gated. How a large result is delivered changes accuracy as much as how well it's compressed. Strong models get a short preview + a retrievable pointer; small/distilled models get a larger inline window and are never handed a pointer they won't follow.
3. Cache-aligned. A volatile segment (a timestamp, "current" state) early in your prompt invalidates the provider's KV cache every turn. winnow aligns a tiered prompt so the stable prefix leads and the cache survives.

Install

npm install winnow

Node ≥ 18, ESM. Core has no runtime deps. Code (AST) compression uses an optional typescript peer.

Quickstart

import { compress, retrieve, stats } from "winnow";

const huge = JSON.stringify(await fetchManyRows()); // e.g. 200 similar objects

const r = await compress(huge);
console.log(r.text);          // head+tail sample, middle elided, + a retrieval footer
console.log(r.compressed);    // true
console.log(stats(huge, r.text)); // { tokensBefore, tokensAfter, tokensSaved, ratio }

// later, if the model needs the full thing:
const original = await retrieve(r.originalId!);

Compress a whole chat array:

import { compressMessages } from "winnow";
const slim = await compressMessages(messages); // compresses each message's content

Benchmark — measured, not claimed

winnow bench runs a fidelity harness: for each case it records token savings and checks whether the "needle" (the fact a model would need) survives compression inline. Anything elided is still recoverable from the store, so recoverable fidelity is 100% by construction — this measures the harder number, what survives without a retrieval round-trip.

winnow fidelity — 6 cases
  json-head    json  save  86%  inline ✓
  json-tail    json  save  86%  inline ✓
  json-middle  json  save  86%  inline · (recoverable)
  log-error    logs  save  99%  inline ✓
  log-dupes    logs  save  99%  inline ✓
  text-prose   text  save   0%  inline ✓

avg savings: 76%   inline needle survival: 83%
by position: head 100% · tail 100% · middle 0% · anywhere 100%
recoverable fidelity: 100% (every elided original is retrievable from the store)

The honest tradeoff is visible: a needle buried deep in the middle of a 200-row array is elided inline — and recoverable in one retrieve call. Logs and head/tail JSON keep their signal at a fraction of the tokens.

API

Export	What it does
compress(text, opts?)	Reversible compress of one block; returns { text, compressed, originalId, tokensBefore, tokensAfter }.
compressMessages(messages, opts?)	Compress each { content } in a chat array.
retrieve(id, dir?)	Read a stored original back by id.
stats(before, after)	Token savings + ratio.
compressText(text, opts?)	Pure router (no I/O, no stashing). opts.tabular → lossless TOON.
crushJson / squashLogs / compressCode	Individual compressors.
encodeTable / decodeTable / toonCompress	TOON — lossless object-array ↔ table (keeps every row).
dedupeBlocks / rehydrateBlocks / dedupeMessages	Collapse repeated blocks/messages anywhere; reversible.
compactHistory(messages, opts?)	Anchored history compaction (injected summarizer, extractive fallback).
pruneText(text, opts?)	LLMLingua-style score-and-drop; inject your own scorer, heuristic fallback.
makeCounter(encode?) / countTokens	Token counting — exact with an injected encoder.
tuneOptions(cases?, grid?, weight?)	Pick compression options that maximize measured survival × savings.
offload(text, opts?)	Size-based offload with the backbone-gated delivery policy.
resolveDelivery / classifyBackbone	The delivery policy primitives.
alignSegments(segments)	Cache-align a tiered prompt; returns the prompt, stable-prefix cacheKey, and breakpoint.

CompressOptions: minTokens (default 400), headItems (3), tailItems (1), maxStringLength (200).

Cache alignment

import { alignSegments, cacheHolds } from "winnow";

const aligned = alignSegments([
  { id: "system", text: SYSTEM, stable: true },
  { id: "tools",  text: TOOLS,  stable: true },
  { id: "clock",  text: now(),  stable: false }, // moved after the stable prefix
]);
aligned.prompt;     // stable segments first → cacheable prefix
aligned.cacheKey;   // equal across turns ⇒ the KV cache can hit
cacheHolds(lastKey, aligned); // did the cached prefix survive this turn?

CLI

winnow bench                 # fidelity benchmark (savings + needle survival)
cat big.json | winnow compress   # compress stdin → stdout (stats on stderr)
winnow retrieve <id>         # print a stored original
winnow mcp                   # start the MCP server (stdio)

MCP server

Expose winnow to any MCP client (editors, agent runtimes) as three tools — winnow_compress, winnow_retrieve, winnow_stats:

winnow mcp

// in your client's MCP config
{ "mcpServers": { "winnow": { "command": "winnow", "args": ["mcp"] } } }

Design notes

• Lossy inline, lossless on demand. Compression always shrinks; the original is one retrieve away. The compressor never keeps a result that didn't actually shrink.
• Read-fidelity is a contract. Precision matters most for code and exact reads — code compression keeps every signature/type/import and only elides bodies (recoverable), so the model still sees the shape.
• Local-first. Originals live in .winnow/ccr/ (override with WINNOW_DIR). Nothing leaves your machine.
• Token counts default to a length/4 heuristic; swap in a real tokenizer where exact numbers matter.

License

MIT © Jason Poindexter