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.

Related MCP server: Copilot Memory Store

Install

Install from GitHub (not on the npm registry — the winnow name there is an unrelated package):

npm install github:jpoindexter/winnow

Pin to a commit for reproducible builds:

npm install "github:jpoindexter/winnow#<commit-sha>"

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

Exactly what it does

No hand-waving — here is the literal transformation each compressor applies, on real input, with real savings. (Token counts use the default length/4 heuristic; inject a real tokenizer for exact figures.)

JSON — keep the edges, elide the middle (recoverable). A 200-object dump:

BEFORE  [ {"id":0,"name":"item-0","active":true,"score":0}, {"id":1,…}, … ×200 ]
AFTER   head (3) + tail (1) objects kept verbatim; the middle becomes an `__elided__` marker
4252 → 112 tokens   (97% saved)

The model sees the schema and a sample; compress() stashes the full array so retrieve(id) returns it intact. An agent reading 200 rows needs the shape and an example — and asks for row 137 when it actually needs it.

Logs — collapse repeats to a count. 40 identical lines:

BEFORE  2026-06-19T21:04:11Z INFO  cache hit for key=session:abc123 (ttl 300s)
        …the same line ×40
AFTER   2026-06-19T21:04:11Z INFO  cache hit for key=session:abc123 (ttl 300s) (×40)
710 → 19 tokens   (97% saved)

"It happened 40 times" is the signal; 40 byte-identical copies are the chaff.

Repeated blocks — reference, don't repeat (reversible). A boilerplate paragraph repeated down a page (¶ = blank line):

BEFORE  # Report ¶ <cookie notice> ¶ Section 1 ¶ <cookie notice> ¶ Section 2 ¶ <cookie notice>
AFTER   # Report ¶ <cookie notice> ¶ Section 1 ¶ ⟦↺#0⟧ ¶ Section 2 ¶ ⟦↺#0⟧
115 → 51 tokens   (56% saved)

The first occurrence stays inline; later identical blocks become ⟦↺#k⟧ pointing at it, and rehydrateBlocks restores the exact original. Repeated nav/footer/disclaimer blocks are the biggest single waste in scraped web and RAG content.

On real agent tool output. compressText detects the type and routes; dedupeBlocks mops up the repeated blocks the router leaves inline. Measured on representative results:

Savings are content-dependent, and that's the honest point: repetition-heavy output (most web / log / RAG content) compresses hard; genuinely unique prose doesn't, and winnow hands it back unchanged rather than mangling it. Everything elided is recoverable — lossy inline, lossless on demand.

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 — 7 cases
  json-head    json  save  86%  inline ✓
  json-tail    json  save  86%  inline ✓
  json-middle  json  save  86%  inline · (recoverable)
  wide-table   json  save  97%  inline · (recoverable)
  log-error    logs  save  99%  inline ✓
  log-dupes    logs  save  99%  inline ✓
  text-prose   text  save   0%  inline ✓

avg savings: 79%   inline needle survival: 71%   CNG: -0.362
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. (CNG, cost-normalized gain, is negative on the default run because it scores inline fidelity only and the default mode is lossy-but-recoverable — it's the conservative number, not a quality loss, since every elided original is retrievable.)

API

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

Run the CLI straight from GitHub, no install:

npx github:jpoindexter/winnow bench

For a persistent winnow command, clone the repo and link it:

git clone https://github.com/jpoindexter/winnow && cd winnow && npm install && npm link

(npm install -g from a git URL is unreliable on some npm versions — use npx or npm link.)

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