@pk-nerdsaver-ai/snapcompact

@pk-nerdsaver-ai/snapcompact

Bitmap-frame context compression for vision-capable LLMs.

Instead of asking an LLM to summarize discarded conversation history, snapcompact serializes it and renders the text into dense PNG frames of pixel-font glyphs that vision models read back directly. The whole pass is local and deterministic — no LLM call, no API key, no latency beyond rendering. Rasterization and PNG encoding happen in native code (@pk-nerdsaver-ai/pi-natives).

Built for oh-my-pi's compaction pipeline, but the rendering API works on arbitrary text.

How it works

1. Discarded history is serialized to compact text (serializeConversation), with per-tool-result and per-argument character caps.
2. Text is normalized for the bundled bitmap fonts (normalize): ANSI sequences stripped, whitespace collapsed, newline runs folded into a single full-block glyph so line structure survives.
3. Pages of text are rasterized into PNG frames (render / renderMany). Frame width is fixed per shape; height hugs the rows actually printed, so a partially filled frame never bills blank pixel rows.
4. Frames persist in the compaction entry's preserveData and are re-attached to the summary message on every context rebuild.

Frame shapes are provider-aware, chosen by SQuAD recall evals (see research/) against real provider billing:

resolveShape({ api, id }) matches the model id, not just the wire API — a Claude routed through Vertex or OpenRouter keeps its Claude shape, priced for the gateway actually carrying the request.

Install

bun add @pk-nerdsaver-ai/snapcompact

Ships TypeScript source directly (no build step); requires Bun ≥ 1.3.14.

Usage

Render arbitrary text into LLM image blocks:

import { renderMany, frames, resolveShape } from "@pk-nerdsaver-ai/snapcompact";

const images = renderMany(longText, { model }); // ImageContent[], first page first
const count = frames(longText, { model });      // frame count without rendering
const shape = resolveShape(model);              // eval-optimal Shape for the reader

Run a full compaction pass over prepared messages:

import { compact } from "@pk-nerdsaver-ai/snapcompact";

const result = await compact(preparation, { model });
// result.summary        — short "resume prior conversation" lead-in, reading guide, and FILES section
// result.preserveData   — bounded archive source + rendered image middle

API surface

• Compaction: compact, CompactionPreparation, CompactionResult, getPreservedArchive, images, historyBlocks
• Rendering: render, renderMany, frames, geometry
• Shapes: SHAPES, SHAPE_VARIANTS, resolveShape, idealShapeVariant, isShape, isShapeVariantName
• Text: serializeConversation, normalize, dimStopwords, wrap
• Budgets: providerImageBudget, MAX_FRAMES_DEFAULT, FRAME_TOKEN_ESTIMATE, HQ_EDGE_FRAMES
• File ops: createFileOps, computeFileLists, upsertFileOperations

References

• Monorepo README
• Compaction architecture
• CHANGELOG