wehub-resource-sync
331 lines
18 KiB
Markdown
331 lines
18 KiB
Markdown
---
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title: "Compression Engines"
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version: 3.8.40
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lastUpdated: 2026-06-28
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---
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# Compression Engines
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OmniRoute compression is built around engine contracts. A mode can run one engine directly
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(`caveman` or `rtk`) or a deterministic stacked pipeline that executes multiple engines in order.
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## Modes
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| Mode | Engine path | Intended input |
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| ------------ | ---------------------------------- | -------------------------------------------- |
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| `off` | none | Exact prompt preservation |
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| `lite` | Caveman lite helpers | Low-risk always-on cleanup |
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| `standard` | Caveman | Natural-language prompt condensation |
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| `aggressive` | Caveman + history/tool summarizers | Long chat sessions |
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| `ultra` | Caveman + pruning helpers | Context-limit recovery |
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| `rtk` | RTK | Terminal, shell, build, test, and git output |
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| `stacked` | Pipeline, default `rtk -> caveman` | Mixed tool logs and prose, max savings |
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## Engine Registry
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The registry lives in `open-sse/services/compression/engines/registry.ts`. Engines expose a shared
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contract:
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- `id`: stable engine id such as `caveman` or `rtk`
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- `apply(text, config)`: legacy execution path used by stacked pipelines
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- `compress(input, config)`: primary execution path returning text + stats
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- `getConfigSchema()`: returns the JSON-Schema-like shape of valid config
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- `validateConfig(config)`: returns `{ valid, errors[] }`
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Registration uses `registerCompressionEngine(engine)` (or `registerEngine` for advanced cases),
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which calls `assertValidEngine()` and `validateConfig(defaultConfig)` before accepting.
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Use `unregisterCompressionEngine(id)` to remove an engine at runtime.
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`strategySelector.ts` registers the built-in engines before compression runs. This lets preview,
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runtime compression, stacked mode, tests, and future engines use the same execution path.
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### MCP description compression (related)
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A separate registry compresses MCP tool description metadata at registry-level — see
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`open-sse/mcp-server/descriptionCompressor.ts` and [MCP-SERVER.md](../frameworks/MCP-SERVER.md). It reuses
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Caveman rules but operates on tool metadata, not request payloads.
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### Additional built-in engines
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Beyond Caveman, RTK, and LLMLingua-2, the registry ships several specialized lossless /
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structural engines (used by stacked pipelines, the playground, and tests):
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| Engine | Id | What it does |
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| ------------- | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| CCR | `ccr` | Content-Compress-Retrieve (H4): replaces large contiguous text blocks with content-addressed references, so repeated/large blocks are sent once and referenced thereafter. |
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| headroom | `headroom` | SmartCrusher (H3 + N5): lossless tabular compaction of homogeneous JSON-array payloads into a columnar `[N rows]` form. |
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| ionizer | `ionizer` | Head/middle/tail row sampling for very large homogeneous blocks, storing the elided middle as a CCR content-addressed reference. |
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| session-dedup | `session-dedup` | Content-addressed cross-turn deduplication (TokenMizer-inspired): elides text already seen in earlier turns of the same session. |
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## Caveman
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Caveman mode focuses on semantic condensation of normal prose:
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- preserves code blocks, URLs, JSON, paths, and structured data
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- removes filler, hedging, repeated context, and verbose connective phrasing
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- supports language-aware file rule packs in `open-sse/services/compression/rules/`
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- remains available through the legacy `standard`, `aggressive`, and `ultra` modes
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The dashboard surface is `Dashboard -> Context & Cache -> Caveman`.
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Caveman upstream reports `~75%` fewer output tokens, `65%` average output savings in benchmarks
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with a `22-87%` range, and a `~46%` input-compression tool. OmniRoute uses the Caveman input-side
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number when documenting stacked prompt/context savings; Caveman output mode remains a separate
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response-behavior feature.
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## RTK
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RTK mode focuses on command and tool output:
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- detects output classes such as `git status`, `git branch`, `git diff`, Vitest/Jest/Pytest,
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Cargo/Go tests, TypeScript/Vite/Webpack builds, ESLint, npm audit/installs, Docker logs,
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shell `find`/`grep`, stack traces, and generic logs
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- applies 49 JSON filters from `open-sse/services/compression/engines/rtk/filters/`
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- supports the RTK-style declarative pipeline: ANSI stripping, replace, match-output short-circuit,
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strip/keep lines, per-line truncation, head/tail/max-line truncation, and on-empty fallback
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- supports trust-gated project filters in `.rtk/filters.json` and global filters in
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`DATA_DIR/rtk/filters.json`
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- strips ANSI sequences, progress noise, repeated lines, and unhelpful boilerplate
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- preserves actionable failures, warnings, summaries, changed files, and tail context
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- can optionally retain redacted raw output for recovery/debugging through authenticated management
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routes
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The dashboard surface is `Dashboard -> Context & Cache -> RTK`.
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Operational details for custom filters, trust, verify, and raw-output recovery live in
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[`RTK_COMPRESSION.md`](./RTK_COMPRESSION.md).
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RTK upstream reports `60-90%` savings for command-output compression. Its README example shows a
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30-minute Claude Code session going from `~118,000` tokens to `~23,900`, or `79.7%` saved.
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## LLMLingua-2 (Semantic Pruning)
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LLMLingua-2 mode performs **semantic token pruning** on prose using a small ONNX token
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classifier, complementing the rule-based Caveman and RTK engines:
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- compresses prose in non-system messages only; fenced code blocks and other preserved
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constructs are never altered
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- runs the `@atjsh/llmlingua-2` backend (ONNX via `@huggingface/transformers`) in a
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worker thread, so model inference never blocks the request event loop
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- is **stackable** (`stackPriority` 35): in a stacked pipeline it runs after the
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structural engines (CCR, session-dedup, headroom, Caveman) but before `ultra`, since
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semantic pruning is most effective on already-structurally-compressed text — e.g.
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`rtk -> caveman -> llmlingua`
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- **fail-opens on any error** (missing optional deps, worker spawn, model load, inference,
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or timeout) → the original text is returned unchanged, never an error
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Engine location: `open-sse/services/compression/engines/llmlingua/`. The dashboard surface
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is `Dashboard -> Context & Cache -> LLMLingua`.
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### Models
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The default model is **TinyBERT** (`atjsh/llmlingua-2-js-tinybert-meetingbank`, ~57 MB,
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fast). A higher-accuracy **BERT-base** model (`Arcoldd/llmlingua4j-bert-base-onnx`,
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~710 MB) is available via the engine config `model` field. `@huggingface/transformers`
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downloads the selected model lazily from the HuggingFace Hub into
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`${DATA_DIR}/models/llmlingua` on the first call (`modelStore.ts`); a `modelPath` config
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override points it at a local copy instead (offline / air-gapped installs).
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### Optional dependencies & on-demand install
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The prunable LLMLingua runtime peer stack is **optional**. Three packages are declared as
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`optionalDependencies` in `package.json` and kept **external** by the production build
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(`scripts/build/prepublish.ts` does not bundle them):
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| Package | Version (pin) | Notes |
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| -------------------- | ------------- | ---------------------------------------------- |
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| `@atjsh/llmlingua-2` | `2.0.3` | Entry package; declares the others as peers |
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| `@tensorflow/tfjs` | `4.22.0` | Heaviest dep — dominates the ~800 MB footprint |
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| `js-tiktoken` | `^1.0.20` | Tokenizer |
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`@huggingface/transformers` is pinned at `3.5.2` as an **optional** dependency (shared with
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the local embeddings path and also traced into the standalone bundle). Keeping it optional prevents
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`onnxruntime-node` CUDA provider postinstall failures on CUDA 11 hosts from aborting the whole
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OmniRoute install; when the optional stack is absent, LLMLingua still fail-opens. Only the three
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packages above are prunable SLM peers. A standard `npm install` (dev) installs the optional stack
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automatically unless optional dependencies are omitted.
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**Why on-demand:** the npm-published package, the standalone bundle, and the Docker image
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ship **without** these deps to stay slim. When they are absent, the worker's dependency
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gate (a `@atjsh/llmlingua-2` resolve probe in `worker.ts`) fails and the engine
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**fail-opens silently** — selecting LLMLingua becomes a no-op (text returned unchanged, no
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error logged). To activate it in a pruned environment, install the optional stack:
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```bash
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# pin to the versions declared in package.json optionalDependencies
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npm install @atjsh/llmlingua-2@2.0.3 @tensorflow/tfjs@4.22.0 js-tiktoken
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```
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Roughly **~800 MB** total: the TensorFlow.js + transformers runtimes dominate; the
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TinyBERT model adds ~57 MB downloaded at first use (not via npm).
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Per environment:
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- **Dev / `npm install`** — installed automatically unless you passed `--omit=optional`
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(or `--no-optional`). No action needed.
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- **Global npm (`npm i -g omniroute`) / standalone** — run the install command above inside
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the installed package directory, or reinstall without omitting optional deps.
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- **Docker** — add the install command in a derived image layer; the published image
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ships slim by design.
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- **VPS (PM2)** — install into the app's `node_modules`, then restart the process so the
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worker re-probes the gate.
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**Verify it is active:** with LLMLingua selected, real prose actually shrinks (the engine
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stops fail-opening), and the first request triggers the model download into
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`${DATA_DIR}/models/llmlingua`. The gate intentionally probes only `@atjsh/llmlingua-2` —
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the other peers are ESM-only and `require.resolve` throws on them even when present — so
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the worker still fail-opens if any peer is genuinely missing at `import()` time.
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## Stacked Pipelines
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Stacked mode runs pipeline steps in order. The default is:
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```txt
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rtk -> caveman
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```
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Use this for coding-agent sessions where a prompt combines command output with human or assistant
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prose. RTK reduces noisy tool logs first, then Caveman compresses remaining natural language.
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Pipeline steps are configured with `stackedPipeline` in compression settings or through compression
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combos.
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When both engines reduce the same eligible payload, savings compound:
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```txt
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combined = 1 - (1 - RTK savings) * (1 - Caveman input savings)
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average = 1 - (1 - 0.80) * (1 - 0.46) = 89.2%
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range = 1 - (1 - 0.60..0.90) * (1 - 0.46) = 78.4-94.6%
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```
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## MCP Accessibility Tree Filter
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The MCP accessibility-tree smart filter is a post-execution compression layer that runs on MCP
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**tool results**, not on prompts or context. It targets the verbose accessibility-tree and browser
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snapshot payloads returned by tools like Playwright, computer-use, and browser-automation MCP
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servers.
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### What it does
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1. **Noise stripping** — removes empty generic/text entries (`- generic:`, `- text: ""`)
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2. **Sibling collapse** — when ≥ `collapseThreshold` (default 30) consecutive lines are structural
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repeats, collapses them into the first `collapseKeepHead` (default 10) lines + a count summary +
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the last `collapseKeepTail` (default 5) lines
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3. **Ref preservation** — `[ref=eXX]` anchors required by Playwright/computer-use are never touched
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4. **Hard truncation** — if the text after collapse still exceeds `maxTextChars` (default 50,000),
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truncates with a navigation hint so the agent can continue working
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### Engine location
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```txt
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open-sse/services/compression/engines/mcpAccessibility/
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index.ts ← smartFilterText() entry point
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collapseRepeated.ts ← sibling-collapse algorithm
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constants.ts ← DEFAULT_MCP_ACCESSIBILITY_CONFIG
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```
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### Configuration
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Controlled by `compression.mcpAccessibility` in global settings (migration 056). Default config:
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```json
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{
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"enabled": true,
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"maxTextChars": 50000,
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"collapseThreshold": 30,
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"collapseKeepHead": 10,
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"collapseKeepTail": 5,
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"minLengthToProcess": 2000
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}
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```
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The filter is only applied to tool-result payloads whose `type` is `"text"` and whose length
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exceeds `minLengthToProcess`. It does not affect prompt compression or request payloads.
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### Expected savings
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60–80% on browser snapshot tool results, depending on page complexity. The collapse algorithm
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is O(n) in line count and adds negligible latency.
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### This filter vs the compression engines above
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| Aspect | Caveman / RTK / Stacked | MCP accessibility filter |
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| ----------- | ------------------------- | -------------------------------------- |
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| Target | Request prompts / context | MCP tool results |
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| Trigger | Compression mode setting | `compression.mcpAccessibility.enabled` |
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| Scope | All SSE messages | Tool results only |
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| Ref anchors | N/A | Preserved unconditionally |
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---
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## Compression Combos
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Compression combos are named compression profiles that can be assigned to routing combos:
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- `compression_combos`: stores mode, pipeline, RTK config, language config, and default marker
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- `compression_combo_assignments`: maps a compression combo to a routing combo
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- runtime integration resolves an assigned compression combo before generic combo overrides
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- analytics include `compression_combo_id` and `engine`
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Dashboard surface: `Dashboard -> Context & Cache -> Compression Combos`.
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## API Surface
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| Route | Purpose |
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| -------------------------------------- | ---------------------------------------------------------------- |
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| `/api/settings/compression` | Global compression settings (includes `mcpAccessibility` config) |
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| `/api/compression/preview` | Preview any compression mode |
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| `/api/compression/language-packs` | List available Caveman language packs |
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| `/api/context/caveman/config` | Caveman settings alias |
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| `/api/context/rtk/config` | RTK defaults and settings |
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| `/api/context/rtk/filters` | RTK filter catalog |
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| `/api/context/rtk/test` | RTK preview/test endpoint |
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| `/api/context/rtk/raw-output/[id]` | Authenticated redacted raw-output recovery |
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| `/api/context/combos` | Compression combo CRUD |
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| `/api/context/combos/[id]/assignments` | Routing-combo assignment CRUD |
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| `/api/context/analytics` | Compression analytics alias |
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Management routes require management authentication or API-key policy checks.
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## MCP Tools
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Compression exposes five MCP tools:
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| Tool | Scope | Purpose |
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| ----------------------------------- | ------------------- | -------------------------------- |
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| `omniroute_compression_status` | `read:compression` | Settings, analytics, cache stats |
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| `omniroute_compression_configure` | `write:compression` | Update global settings |
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| `omniroute_set_compression_engine` | `write:compression` | Set mode and optional pipeline |
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| `omniroute_list_compression_combos` | `read:compression` | List compression combos |
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| `omniroute_compression_combo_stats` | `read:compression` | Read combo/engine analytics |
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## Known limitations
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- **LLMLingua-2 (SLM) requires co-located optional deps.** The worker only runs in a
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production build when `@atjsh/llmlingua-2` + peers are co-located into
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`dist/node_modules` (see `scripts/build/colocateOptionals.mjs`, #4286). Without them the
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engine fail-opens (returns the original text). Worker resolution no longer depends on
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`import.meta.url` (it dies in the standalone bundle) — it anchors on the runtime
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cwd / `argv[1]`.
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- **Caveman language packs `de` / `fr` / `ja` are partial.** They ship `context` +
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`filler` + `structural` rules but no `dedup` / `ultra` packs, so `ultra` intensity is
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no stronger than `full` for those languages (they use only their own rules — there is no
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silent fall-back to the English `dedup`/`ultra` rules, which would mangle foreign text).
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`en` / `es` / `id` / `pt-BR` are complete. Contributions of `dedup.json` + `ultra.json`
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for the partial packs are welcome.
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- **Stacked telemetry only lists engines that compressed.** A stacked-pipeline step whose
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engine ran but produced 0 % savings returns `stats:null` and so does not appear in
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`engineBreakdown` — indistinguishable from a step that was skipped. Distinguishing
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"ran, 0 %" from "skipped" would require a breakdown-model change and is deferred.
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## Validation
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The focused gates for this area are:
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```bash
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node --import tsx/esm --test tests/unit/compression/rtk-*.test.ts tests/unit/compression/pipeline-integration.test.ts tests/unit/compression/context-compression-api.test.ts
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node --import tsx/esm --test tests/unit/compression/*.test.ts tests/golden-set/*.test.ts tests/integration/compression-pipeline.test.ts tests/unit/api/compression/compression-api.test.ts
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node --import tsx/esm --test tests/unit/compression/mcpAccessibility*.test.ts
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npm run typecheck:core
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```
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