cendor-squeeze

Shrink verbose context — JSON, logs, code, prose — without throwing anything away. Compression returns a handle; the original is always restorable. Content-aware: each type gets a purpose-built, deterministic compressor — no LLM, no model download, byte-reproducible.

Up to ~99% smaller on repetition-heavy logs (~50% on structured data), 100% reversible — no ML dependency, deterministic output.

· pip install cendor-squeeze

Using an AI coding assistant? npx @cendor/init (TS) / uvx cendor-init (Python) wires it up — or point it at cendor.ai/docs/for-ai-assistants.

from cendor.squeeze import compress

small, handle = compress(huge_json, kind="auto")                 # detect + route
small, handle = compress(source_code, kind="code", fidelity="aggressive")
small, handle = compress(logs, kind="logs", target_tokens=400)   # compress to a budget
original = handle.expand()                                        # restore, byte-for-byte

Highlights

• Four purpose-built compressors — JSON (minify + drop nulls; budget-shrink drops keys/elements structurally, staying valid JSON), logs (normalize timestamps/UUIDs/IPs/hex/integers + dedup repeats into (×N), chronological), code (strip comments — string-aware, so a // or # inside a literal stays put; keeps preprocessor & shebang), prose (extractive, abbreviation-aware sentence splitting). detect() auto-routes.
• Compress to a budget — target_tokens is never exceeded; fidelity="lossless" | "balanced" | "aggressive". No LLM, no download, byte-reproducible.
• 100% reversible — a content-addressed store (deduped by hash) keeps every original; handle.expand() restores it byte-for-byte no matter how hard you squeeze.
• Survives restarts — handle.to_dict() / Handle.from_dict() next to a durable squeeze.store.SQLiteStore; or a bounded MemoryStore(max_items=…) via use_store(...).
• The deterministic default, swappable — wired through core's Compressor protocol (not a hard import), so contextkit.use_compressor(...) can replace it globally with any backend (even a heavier ML compressor). squeeze stays the pick for reproducible, offline, audit-friendly output.

Inbound — usually contextkit calls it for you (Block(evict="compress")). Ratios are content-dependent (logs compress most, structured JSON least) — see the benchmarks.

See docs/squeeze.md · CHANGELOG. Part of the Cendor stack — github.com/cendorhq/cendor-libs. Powered by PowerAI Labs. Apache-2.0; provided "as is", without warranty — use at your own risk (LICENSE §7–8).