alibaizhanov/densely

Lossless context compression for LLMs: 2x-8x fewer tokens, byte-exact reconstruction (sha256-verified). MCP server for Claude Code & Cursor.

About densely

Lossless context compression for LLMs. Pack any text into 2x–8x fewer tokens with guaranteed byte-exact reconstruction — verified by sha256 on every decompress.

An o200k token can carry up to ~17.6 bits of information, but typical code occupies tokens at only ~5–6 bits each. densely reclaims the difference:

Each carrier word (" the", " of", …) costs exactly 1 token — the o200k pre-tokenizer never merges across word boundaries — so every token in the payload carries 2 bytes of compressed data (16 of the ~17.6 theoretically available bits, 91% of channel capacity).

Reproduce with python3 bench.py (fixed seeds, stdlib code sample):

Scenario Backend Tokens (o200k) Ratio Saved ------------------------------------------------------------------------- Code (argparse.py + densely) lzma 21,659 → 10,726 2.02x 50.5% Code (same sample) neural 21,659 → 2,538 8.53x 88.3% Code never seen by the model neural 3,433 → 472 7.27x 86.3% JSON (code search, 100 hits) lzma 15,465 → 1,995 7.75x 87.1% Logs (SRE incident, ~1600 ln) lzma 117,766 → 16,962 6.94x 85.6%

The neural backend (--backend neural, python3 bench.py --neural) drives an integer arithmetic coder with next-token probabilities from Qwen2.5-Coder-0.5B, NNCP-style batched across segments. The 88.3% figure benefits from the model having seen Python's stdlib during training; the 86.3% row is this repo's own sources — code that did not exist before 2026-08-08 — and is the honest number for novel code (~0.56 bit/byte).

From the project README.

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