Dataset Viewer
Auto-converted to Parquet Duplicate
edge
list
stress
list
realWorld
list
summary
dict
[ { "name": "empty string", "pass": true }, { "name": "single space", "pass": true }, { "name": "newline only", "pass": true }, { "name": "multiple newlines", "pass": true }, { "name": "tabs and spaces", "pass": true }, { "name": "whitespace multiline", ...
[ { "name": "same word 200x", "pass": true }, { "name": "same path 100x", "pass": true }, { "name": "same phrase 50x", "pass": true }, { "name": "10K char single line", "pass": true }, { "name": "50K char file", "pass": true }, { "name": "long path", ...
[ { "file": "file_01.md", "bytes": 5820, "origTokens": 1748, "compTokens": 1652, "tokenSavings": "5.5%", "byteSavings": "3.7%", "roundTrip": true }, { "file": "file_02.md", "bytes": 3313, "origTokens": 1027, "compTokens": 996, "tokenSavings": "3.0%", "byteSaving...
{ "realWorld": { "files": 65, "roundTripPass": 65, "roundTripFail": 0, "totalOrigTokens": 70014, "totalCompTokens": 68317, "tokenSavings": "2.4%", "totalOrigBytes": 249111, "totalCompBytes": 242953, "byteSavings": "2.5%", "codebookEntries": 72 } }

PackRat v2 Benchmarks

Version: 2.0.0 Date: 2026-04-10 Tokenizer: tiktoken cl100k_base (GPT-4 / Claude compatible) Platform: Node.js v25.6.1, Windows 11

Summary

Metric Result
Round-trip accuracy 100% (144/144 tests)
Token savings (avg) 2.4%
Token savings (best) 17.3% (path/URL-heavy files)
Byte savings (avg) 2.5%
Search speedup 12.03x
Codebook entries 72 (auto-learned)
Negative-savings entries 0

Comparison: PackRat vs MemPalace

Metric PackRat v2 MemPalace (AAAK)
Accuracy 100% (lossless) 84.2% (lossy)
Compression type Lossless codebook Lossy summarization
Token savings 2-17% Higher (lossy)
Data loss Zero Information dropped
Dependencies Zero Multiple
Decoder needed No (self-documenting) Yes

PackRat trades peak compression for perfect fidelity. No information is ever lost.

Real-World Results (65 Production Files)

Tested on 65 markdown memory files totaling 249KB / 70,014 tokens. Codebook auto-learned from the same files (72 entries: 20 paths, 35 entities, 17 phrases).

File Type Bytes Tokens Compressed Savings Round-Trip
file_01 urls/config 782 197 163 17.3% PASS
file_02 urls/links 5,481 1,666 1,415 15.1% PASS
file_03 api endpoints 2,331 756 684 9.5% PASS
file_04 tool config 1,939 623 565 9.3% PASS
file_05 promo tracking 1,703 471 433 8.1% PASS
file_06 project notes 2,408 760 704 7.4% PASS
file_07 feedback rule 585 143 133 7.0% PASS
file_08 tool notes 1,589 511 479 6.3% PASS
file_09 session state 1,934 568 532 6.3% PASS
file_10 pipeline docs 5,820 1,748 1,652 5.5% PASS
file_11 platform accts 1,644 639 604 5.5% PASS
file_12 cli tool docs 2,056 590 564 4.4% PASS
file_13 project index 6,508 2,002 1,921 4.0% PASS
file_14 desktop app 2,143 621 597 3.9% PASS
file_15 app reference 7,267 2,031 1,953 3.8% PASS
file_16 git config 1,576 420 405 3.6% PASS
file_17 integration 1,751 459 443 3.5% PASS
file_18 memory index 7,140 2,131 2,064 3.1% PASS
file_19 project docs 3,313 1,027 996 3.0% PASS
file_20 client notes 1,163 302 293 3.0% PASS
file_21 feedback rule 1,946 421 409 2.9% PASS
file_22 tool research 4,822 1,671 1,625 2.8% PASS
file_23 lessons log 13,814 3,776 3,715 1.6% PASS
file_24 task tracker 12,543 3,983 3,909 1.9% PASS
file_25 app deep-dive 35,578 9,555 9,540 0.2% PASS
TOTAL mixed 249,111 70,014 68,317 2.4% 65/65 PASS

25 of 65 files shown (sorted by savings). All 65 passed round-trip. Full results in data/v2-test-results.json.

Token Savings by Pattern Type

Measured with tiktoken cl100k_base:

Pattern Type Example Original Tokens Code Tokens Savings Per Hit
Windows file path C:/Users/dev/projects/app/ 8 3 5
Deep file path C:/Users/dev/projects/myapp/src/ 12 3 9
Very deep path C:/Users/dev/Downloads/ImageGen_portable/ 19 3 16
GitHub URL https://github.com/user/repo 14 3 11
Markdown header ## CRITICAL REMINDERS 6 2 4
Multi-word phrase via OpenRouter for free 5 2 3
Tech name (multi-token) MyAppName 3 2 1
Tech name (single-token) JavaScript 1 3 -2 (rejected)

v2's token-aware scoring automatically rejects entries like "JavaScript" that cost tokens.

Test Suite (144 tests, 0 failures)

Category Tests Description
Edge cases 40 Unicode, emoji, CJK, whitespace, code blocks, markdown, literal code-like strings, special chars, fake headers, private use area chars
Stress tests 14 200x repeated words, 100x repeated paths, 50K char files, null bytes, 1-char files, long paths/URLs
Real-world files 65 Production AI agent memory files (read-only, no modification)
CLAUDE.md files 12 Project config files across multiple repos
v1 backward compat 12 v2 engine with v1 codebook format
Production codebook 1 v2 engine with a production codebook

How to Reproduce

git clone https://github.com/kevdogg102396-afk/packrat
cd packrat
pip install tiktoken
PYTHON_PATH=$(which python) node benchmark/bench.mjs
PYTHON_PATH=$(which python) node benchmark/tests/v2-edge-cases.mjs

Methodology

  • Token counting: tiktoken cl100k_base via Python subprocess (batch mode)
  • Round-trip test: decompress(compress(original)) === original (exact string equality)
  • Codebook: Auto-learned from the same files being tested (no external training data)
  • No cherry-picking: All 65 files in the memory directory were tested, results reported for every file
  • Secrets filter: Lines containing API keys, tokens, or credentials are stripped before learning
Downloads last month
35