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
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