# mem0ai/mem0 SLOC report

> As of 2026-09-17 (commit 0df3e4b87df2), mem0ai/mem0 contains 205,126 total lines: 164,064 code, 15,474 comments, 25,588 blank, across 993 files in 18 languages (top: Python 37.4%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

mem0ai/mem0 has 164,064 source lines of code out of 205,126 total lines across 993 files, counted from the 0df3e4b87df20785f0741370c75e44428796193e ref at commit 0df3e4b87df2 by the OctoCounts tokei engine on 2026-09-17. The report is cached by commit, tokei version, and analysis options, so recounting the same revision returns exactly these numbers.

OctoCounts produced this report by resolving mem0ai/mem0 to commit 0df3e4b87df2, downloading the repository source archive, and counting every source file with tokei, the open-source line counter written in Rust. The table below breaks the count down by programming language into files, total lines, code lines, comment lines, and blank lines, so the figures can be compared across languages and projects. Results are cached by commit, tokei version, and analysis options, so counting the same revision again reproduces exactly these numbers.

## Repository size insights

This is a large codebase by counted code lines. Code represents 80.0% of all lines, comments represent 7.5%, and the repository averages 165 code lines per file. Python accounts for 37.4% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 266 | 71,008 | 61,418 | 1,453 | 8,137 |
| TypeScript | 291 | 52,482 | 44,525 | 2,990 | 4,967 |
| YAML | 19 | 42,011 | 36,007 | 12 | 5,992 |
| TSX | 136 | 11,962 | 10,903 | 40 | 1,019 |
| JSON | 66 | 3,340 | 3,332 | 0 | 8 |
| Shell | 6 | 2,590 | 2,050 | 145 | 395 |
| CSS | 9 | 2,363 | 2,027 | 36 | 300 |
| JavaScript | 24 | 2,222 | 1,768 | 218 | 236 |
| Jupyter Notebooks | 7 | 1,027 | 538 | 301 | 188 |
| SVG | 23 | 401 | 401 | 0 | 0 |
| HTML | 4 | 387 | 373 | 5 | 9 |
| TOML | 3 | 331 | 292 | 6 | 33 |

Top language (Python 37.4%). Generated at 2026-09-17T03:23:10.142667832+00:00. Canonical report: https://octocounts.com/github/mem0ai/mem0

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/mem0ai/mem0/commit/0df3e4b87df20785f0741370c75e44428796193e?analysis=%7B%22ignoredDirs%22%3A%5B%5D%2C%22ignoredLanguages%22%3A%5B%5D%2C%22profile%22%3A%22default%22%2C%22includeDocs%22%3Afalse%2C%22includeTests%22%3Afalse%2C%22includeGenerated%22%3Afalse%7D).

## Report FAQ

### How many lines of code does mem0ai/mem0 have?

mem0ai/mem0 has 205,126 total lines, including 164,064 code lines, 15,474 comment lines, and 25,588 blank lines.

### How was the mem0ai/mem0 line count measured?

OctoCounts resolved the public GitHub repository to commit 0df3e4b87df2, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for mem0ai/mem0?

This OctoCounts report was generated from 0df3e4b87df20785f0741370c75e44428796193e at commit 0df3e4b87df2 on 2026-09-17T03:23:10.142667832+00:00.

## Similar repository reports

- [datawhalechina/hello-agents](https://octocounts.com/github/datawhalechina/hello-agents) — Python, 159,917 code lines
- [crewAIInc/crewAI](https://octocounts.com/github/crewAIInc/crewAI) — Python, 168,690 code lines
- [meta-pytorch/torchrec](https://octocounts.com/github/meta-pytorch/torchrec) — Python, 158,832 code lines
- [tradermonty/claude-trading-skills](https://octocounts.com/github/tradermonty/claude-trading-skills) — Python, 172,153 code lines
- [langchain-ai/open-swe](https://octocounts.com/github/langchain-ai/open-swe) — Python, 154,660 code lines
- [Blaizzy/mlx-audio](https://octocounts.com/github/Blaizzy/mlx-audio) — Python, 154,095 code lines

## Related OctoCounts pages

- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [TensorFlow vs PyTorch](https://octocounts.com/compare/tensorflow-vs-pytorch)
- [Django vs Ruby on Rails](https://octocounts.com/compare/django-vs-rails)
- [Laravel vs Django](https://octocounts.com/compare/laravel-vs-django)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [OctoCounts API docs](https://octocounts.com/docs/api)
