# ValueCell-ai/valuecell SLOC report

> As of 2026-09-07 (commit a012b384daa2), ValueCell-ai/valuecell contains 67,431 total lines: 55,081 code, 4,571 comments, 7,779 blank, across 457 files in 16 languages (top: Python 62.9%). Counted with tokei via OctoCounts.

OctoCounts produced this report by resolving ValueCell-ai/valuecell to commit a012b384daa2, 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 medium-sized codebase by counted code lines. Code represents 81.7% of all lines, comments represent 6.8%, and the repository averages 121 code lines per file. Python accounts for 62.9% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 201 | 42,147 | 34,640 | 2,131 | 5,376 |
| TSX | 134 | 13,985 | 12,574 | 275 | 1,136 |
| JSON | 23 | 3,172 | 3,172 | 0 | 0 |
| TypeScript | 42 | 3,617 | 2,898 | 323 | 396 |
| YAML | 13 | 842 | 465 | 238 | 139 |
| Rust | 5 | 457 | 377 | 20 | 60 |
| PowerShell | 2 | 358 | 280 | 32 | 46 |
| Shell | 3 | 286 | 216 | 24 | 46 |
| CSS | 1 | 193 | 169 | 0 | 24 |
| SVG | 14 | 150 | 150 | 0 | 0 |
| TOML | 2 | 130 | 113 | 4 | 13 |
| Dockerfile | 1 | 24 | 12 | 5 | 7 |

Top language (Python 62.9%). Generated at 2026-09-07T21:11:45.450517272+00:00. Canonical report: https://octocounts.com/github/ValueCell-ai/valuecell

## Report FAQ

### How many lines of code does ValueCell-ai/valuecell have?

ValueCell-ai/valuecell has 67,431 total lines, including 55,081 code lines, 4,571 comment lines, and 7,779 blank lines.

### How was the ValueCell-ai/valuecell line count measured?

OctoCounts resolved the public GitHub repository to commit a012b384daa2, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for ValueCell-ai/valuecell?

This OctoCounts report was generated from a012b384daa256ce2280033c20c923dbe9177e36 at commit a012b384daa2 on 2026-09-07T21:11:45.450517272+00:00.

## Similar repository reports

- [YanJiangJerry/Block-R1](https://octocounts.com/github/YanJiangJerry/Block-R1) — Python, 55,217 code lines
- [datawhalechina/vibe-blog](https://octocounts.com/github/datawhalechina/vibe-blog) — Python, 55,931 code lines
- [Alishahryar1/free-claude-code](https://octocounts.com/github/Alishahryar1/free-claude-code) — Python, 55,979 code lines
- [HKUDS/OpenHarness](https://octocounts.com/github/HKUDS/OpenHarness) — Python, 54,162 code lines
- [Lightricks/LTX-2](https://octocounts.com/github/Lightricks/LTX-2) — Python, 56,339 code lines
- [renpy/renpy-build](https://octocounts.com/github/renpy/renpy-build) — Python, 56,614 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)
- [OctoCounts API docs](https://octocounts.com/docs/api)