# Ralph-Workflow/Ralph-Workflow SLOC report

> As of 2026-10-24 (commit e335304c1641), Ralph-Workflow/Ralph-Workflow contains 337,307 total lines: 263,301 code, 37,008 comments, 36,998 blank, across 1,632 files in 14 languages (top: Python 98.4%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

Ralph-Workflow/Ralph-Workflow has 263,301 source lines of code out of 337,307 total lines across 1,632 files, counted from the main ref at commit e335304c1641 by the OctoCounts tokei engine on 2026-10-24. 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 Ralph-Workflow/Ralph-Workflow to commit e335304c1641, 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 78.1% of all lines, comments represent 11.0%, and the repository averages 161 code lines per file. Python accounts for 98.4% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 1,435 | 314,140 | 259,168 | 22,255 | 32,717 |
| Jinja2 | 39 | 1,663 | 1,263 | 34 | 366 |
| TOML | 10 | 1,796 | 817 | 818 | 161 |
| JSON | 5 | 807 | 807 | 0 | 0 |
| Shell | 9 | 902 | 605 | 231 | 66 |
| Makefile | 2 | 530 | 274 | 168 | 88 |
| JavaScript | 2 | 210 | 181 | 0 | 29 |
| Dockerfile | 2 | 146 | 78 | 32 | 36 |
| INI | 2 | 73 | 41 | 28 | 4 |
| BASH | 1 | 49 | 31 | 11 | 7 |
| YAML | 2 | 46 | 24 | 20 | 2 |
| Ruby | 1 | 42 | 12 | 26 | 4 |

Top language (Python 98.4%). Generated at 2026-10-24T11:55:34.564838353+00:00. Canonical report: https://octocounts.com/github/Ralph-Workflow/Ralph-Workflow

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/Ralph-Workflow/Ralph-Workflow/commit/e335304c1641b66ea1005463b0ff3db32dfce95d?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&reportId=10bcabdd22e909aa90b22071800f425348f2192a088bfcb095261258a044e716).

## Report FAQ

### How many lines of code does Ralph-Workflow/Ralph-Workflow have?

Ralph-Workflow/Ralph-Workflow has 337,307 total lines, including 263,301 code lines, 37,008 comment lines, and 36,998 blank lines.

### How was the Ralph-Workflow/Ralph-Workflow line count measured?

OctoCounts resolved the public GitHub repository to commit e335304c1641, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for Ralph-Workflow/Ralph-Workflow?

This OctoCounts report was generated from main at commit e335304c1641 on 2026-10-24T11:55:34.564838353+00:00.

## Similar repository reports

- [langchain-ai/open-swe](https://octocounts.com/github/langchain-ai/open-swe) — Python, 263,502 code lines
- [pipecat-ai/pipecat](https://octocounts.com/github/pipecat-ai/pipecat) — Python, 263,029 code lines
- [ZhuLinsen/daily_stock_analysis](https://octocounts.com/github/ZhuLinsen/daily_stock_analysis) — Python, 264,962 code lines
- [mehakkgupta1107/PlanKaro](https://octocounts.com/github/mehakkgupta1107/PlanKaro) — Python, 265,986 code lines
- [langchain-ai/deepagents](https://octocounts.com/github/langchain-ai/deepagents) — Python, 268,261 code lines
- [matplotlib/matplotlib](https://octocounts.com/github/matplotlib/matplotlib) — Python, 256,721 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)
