# angr/angr SLOC report

> As of 2026-09-16 (commit 9b0bfed7c731), angr/angr contains 347,214 total lines: 274,750 code, 23,586 comments, 48,878 blank, across 1,427 files in 15 languages (top: Python 82.0%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

angr/angr has 274,750 source lines of code out of 347,214 total lines across 1,427 files, counted from the 9b0bfed7c731bb2b44fd474735e56b9f2eaa29e9 ref at commit 9b0bfed7c731 by the OctoCounts tokei engine on 2026-09-16. 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 angr/angr to commit 9b0bfed7c731, 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 79.1% of all lines, comments represent 6.8%, and the repository averages 193 code lines per file. Python accounts for 82.0% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 1,276 | 286,533 | 225,189 | 18,593 | 42,751 |
| Rust | 113 | 51,524 | 43,819 | 2,500 | 5,205 |
| C++ | 2 | 3,387 | 2,743 | 410 | 234 |
| Protocol Buffers | 9 | 1,293 | 918 | 188 | 187 |
| Shell | 6 | 917 | 703 | 51 | 163 |
| C++ Header | 1 | 1,000 | 689 | 128 | 183 |
| TOML | 8 | 413 | 358 | 14 | 41 |
| C | 2 | 194 | 143 | 26 | 25 |
| C Header | 2 | 120 | 89 | 21 | 10 |
| Module-Definition | 1 | 52 | 51 | 0 | 1 |
| Makefile | 1 | 48 | 41 | 0 | 7 |
| Autoconf | 1 | 7 | 7 | 0 | 0 |

Top language (Python 82.0%). Generated at 2026-09-16T21:31:45.231808670+00:00. Canonical report: https://octocounts.com/github/angr/angr

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/angr/angr/commit/9b0bfed7c731bb2b44fd474735e56b9f2eaa29e9?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 angr/angr have?

angr/angr has 347,214 total lines, including 274,750 code lines, 23,586 comment lines, and 48,878 blank lines.

### How was the angr/angr line count measured?

OctoCounts resolved the public GitHub repository to commit 9b0bfed7c731, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for angr/angr?

This OctoCounts report was generated from 9b0bfed7c731bb2b44fd474735e56b9f2eaa29e9 at commit 9b0bfed7c731 on 2026-09-16T21:31:45.231808670+00:00.

## Similar repository reports

- [kovidgoyal/kitty](https://octocounts.com/github/kovidgoyal/kitty) — Python, 272,599 code lines
- [alirezarezvani/claude-skills](https://octocounts.com/github/alirezarezvani/claude-skills) — Python, 277,686 code lines
- [openai/openai-python](https://octocounts.com/github/openai/openai-python) — Python, 270,647 code lines
- [imbad0202/academic-research-skills](https://octocounts.com/github/imbad0202/academic-research-skills) — Python, 279,102 code lines
- [Imbad0202/academic-research-skills](https://octocounts.com/github/Imbad0202/academic-research-skills) — Python, 285,318 code lines
- [saleor/saleor](https://octocounts.com/github/saleor/saleor) — Python, 286,919 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)