# BerriAI/litellm SLOC report

> As of 2026-10-22 (commit f2118fc7670b), BerriAI/litellm contains 2,089,078 total lines: 1,749,068 code, 138,825 comments, 201,185 blank, across 7,977 files in 23 languages (top: Python 49.3%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

BerriAI/litellm has 1,749,068 source lines of code out of 2,089,078 total lines across 7,977 files, counted from the main ref at commit f2118fc7670b by the OctoCounts tokei engine on 2026-10-22. 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 BerriAI/litellm to commit f2118fc7670b, 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 very large codebase by counted code lines. Code represents 83.7% of all lines, comments represent 6.6%, and the repository averages 219 code lines per file. Python accounts for 49.3% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 3,169 | 1,028,457 | 861,799 | 35,316 | 131,342 |
| TSX | 1,632 | 352,414 | 307,680 | 5,166 | 39,568 |
| JSON | 94 | 295,160 | 295,148 | 0 | 12 |
| TypeScript | 736 | 180,445 | 134,685 | 34,418 | 11,342 |
| Rust | 620 | 100,133 | 92,061 | 146 | 7,926 |
| Go | 105 | 24,612 | 21,553 | 434 | 2,625 |
| YAML | 157 | 14,422 | 11,971 | 1,801 | 650 |
| HCL | 39 | 6,498 | 4,793 | 769 | 936 |
| JavaScript | 197 | 4,213 | 3,947 | 27 | 239 |
| SVG | 327 | 2,682 | 2,623 | 26 | 33 |
| SQL | 232 | 3,767 | 2,377 | 803 | 587 |
| Shell | 24 | 3,056 | 2,336 | 419 | 301 |

Top language (Python 49.3%). Generated at 2026-10-22T06:09:59.094237281+00:00. Canonical report: https://octocounts.com/github/BerriAI/litellm

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/BerriAI/litellm/commit/f2118fc7670ba3fa736d9b8f4deef9077a45a720?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=e87523caf835863d9f4c2b6ab6c615e812d14473324fd9b12ac3953002b0c46c).

## Report FAQ

### How many lines of code does BerriAI/litellm have?

BerriAI/litellm has 2,089,078 total lines, including 1,749,068 code lines, 138,825 comment lines, and 201,185 blank lines.

### How was the BerriAI/litellm line count measured?

OctoCounts resolved the public GitHub repository to commit f2118fc7670b, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for BerriAI/litellm?

This OctoCounts report was generated from main at commit f2118fc7670b on 2026-10-22T06:09:59.094237281+00:00.

## Similar repository reports

- [sgl-project/sglang](https://octocounts.com/github/sgl-project/sglang) — Python, 1,740,273 code lines
- [home-assistant/core](https://octocounts.com/github/home-assistant/core) — Python, 1,655,962 code lines
- [NousResearch/hermes-agent](https://octocounts.com/github/NousResearch/hermes-agent) — Python, 1,928,554 code lines
- [vllm-project/vllm](https://octocounts.com/github/vllm-project/vllm) — Python, 1,420,031 code lines
- [unslothai/unsloth](https://octocounts.com/github/unslothai/unsloth) — Python, 1,406,112 code lines
- [houmo-ai/postmo-quantflow](https://octocounts.com/github/houmo-ai/postmo-quantflow) — Python, 1,321,386 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)
