# mlc-ai/mlc-llm SLOC report

> As of 2026-10-12 (commit 8978ea9aec62), mlc-ai/mlc-llm contains 97,413 total lines: 79,941 code, 6,995 comments, 10,477 blank, across 549 files in 24 languages (top: Python 69.4%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

mlc-ai/mlc-llm has 79,941 source lines of code out of 97,413 total lines across 549 files, counted from the main ref at commit 8978ea9aec62 by the OctoCounts tokei engine on 2026-10-12. 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 mlc-ai/mlc-llm to commit 8978ea9aec62, 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 82.1% of all lines, comments represent 7.2%, and the repository averages 146 code lines per file. Python accounts for 69.4% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 346 | 63,837 | 55,509 | 1,455 | 6,873 |
| C++ | 43 | 16,928 | 13,174 | 2,234 | 1,520 |
| C Header | 41 | 5,746 | 2,850 | 2,087 | 809 |
| Kotlin | 14 | 2,493 | 2,237 | 46 | 210 |
| SVG | 7 | 2,038 | 2,038 | 0 | 0 |
| Swift | 18 | 2,296 | 1,871 | 165 | 260 |
| Shell | 13 | 824 | 607 | 118 | 99 |
| CMake | 2 | 308 | 253 | 11 | 44 |
| JSON | 14 | 251 | 251 | 0 | 0 |
| Sass | 1 | 238 | 200 | 1 | 37 |
| XML | 17 | 221 | 160 | 48 | 13 |
| Batch | 3 | 192 | 143 | 3 | 46 |

Top language (Python 69.4%). Generated at 2026-10-12T16:24:58.051408637+00:00. Canonical report: https://octocounts.com/github/mlc-ai/mlc-llm

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/mlc-ai/mlc-llm/commit/8978ea9aec626d64096aaa92fbc89bda2e3ea802?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=81642d22713567a3f0bf95a2131e4d72864361cf28f6a9b68b47fadbc500bc9c).

## Report FAQ

### How many lines of code does mlc-ai/mlc-llm have?

mlc-ai/mlc-llm has 97,413 total lines, including 79,941 code lines, 6,995 comment lines, and 10,477 blank lines.

### How was the mlc-ai/mlc-llm line count measured?

OctoCounts resolved the public GitHub repository to commit 8978ea9aec62, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for mlc-ai/mlc-llm?

This OctoCounts report was generated from main at commit 8978ea9aec62 on 2026-10-12T16:24:58.051408637+00:00.

## Similar repository reports

- [EverMind-AI/EverOS](https://octocounts.com/github/EverMind-AI/EverOS) — Python, 79,894 code lines
- [github/spec-kit](https://octocounts.com/github/github/spec-kit) — Python, 79,866 code lines
- [swat-model/swatplus-editor](https://octocounts.com/github/swat-model/swatplus-editor) — Python, 80,731 code lines
- [e2b-dev/e2b](https://octocounts.com/github/e2b-dev/e2b) — Python, 81,298 code lines
- [michaelegner/architecture-intelligence-platform](https://octocounts.com/github/michaelegner/architecture-intelligence-platform) — Python, 81,575 code lines
- [langchain-ai/langgraph](https://octocounts.com/github/langchain-ai/langgraph) — Python, 81,770 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)