# deepset-ai/haystack SLOC report

> As of 2026-09-18 (commit fc2f66e84ca5), deepset-ai/haystack contains 565,984 total lines: 59,512 code, 338,139 comments, 168,333 blank, across 3,829 files in 14 languages (top: Python 73.5%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

deepset-ai/haystack has 59,512 source lines of code out of 565,984 total lines across 3,829 files, counted from the fc2f66e84ca5a37829c162ac953260250c6ec6de ref at commit fc2f66e84ca5 by the OctoCounts tokei engine on 2026-09-18. 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 deepset-ai/haystack to commit fc2f66e84ca5, 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 10.5% of all lines, comments represent 59.7%, and the repository averages 16 code lines per file. Python accounts for 73.5% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 285 | 52,698 | 43,767 | 2,695 | 6,236 |
| YAML | 978 | 7,174 | 6,851 | 51 | 272 |
| JSON | 16 | 4,431 | 4,431 | 0 | 0 |
| JavaScript | 14 | 2,290 | 2,065 | 111 | 114 |
| CSS | 4 | 1,698 | 1,260 | 162 | 276 |
| SVG | 13 | 417 | 417 | 0 | 0 |
| TOML | 2 | 411 | 335 | 30 | 46 |
| TSX | 4 | 343 | 265 | 33 | 45 |
| TypeScript | 1 | 73 | 61 | 2 | 10 |
| HCL | 1 | 40 | 31 | 2 | 7 |
| Shell | 1 | 54 | 29 | 13 | 12 |
| MDX | 1,911 | 228,925 | 0 | 165,589 | 63,336 |

Top language (Python 73.5%). Generated at 2026-09-18T03:34:34.772613621+00:00. Canonical report: https://octocounts.com/github/deepset-ai/haystack

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/deepset-ai/haystack/commit/fc2f66e84ca5a37829c162ac953260250c6ec6de?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 deepset-ai/haystack have?

deepset-ai/haystack has 565,984 total lines, including 59,512 code lines, 338,139 comment lines, and 168,333 blank lines.

### How was the deepset-ai/haystack line count measured?

OctoCounts resolved the public GitHub repository to commit fc2f66e84ca5, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for deepset-ai/haystack?

This OctoCounts report was generated from fc2f66e84ca5a37829c162ac953260250c6ec6de at commit fc2f66e84ca5 on 2026-09-18T03:34:34.772613621+00:00.

## Similar repository reports

- [jianchang512/pyvideotrans](https://octocounts.com/github/jianchang512/pyvideotrans) — Python, 59,804 code lines
- [usestrix/strix](https://octocounts.com/github/usestrix/strix) — Python, 59,879 code lines
- [mnemosyne-oss/mnemosyne](https://octocounts.com/github/mnemosyne-oss/mnemosyne) — Python, 60,163 code lines
- [nvidia-cosmos/cosmos-transfer1](https://octocounts.com/github/nvidia-cosmos/cosmos-transfer1) — Python, 60,502 code lines
- [opendatalab/mineru](https://octocounts.com/github/opendatalab/mineru) — Python, 60,506 code lines
- [opendatalab/MinerU](https://octocounts.com/github/opendatalab/MinerU) — Python, 60,584 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)
