This page compares the source lines of code (SLOC) of langchain-ai/langchain and run-llama/llama_index using cached OctoCounts reports. Code size is not code quality: a larger count only means more source material, not a better or worse project.
As of 2026-09-07, langchain-ai/langchain contains 244,955 total lines (200,686 code) across 1,900 files, while run-llama/llama_index contains 406,228 total lines (311,078 code) across 4,545 files as of 2026-08-26. run-llama/llama_index is about 1.6x the size of langchain-ai/langchain by code lines. Code size is not code quality: a larger count only means more source material, not a better or worse project.
| Metric | langchain-ai/langchain | run-llama/llama_index |
|---|---|---|
| Files | 1,900 | 4,545 |
| Total lines | 244,955 | 406,228 |
| Code lines | 200,686 | 311,078 |
| Comment lines | 14,842 | 33,854 |
| Blank lines | 29,427 | 61,296 |
| Languages counted | 9 | 12 |
Top languages in langchain-ai/langchain: Python (97.8% of code), TOML (1.4% of code), Makefile (0.6% of code). Top languages in run-llama/llama_index: Python (84.1% of code), TOML (11.8% of code), Makefile (2.4% of code). Python, TOML, Makefile appear in both top language lists; Shell, XSL appear only in langchain-ai/langchain's top languages; Jupyter Notebooks, JavaScript appear only in run-llama/llama_index's top languages.
Methodology: both counts come from cached OctoCounts reports generated with tokei. langchain-ai/langchain was counted at ref master (commit fa942aec719a) on 2026-09-07; run-llama/llama_index was counted at ref main (commit d8021225eb7e) on 2026-08-26. See the counting methodology for ignored directories and analysis options.
Evidence and next steps:
Note: code size is not code quality. OctoCounts only reports reproducible line counts and makes no claim that either project is better.
run-llama/llama_index has more code: 311,078 code lines versus 200,686 for langchain-ai/langchain, about 1.6x as much, based on cached OctoCounts reports as of 2026-09-07.
Not necessarily. SLOC measures size, not complexity, quality, or maintainability. A larger codebase can mean more features, more generated or vendored code, more verbose language idioms, or more tests — none of which imply the code is harder to work with. Use SLOC to gauge the scale of what you'd be reading or maintaining, not as a quality signal for langchain-ai/langchain, run-llama/llama_index, or any repository.
Methodology: both counts come from cached OctoCounts reports generated with tokei. langchain-ai/langchain was counted at ref master (commit fa942aec719a) on 2026-09-07; run-llama/llama_index was counted at ref main (commit d8021225eb7e) on 2026-08-26. See the counting methodology for ignored directories and analysis options.
Yes. This page shows the default branch for each repository. Use the interactive comparison tool to pick any public GitHub repository, branch, tag, or commit SHA for both sides and get a fresh side-by-side report.