- Dataset
- 2026-10-27-catalog-ai-v2
- Ruleset
- b2e8889c66f7
- Scanned
- 2026-10-27
- Window
- February–September 15, 2026
LangChain 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 LangChain을 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.
01Result
teddylee777/langchain-kr ↗teddylee777/langchain-kr AI coding statistics: 0%
No signed agent commits found
No agent signatures were found in the last 0 considered commits. That is a real result, not a failure to measure.
It does not mean no AI was used here. A developer can use an agent and commit by hand without leaving a detectable git signature.
02Drivers
What shapes this result
Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.
03Evidence
Inspect the matches
Public commit samples let you inspect the signatures behind the aggregate result.
04Method
What entered the denominator
The result begins with commit history, removes merges and routine automation, and reports literal agent signatures as a lower bound.
Exclusion ledger
| Commits in window | 0 |
| Merge commits removed | −0 |
| CI / dependency bots removed | −0 |
| Considered | 0 |
Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.
Continue exploring
Related coding-agent evidence
Run the same detector locally.
Deterministic signature matching; no code-style inference. Squash merges can remove trailers, so this result remains a measured floor.
npx qmmit-cli teddylee777/langchain-kr