# whylabs/langkit

🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safety & security. 🛡️ Features include text quality, relevance metrics, & sentiment analysis. 📊 A comprehensive tool for LLM observability. 👀

Repository: https://github.com/whylabs/langkit
Canonical: https://ross.abutalabs.com/products/langkit
Homepage: https://whylabs.ai
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: large-language-models, machine-learning, nlg, nlp, observability, prompt-engineering, prompt-injection
Last push: 2024-11-22T20:02:14+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 40, longevity 87
- inputs: {"age_days": 1225, "days_push": 649, "days_rel": 665, "gap_med": 8, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 996, forks 74 (observed 2026-09-03T02:15:20.510808+00:00)

## Summary
No AI-extracted summary yet.

## Member repositories
- whylabs/langkit (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:20.510808+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Data as of 2026-08-30T08:39:29.467469+00:00.