⛨GuardrailCybersecurityFree
LangKit
Open-source toolkit that extracts safety and quality signals — injection, PII, toxicity, sentiment, relevance — from LLM prompts and responses.
LangKit — safety & quality metrics for LLM prompts and responses
LangKit is an open-source text-metrics toolkit that extracts safety, quality, and relevance signals from LLM prompts and responses, so you can monitor and guardrail models in production rather than trusting them blind.
Key features
- Prompt-injection and jailbreak similarity scoring against known-attack themes
- PII pattern detection, toxicity, and sentiment analysis on both inputs and outputs
- Text-quality/readability metrics and prompt-response relevance via semantic similarity
- Consistency and refusal signals to surface likely hallucinations or off-policy replies
- Emits metrics compatible with whylogs for drift monitoring, dashboards, and alerting
Unlike a single classifier, LangKit produces a bundle of interpretable signals you can threshold and combine into your own guardrail policy, making it a practical observability layer for LLM safety and security.
Curated mirror of the open-source LangKit (Apache-2.0). Get it from the source.