LangKit

open_source

LangKit is an open-source Python toolkit for monitoring Large Language Models. Extract text quality, sentiment, relevance, and safety signals from prompts and responses.

Data & Analytics

AI Models & Infrastructure

LLM Developer Tools

About

LangKit is an open-source text metrics toolkit designed to help developers monitor and observe the behavior of Large Language Models (LLMs) in production environments. Built by WhyLabs and licensed under Apache-2.0, it extracts a wide range of signals from both LLM inputs (prompts) and outputs (responses), enabling teams to track quality, safety, and performance over time. Key capabilities include text quality metrics (readability, complexity, token counts), sentiment analysis, relevance and semantic similarity scoring between prompts and responses, and safety/security features such as prompt injection detection and toxicity scoring. All extracted metrics are designed to be compatible with whylogs, WhyLabs' open-source data logging library, making it easy to integrate LangKit into broader ML observability stacks. LangKit is ideal for ML engineers and data scientists who need to go beyond basic logging and want structured, quantitative visibility into how their LLM applications are performing. It supports use cases ranging from content moderation and compliance monitoring to debugging hallucinations and measuring response drift in RAG pipelines. As a pure Python library, it can be incorporated into existing workflows with minimal overhead, and it works with any LLM provider or framework. With nearly 1,000 GitHub stars and an active open-source community, LangKit is a reliable foundation for production LLM monitoring.

Key Features

Prompt & Response Signal Extraction: Automatically extracts quantitative signals from LLM inputs and outputs, including token counts, text complexity, and readability metrics.
Sentiment & Toxicity Analysis: Runs sentiment scoring and toxicity detection on prompts and responses to flag unsafe or negative content before it reaches end users.
Relevance & Semantic Similarity Metrics: Measures semantic relevance between prompts and model responses, helping identify hallucinations or off-topic outputs in production.
Prompt Injection & Security Detection: Includes built-in checks to detect prompt injection attempts and other adversarial inputs that could compromise LLM behavior.
WhyLogs Integration: Natively compatible with the whylogs data logging library, enabling seamless integration into WhyLabs observability pipelines and dashboards.

Use Cases

Monitoring LLM response quality in production applications to detect degradation or drift over time.
Detecting prompt injection attacks and adversarial inputs before they affect model behavior.
Analyzing sentiment and toxicity in user-generated prompts to enforce content policies.
Measuring semantic relevance between prompts and responses in RAG pipelines to identify hallucinations.
Building comprehensive LLM observability pipelines by combining LangKit metrics with whylogs and the WhyLabs platform.

Pros

Truly Open Source: Released under Apache-2.0, LangKit is free to use, modify, and deploy commercially with no licensing restrictions.
Comprehensive Metric Coverage: Covers a broad range of signal types—quality, safety, sentiment, and relevance—from a single unified library, reducing the need for multiple tools.
Easy Integration with Existing Stacks: Works as a standard Python library and integrates natively with whylogs, making it straightforward to add to existing ML pipelines.
Provider-Agnostic: Works with any LLM provider or framework (OpenAI, Anthropic, open-source models, etc.) without vendor lock-in.

Cons

Python-Only: Currently limited to Python environments, which may exclude teams using other languages or non-technical stakeholders who need no-code solutions.
Requires Additional Infrastructure for Dashboards: LangKit handles metric extraction but relies on whylogs and the WhyLabs platform for visualization and alerting, adding setup complexity.
Limited Real-Time Streaming Support: Primarily designed for batch and asynchronous monitoring workflows; real-time streaming observability may require additional configuration.

Frequently Asked Questions

LangKit is an open-source Python toolkit for monitoring LLMs in production. It is designed for ML engineers, data scientists, and developers who want structured visibility into LLM prompt/response quality, safety, and performance.

Yes. LangKit is fully open-source under the Apache-2.0 license, meaning it is free to use, modify, and distribute, including for commercial purposes.

LangKit can extract text quality metrics (token counts, readability), sentiment scores, toxicity levels, semantic relevance between prompts and responses, and security-related signals like prompt injection indicators.

LangKit is designed to be compatible with whylogs, WhyLabs' open-source data logging library. Extracted metrics can be logged as whylogs profiles and sent to the WhyLabs platform for monitoring, dashboards, and alerting.

Yes. LangKit is provider-agnostic and works with any LLM, including OpenAI, Anthropic, Cohere, Hugging Face models, and locally hosted open-source models.