About
LangCheck, developed by Citadel AI, is an open-source Python toolkit designed to make LLM application evaluation straightforward and Pythonic. It gives developers a rich set of metrics to programmatically assess the quality, safety, and correctness of text generated by any large language model. The library installs easily via pip and supports language-specific metric packages — English-only, English plus Japanese, or all supported languages (English, Japanese, Chinese, German) — making it adaptable for global teams and multilingual applications. With LangCheck, developers can evaluate outputs for fluency, factual consistency, toxicity, sentiment, relevance, and more without needing to build evaluation infrastructure from scratch. Its composable design means metrics can be combined into custom evaluation pipelines that integrate directly into CI/CD workflows, model fine-tuning loops, or prompt engineering experiments. The library is fully open source under the MIT license, making it accessible for startups, research teams, and enterprise developers alike. It integrates with any LLM library — simply pass generated outputs to LangCheck's evaluation functions and receive structured, interpretable scores. Comprehensive documentation, multilingual README files, and a growing community make it easy to adopt. LangCheck is ideal for AI engineers, researchers, and MLOps teams who need reliable, repeatable quality gates for LLM-powered products.
Key Features
- Comprehensive Evaluation Metrics: Provides a broad suite of metrics covering fluency, factual consistency, toxicity, sentiment, and relevance to assess LLM output quality.
- Pythonic API: Designed with simplicity in mind — pass generated outputs directly to evaluation functions and receive structured, interpretable scores.
- Multilingual Support: Supports English, Japanese, Chinese, and German metrics via optional pip install extras, enabling evaluation of global, multilingual applications.
- LLM-Agnostic Integration: Works with outputs from any LLM library, making it easy to drop into existing AI development workflows regardless of the underlying model.
- Composable Evaluation Pipelines: Metrics are modular and composable, allowing teams to build custom evaluation pipelines for CI/CD, prompt engineering, or model fine-tuning.
Use Cases
- Automatically evaluating LLM output quality in CI/CD pipelines to catch regressions before deployment.
- Assessing factual consistency and fluency of AI-generated content in production applications.
- Benchmarking and comparing prompt variations during prompt engineering experiments.
- Monitoring LLM application safety by detecting toxic or harmful outputs at scale.
- Supporting researchers who need reproducible, standardized evaluation metrics for multilingual NLP studies.
Pros
- Fully Open Source: MIT-licensed and freely available, with no vendor lock-in and full access to source code for customization or contribution.
- Easy to Get Started: Simple pip installation and a clean Pythonic API mean developers can start evaluating LLM outputs in minutes with minimal boilerplate.
- Multilingual Out of the Box: Rare among evaluation libraries, LangCheck natively supports Japanese, Chinese, and German in addition to English.
Cons
- Python-Only: Currently a Python library with no native SDKs for other languages, limiting adoption for teams outside the Python ecosystem.
- No Hosted UI: LangCheck is purely a code library without a visual dashboard or no-code interface, requiring programming knowledge to use effectively.
Frequently Asked Questions
What is LangCheck?
LangCheck is an open-source Python library that provides simple, composable metrics to evaluate the quality, safety, and accuracy of text generated by large language models (LLMs).
How do I install LangCheck?
You can install it via pip: `pip install langcheck` for English-only metrics, `pip install langcheck[ja]` for English and Japanese, or `pip install langcheck[all]` for all supported languages.
Which LLMs does LangCheck support?
LangCheck is LLM-agnostic — it evaluates text outputs regardless of which model produced them, so it works with OpenAI, Anthropic, open-source models, or any other LLM library.
What languages does LangCheck support for evaluation metrics?
LangCheck supports English, Japanese, Chinese, and German through its optional install extras, making it suitable for multilingual AI applications.
Is LangCheck free to use?
Yes, LangCheck is fully open source and released under the MIT license, meaning it is free to use, modify, and distribute.
