DeepEval
The Open-Source LLM Evaluation Framework.
Overview
DeepEval is an open-source evaluation framework for Large Language Models that allows developers to unit test their LLM applications. It provides a suite of metrics to evaluate the performance of LLMs on various aspects such as factual consistency, relevance, and coherence. DeepEval is designed to be easy to use and integrate into existing MLOps workflows. It is maintained by Confident AI, which also provides a commercial platform for more advanced features.
✨ Key Features
- Unit testing for LLMs
- Factual consistency checking
- Relevance and coherence scoring
- Bias and toxicity detection
- Synthetic data generation
- Integration with popular LLM frameworks
🎯 Key Differentiators
- Open-source and developer-focused
- Unit testing paradigm for LLMs
- Comprehensive and customizable metrics
Unique Value: DeepEval brings the familiar and powerful paradigm of unit testing to the world of LLM evaluation, making it easy for developers to ensure the quality and reliability of their AI applications.
🎯 Use Cases (4)
✅ Best For
- Unit testing LLM outputs for factual consistency and relevance.
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Not a full-fledged MLOps platform for model training and deployment.
🏆 Alternatives
Compared to more comprehensive MLOps platforms, DeepEval offers a lightweight and focused solution for LLM evaluation. Its open-source nature and developer-centric design make it a popular choice for teams that want to have granular control over their testing workflows.
💻 Platforms
🔌 Integrations
🛟 Support Options
- ✓ Email Support
- ✓ Live Chat
- ✓ Dedicated Support (Enterprise tier)
🔒 Compliance & Security
💰 Pricing
✓ 14-day free trial
Free tier: Free forever for open-source use.
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