DeepEval
UnclaimedThe comprehensive LLM evaluation framework for building reliable AI applications.
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TL;DR - DeepEval
- An open-source LLM evaluation framework for testing AI systems.
- Offers 50+ research-backed metrics, including G-Eval, DAGA, and QAG.
- Integrates with Pytest and supports multi-modal, single/multi-turn evaluations.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Comprehensive set of evaluation metrics for LLMs
- Seamless integration into existing Python testing frameworks (Pytest)
- Supports complex AI systems with multi-turn and multi-modal capabilities
- Ability to generate synthetic data for testing when real data is scarce
- Open-source framework with a cloud platform option for advanced features and collaboration
Cons
- Requires some technical knowledge to set up and integrate
- Advanced features like online monitoring and team collaboration are part of the Confident AI platform, which may have additional costs
Key Features
Native integration with Pytest for CI workflows50+ research-backed LLM-as-a-Judge metrics (G-Eval, DAGA, QAG)Support for single and multi-turn evaluationsNative multi-modal support (text, images, audio)Synthetic data generation and conversation simulationAutomatic prompt optimizationIntegration with Confident AI for team-wide collaboration, regression testing, and online monitoringCompatibility with OpenAI, LangChain, Pydantic AI, LlamaIndex, LangGraph, OpenAI Agents, Crew AI, Anthropic
Pricing
Freemium
DeepEval offers a generous free tier with optional paid upgrades for advanced features.
About DeepEval
By Toolradar Team·
DeepEval is an open-source LLM evaluation framework designed to help developers build and test reliable AI systems. It provides a robust set of tools for evaluating large language models (LLMs) and other AI components, integrating seamlessly into existing development workflows, particularly with Python's Pytest.
The framework offers a wide array of research-backed metrics, including advanced techniques like G-Eval, DAGA, and QAG, to provide nuanced and objective scoring for various AI use cases. It supports both single and multi-turn evaluations, handles multi-modal data (text, images, audio), and can even generate synthetic test data to address a lack of real-world examples. DeepEval is built for production-grade standards and integrates with popular AI stacks like OpenAI, LangChain, and Anthropic, making it suitable for enterprises and individual developers focused on ensuring the quality and reliability of their AI applications.
For team-wide collaboration and advanced features like regression testing, AI experiments, and online monitoring, DeepEval can be used on Confident AI, a cloud-based LLM evaluation platform developed by the creators of DeepEval.
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DeepEval FAQ
What is DeepEval?
DeepEval is an open-source LLM evaluation framework that allows developers to build reliable evaluation pipelines to test any AI system. It provides research-backed metrics and integrates with Python's Pytest for comprehensive AI application testing.
How much does DeepEval cost?
DeepEval is available for free as an open-source framework. There is also an option to use DeepEval on Confident AI, a cloud platform, which offers additional features for team-wide collaboration and advanced AI testing, implying a potential paid model for the cloud service.
Is DeepEval free?
Yes, DeepEval is free as an open-source framework that you can install and use. There is also a free trial available for Confident AI, the cloud platform that extends DeepEval's capabilities.
Who is DeepEval for?
DeepEval is for developers, AI engineers, and teams building and deploying AI applications, particularly those involving Large Language Models (LLMs), who need to ensure the reliability, quality, and performance of their AI systems.
Source: deepeval.com