DeepEval
The LLM Evaluation Framework
About
Found in: Yigtwxx/Awesome-RAG-Production
Details
DeepEval is an open-source LLM evaluation framework designed for unit testing large language model systems, similar to Pytest. It incorporates the latest research to run evaluations using metrics such as G-Eval, task completion, answer relevancy, hallucination, and more, leveraging LLM-as-a-judge and other NLP models that run locally. The framework supports a wide variety of use cases including AI agents, RAG pipelines, and chatbots, and integrates with popular tools like LangChain and OpenAI. Users can evaluate custom criteria, compare models, prevent prompt drift, and transition between LLM providers with confidence. The project also offers Confident AI, a platform for managing eval results, generating reports, and collaborating.
Key Features
- G-Eval: a research-backed LLM-as-a-judge metric for custom criteria
- DAG: graph-based deterministic LLM-as-a-judge metric builder
- Agentic metrics: Task Completion, Tool Correctness, Goal Accuracy, Step Efficiency, Plan Adherence, Plan Quality, Tool Use, Argument Correctness
- RAG metrics: Answer Relevancy, Faithfulness, Contextual Recall, Contextual Precision
- LLM-as-a-judge evaluation using any LLM of your choice
- NLP models that run locally on your machine
- Integrates with LangChain, OpenAI, and other LLM frameworks
- Similar to Pytest for unit testing LLM apps
- Open source and free to use
Use Cases
- Evaluating RAG pipelines for faithfulness and answer relevance
- Testing AI agents for task completion and tool usage correctness
- Comparing different LLM models, prompts, and architectures
- Preventing prompt drift in production LLM applications
- Transitioning between LLM providers (e.g., OpenAI to Claude) with confidence
- Running automated unit tests for LLM systems in CI/CD pipelines
Pros & Cons
Pros
- Open source and completely free
- Wide variety of ready-to-use evaluation metrics covering custom, agentic, and RAG use cases
- Supports local execution of NLP models, reducing API costs and latency
- Research-backed metrics like G-Eval for higher accuracy
- Integrates seamlessly with popular LLM frameworks and tools
- Allows users to determine optimal models, prompts, and architecture for their AI quality
Cons
- Requires programming knowledge (Python) to set up and use
- Some metrics rely on LLM API calls, which may incur costs even with the framework being free
- Documentation and community support may be limited compared to established testing frameworks
Frequently Asked Questions
What is DeepEval?
DeepEval is an open-source LLM evaluation framework for unit testing large language model systems, similar to Pytest.
What metrics does DeepEval support?
DeepEval supports a wide range of metrics including G-Eval, task completion, answer relevancy, faithfulness, contextual recall/precision, and many agentic metrics.
Does DeepEval require an internet connection?
Some metrics use local NLP models, while others rely on LLM-as-a-judge which may require an API call to an external LLM.
Can I use DeepEval with my existing LLM framework?
Yes, DeepEval integrates with popular frameworks like LangChain and OpenAI, and can be adapted to any LLM system.
Is there a paid version or platform for DeepEval?
DeepEval itself is free and open source. Confident AI is a complementary platform for managing evaluation data and generating reports, which may have its own pricing.