DeepEval
An open-source evaluation framework for LLM applications, offering multiple evaluation metrics including hallucination detection, answer relevance, and RAG assessment. It supports CI/CD integration and is a representative tool in the AI evaluation space.
Timeline (last 90 days)
When choosing only one framework, if you rely heavily on pytest and have no YAML-driven evaluation system, you should choose DeepEval, but you must fix evaluation_steps on day one.
DeepEval's first-time configuration took about 40 minutes, running 40 data items took 4 minutes 48 seconds, the cost was $1.31, and the agreement rate with manual annotation was 31/40.
DeepEval does not raise an error when the sum of scoring rubric weights is not 1.0, and performs no sanity check.
When evaluation_steps is not passed, DeepEval's G-Eval metric automatically generates scoring steps based on criteria, causing the same response to potentially get opposite conclusions on re-runs, producing drift.
In the article, under DeepEval's default mode, 7 responses had their pass/fail conclusion flip at least once across three re-runs; after manually fixing evaluation_steps, the number of flips dropped to 3, and the agreement rate rose from 31/40 to 35/40.
After cleaning up the anchors and prompts, promptfoo's agreement rate rose from 34/40 to 37/40, and DeepEval's rose from 35/40 to 36/40.
promptfoo integrates with CI via CLI and YAML, while DeepEval integrates via pytest.
RAGAS and DeepEval are commonly used agent/model evaluation frameworks.
Evaluation frameworks such as Ragas and DeepEval focus on offline batch testing of RAG pipeline or single-inference quality, making them suitable for running in CI workflows.
The AI agent evaluation (LLM Evaluation) space has seen the emergence of representative products such as Ragas, DeepEval, LangSmith, and Brainlid.
4 more timeline events
All Facts (14)
When choosing only one framework, if you rely heavily on pytest and have no YAML-driven evaluation system, you should choose DeepEval, but you must fix evaluation_steps on day one.
50%UnverifiedDeepEval's first-time configuration took about 40 minutes, running 40 data items took 4 minutes 48 seconds, the cost was $1.31, and the agreement rate with manual annotation was 31/40.
50%UnverifiedDeepEval does not raise an error when the sum of scoring rubric weights is not 1.0, and performs no sanity check.
50%UnverifiedWhen evaluation_steps is not passed, DeepEval's G-Eval metric automatically generates scoring steps based on criteria, causing the same response to potentially get opposite conclusions on re-runs, producing drift.
50%UnverifiedIn the article, under DeepEval's default mode, 7 responses had their pass/fail conclusion flip at least once across three re-runs; after manually fixing evaluation_steps, the number of flips dropped to 3, and the agreement rate rose from 31/40 to 35/40.
50%UnverifiedAfter cleaning up the anchors and prompts, promptfoo's agreement rate rose from 34/40 to 37/40, and DeepEval's rose from 35/40 to 36/40.
50%Unverifiedpromptfoo integrates with CI via CLI and YAML, while DeepEval integrates via pytest.
50%UnverifiedRAGAS and DeepEval are commonly used agent/model evaluation frameworks.
50%UnverifiedEvaluation frameworks such as Ragas and DeepEval focus on offline batch testing of RAG pipeline or single-inference quality, making them suitable for running in CI workflows.
50%UnverifiedThe AI agent evaluation (LLM Evaluation) space has seen the emergence of representative products such as Ragas, DeepEval, LangSmith, and Brainlid.
50%UnverifiedLLM evaluation engineering typically relies on frameworks such as RAGAS and DeepEval, combined with human annotation or using another LLM as a judge for scoring.
50%UnverifiedMainstream RAG evaluation frameworks include RAGAS, TruLens, and DeepEval, which typically score across two dimensions—retrieval quality and generation quality—but lack built-in support for specialized requirements such as exact identifier matching and rule clause completeness
50%UnverifiedRagas、TruLens、DeepEval等开源框架提供RAG指标的自动化计算，其中不少采用LLM as a judge方式评分
50%Unverified开源评估框架RAGAS专注于RAG系统评估，与DeepEval等工具正在将评估最佳实践标准化
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