DeepEval vs UpTrain
Side-by-side comparison of two AI agent tools
Short answer
- UpTrain has had no commit in 26 months; DeepEval is actively maintained (545 commits in the last 90 days).
- DeepEval is growing faster: +676 GitHub stars in the last 30 days vs +4 for UpTrain.
- Pick DeepEval for: the LLM Evaluation Framework. Pick UpTrain for: open-source platform to evaluate and improve generative AI applications with 20+ preconfigured evaluations.
From GitHub data refreshed daily.
DeepEvalopen-source
The LLM Evaluation Framework
UpTrainopen-source
Open-source platform to evaluate and improve generative AI applications with 20+ preconfigured evaluations
Metrics
| DeepEval | UpTrain | |
|---|---|---|
| Stars | 18.6k | 2.4k |
| Star velocity /mo | 675.8730158730159 | 4.285714285714286 |
| Commits (90d) | 545 | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8347115555103475 | 0.19094120364152495 |
Pros
- +Research-backed evaluation metrics including G-Eval, hallucination detection, and answer relevancy that leverage latest academic advances
- +Pytest-like interface provides familiar testing paradigm for developers already comfortable with Python testing frameworks
- +LLM-as-a-judge approach enables nuanced, contextual evaluation that captures semantic meaning rather than just exact matches
- +Open-source platform with active community support and transparency
- +Comprehensive evaluation framework with 20+ preconfigured checks covering multiple AI use cases
- +Unified platform approach that handles both evaluation and improvement recommendations
Cons
- -LLM-as-a-judge evaluation may introduce variability and potential bias depending on the judge model used
- -Evaluation costs can accumulate quickly when using external LLM APIs for assessment across large test suites
- -As a specialized framework, it requires understanding of LLM-specific evaluation concepts beyond traditional software testing
- -May require technical expertise to implement and configure effectively
- -Evaluation accuracy depends on the quality and relevance of preconfigured checks
Use Cases
- •Unit testing LLM applications to ensure consistent performance across different inputs and edge cases
- •Evaluating chatbots and conversational AI systems for answer relevancy and factual accuracy
- •Detecting and measuring hallucination rates in content generation applications before production deployment
- •Evaluating LLM application performance before production deployment
- •Systematic testing of code generation and language processing AI models
- •Quality assurance for embedding-based applications and retrieval systems
FAQ
- Which is more popular, DeepEval or UpTrain?
- DeepEval has more GitHub stars (18,570 vs 2,366).
- Which is more actively developed, DeepEval or UpTrain?
- DeepEval had more commits in the last 90 days (545 vs 0).
- Should I use DeepEval or UpTrain?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.