DeepEval vs OpenLLMetry
Side-by-side comparison of two AI agent tools
Short answer
- DeepEval is growing faster: +676 GitHub stars in the last 30 days vs +80 for OpenLLMetry.
- Pick DeepEval for: the LLM Evaluation Framework. Pick OpenLLMetry for: open-source observability for your GenAI or LLM application, based on OpenTelemetry.
From GitHub data refreshed daily.
DeepEvalopen-source
The LLM Evaluation Framework
OpenLLMetryopen-source
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Metrics
| DeepEval | OpenLLMetry | |
|---|---|---|
| Stars | 18.6k | 7.5k |
| Star velocity /mo | 675.7894736842105 | 80.36842105263159 |
| Commits (90d) | 553 | 12 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | 2.5M | — |
| Overall score | 0.8238556798691397 | 0.5912367252217405 |
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
- +Built on OpenTelemetry standard with official semantic conventions integration, ensuring compatibility with existing observability infrastructure
- +Open-source with strong community support (6,900+ GitHub stars) and active development backed by Y Combinator
- +Multi-language support covering both Python and JavaScript/TypeScript ecosystems for broad developer adoption
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
- -Requires familiarity with OpenTelemetry concepts and infrastructure setup, which may have a learning curve for teams new to observability
- -As a specialized tool for LLM observability, it may be overkill for simple AI applications or proof-of-concepts
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
- •Production LLM application monitoring to track performance metrics, token usage, and error rates across different models and providers
- •Debugging complex GenAI workflows by tracing requests through multiple AI services and identifying bottlenecks or failures
- •Cost optimization and performance analysis of AI applications to understand usage patterns and optimize model selection
FAQ
- Which is more popular, DeepEval or OpenLLMetry?
- DeepEval has more GitHub stars (18,592 vs 7,467).
- Which is more actively developed, DeepEval or OpenLLMetry?
- DeepEval had more commits in the last 90 days (553 vs 12).
- Should I use DeepEval or OpenLLMetry?
- 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.