DeepEval vs UQLM

Side-by-side comparison of two AI agent tools

Short answer

  • DeepEval is growing faster: +676 GitHub stars in the last 30 days vs +12 for UQLM.
  • Pick DeepEval for: the LLM Evaluation Framework. Pick UQLM for: uQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination.

From GitHub data refreshed daily.

DeepEvalopen-source

The LLM Evaluation Framework

UQLMopen-source

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

Metrics

DeepEvalUQLM
Stars18.6k1.2k
Star velocity /mo675.789473684210512.157894736842104
Commits (90d)55391
Releases (6m)1010
Downloads (30d, npm + PyPI)2.5M1.5K
Overall score0.82385567986913970.5096030701293031

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
  • +Research-backed uncertainty quantification methods published in top-tier academic journals (JMLR, TMLR)
  • +Multiple scorer types offering different trade-offs between latency, cost, and accuracy for flexible deployment
  • +Simple installation and integration with existing LLM workflows through PyPI distribution

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 Python 3.10+ which may limit compatibility with older environments
  • -Different scorers add varying levels of latency and computational cost to LLM inference
  • -Limited to response-level scoring rather than token-level or real-time uncertainty detection

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 applications requiring confidence scores to filter or flag potentially unreliable outputs
  • •Research and development of hallucination detection systems and uncertainty quantification methods
  • •Quality assurance workflows for LLM-generated content in critical domains like healthcare or finance
View DeepEval DetailsView UQLM Details

FAQ

Which is more popular, DeepEval or UQLM?
DeepEval has more GitHub stars (18,592 vs 1,207).
Which is more actively developed, DeepEval or UQLM?
DeepEval had more commits in the last 90 days (553 vs 91).
Should I use DeepEval or UQLM?
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.