Which is more popular, Auto-evaluator or DeepEval?

DeepEval has more GitHub stars (18,592 vs 1,104).

Which is more actively developed, Auto-evaluator or DeepEval?

DeepEval had more commits in the last 90 days (553 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Auto-evaluator has had no commit in 41 months; DeepEval is actively maintained (553 commits in the last 90 days).
DeepEval is growing faster: +676 GitHub stars in the last 30 days vs +51 for Auto-evaluator.
Pick Auto-evaluator for: evaluation tool for LLM QA chains. Pick DeepEval for: the LLM Evaluation Framework.

From GitHub data refreshed daily.

Evaluation tool for LLM QA chains

DeepEvalopen-source

The LLM Evaluation Framework

+Fully automated evaluation pipeline that generates question-answer pairs from documents without manual dataset creation
+Comprehensive configuration testing across multiple parameters including chunk sizes, retrieval methods, and embedding approaches
+User-friendly Streamlit interface with hosted versions available on HuggingFace and langchain.com for easy access

+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

-Requires paid API access to both OpenAI (GPT-4) and Anthropic services for full functionality
-Limited to GPT-3.5-turbo for both question generation and response scoring, which may introduce model-specific biases
-Evaluation quality depends on the automatic question generation, which may not capture all important aspects of document content

-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

•Optimizing RAG system parameters by testing different chunk sizes, overlap settings, and retrieval strategies on domain-specific documents
•Benchmarking multiple embedding methods and language models to find the best combination for specific document types and query patterns
•Conducting systematic performance comparisons when migrating between different QA architectures or upgrading model versions

•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

Which is more popular, Auto-evaluator or DeepEval?: DeepEval has more GitHub stars (18,592 vs 1,104).
Which is more actively developed, Auto-evaluator or DeepEval?: DeepEval had more commits in the last 90 days (553 vs 0).
Should I use Auto-evaluator or DeepEval?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.