auto-evaluator vs promptfoo
Side-by-side comparison of two AI agent tools
auto-evaluatorfree
Evaluation tool for LLM QA chains
promptfooopen-source
Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and
Metrics
| auto-evaluator | promptfoo | |
|---|---|---|
| Stars | 782 | 18.9k |
| Star velocity /mo | 0 | 1.7k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.2903286660805505 | 0.7957593044797683 |
Pros
- +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
- +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
- +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
- +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments
Cons
- -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
- -Requires API keys and credits for multiple LLM providers, which can become expensive for extensive testing
- -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
- -Limited to evaluation and testing - does not provide actual LLM application development capabilities
Use Cases
- •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
- •Automated testing and evaluation of prompt performance across different models before production deployment
- •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
- •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture