DeepEval vs Promptfoo
Side-by-side comparison of two AI agent tools
Short answer
- Promptfoo is growing faster: +1,112 GitHub stars in the last 30 days vs +676 for DeepEval.
- Pick DeepEval for: the LLM Evaluation Framework. Pick Promptfoo for: open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps.
From GitHub data refreshed daily.
DeepEvalopen-source
The LLM Evaluation Framework
Promptfooopen-source
Open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps
Metrics
| DeepEval | Promptfoo | |
|---|---|---|
| Stars | 18.6k | 25.6k |
| Star velocity /mo | 675.8730158730159 | 1.1k |
| Commits (90d) | 545 | 913 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8347115555103475 | 0.8742860723201702 |
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
- +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
- -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 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
- •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
- •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
FAQ
- Which is more popular, DeepEval or Promptfoo?
- Promptfoo has more GitHub stars (25,640 vs 18,570).
- Which is more actively developed, DeepEval or Promptfoo?
- Promptfoo had more commits in the last 90 days (913 vs 545).
- Should I use DeepEval or Promptfoo?
- 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.