Promptfoo vs Superagent
Side-by-side comparison of two AI agent tools
Short answer
- Promptfoo is growing faster: +1,110 GitHub stars in the last 30 days vs +41 for Superagent.
- Pick Promptfoo for: open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps. Pick Superagent for: superagent protects your AI applications against prompt injections, data leaks, and harmful outputs.
From GitHub data refreshed daily.
Promptfooopen-source
Open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps
Superagentopen-source
Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers.
Metrics
| Promptfoo | Superagent | |
|---|---|---|
| Stars | 25.7k | 6.8k |
| Star velocity /mo | 1.1k | 41.36842105263158 |
| Commits (90d) | 920 | 8 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 3.0M | — |
| Overall score | 0.8639349362705032 | 0.35990158642254044 |
Pros
- +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
- +Comprehensive AI security coverage with multiple protection layers including prompt injection detection, PII redaction, and repository scanning
- +Production-ready SDK with dual language support (TypeScript and Python) and straightforward API integration
- +Open-source with strong community backing (6,500+ GitHub stars) and Y Combinator validation
Cons
- -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
- -Requires API key and external service dependency, potentially adding latency to AI application workflows
- -Red team testing feature is still in development (marked as 'coming soon')
- -May introduce additional complexity and cost considerations for high-volume AI applications
Use Cases
- •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
- •Protecting customer-facing chatbots from prompt injection attacks that could expose system prompts or cause harmful outputs
- •Sanitizing AI-processed documents and conversations to automatically redact sensitive information like SSNs, emails, and medical data for compliance
- •Securing AI development pipelines by scanning code repositories for malicious instructions or AI agent poisoning attempts
FAQ
- Which is more popular, Promptfoo or Superagent?
- Promptfoo has more GitHub stars (25,665 vs 6,762).
- Which is more actively developed, Promptfoo or Superagent?
- Promptfoo had more commits in the last 90 days (920 vs 8).
- Should I use Promptfoo or Superagent?
- 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.