DeepEval vs Superagent
Side-by-side comparison of two AI agent tools
Short answer
- DeepEval is growing faster: +676 GitHub stars in the last 30 days vs +41 for Superagent.
- Pick DeepEval for: the LLM Evaluation Framework. Pick Superagent for: superagent protects your AI applications against prompt injections, data leaks, and harmful outputs.
From GitHub data refreshed daily.
DeepEvalopen-source
The LLM Evaluation Framework
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
| DeepEval | Superagent | |
|---|---|---|
| Stars | 18.6k | 6.8k |
| Star velocity /mo | 675.7894736842105 | 41.36842105263158 |
| Commits (90d) | 553 | 8 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 2.5M | — |
| Overall score | 0.8238556798691397 | 0.35990158642254044 |
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 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
- -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 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
- •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
- •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, DeepEval or Superagent?
- DeepEval has more GitHub stars (18,592 vs 6,762).
- Which is more actively developed, DeepEval or Superagent?
- DeepEval had more commits in the last 90 days (553 vs 8).
- Should I use DeepEval 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.