agentic-radar vs DeepEval
Side-by-side comparison of two AI agent tools
Short answer
- agentic-radar has had no commit in 10 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 +19 for agentic-radar.
- Pick agentic-radar for: a security scanner for your LLM agentic workflows. Pick DeepEval for: the LLM Evaluation Framework.
From GitHub data refreshed daily.
agentic-radaropen-source
A security scanner for your LLM agentic workflows
DeepEvalopen-source
The LLM Evaluation Framework
Metrics
| agentic-radar | DeepEval | |
|---|---|---|
| Stars | 1.1k | 18.6k |
| Star velocity /mo | 19.26315789473684 | 675.7894736842105 |
| Commits (90d) | 0 | 553 |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | 5.3K | 2.5M |
| Overall score | 0.20717140935755865 | 0.8238556798691397 |
Pros
- +Specialized focus on LLM agentic workflow security vulnerabilities that traditional scanners miss
- +Includes built-in visualization tools for clear security assessment reporting and analysis
- +Integrates with popular frameworks like CrewAI and provides easy PyPI installation
- +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
Cons
- -Appears to be a relatively new tool with limited documentation visibility from the provided materials
- -May require specialized knowledge of agentic systems to effectively interpret and act on scan results
- -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
Use Cases
- •Security assessment of autonomous AI agent systems before production deployment
- •Compliance auditing for organizations using LLM-powered workflows in regulated industries
- •Continuous security monitoring of agentic systems to detect emerging vulnerabilities
- •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
FAQ
- Which is more popular, agentic-radar or DeepEval?
- DeepEval has more GitHub stars (18,592 vs 1,057).
- Which is more actively developed, agentic-radar or DeepEval?
- DeepEval had more commits in the last 90 days (553 vs 0).
- Should I use agentic-radar or DeepEval?
- 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.