agentic-radar vs PentAGI

Side-by-side comparison of two AI agent tools

Short answer

  • agentic-radar has had no commit in 10 months; PentAGI is actively maintained (311 commits in the last 90 days).
  • PentAGI is growing faster: +765 GitHub stars in the last 30 days vs +20 for agentic-radar.
  • Pick agentic-radar for: a security scanner for your LLM agentic workflows. Pick PentAGI for: fully autonomous AI Agents system capable of performing complex penetration testing tasks.

From GitHub data refreshed daily.

agentic-radaropen-source

A security scanner for your LLM agentic workflows

P
PentAGIopen-source

Fully autonomous AI Agents system capable of performing complex penetration testing tasks

Metrics

agentic-radarPentAGI
Stars1.1k25.2k
Star velocity /mo19.52380952380953765
Commits (90d)0311
Releases (6m)02
Overall score0.223971183706991430.7687359680048651

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

    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

      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

        FAQ

        Which is more popular, agentic-radar or PentAGI?
        PentAGI has more GitHub stars (25,199 vs 1,058).
        Which is more actively developed, agentic-radar or PentAGI?
        PentAGI had more commits in the last 90 days (311 vs 0).
        Should I use agentic-radar or PentAGI?
        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.