agentic-radar vs DBX

Side-by-side comparison of two AI agent tools

Short answer

  • agentic-radar has had no commit in 10 months; DBX is actively maintained (4,812 commits in the last 90 days).
  • DBX is growing faster: +11,295 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 DBX for: 25 MB cross-platform client for 100+ databases with a built-in AI assistant and MCP Server.

From GitHub data refreshed daily.

agentic-radaropen-source

A security scanner for your LLM agentic workflows

D
DBXopen-source

25 MB cross-platform client for 100+ databases with a built-in AI assistant and MCP Server

Metrics

agentic-radarDBX
Stars1.1k23.8k
Star velocity /mo19.5238095238095311.3k
Commits (90d)04.8k
Releases (6m)010
Overall score0.223971183706991430.950750801484004

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
        View agentic-radar DetailsView DBX Details

        FAQ

        Which is more popular, agentic-radar or DBX?
        DBX has more GitHub stars (23,828 vs 1,058).
        Which is more actively developed, agentic-radar or DBX?
        DBX had more commits in the last 90 days (4,812 vs 0).
        Should I use agentic-radar or DBX?
        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.