Which is more popular, garak or Guardrails?

garak has more GitHub stars (9,414 vs 7,237).

Which is more actively developed, garak or Guardrails?

garak had more commits in the last 90 days (227 vs 122).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Pick garak for: the LLM vulnerability scanner. Pick Guardrails for: neMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based.

From GitHub data refreshed daily.

garakopen-source

the LLM vulnerability scanner

NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.

+Open-source toolkit backed by NVIDIA with comprehensive documentation and active development
+Flexible programming model supporting multiple types of guardrails from content filtering to structured data extraction
+Production-ready with multi-platform support (Linux, Windows, macOS) and extensive testing infrastructure

-Requires C++ dependencies (annoy library) which may complicate deployment in some environments
-Additional complexity layer that may impact response latency in high-throughput applications
-Learning curve for configuring effective guardrails rules and understanding the programming model

•Content moderation for customer service chatbots to prevent discussions of sensitive topics like politics or inappropriate content
•Enforcing specific dialog flows and response formats for structured interactions like form filling or guided troubleshooting
•Extracting and validating structured data from conversational inputs while maintaining consistent output formatting

Which is more popular, garak or Guardrails?: garak has more GitHub stars (9,414 vs 7,237).
Which is more actively developed, garak or Guardrails?: garak had more commits in the last 90 days (227 vs 122).
Should I use garak or Guardrails?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.