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Colang is a domain-specific language created by NVIDIA specifically for defining conversational guardrails. It uses an event-driven model where you define flows describing how the AI should behave. The syntax is relatively simple and purpose-built — most developers can write basic guardrails within a few hours of reading the docs.
Each rail layer adds 50-200ms depending on complexity. Input rails run before the LLM call, so they add to perceived latency. Output rails run after. Simple topic checks are fast; complex fact-checking rails that require additional LLM calls are slower. GPU acceleration reduces this significantly.
No guardrail system can prevent 100% of jailbreak attempts. NeMo Guardrails significantly reduces the attack surface through multi-layered detection, but determined adversaries with novel techniques may find bypasses. It's best used as part of a defense-in-depth strategy alongside prompt engineering and monitoring.
NeMo Guardrails works with any LLM including OpenAI, Anthropic, Google, open-source models, and NVIDIA's own models. The guardrails wrap the LLM interaction, so the underlying model is interchangeable. Some rails use a secondary LLM for evaluation, which can be any supported provider.
Compare features, test the interface, and see if it fits your workflow.