Guardrails AI

Use it when

• Your LLM app can return toxic text, competitor mentions, or policy-violating content to users and you need programmatic input/output checks that block or raise on failure
• You need LLM responses to conform to a Pydantic schema reliably, with validation and structured extraction handled for models with or without function calling

What it solves

Not the fit when

• Not a benchmark harness for scoring model quality on academic tasks
• Validators mitigate risk statistically; they do not guarantee zero unsafe outputs
• Some hub validators call ML models or LLMs and add latency per request
• JavaScript support exists but other languages are still in progress
• standardized academic benchmark scoring
• prompt experiment tracking and tracing
• model serving or inference acceleration
• training-time alignment or fine-tuning

Install

pip install guardrails-ai && guardrails configure (then install validators, e.g. pip install guardrails-ai-toxic-language)

Invoke

Python: guard = Guard().use(ToxicLanguage(threshold=0.5, on_fail=OnFailAction.EXCEPTION)); guard.validate(text). Structured output: Guard.for_pydantic(output_class=MyModel, prompt=...). Server: guardrails start --config=./config.py

Alternatives

No reviewed alternatives recorded yet.