Guardrails AI · 2024-10-20 · major
Guardrails AI — LLM Output Validation Framework
Open-source framework for adding structural and semantic validation to LLM outputs. Enforces JSON schemas, detects PII/toxicity, and auto-retries on failure.
Validate, structure, and secure LLM outputs with declarative guardrails.
Key specs
| GitHub stars | 4.5k+ |
|---|---|
| Validators | 100+ |
What is it?
Guardrails AI wraps your LLM calls with validation logic. Define what valid output looks like (JSON schema, no PII, no toxic content) and Guardrails enforces it — auto-retrying if the model fails.
How does it work?
You define a Guard with validators from their hub (100+ available). When you call the LLM through Guardrails, it validates the response, masks PII, checks for toxicity, and retries if needed.
Why does it matter?
Raw LLM output is unpredictable. Guardrails makes it production-safe — you get structured, validated, safe responses or explicit failures.
Who is it for?
Teams building LLM-powered apps that need reliable, safe outputs.
Try it
pip install guardrails-ai