Guardrails AI

Guardrails AI is an open source framework for making LLM applications reliable by validating and, where possible, correcting model outputs. It does two main things. It runs input and output guards that detect, quantify, and mitigate specific risks, sitting before or after an LLM call to catch problems like personally identifiable information, toxic language, prompt injection, hallucinations, or off topic responses. And it helps generate structured data, using a Pydantic schema or its own RAIL markup so an LLM returns typed, validated fields rather than free text. The core framework is Apache 2.0 licensed and installs as a Python package.

The center of the ecosystem is the Guardrails Hub, a catalog of prebuilt validators contributed by the community, with dozens covering categories like PII detection, toxicity across multiple dimensions, jailbreak and injection scanning, factuality and provenance, competitor mention filtering, format and schema checks, and RAG context relevance. Validators are composable, so a team can chain several into a single guard, for example running toxicity detection, PII scanning, and factual grounding on every response. Each validator declares how it behaves on failure, whether to raise an exception, refrain from returning the output, filter it, or ask the model to try again, and the project also publishes a Guardrails Index benchmark comparing validator accuracy and latency.

Guardrails can run inline in application code or as a standalone service started with a single command, exposing an OpenAI compatible REST API so existing clients route through it with a changed base URL, and it is recommended to run with Docker behind a production server. It is model agnostic, working with proprietary and open source LLMs alike. Beyond the framework, the company has expanded into a broader reliability platform that adds synthetic data generation for fine tuning and prompt optimization, dynamic evaluation datasets that target edge cases, and managed runtime guardrails that block policy violations and data leakage before they reach users.

The open source framework is free, and Guardrails Pro is a managed service that adds hosted validation, observability dashboards, and enterprise support, removing the operational work of running validators at scale. Guardrails AI was founded by Shreya Rajpal, formerly of Apple and Drive.ai, and Diego Oppenheimer, who founded Algorithmia, and it raised a $7.5M seed round in early 2024 from Zetta Venture Partners, Bloomberg Beta, and Pear VC. The company says it is used by enterprises, startups, and government agencies. Pricing for the managed tier is not listed publicly.