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Guardrails

Freemium
llmaideveloper toolopen sourceoutput parsingvalidationpythondata qualityprompt engineering

Guardrails is an open-source library for specifying structure and type, validating, and correcting the outputs of large language models (LLMs), ensuring applications are reliable, safe, and adhere to defined formats.

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Guardrails addresses the challenge of unpredictable LLM outputs by allowing developers to enforce specific rules and structures. It acts as a wrapper around LLM API calls, intercepting the output to validate it against a predefined schema. If the output doesn't conform, Guardrails can automatically correct it or re-prompt the model until a valid response is generated. This makes it invaluable for developers building production-grade applications that require reliable, structured data, such as JSON or specific text formats. Its key value is providing a deterministic layer on top of non-deterministic models, enhancing application safety and trustworthiness without extensive manual checks.

Pros

  • Open-source library providing full transparency and community support.
  • Enforces strict structural and type guarantees (e.g., valid JSON) on LLM outputs.
  • Supports corrective actions, like re-prompting the LLM, to automatically fix invalid outputs.
  • Provides a declarative way to define output requirements using a RAIL (Reliable AI Markup Language) specification or Pydantic models.
  • Compatible with a wide range of LLMs through integrations with libraries like LangChain and direct API calls.

Cons

  • Adds latency to LLM calls due to the validation and potential re-prompting steps.
  • Learning the RAIL specification adds another layer of complexity to the development workflow.
  • For highly complex validation logic, the RAIL specification can become verbose and difficult to manage.
  • Enterprise and managed hub offerings lack transparent public pricing.

Key features

  • Output validation against RAIL (.rail) specifications or Pydantic models
  • Automatic output correction through multi-step re-prompting
  • Quality assurance checks for toxicity, bias, and adherence to factual information
  • Library of pre-built validators for common data types and custom logic (e.g., regex, SQL)
  • Streaming support to validate LLM outputs as they are generated
  • Integration with popular LLM orchestration frameworks like LangChain and LlamaIndex

Integrations

OpenAIHugging FaceLangChainLlamaIndexAnthropicGoogle AI (Vertex AI)CohereAzure OpenAI Service

Target audience

AI/ML engineers and developers building applications on top of large language models (LLMs) who need to ensure output reliability, structure, and safety in production environments.

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Founded

2023

Headquarters

San Francisco, USA

Pricing Tiers

Open Source

The core open-source Python library for self-hosting. Includes output structuring, validation, and correction capabilities for use in any environment.

Free

Guardrails Hub

A managed service for hosting and managing validators, providing a private, secure, and scalable environment. Access is currently via a waitlist.

Contact for Pricing

Enterprise

Commercial offering from Shua Group with enterprise-grade features, enhanced security, performance benchmarks, and dedicated support for large-scale deployments.

Contact for Pricing

Frequently Asked Questions

Top Alternatives to Guardrails

Instructor

For developers who prefer defining output structure directly in Python using Pydantic models, Instructor offers a simpler and more lightweight approach.

Outlines

This library guides LLM generation by constraining it to a specific format like a regex or JSON schema, which can be more efficient than Guardrails' validate-and-retry method.

TypeChat

Developed by Microsoft, TypeChat is ideal for developers in the TypeScript ecosystem, as it focuses on generating JSON that conforms to TypeScript type definitions.

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