What tools or libraries are available for adding LLM guardrails?

Several tools and libraries are available to help developers implement guardrails for large language models (LLMs), focusing on safety, reliability, and adherence to specific guidelines. These solutions generally fall into three categories: open-source libraries, cloud-based services, and custom validation frameworks. Each approach provides mechanisms to filter inputs and outputs, enforce constraints, or detect harmful content. The choice depends on factors like integration complexity, required control, and scalability.

Open-source libraries like Guardrails AI, NVIDIA NeMo Guardrails, and Microsoft Guidance are popular for adding customizable guardrails. Guardrails AI, for example, uses a declarative XML-based syntax to define rules for input validation, output formatting, and content filtering. For instance, you can enforce that an LLM-generated response must include a valid SQL query or block answers containing profanity. NVIDIA NeMo Guardrails uses Python-based configurations to create conversational boundaries, such as preventing a customer support bot from discussing unrelated topics. Microsoft Guidance employs templating to constrain outputs—like ensuring a medical chatbot avoids speculative diagnoses. These tools are ideal for teams wanting full control over rule definitions without relying on external services.

Cloud-based services, such as Azure AI Content Safety and AWS Bedrock Guardrails, offer managed solutions for content moderation. Azure’s service provides APIs to detect harmful content (e.g., hate speech, self-harm) in both prompts and responses. AWS Bedrock allows developers to define denied topics, such as politics or violence, which the LLM will refuse to address. These services are scalable and require minimal setup, making them suitable for applications needing quick integration. For example, a social media platform could use Azure’s API to filter toxic comments generated by an LLM before they reach users.

For tailored use cases, developers often build custom validation logic using libraries like Presidio (for PII detection) or regex-based filters. A financial app might use Presidio to redact account numbers from LLM outputs, while a regex pattern could enforce that phone numbers in responses match a specific format. Combining these methods with LLM-based evaluation—like using a secondary model to score response appropriateness—adds another layer of safety. While this approach demands more development effort, it allows fine-grained adjustments, such as blocking slang in formal emails or ensuring compliance with industry regulations.