What should I use to add guardrails to AI API requests?

For guardrails on AI API requests, the best choice depends on what you’re trying to protect against:

Good options

• Input validation / schema enforcement
  Use a strict request schema (for example with JSON Schema, Pydantic, or Zod) to ensure only expected fields and formats reach the model.

• Prompt firewall / policy layer
  Put a middleware layer in front of the AI API that checks:

  • user prompt content
  • allowed tools/functions
  • max token limits
  • role-based access
  • banned topics or sensitive data

• LLM-specific guardrail frameworks

  • Guardrails AI — popular for validating LLM outputs against schemas and constraints.
  • NeMo Guardrails — good if you want conversational policy enforcement and refusal flows.
  • OpenAI moderation / safety filters — useful for content moderation and abuse prevention.
  • LangChain / LlamaIndex guards — if you already use those stacks, they can help with structured outputs and tool restrictions.

• Output validation Always validate the model’s response before using it downstream. Don’t trust the model to stay within format or policy on its own.

• Human-in-the-loop for sensitive cases For high-risk actions like payments, account changes, or legal/medical advice, require review or confirmation.

Practical recommendation

If you want a simple, effective setup:

1. Validate inputs with a schema
2. Moderate prompts before sending them to the model
3. Constrain outputs with structured output / JSON schema
4. Add logging and rate limits
5. Escalate risky requests to human review

If you want, I can recommend a specific guardrail stack for:

• OpenAI / Anthropic APIs
• Python
• Node.js
• RAG / tool-using agents