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Overview

This demo is a practical guide that demonstrates:
  • Three types of AI guardrails: Input validation, output checking, and real-time streaming monitoring
  • Integration of the OpenAI Agent SDK for Python with Trigger.dev for production AI workflows
  • Triggering Python scripts from tasks using our Python build extension
  • Educational examples of implementing guardrails for AI safety and control mechanisms
  • Real-world scenarios like math tutoring agents with content validation and complexity monitoring
Guardrails are safety mechanisms that run alongside AI agents to validate input, check output, monitor streaming content in real-time, and prevent unwanted or harmful behavior.

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GitHub repo

View the OpenAI Agent SDK Guardrails repo

Click here to view the full code for this project in our examples repository on GitHub. You can fork it and use it as a starting point for your own project.

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Video

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Relevant code

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Trigger.dev Tasks

  • inputGuardrails.ts - Passes user prompts to Python script and handles InputGuardrailTripwireTriggered exceptions
  • outputGuardrails.ts - Runs agent generation and catches OutputGuardrailTripwireTriggered exceptions with detailed error info
  • streamingGuardrails.ts - Executes streaming Python script and parses JSON output containing guardrail metrics

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Python Implementations

  • input-guardrails.py - Agent with @input_guardrail decorator that validates user input before processing (example: math tutor that only responds to math questions)
  • output-guardrails.py - Agent with @output_guardrail decorator that validates generated responses using a separate guardrail agent
  • streaming-guardrails.py - Processes ResponseTextDeltaEvent streams with async guardrail checks at configurable intervals (example: stops streaming if language is too complex for a 10-year-old)

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Configuration

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