Structured Outputs with Pydantic: Type-Safe Agent Responses
Learn how to use Pydantic models with the OpenAI Agents SDK output_type parameter to get type-safe, validated, structured JSON responses from your agents.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Learn how to use Pydantic models with the OpenAI Agents SDK output_type parameter to get type-safe, validated, structured JSON responses from your agents.
Learn how to implement output guardrails in the OpenAI Agents SDK to inspect, validate, and block unsafe agent responses before they reach end users — including PII detection and compliance filtering.
Learn how to run multiple OpenAI agents concurrently using asyncio.gather for dramatic performance improvements, with error handling strategies and a complete market research example.
Learn how to build a hybrid regex and LLM-based PII detection and redaction system for AI agent pipelines, with output sanitization, reversible tokenization, and GDPR compliance patterns.
Learn how to build production monitoring and alerting for AI agent systems including latency tracking, error rate dashboards, token usage analytics, alerting pipelines, and SLA enforcement.
Build a documentation chatbot using the OpenAI Agents SDK FileSearchTool with vector stores, citation handling, and hybrid retrieval for production-grade RAG chat agents.
Learn how to implement token budgets, max_turns safety limits, IP and user-level throttling, and abuse detection for production AI agent systems using the OpenAI Agents SDK.
Set up RedisSession in the OpenAI Agents SDK for distributed AI agent deployments with session sharing across instances, production configuration, and worker coordination.
Master OpenAIResponsesCompactionSession for automatic and manual compaction of long agent conversations including token management, custom triggers, and compaction strategies.