Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Get started with the Instructor library to extract typed, validated Python objects from LLM responses. Covers setup, OpenAI patching, automatic retry logic, and validation-based error correction.
Learn how to implement checkpointing for AI agents — serializing agent state, persisting progress to disk or database, and resuming interrupted tasks with idempotent operations and recovery strategies.
Learn how to build tool registries, tool factories, and shared tool modules that work across multiple agents. Covers composable design patterns, parameterized tools, dependency injection, and packaging tools for reuse.
Discover why single-agent architectures hit a ceiling for complex tasks and how multi-agent systems use specialization, parallel execution, and separation of concerns to build more reliable AI workflows.
Learn how to build an orchestrator agent that breaks complex tasks into subtasks, delegates to specialist agents, aggregates results, and delivers a unified response using the OpenAI Agents SDK.
Learn how to implement clean agent handoffs using the OpenAI Agents SDK, including handoff triggers, context transfer, conversation continuity, and patterns for preserving user experience across agent boundaries.
Build a triage agent that classifies incoming requests, routes them to the correct specialist agent with confidence scoring, and handles ambiguous or unclassifiable inputs with fallback strategies.
Master shared state management in multi-agent systems using the OpenAI Agents SDK's RunContext, including shared context objects, state mutation patterns, race conditions, and consistency strategies.
