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OpenAI Developers

API deployment checklist | OpenAI API Sora 2 Prompting Guide Codex Prompting Guide Docs MCP | OpenAI Developers Gpt-image-1.5 Prompting Guide GPT-5.2 Prompting Guide Transcribing User Audio with a Separate Realtime Request Modernizing your Codebase with Codex GitHub - openai/openai-sora-sample-app: Sample app to get started using the Video API with Sora GitHub - openai/openai-apps-sdk-examples: Example apps for the Apps SDK GitHub - openai/openai-chatkit-advanced-samples: Starter app to build with OpenAI ChatKit SDK GitHub - openai/openai-chatkit-starter-app: Starter app to build with OpenAI ChatKit + Agent Builder Rate limits | OpenAI API Web search | OpenAI API Getting started with datasets | OpenAI API Prompt optimizer | OpenAI API Verifying gpt-oss implementations How to run gpt-oss locally with LM Studio Fine-tuning with gpt-oss and Hugging Face Transformers How to run gpt-oss locally with Ollama Function calling | OpenAI API Models | OpenAI API Reasoning best practices | OpenAI API Reasoning models | OpenAI API Background mode | OpenAI API Batch API | OpenAI API Conversation state | OpenAI API File search | OpenAI API Flex processing | OpenAI API MCP and Connectors | OpenAI API Code Interpreter | OpenAI API Quickstart - OpenAI Agents SDK Build Hour: Agentic Tool Calling Build Hour: Built-In Tools Reasoning best practices | OpenAI API Graders | OpenAI API Evaluation best practices | OpenAI API Working with evals | OpenAI API Guardrails - OpenAI Agents SDK Latency optimization | OpenAI API Optimizing LLM Accuracy | OpenAI API Agent orchestration - OpenAI Agents SDK Production best practices | OpenAI API Realtime transcription | OpenAI API Optimizing LLM Accuracy | OpenAI API Realtime and audio | OpenAI API Realtime conversations | OpenAI API Responses guide Migrate to the Responses API | OpenAI API Speech to text | OpenAI API Supervised fine-tuning | OpenAI API Tracing - OpenAI Agents SDK Vision fine-tuning | OpenAI API Audio and speech | OpenAI API GitHub - openai/openai-cs-agents-demo: Demo of a customer service use case implemented with the OpenAI Agents SDK Voice agents | OpenAI API Fine-tuning best practices | OpenAI API GitHub - openai/openai-agents-python: A lightweight, powerful framework for multi-agent workflows GitHub - openai/openai-agents-js: A lightweight, powerful framework for multi-agent workflows and voice agents Using tools | OpenAI API Computer use | OpenAI API GitHub - openai/openai-cua-sample-app: Learn how to use CUA (our Computer Using Agent) via the API on multiple computer environments. GitHub - openai/openai-testing-agent-demo: Demo of a UI testing agent using the OpenAI CUA model and the Responses API. Model optimization | OpenAI API GitHub - openai/openai-fm: Code for openai.fm, a demo for the OpenAI Speech API Predicted Outputs | OpenAI API GitHub - openai/openai-realtime-console: React app for inspecting, building and debugging with the Realtime API Building Voice Agents GitHub - openai/openai-realtime-solar-system: Demo showing how to use the OpenAI Realtime API to navigate a 3D scene via tool calling GitHub - openai/openai-realtime-twilio-demo Reinforcement fine-tuning | OpenAI API GitHub - openai/openai-responses-starter-app: Starter app to build with the OpenAI Responses API Structured model outputs | OpenAI API GitHub - openai/openai-structured-outputs-samples: Sample apps to help developers get started with Structured Outputs Voice agents | OpenAI API Model optimization | OpenAI API GitHub - openai/openai-realtime-agents: This is a simple demonstration of more advanced, agentic patterns built on top of the Realtime API. GitHub - openai/openai-support-agent-demo: Demo of a customer support agent interface using NextJS and the OpenAI Responses API with File Search Building Voice Agents Generate images with high input fidelity AI app development: Concept to production Model optimization Building agents Eval Driven System Design - From Prototype to Production Multi-Agent Portfolio Collaboration with OpenAI Agents SDK o3/o4-mini Function Calling Guide Exploring Model Graders for Reinforcement Fine-Tuning Guide to Using the Responses API Reinforcement Fine-Tuning for Conversational Reasoning with the OpenAI API Evals API Use-case - Responses Evaluation Comparing Speech-to-Text Methods with the OpenAI API Generate images with GPT Image Multi-Tool Orchestration with RAG approach using OpenAI Multi-Language One-Way Translation with the Realtime API Doing RAG on PDFs using File Search in the Responses API How to use the Usage API and Cost API to monitor your OpenAI usage Leveraging model distillation to fine-tune a model Orchestrating Agents: Routines and Handoffs Prompt Caching 101 Developing Hallucination Guardrails
Agents SDK | OpenAI API
2025-07-18 · via OpenAI Developers

Agents are applications that plan, call tools, collaborate across specialists, and keep enough state to complete multi-step work.

  • Use the Responses API when one model call plus tools and application-owned logic is enough.
  • Use the Agents SDK pages when your application owns orchestration, tool execution, approvals, and state.

Start with the Agents SDK quickstart to install the SDK, define one agent, and run it. Once that works, return here to choose the next capability your application needs.

Use the GitHub repositories for more examples, issues, and language-specific reference details.

If you want toStart hereWhy
Build a code-first agent appQuickstartThis is the shortest path to a working SDK integration.
Define one specialist cleanlyAgent definitionsStart here when you are still shaping the contract for a single agent.
Choose models, defaults, and transportModels and providersUse this when model choice, provider setup, or transport strategy affects the workflow.
Understand the runtime loop and stateRunning agentsThis is where the agent loop, streaming, and continuation strategies live.
Run work in a container-based environmentSandbox agentsUse this when the agent needs files, commands, packages, snapshots, mounts, or provider links.
Design specialist ownershipOrchestration and handoffsUse this when you need more than one agent and must decide who owns the reply.
Add validation or human reviewGuardrails and human reviewUse this when the workflow should block or pause before risky work continues.
Understand what a run returnsResults and stateThis page explains final output, resumable state, and next-turn surfaces.
Add hosted tools, function tools, or MCPUsing tools and Integrations and observabilityTool semantics live in the platform tools docs; SDK-specific MCP and tracing live here.
Inspect and improve runsIntegrations and observability and evaluate agent workflowsUse traces for debugging first, then move into evaluation loops.
Build a voice-first workflowVoice agentsUse the SDK’s voice pipeline and realtime agent patterns.

Use the SDK track when your server owns orchestration, tool execution, state, and approvals. That path is the best fit when you want:

  • typed application code in TypeScript or Python
  • direct control over tools, MCP servers, and runtime behavior
  • custom storage or server-managed conversation strategies
  • tight integration with existing product logic or infrastructure

A typical SDK reading order is: