Master OpenAI Agents SDK with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install the Python package from the official OpenAI Agents SDK documentation. Create an agent with instructions, a model, and any function tools or MCP server tools it needs. Run the agent with the SDK runner, then inspect traces and add guardrails before exposing the workflow to users. Estimate runtime costs from the selected OpenAI model rates, tool
call usage, realtime usage, containers, and expected token volume.
💡 Quick Start: Follow these 2 steps in order to get up and running with OpenAI Agents SDK quickly.
Explore the key features that make OpenAI Agents SDK powerful for ai agent builders workflows.
Built on a small set of core abstractions, including Agents, Handoffs, and Guardrails, plus Python as the orchestration language. This reduces the amount of framework-specific structure developers need to learn.
A developer builds a multi-agent customer support prototype using standard Python patterns, without adopting a large visual workflow system.
Agents can delegate tasks to specialized agents mid-conversation, with context passed through the workflow. This supports modular agent architectures.
A triage agent routes customer inquiries to specialized billing, technical support, or sales agents based on intent, with relevant conversation context included.
Input validation and safety checks can be added around agent execution, with fast-fail behavior when checks do not pass.
A financial workflow validates user inputs for sensitive data and checks outputs for policy requirements before returning a response.
Support for MCP server tools helps agents connect to standardized external tool interfaces alongside native function tools.
An agent connects to a company's MCP servers for database access, document retrieval, or internal API calls.
Session support helps maintain working context within and across agent runs, enabling stateful conversations and longer workflows when backed by an appropriate session store.
A research assistant maintains context about a user's ongoing project across multiple conversation sessions.
Tracing helps developers inspect tool calls, handoffs, guardrail checks, and model interactions while debugging or evaluating agent workflows.
A team reviews traces to identify where an agent is making unnecessary tool calls or passing incomplete context to a specialist agent.
OpenAI Agents SDK is used to build agentic AI applications in Python with managed tool calls, handoffs between agents, guardrails, sessions, tracing, and realtime or voice agent support.
The Responses API is lower-level, while the Agents SDK gives developers a higher-level runtime for agent behavior. The SDK includes a built-in agent loop that invokes tools, sends results back to the model, and continues execution until a final result is produced.
The official introduction lists 3 core primitives: Agents, Agents as tools or Handoffs, and Guardrails. Agents are LLMs equipped with instructions and tools, handoffs let agents delegate work to other agents, and guardrails validate inputs or outputs.
Yes. The documentation includes a Sessions section and lists several session implementations and extensions, including SQLAlchemySession, Async SQLite session, RedisSession, MongoDBSession, DaprSession, EncryptedSession, and AdvancedSQLiteSession.
The SDK itself is open source and free to install. Runtime costs are separate and depend on the selected model and tools. For example, OpenAI's API pricing page lists GPT-5.4 mini text tokens at $0.75 per 1M input tokens, $0.075 per 1M cached input tokens, and $4.50 per 1M output tokens.
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Tutorial updated March 2026