For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL.

Agents can plan and complete tasks using tools, work with other agents, and maintain context across steps. Choose a runtime based on where you want orchestration to run and who should manage the state between tasks.

Choose your starting point

You want to	Start here
Run an agent with the Codex harness managed by OpenAI	Agents API
Control the agent loop in your application with reusable agents, tools, and handoffs	Agents SDK
Work directly with model responses and control your integration	Responses API
Add an embedded chat experience	ChatKit

Compare agent runtime options

	Agents API	Agents SDK	Responses API
Use for	Long-running tasks where OpenAI manages the agent and saves its progress	Building agents with custom tools and workflows in your application	Calling models directly or building an agent from scratch
Where the agent runs	OpenAI runs a managed Codex harness	The SDK runs inside your application	Your application, with optional hosted orchestration
Agent integration effort	Low	Medium	High
State between tasks	Saved session configuration, turns, and items	Your storage and SDK sessions, or Responses conversation state	Manual history, response chaining, or Conversations
Tool execution	Service-connected tools, application function handlers, and an optional sandbox	Tools and integrations configured in your application	Hosted tools and tools your application runs
Execution environment	OpenAI hosted sandbox, self-hosted sandbox, or no sandbox	Your runtime and sandbox provider integrations	Your own execution environment
Start here	Agents API overview	Agents SDK overview	Responses guide

The Agents API runs the Codex harness and manages the underlying agent infrastructure so you can focus on what your agents do. It includes automatic context compaction, multi-agent orchestration, programmatic tool calling, and support for MCP servers. See Architecture.

The Agents SDK gives your application control over deployment, storage, approvals, and runtime integration. Its runner handles the agent loop and handoffs. See Running agents.

Add tools, skills, and prompt caching

Tool design, reusable skills, and prompt caching apply across agent workflows. Their configuration and lifecycle can differ by API.

Start with Using tools for function calling, MCP, and hosted capabilities.
Read Programmatic Tool Calling for orchestration with JavaScript and the configuration for each API.
Use Skills for reusable instructions and the supported loading mechanisms.
Read Prompt caching for shared caching behavior, then Agents API observability and usage for session accounting.

An Agents API session, an SDK session, a Responses conversation, and a sandbox are different resources. Follow the state and cleanup instructions for the runtime you choose.