OpenAI Agent Builder: What It Is and How to Get Started (2026)

OpenAI's Agent Builder is the company's platform for creating custom AI agents. If you've used GPTs or the Assistants API, Agent Builder is the next evolution — it lets you build agents that can use tools, follow complex instructions, and work across OpenAI's ecosystem.

Quick Answer: OpenAI's Agent Builder is a no-code/low-code platform for creating custom AI agents with a visual interface for defining behavior, connecting tools, and testing interactions, positioned between custom GPTs and the Assistants API.

What Agent Builder is

Agent Builder is OpenAI's no-code and low-code platform for creating AI agents. It sits between ChatGPT's custom GPTs (simple, limited) and the full Assistants API (powerful, requires coding). You get a visual interface for defining agent behavior, connecting tools, and testing interactions.

Agents built with Agent Builder can access the web, run code, use MCP servers, connect to external APIs through actions, and follow detailed instructions. They can be deployed within ChatGPT, embedded in applications through the API, or used as standalone agents.

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How it compares to Claude Code and Codex CLI

Agent Builder and terminal-based coding agents serve different purposes, though they're converging:

Agent Builder is primarily for creating agents that others will use. You define the agent's behavior, tools, and knowledge, then deploy it. It's more about agent creation than daily coding.

Claude Code and Codex CLI are agents you use directly in your terminal for development work. They interact with your local codebase, run commands, and help you write and refactor code.

The overlap is growing. Codex CLI — OpenAI's own terminal agent — shares Agent Builder's underlying models and tool-use capabilities. And Agent Builder agents increasingly support development-focused tasks.

Getting started

1. Visit platform.openai.com and navigate to the Agent Builder section
2. Create a new agent and give it a name and description
3. Write system instructions that define the agent's behavior, expertise, and constraints
4. Connect tools — built-in tools (web search, code execution) and external tools (MCP servers, API actions)
5. Add knowledge files if the agent needs reference material
6. Test the agent in the playground
7. Deploy through the API or ChatGPT

Adding SKILL.md to your agents

If you're building development-focused agents with Agent Builder, SKILL.md skills can define the agent's coding behavior. Upload a SKILL.md file as a knowledge source, and the agent follows those instructions when performing development tasks. This works particularly well for agents that help teams with specific coding patterns, review standards, or deployment procedures.

Browse development skills on Agensi that can enhance your Agent Builder agents.

MCP integration with Agent Builder

Agent Builder supports connecting to MCP servers through the actions panel. This means your custom agents can access databases, external services, and development tools through the same MCP servers used by Claude Code, Codex CLI, and Cursor.

The practical implication: you can build an internal agent for your team that connects to your company's database, documentation, and project management tools through MCP, then deploy it through ChatGPT where everyone can access it.

When to use Agent Builder vs a coding agent

Use Agent Builder when you want to create an agent for other people to use — a customer support agent, an internal knowledge bot, a specialized research assistant, or a team-facing development tool.

Use a coding agent (Claude Code, Codex CLI, Gemini CLI) when you're doing hands-on development work — writing code, debugging, refactoring, deploying.

Use both when you want a coding agent for your daily work and a custom agent that your non-technical team members can access for development-adjacent tasks like checking deployment status or querying production metrics.