How to Build an Agent with OpenAI AgentKit in 5 Steps
OpenAI AgentKit is a bundle of tools for building agents: Agent Builder for visual workflows, an Agents SDK for code, Connector Registry for data sources, and eval tools. It is included with API usage and works across OpenAI models.
The interesting design choice is that AgentKit does not force you to pick code or no-code. You can wire a flow visually and then drop into code for the parts that need logic. For teams where a PM and an engineer both touch the same agent, that shared surface is the main selling point of OpenAI AgentKit.
What is in the box?
Four pieces. Agent Builder is a canvas where you drag nodes for model calls, tools, and branching logic. The Agents SDK is the code-first path, with multi-agent handoffs. Connector Registry exposes third-party data sources as tools. Evals let you test agent output against defined criteria.
Evals are the underrated one. Without them you are guessing whether a prompt change actually improved anything.
How do you build your first agent?
- Open Agent Builder in the OpenAI platform dashboard.
- Add a start node and a model node; pick a model like GPT-4o or a newer release.
- Write the agent instructions in the model node — purpose, tone, boundaries.
- Attach one tool, such as a web search or a connector.
- Run a test prompt in the preview panel, then export the workflow or wire it to the SDK.
One tool again. Multi-tool agents fail in ways that are painful to debug on day one.
How much does AgentKit cost?
There is no separate AgentKit fee. You pay standard OpenAI API token rates for the model calls your agent makes. A small agent using a mid-tier model costs fractions of a cent per interaction; agent loops with long context can reach several cents. The eval runs also consume tokens, which surprises people who run them on every commit.
Should you use the visual builder or the SDK?
| Path | Best for | Trade-off |
|---|---|---|
| Agent Builder | Prototypes, non-engineers | Less control over edge cases |
| Agents SDK | Production, version control | Code required |
| Hybrid | PM drafts, engineer extends | Needs discipline to keep in sync |
Prototype visually, then port to the SDK before you ship. Visual flows are hard to review in a pull request.
What are the limitations?
You are inside OpenAI’s ecosystem. Model choice means OpenAI models, and connectors are the ones supported by the registry rather than anything you can imagine. Teams committed to a multi-vendor model strategy will find that constraining.
Also, long-running agents with many steps get expensive quickly, and there is no built-in cost guardrail. You add spend limits at the account level.
Suits product teams wanting to ship an internal assistant fast with evaluation built in. Less suited to teams already deep in another framework who would be duplicating orchestration.
Quick Summary
- AgentKit bundles Agent Builder, Agents SDK, Connector Registry and evals.
- No extra fee; you pay standard OpenAI token rates.
- Start with one tool and clear instructions, then expand.
- Prototype in the visual builder, port to the SDK for production.
- OpenAI-model-only and no built-in spend limits.
