Agents SDK App Builder

What it does

Agents SDK App Builder guides Codex from an idea or existing project to a locally runnable OpenAI Agents SDK application. It starts with a defined input/output contract and one agent, adding tools or specialists only when justified. Evaluation and local deployment are separate paths in the workflow. This skill builds an application; it does not provide a hosted agent service by itself.

When to use it

• Build a support assistant that answers from an approved article set and escalates unsupported questions.
• Add a narrow agent layer to an existing application with deterministic lookup tools.
• Create focused evaluations or deploy an already working prototype through the documented local manager.

Step-by-step workflow

1. Inspect the project and current official Agents SDK documentation. Classify whether the request is a new build, adaptation, evaluation or deployment.
2. Define inputs, outputs, tools, state, approval boundaries and a local command that proves the workflow. Confirm API access through the environment’s credential process without exposing secrets.
3. Start with one Agent and a runnable entrypoint. Use the project’s package manager and dependency conventions; the source prefers Python unless TypeScript is requested.
4. Add narrowly scoped tools with explicit schemas. Use sandbox execution only when the task actually needs workspace files or shell access.
5. Run a meaningful smoke scenario. If evaluation is requested, exercise the real agent path with cases for supported answers, missing evidence and escalation. Deploy only within the requested scope and verify readiness.

Example request

Use agents-sdk to build a support assistant over the approved articles in data/help.json. Return the article IDs supporting an answer. If evidence is missing, return an escalation result rather than inventing a policy. Start with one agent and a read-only lookup tool. Provide a local smoke command and evaluations for supported, unsupported and ambiguous questions. Keep the API key outside source files. Do not deploy until local behavior is verified.

Worked example

A support prototype should prove its evidence boundary before adding a web UI. One case asks a documented question; another asks about a refund policy absent from the source. The second must escalate. A fluent unsupported answer is a failed workflow even if the application starts successfully. Tool schemas and evaluation cases should make that boundary explicit.

What you get

A runnable agent app, a smoke command and optionally evaluation or deployment artifacts.

How to evaluate the result

• Expect runnable agent code, dependency metadata, sample input and an observed smoke result. Evaluation and deployment files should be identified separately when present.
• Verify credentials are loaded securely and excluded from generated artifacts. API calls incur model usage costs.
• For a deployed HTTP service, check the actual agent request path as well as readiness. A healthy endpoint alone does not prove useful answers.

What you need

A supported Python or TypeScript runtime, OpenAI API access and the dependencies required by the project.

Setup and activation

This skill is part of the openai-developers plugin in openai/plugins. Use the plugin workflow supported by your Codex client, then request the skill by name. Preserve companion references and required tool integrations. The repository describes plugin manifests and marketplace metadata; it does not establish one universal installation command for every Codex environment. This page installs nothing. Before local installation, review and security-scan the exact source/version; catalogue inclusion is not security approval.

Limits and considerations

Do not start with multiple agents when one proves the task. The deployment path requires its own manager/runtime setup; copying SKILL.md does not install that infrastructure. A prototype still needs domain-specific permissions, error handling and production review.

Related workflows

• Use MCP Builder when the agent needs a reusable external-service integration.
• Use task-based evaluations before exposing tools with consequential side effects.

License and reuse

Check the plugin and repository license terms.

Further reading

• Publisher setup and repository README