How to Use ChatGPT for OpenClaw: Setup and Task Approvals

To use ChatGPT for OpenClaw, configure an OpenAI model connection in OpenClaw, complete the supported account sign-in or API-key setup, and verify a reply. Then restrict the tools available to the assistant and test task approvals before giving it real work. The model connection and permission to act are separate setup choices.

A successful connection still needs a clear work brief: the goal, allowed actions, reviewed results, and next step. fenbs can hold that record between runs; copy the relevant details into OpenClaw manually. This gives you a consistent starting point when you return to the task.

How do you connect OpenClaw to an OpenAI model?

1. Install OpenClaw using its getting-started guide↗ (opens in a new tab), including the requirements for your operating system. Complete onboarding and open its dashboard. Keep the local OpenClaw service running while you test.
2. In Settings, open Models and select the assistant you want to configure. Choose Connect provider, then OpenAI. Check that you are configuring the intended installation and assistant before signing in.
3. Choose the supported authentication method for your account. Complete the browser authorization or supply a Platform API key through the credential setup. Read the requested access before approving it.
4. Select an available model and choose Test & use. Start a new conversation, inspect its selected model and account, and send a small text request that needs no outside tools.
5. Before enabling real work, configure tool access and execution approvals. Use a disposable test task to check both that permitted work succeeds and that work requiring approval waits for you.

For a terminal check, OpenClaw provides the following model commands. Its model reference↗ (opens in a new tab) explains how selection and available models work. Check the configured connection rather than choosing a model name from an old tutorial.

openclaw models status
openclaw models list

What does the account connection actually authorize?

OpenClaw documents three OpenAI connection choices: Codex login, Sign in with ChatGPT, and a Platform API key. They have different model access and account requirements. Use its OpenAI authentication guide↗ (opens in a new tab) to choose the route available to you; our ChatGPT subscription and OpenClaw guide covers the separate subscription question.

Signing in does not bring your ChatGPT conversations into OpenClaw. OpenAI distinguishes identity permissions from permission to make model requests in its Sign in with ChatGPT documentation↗ (opens in a new tab). An account can be recognized without having the access needed to answer your prompt.

For the API-key route, use credentials for the intended Platform project. Follow OpenAI’s API authentication guidance↗ (opens in a new tab): keep keys in protected environment settings or secret storage, away from prompts, shared task notes, and source files. If you need background on the coding product, see ChatGPT and Codex.

What should your first task ask OpenClaw to do?

Start with a draft you can inspect without changing anything outside the conversation. For example, give it a sanitized project update and ask for a proposed checklist. A useful test shows whether the assistant follows your scope and identifies missing information.

Goal: Turn the project update below into a draft checklist.
Input: Use only the text I provide.
Allowed: Summarize facts and suggest next steps in this chat.
Approval required: Ask before using tools, changing files, or sending anything.
Output: Separate confirmed facts, suggestions, and questions.
Stop after the draft so I can review it.

This prompt describes your expectations; tool settings must enforce the practical limits. Review the draft against the supplied update. Did a tentative idea become a confirmed fact? Did the assistant invent a deadline? Correct the work brief before making the task more complex.

Once a person approves a continuing rule, record it in fenbs’s Decisions and rules so it remains available in the project context used to prepare later runs.

How do you check task approvals before real work?

Inspect the execution policy for the machine where commands will run. OpenClaw’s execution approval documentation↗ (opens in a new tab) describes command restrictions, approval prompts, and host-specific settings. These approvals govern command execution; they do not make every enabled tool harmless or every folder read-only.

• Check the target: confirm which machine and assistant the policy applies to. Inspect the effective policy, including host restrictions, rather than relying on one visible setting.
• Check the prompt: in a disposable folder, request a harmless command that your policy requires you to approve. Confirm it waits; deny it and check that it stays blocked.
• Check the scope: when you approve a command, inspect what it will run and where. Prefer permission for one execution while learning the workflow.
• Check other tools: separately restrict file access, message destinations, and external services. Leave unnecessary tools disconnected for the first task.

Decide explicitly who may approve customer messages, publication, or changes to live information. For workplace use, agree on the owner of each connection and review point before sharing access. Our ChatGPT for business guide discusses the broader work setup.

What should you check when the first request fails?

Use the failure to identify which layer needs attention. A model request that cannot authenticate needs a different fix from a command that correctly waits for approval. Repeat the smallest text-only test before adding another tool.

• Dashboard unavailable: check whether the local OpenClaw service is running before changing model credentials.
• Account connected but no reply: inspect the account and model selected in that conversation. For Sign in with ChatGPT, verify that the authorization includes model requests and that your workspace permits the requested access.
• Unavailable model: choose from the models offered for the configured connection. A successful sign-in does not guarantee access to every model.
• Command denied or waiting: inspect the effective execution policy and pending approval. A working restriction is not a reason to grant unrestricted execution.
• Reply ignores project facts: check which work brief you actually supplied. Test with one short, reviewed brief before adding more background.

What should you save before the next assistant run?

Save enough for someone else to continue: the goal, input references, actual output, review result, unresolved questions, and next permitted action. Say what was tested and what still needs checking. Keep proposed actions distinct from actions a person approved.

For example: “Draft checklist reviewed against the supplied update. The delivery date is still unknown. Next run may suggest clarification questions; sending them requires approval.” That record gives the next session a specific job without silently expanding its authority.

If you later change assistants, Claude versus ChatGPT can help with that separate choice. Keep the reviewed work in fenbs’s handoff record, then supply the relevant details to the next OpenClaw run yourself.

How to do this with fenbs

1. Create a free account, then add a project for your OpenClaw workflow. fenbs is free for one person, no credit card.
2. Add a task with the desired outcome, input references, and a plan covering allowed actions and review checks. Keep credentials outside the work record.
3. Record the rules a person chose in Decisions and rules. Before each OpenClaw run, copy the relevant rules and task details into its work brief.
4. Review the output, update the task, and save Where we left off with the verified result, unresolved questions, and next step.
5. If you also use an assistant supported by fenbs, follow the connection guides and ask it to read project context before work and save progress afterward.

fenbs does not connect directly to OpenClaw or control its execution approvals. It is unnecessary for a single disposable test; it becomes useful when you need a durable work record across runs and assistants. For more on planning and reviewing assistant workflows, visit the AI agents hub.