OpenClaw OpenAI Integration

Supported OpenAI Models in OpenClaw (GPT-4o, o3, GPT-5 Codex)

OpenClaw model support follows two things: the OpenClaw provider catalog and the models enabled for your OpenAI account.

GPT-4o and o3 may appear as legacy or compatibility choices. GPT-5 Codex naming also depends on the current API catalog and the authentication path your setup uses.

List the available provider routes before choosing a model:

openclaw models list

OpenClaw uses the openai/* route format for OpenAI-backed models, so a model setting usually follows this pattern:

openai/<model-id>

Choose the newest supported model that fits the task. Use a fast, lower-cost model for classification, routing, extraction, and simple edits. Use a stronger reasoning or coding model for multi-file changes, complex debugging, and review-heavy work.

Do not hard-code an old model ID without checking availability. If a Codex-related route, OAuth profile, or API-key profile behaves differently in your environment, confirm the supported path before assuming every OpenAI model ID works the same way.

API Key Configuration in openclaw.json

Use onboarding to connect OpenClaw to OpenAI:

openclaw onboard

Select OpenAI. Add the credential through the supported auth flow. Then set the model route:

openclaw models set openai/<model-id>

Keep the model route and the secret source separate. A project config can point to openai/<model-id> while the actual credential lives in a protected auth profile, deployment secret, or environment variable.

Avoid storing a raw key in a shared openclaw.json. Shared config files can enter repository history, build logs, and teammate machines. Protected profiles and environment variables make key rotation safer and reduce accidental exposure.

For team environments, use one of these patterns:

• Local development: each developer stores a personal key in a protected local profile.
• Shared development workspace: the workspace reads the key from an environment variable or secret manager.
• CI or automation: the job injects the key at runtime and never writes it to the repo.
• Rotated credentials: update the protected secret source, restart the service, and keep the model route unchanged.

After changing credentials, restart any long-running OpenClaw gateway or worker process that cached the old environment.

Token Budgeting and Rate Limit Strategies

Agent loops can create many model calls because each planning step, tool result, retry, and review pass may add tokens.

Control usage with:

• Short system instructions.
• Compact tool output.
• Session summaries.
• Per-task model routing.
• Retry limits.
• Usage alerts.
• Concurrency caps.
• Escalation rules for stronger models.

Use a smaller model for routing, summarization, extraction, and simple file inspection. Reserve stronger models for decisions that need deeper reasoning, such as architecture changes, failing test analysis, security-sensitive edits, and final review.

A practical budget should track four counts per task:

input tokens + output tokens + retry calls + tool-result size

Reduce token growth by summarizing long tool outputs, excluding irrelevant logs, and asking agents to inspect only the files needed for the current step. Large stack traces, dependency trees, and generated files should be summarized before they enter another model call.

For rate limits, control both speed and volume. Lower parallel calls when several agents run at once, add exponential backoff for retryable failures, and stop retries after a fixed limit. Immediate retry loops can turn one failed task into a burst of blocked requests.

To compare rate-limit behavior and model-routing tradeoffs against Anthropic-backed agent runs, use OpenClaw + Claude Integration alongside the budgeting approach outlined here.

For source-level validation, Steipete is worth checking after you understand the OpenClaw OpenAI Integration workflow described here.

Fix '401 Missing Auth' and '429 Rate Limit Reached' Errors

401 Missing Auth

401 Missing Auth means OpenClaw reached the provider path without a valid credential. Confirm the key exists in the gateway or worker environment, then restart the service after changing credentials.

Also check:

• The selected provider is OpenAI.
• The model route uses the expected openai/<model-id> format.
• The key is present in the process that runs the request.
• The account has access to the selected model.
• The key has not expired, been revoked, or been replaced.
• The project, organization, or billing context matches the credential.

If the same key works locally but fails in a shared environment, compare the local shell, service environment, and deployment secret. The failing process may not be reading the same credential source.

429 Rate Limit Reached

429 Rate Limit Reached means the provider is throttling request volume, token volume, or both. Reduce parallel calls, add exponential backoff, and check account limits and billing status.

Use these controls before retrying a busy agent workflow:

• Lower worker concurrency.
• Add jitter to retries so agents do not retry at the same moment.
• Cap retry attempts per task.
• Route simple steps to a smaller model when supported.
• Summarize long context before retrying.
• Queue non-urgent tasks instead of running every agent at once.

Do not loop immediate retries. A tight retry loop can increase cost, consume remaining quota, and delay the task that originally failed.

To reduce repeat throttling across agent workflows, compare provider routing and fallback choices in OpenClaw Model Integrations before increasing concurrency again.

When details such as limits or setup steps matter, the official documentation can help confirm the latest implementation surface.

OpenAI vs Claude Sonnet 4.5 for Multi-Turn Agent Tasks

Claude Sonnet 4.5 is a historical model name. Compare against the current Sonnet-tier model available to your account.

Provider benchmarks do not predict every agent loop. Use real tasks and fixed evaluation criteria: successful completion, number of retries, tool-call accuracy, code quality, review findings, and final merge readiness.

For multi-turn agent work, the model is only one layer. The surrounding workflow determines whether the system plans the change, limits unnecessary tool calls, isolates branch work, and catches mistakes before merge.

> From model output to merged work > > Verdent reached 76.1% on SWE-bench Verified. The number matters because Code Verification measures completed software work rather than persuasive chat output. > > The four layers work together: plan the change, split the work, isolate each branch, then verify before merge.

To separate provider choice from local-runtime constraints, test the same agent workflow with OpenClaw Ollama Integration before standardizing on a hosted model.

Before you budget a real project around OpenClaw OpenAI Integration, compare the claims here with Reddit.

Cost Per Task: OpenAI vs Claude vs Local Models

Cost per token is incomplete because agent work includes retries, tool calls, review cycles, and developer follow-up.

Measure complete task cost with this model:

task cost = model calls + retries + tool calls + review time

Track the same fields for each provider comparison:

• Model route used for the task.
• Input and output token counts when available.
• Number of retries and failed calls.
• Number of tool invocations.
• Wall-clock time to completion.
• Human review time after the agent finishes.
• Whether the output passed tests or review on the first attempt.

OpenClaw cost visibility can depend on provider metadata, configured pricing, and whether the call uses an API-key route or another supported auth path. When estimated cost is unavailable, record token counts, retry counts, tool invocations, and completion quality so the comparison remains useful.

Verdent helps compare models inside a structured development process. It also adds Plan Mode, parallel agents, isolated workspaces, and verification, so teams can evaluate provider cost against completed work rather than single-call price.