How to Build OpenClaw Provider Plugins

A provider plugin adds a model provider to OpenClaw’s inference loop. This is the right shape for an OpenAI-compatible proxy, a custom LLM API, or a model catalog that should appear as provider/model refs. It is not the right place to hide a full external agent daemon. The provider docs explicitly say that if the model must run through a native daemon that owns threads, compaction, or tool events, pair the provider with an agent harness instead.

30-second answer

Create a plugin package and manifest, declare provider ids and auth metadata, then register the provider with id, label, docs path, auth methods, and catalog behavior. A minimal provider can expose API-key auth and a model catalog. Users can then onboard with the provider’s auth choice and select models using the provider/model ref.

Where it fits

Build provider plugins when OpenClaw should treat a model backend as a first-class model provider. That means model listing, auth detection, default model selection, costs, context windows, input capabilities, and runtime transport matter. For a one-off HTTP call, use a tool. For a full coding agent daemon, use a harness. For model inference, use a provider plugin.

Docs-grounded facts

• Provider plugins add models to OpenClaw’s normal inference loop.
• The manifest can declare providers, modelSupport, providerAuthEnvVars, providerAuthAliases, and providerAuthChoices.
• providerAuthEnvVars lets OpenClaw detect credentials without loading runtime code.
• A minimal provider registers id, label, auth, and catalog.
• defineSingleProviderPluginEntry exists for bundled/simple providers.
• Use an agent harness when a native daemon owns threads, compaction, or tool events.

Set it up deliberately

The docs show package.json openclaw metadata with extensions, providers, compat, and build fields, plus an openclaw.plugin.json manifest with id, name, providers, modelSupport, providerAuthEnvVars, providerAuthAliases, providerAuthChoices, and configSchema. The manifest lets OpenClaw detect credentials and model prefixes before the runtime plugin fully loads.

Use it safely

Do not hardcode secrets or force runtime imports during discovery. Use providerAuthEnvVars and SecretRef-compatible config paths. If a provider variant should reuse another provider id’s auth, declare providerAuthAliases. If you publish on ClawHub, include compat and build metadata so users and tooling can check gateway and SDK compatibility.

Common mistakes

The common mistake is mocking register(api) in unit tests and assuming the loader will accept the plugin. The testing docs recommend at least one loader-backed smoke test because the real loader enforces metadata and capability ownership. Another mistake is replacing the whole stream path when a small input or output text transform would solve provider-specific token wording.

Verification checklist

Test auth resolution with and without keys, catalog output when auth is present, dynamic model resolution if you support it, and one real model call in a safe environment. Add loader-backed registration tests. Check that model ids, costs, context windows, max tokens, reasoning flags, and input capabilities are represented accurately enough for routing.

Playbook angle

The OpenClaw Playbook’s provider-plugin advice is to make the catalog honest. A provider that reports capabilities cleanly is easy to route, budget, and debug. A provider that hides auth and model facts becomes another mystery layer for agents to trip over.

Operator note

How to Build OpenClaw Provider Plugins works best when it is written into a small runbook instead of left as tribal knowledge. Record the intended owner, the exact config surface, the channel where results should appear, the allowed inputs, the expected output, and the rollback step. OpenClaw gives agents broad tools, but the durable value comes from making each tool boring, repeatable, and auditable. I would rather have one well-scoped provider plugin workflow that survives a restart than five clever demos nobody can safely run next week. If the runbook cannot explain when not to use it, keep refining before automation becomes default.