OpenClaw Agent Harness SDK Explained

An agent harness is one of the deepest OpenClaw extension points. The docs define it as the low-level executor for one prepared OpenClaw agent turn. It is not a model provider, not a channel, and not a tool registry.

30-second answer

Register an agent harness only when a model family has its own native session runtime and the normal provider transport is the wrong abstraction. Examples include a native coding-agent server that owns threads and compaction, a local CLI or daemon that streams native plan and tool events, or a runtime that needs its own resume id in addition to OpenClaw's session transcript.

What core still owns

Before a harness is selected, OpenClaw has already resolved provider and model, runtime auth state, thinking level, context budget, transcript, workspace, sandbox, tool policy, channel reply callbacks, streaming callbacks, model fallback, and live model switching policy. The harness runs a prepared attempt; it does not silently choose providers or replace delivery policy.

That split is the safety model. The harness can execute the attempt through a specialized runtime, but OpenClaw still owns the shared policy layer around the attempt. If a plugin tries to use a harness to bypass auth, sandbox, or tool policy, it is using the wrong extension point.

Provider versus harness

If you are adding a normal HTTP or WebSocket model API, build a provider plugin. Provider plugins are the right abstraction for request parameters, authentication, model catalogs, and fallback hints. Harness plugins are for runtimes with their own session and execution semantics.

Runtime plan boundary

The docs mention params.runtimePlan as an OpenClaw-owned policy bundle passed into prepared attempts. It includes provider-aware tool schema policy and transcript policy. A harness should use those shared decisions rather than inventing separate behavior for the same session.

Who should touch it

The docs limit this surface to bundled or trusted native plugins. The contract is experimental because parameter types intentionally mirror the current embedded runner. External plugin authors should be cautious; if the goal is ordinary provider support, the provider SDK is safer and more stable.

Operator checklist

Before enabling a harness-backed runtime, verify which plugin owns it, which agent runtimes can select it, how it streams output, how it handles resume ids, how tool calls are normalized, and how fallback behaves. Test aborts, timeouts, compaction, and empty or reasoning-only attempts before production use.

The OpenClaw Playbook frames harnesses as an advanced escape hatch: powerful when a native runtime deserves first-class execution, dangerous when used to avoid the normal provider contract.

Rollout plan

Treat OpenClaw Agent Harness SDK Explained as a workflow you roll out in stages, not a switch you flip once. Start with the smallest harmless proof: a status check, dry run, local-only call, private session, or read-only inspection. Confirm the documented behavior matches your installed OpenClaw version, then write the exact commands and expected output into the workspace so the next agent does not rely on memory or vibes.

For a production runbook, document decision owner, source document, acceptance check, upgrade risk, and where future agents should look before changing the behavior. Also write down what the agent may do alone, what requires approval, and what must stop immediately. That boundary is the difference between useful autonomy and a workflow that surprises the operator at the worst possible time.

Keep one rollback note beside the guide. It can be as simple as the command to disable a plugin, the channel to pause, the config key to revert, or the owner who must approve the next run. Include the proof that tells you rollback worked, and keep it visible near the production checklist for future maintainers. Agents are most useful when recovery is obvious.

After the first live run, review the transcript or logs while the details are fresh. Look for missing prerequisites, stale assumptions, broad prompts, confusing errors, and any external side effect that should have been gated. Tighten the guide, then repeat with one wider scope. The OpenClaw Playbook is built around this operating rhythm: cautious first proof, written runbook, verified automation, then gradual autonomy once the evidence is boring.