Node version
Start with the official baseline first: Node 24 is recommended, and Node 22.14+ is still supported. If the version is wrong, fix that before you blame the workflow.
node --versionOpenClaw quickstart
OpenClaw quickstart: make the browser agent stable before you try to scale it.
This is a second entry point for readers who want a more practical starting page. It focuses on the parts that usually create early drop-off: environment checks, onboarding and gateway readiness, agent reasoning loops, practical templates, and the first debugging order.
3-minute preflight
Do the three-minute check before you lose the next three hours
Most early browser-agent failures are not concept failures. They are environment failures.
Gateway status
After onboarding, verify that the Gateway is actually listening before you assume the local setup is healthy.
openclaw gateway statusLocal dashboard
Open the official local dashboard and confirm the browser can reach the Gateway on the expected port before you expand into larger workflows.
openclaw dashboardFast start
Start small enough to prove the browser works before you call it a workflow
If you ask the system to search, classify, organize, and export everything in one go, debugging becomes blurry immediately.
Recommended order
- Confirm Node is on a supported version and finish the default onboarding flow first.
- Verify the Gateway is running before you diagnose anything at the prompt layer.
- Open the dashboard, then let the agent complete one tiny browser action first.
- Only then increase step count, add review checkpoints, and expand into multi-agent work.
Agent logic
Understand the loop before you try to optimize the prompt
The core of a browser-agent workflow is not one giant prompt. It is a visible rhythm of reasoning and observation.
1. User input
Turn the task into one clear objective instead of asking the system to search, judge, organize, contact, and deliver everything at once.
2. Thought
The supervisor forms a short plan, decides which page to inspect first, and chooses what to verify before moving forward.
3. Action
The browser agent performs one explicit step, such as opening a page, clicking, searching, or extracting structured information.
4. Observation
The system checks the page state and result, then decides whether to continue, retry, ask for approval, or stop.
5. Final output
The result becomes a summary, table, to-do list, or review-ready report instead of a premature claim that the task is finished.
Thought → Action → Observation
This loop is the unit you should improve, not a single giant block of prompt text
Once you understand that the agent looks, acts, observes, and adjusts step by step, you naturally start writing better instructions. You stop asking for omniscience and start defining success conditions, stop conditions, and human review points.
Canadian templates
These three practical templates are more useful than generic demos
The point is not to encourage reckless scraping. The point is to show when a browser agent can save time in real work.
Scenario A: Track new real estate listings in Canada
Value: Useful for consultants, investors, and research-heavy operators who keep checking the same listing pages repeatedly.
Workflow: Review selected listing pages on a schedule, extract price, address, and status changes, then produce a summary for human review.
Caution: Check the site terms and access limits first. Do not treat high-frequency scraping as the default.
Scenario B: Monitor grant and funding pages
Value: Useful for consultants, immigration support operators, startup advisors, and small-business researchers.
Workflow: Check grant or funding pages periodically, extract deadlines, eligibility rules, and update timestamps, then turn them into a reviewable alert list.
Caution: This type of workflow works best as low-frequency, field-specific extraction rather than open-ended crawling.
Scenario C: Organize LinkedIn job search signals
Value: Useful for job seekers, consultants, and researchers who want a clearer pattern from repeated searches.
Workflow: Run fixed search criteria, collect job title, location, skill signals, and posting time, then hand the results to a human for screening.
Caution: Social platforms are more sensitive to bot detection, so keep review and compliance judgment in the loop.
Implementation notes
Do not just copy the parameters. Understand what they change.
- max_steps: Controls how many steps a task can take. More steps do not make the system smarter. They often just bury the failure deeper.
- use_vision: Turn vision on only when the task actually depends on visual cues. It should not be the default for every workflow.
- browser profile: Use a restricted, separated browser profile whenever possible instead of mixing the agent with your main personal workspace.
If it fails
If the first run breaks, debug in this order instead of blaming the model
- Reduce the task to one minimal reproducible step, such as opening the page and checking whether the target element exists.
- Confirm the browser can really launch and that session state or login state is not broken.
- Lower `max_steps` so the failure point stays visible instead of disappearing inside a long run.
- If the site appears to be blocking automation, stop and adjust the workflow and frequency instead of forcing harder retries.
- Only after the environment and page state look healthy should you change the prompt, vision settings, or task strategy.
Security
The boring rules are often the ones that actually protect you
- Do not upload `.env` files to GitHub, including test keys and throwaway setups.
- Prefer smaller-scope API keys and separated browser profiles wherever possible.
- Start with restricted tools and approval checkpoints instead of full access from day one.
Compliance
Browser automation is not a free pass around site rules and access boundaries
- Check robots guidance and published terms before touching a target site.
- Avoid treating high-frequency extraction, login bypass, or sensitive personal data as normal defaults.
- Design workflows so they stay reviewable, stoppable, and narrow enough to control.
Flagship article
Read the flagship guide when you want the full mental model first
The longer guide places OpenClaw back in the right frame: architecture, permissions, workflow logic, and business reality.
Want structure?
Take the free course if you want the safer order to follow next
The course is better for readers who want setup, browser safety, approvals, and operating habits in a clearer sequence.
Want deeper implementation?
Go to the playbooks when you want reusable implementation material
The product library is better for readers who want concentrated guides they can keep, reuse, or turn into deployment briefs.