Self-Healing Browser Automation
Automate logins, data collection, and visual checks in real browsers, with AI steps that repair broken selectors and secure code execution when you need it.
The problem
Browser automations break the moment a page changes: selectors stop matching, logins expire, and scripts fail silently until someone notices the missing data.
How it runs today
Teams maintain brittle scripts by hand, re-record flows after every UI change, and keep session cookies in ad hoc places nobody can audit.
With Heym
Build the flow as Playwright steps on the canvas, or write Playwright Python that runs in a hardened sandbox. Describe difficult steps in natural language and the AI step generates the actions; when a selector fails, auto heal proposes a working alternative and the run continues. Sessions restore from cookies or storage state, an authenticated selector check verifies the login, and scripted login steps run as a fallback when the session is stale.
The workflow, end to end
The Playwright Visual AI Monitor template is an importable starting point: it takes a full-page screenshot on a schedule, has an LLM inspect the page for anomalies, and sends a Slack alert when something looks off.
Where control lives
Code mode is disabled by default and always runs sandboxed, auth state is verified before steps execute, and every run keeps screenshots and captured network data for inspection.
Browser automations that survive UI changes and expired sessions instead of failing quietly.
Built with these Heym capabilities
Common applications
Deployment and integration
- Self-host with Docker or Kubernetes, keeping data on your infrastructure.
- Connect your own model providers: OpenAI, Ollama, vLLM, and more.
- Integrate over HTTP, webhooks, Slack, email, and MCP tools.
- Expose finished workflows as APIs, portals, or MCP servers.
Have a process that fits this pattern?
Show us the workflow, data sources, tools, and human decisions involved. We will help map it to Heym.