Stagehand

Stagehand's pitch is a positioning argument as much as a technical one: Playwright was built for testing, Stagehand is built for agents. The technical substance behind that claim is real in three places. First, the runtime. Stagehand runs as an extension next to the page over the Chrome DevTools Protocol rather than driving a remote browser, and the vendor's benchmark puts a batch click-and-type task at 4.7 seconds versus 9.0 seconds for Playwright. Second, context discipline. Hybrid accessibility tree trimming produces 7.4k tokens for a representative extract() call where the Playwright path consumes 35.7k — that is roughly 80% fewer tokens per page read, which is the difference between an agent loop that is affordable at volume and one that isn't. Third, DOM coverage. Out-of-process iframes, closed Shadow DOMs, and deep locators for nested iframes are handled natively, and self-healing actions re-resolve when markup changes instead of throwing a selector error at 3am. The v4.0.0 release is the most interesting piece for production teams. Built-in caching skips repeated operations, which compounds with the token savings. Network-level domain allowlist and blocklist is a security control, not a feature bullet — if your agent browses untrusted pages, prompt injection is the threat that actually bites, and gating egress at the network layer is a defensible answer. WebMCP support, clipboard access, batched commands, and built-in OTel tracing with per-method metrics() round out the observability story that most AI browser tools skip entirely. The honest constraints are structural. This is an SDK, not a product: there is no visual recorder, no hosted dashboard, and no support contract implied by a repo. You supply the agent loop — Stagehand positions itself as the hands while you bring the brain via LangChain, CrewAI, Mastra, or your own orchestration. It targets desktop Chromium (Chrome, Edge, Arc, Brave), so mobile browser automation isn't in scope. Scaling past a local Chromium instance is your problem or Browserbase's, depending on the path you pick. And if your scraping job is a static page that requests handles fine, adding an agent layer is overhead. Where it fits: teams replacing selector maintenance in Playwright or Selenium with self-healing primitives; AI engineers wiring browser control into an existing agent framework; QA groups running end-to-end tests on dynamic pages; anyone scraping frequently-changing sites without public APIs who needs typed, schema-validated output rather than parsing HTML by hand. Where it doesn't: non-technical users, teams who want a managed black-box agent with no code to own, and anyone unwilling to own browser lifecycle and retry logic.