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Browser Use To Stagehand

BrowserbaseOfficialPublished by Browserbase

  • Skill
  • Workflow
  • Software development
  • MIT
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From the author

Migrate browser-use (Python) browser-automation scripts to Stagehand v3 (TypeScript) on Browserbase. Use when the user wants to convert, port, rewrite, or migrate a browser-use Agent script to Stagehand, map browser-use features/APIs to Stagehand primitives (act/extract/observe/agent), or move agentic browser automation onto Browserbase with more determinism. Triggers on "browser-use", "browser_use", or "Agent(task=...)".
— the description by Browserbase

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured methodology for porting browser-use (Python) agentic scripts to Stagehand v3 (TypeScript) on Browserbase. Rather than a direct transpilation, it emphasizes refactoring opaque agent loops into inspectable, deterministic pipelines. By applying a determinism spectrum, it helps developers replace LLM-heavy agentic steps with specific Stagehand primitives (act, extract, observe) where the page flow is predictable, reserving AI-driven agents only for genuinely open-ended tasks.

What it does

  • Analyzes browser-use scripts to extract tasks, browser configurations, and custom actions.
  • Maps agentic browser-use steps to Stagehand v3 primitives based on a determinism decision tree.
  • Generates runnable TypeScript code using Stagehand v3 and Zod for structured data extraction.
  • Provides setup templates for package.json, environment variables, and index.ts.
  • Offers a trace-assisted workflow for debugging and migrating flaky or opaque agent scripts.
  • Ensures best practices for secret management and browser session handling.

When to use it

  • When you need to migrate an existing browser-use Python script to a more stable TypeScript environment.
  • When you want to improve the reliability and speed of browser automation by reducing unnecessary LLM calls.
  • When you need to move agentic workflows onto the Browserbase platform for better observability.
  • When you are refactoring an opaque, flaky agent script into a maintainable, deterministic pipeline.

Advantages

  • Increases automation reliability by replacing non-deterministic agent loops with predictable primitives.
  • Reduces token costs and latency by using AI only where the page structure is unpredictable.
  • Improves maintainability through structured, inspectable TypeScript code.
  • Leverages Browserbase platform features for better session management and debugging.

Usage examples

“Migrate my browser-use script that searches for products on Amazon and extracts prices.”
The agent will inventory your script, identify the search and extraction steps, and provide a Stagehand v3 script using page.goto for navigation and stagehand.extract for price gathering.
“I have a flaky browser-use agent that logs into a portal. How do I move it to Stagehand?”
The agent will guide you through mapping the login flow to a deterministic Stagehand sequence, potentially using Browserbase context to handle authentication reliably.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/browserbase/browser-use-to-stagehand/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip browser-use-to-stagehand.zip -d .agents/skills

Documentation by Browserbase

browser-use → Stagehand on Browserbase (/browser-use-to-stagehand)

Convert a browser-use (Python) script into an idiomatic Stagehand v3 (TypeScript) script on Browserbase, choosing the right level of determinism at each step rather than producing a one-to-one agentic copy.

Core principle: browser-use is agentic-by-default (the LLM decides every action). Stagehand lets you choose how much AI to use. A good migration replaces opaque agent loops with an inspectable, mostly-deterministic pipeline — using AI only where the page is genuinely unpredictable. This is a refactor with judgment, not a transpile.

Source of truth & versions. This skill's durable value is the judgment — the determinism spectrum and the decompose-vs-agent decision — not the API specifics, which drift every release. The code mappings here are a snapshot validated against @browserbasehq/stagehand 3.6.x and browser-use 0.13.x (2026-06). On any conflict, the live docs win — always verify against the installed package and these sources before emitting code:

If the installed Stagehand major is not 3, treat this skill as conceptual only and follow the live docs for every signature.

Reference files (read as needed)

  • references/api-mapping.md — the mechanical browser-use → Stagehand mapping: variant detection, the full feature table, before/after code, Browserbase platform options, and v3 version gotchas. Read this for any non-trivial construct.

  • references/determinism.md — how to choose agent() vs act/extract/observe vs cached observe→act. The decision tree. Read this when deciding how to translate an Agent(task=…).

  • references/trace-assisted.md — the optional "run it on Browserbase, read the logs, then rewrite" workflow for opaque/flaky scripts.

  • references/guide.md — the human migration guide: philosophy shift, feature mapping, the determinism spectrum, and a recommended migration path.

  • references/prompt.md — a self-contained, tool-agnostic version of this skill; paste it into any AI assistant along with a browser-use script.

  • EXAMPLES.md — before/after script pairs.

Workflow

1. Get the source

Obtain the browser-use script(s). If the user only described a script, ask for the file(s). Note the target: TypeScript Stagehand on Browserbase unless they say otherwise.

First, gate on scope — is this even migratable? Not every browser-use file is an Agent(task=…) script. If the source is browser-use running as an MCP server (uvx browser-use --mcp, a mcpServers config) there is no Stagehand equivalent — flag it as out of scope, don't invent one (see api-mapping §3.7b). If the browser-use call is embedded in a larger app (a class/tool wrapper, web route, queue task), convert only the browser-use surface and preserve the surrounding app glue — see api-mapping §3.8.

2. Detect the browser-use variant

Identify legacy (pre-0.12) vs stable vs Rust beta (only when imports come from browser_use.beta) — see api-mapping §1. Note: the classic top-level from browser_use import Agent, ChatBrowserUse surface is alive and well in 0.13.x — ChatBrowserUse alone is not a beta tell; only a browser_use.beta import is. All variants translate identically, so when unsure, proceed with the stable mapping. Normalize legacy names before translating. State which variant you found.

3. Inventory the script

Extract a structured inventory before writing any TypeScript:

  • Task(s) — the task= string(s); split each into its implied ordered steps.

  • Model — the Chat* provider + model id.

  • Browser config — local vs cdp_url/Browserbase; headless; proxies; user_data_dir/storage_state.

  • Structured output — any output_model_schema Pydantic models.

  • Secrets — sensitive_data, env-var usage, login flows.

  • Guardrails — allowed_domains, max_steps.

  • Custom actions — @tools.action / Controller functions, and whether each is a deterministic side-effect or an agent capability.

  • Setup — initial_actions, secondary models (page_extraction_llm, planner_llm).

4. Decide the determinism level per step

For each step from the inventory, apply the decision tree in determinism.md:

  • Navigate to a known URL → page.goto(url) on the Stagehand page (no AI).

  • On-page action → act("…"); if it repeats, observe() once then replay act(action) (no LLM call).

  • Reading data → extract("…", zodSchema).

  • Genuinely open-ended → keep stagehand.agent().execute(...) (tightened with maxSteps/systemPrompt).

Default to decomposition when the flow is known; keep agent() only where it isn't. For a first lift-and-shift, a faithful agent() translation is acceptable — say so and note the optimization path.

5. Produce the Stagehand v3 rewrite

First, verify the API. Before writing, confirm the exact signatures you're about to use against the installed package (node_modules/@browserbasehq/stagehand types) or https://docs.stagehand.dev/v3. The mappings below are a 3.6.x snapshot; if anything differs in the installed version, the installed version wins. Then emit runnable TypeScript. Always:

  • import { Stagehand } from "@browserbasehq/stagehand"; and import { z } from "zod"; when extracting.

  • Get the page via const page = stagehand.context.pages()[0];.

  • Call AI methods on the instance: stagehand.act(...), stagehand.extract(...), stagehand.observe(...) — never page.act(...).

  • Set the model as a "provider/model" string.

  • Default to env: "BROWSERBASE"; show env: "LOCAL" as the dev option.

  • Pass secrets via variables and process.env, never hardcoded.

  • await stagehand.init() at the start, await stagehand.close() in a finally.

Include the project setup so it runs (see the templates below).

6. Write the migration summary

Alongside the code, produce a short summary:

  • Variant detected and the determinism choices made (which steps became deterministic vs AI vs agent), with the reasoning.

  • Needs human review — anything that didn't map 1:1: lost allowed_domains guardrails, custom-action logic, secondary-model intent, ambiguous task strings.

  • Recommended next step — Browserbase Context for auth reuse, caching for production, or the trace-assisted path if the flow was opaque.

7. Offer the trace-assisted path (only if warranted)

If the source was one large opaque agent(task=…), was flaky, or your rewrite can't be confidently mapped, offer the trace-assisted workflow (trace-assisted.md): run the original on Browserbase, pull sessions.logs.list, and rewrite from observed behavior. Don't run anything without the user's go-ahead.

Output templates

package.json

{
  "name": "stagehand-migration",
  "type": "module",
  "scripts": { "start": "tsx index.ts" },
  "dependencies": {
    "@browserbasehq/stagehand": "^3.0.0",
    "dotenv": "^16.0.0",
    "zod": "^3.25.0"
  },
  "devDependencies": { "tsx": "^4.0.0", "typescript": "^5.0.0" }
}

Add "ai": "^5.0.0" (Vercel AI SDK) only if a custom browser-use action maps to an agent tool. Pin v5, not v4 — Stagehand 3.6.x bundles ai v5 and types agent({ tools }) as the v5 ToolSet, where a tool's schema field is inputSchema. The v4 tool() helper emits parameters instead and will fail to type-check against Stagehand's v5 ToolSet. If you can't control the hoisted ai version, skip the tool() helper and pass a plain object { description, inputSchema: zodSchema, execute } — it satisfies the v5 ToolSet regardless of which ai major resolves.

.env

BROWSERBASE_API_KEY=...
BROWSERBASE_PROJECT_ID=...
ANTHROPIC_API_KEY=...   # or the provider matching your model string

index.ts skeleton (decomposed, the preferred shape)

import "dotenv/config";
import { Stagehand } from "@browserbasehq/stagehand";
import { z } from "zod";

async function main() {
  const stagehand = new Stagehand({
    env: "BROWSERBASE",
    model: "anthropic/claude-sonnet-4-6",
  });
  await stagehand.init();
  try {
    const page = stagehand.context.pages()[0];

    await page.goto("https://example.com");          // deterministic skeleton
    await stagehand.act("…");                          // AI where the page varies
    const data = await stagehand.extract("…", z.object({ /* … */ }));  // structured reads

    console.log(data);
  } finally {
    await stagehand.close();
  }
}

main().catch((err) => { console.error(err); process.exit(1); });

Validation checklist (before declaring done)

  • [ ] AI methods are on the instance (stagehand.act/extract/observe), not the page.

  • [ ] Page obtained via stagehand.context.pages()[0].

  • [ ] Model is a "provider/model" string; the matching provider key is in .env.

  • [ ] extract uses a zod schema; zod is in dependencies.

  • [ ] Secrets use variables + process.env; nothing hardcoded.

  • [ ] init() / close() present; close() in finally.

  • [ ] Each browser-use step is accounted for, placed deliberately on the determinism spectrum.

  • [ ] Migration summary lists determinism choices and "needs human review" items.

Common mistakes to avoid

  • Copying v2 syntax (page.act(), stagehand.page, modelName/modelClientOptions, enableCaching) from old blog posts. Use v3 — see api-mapping "Version notes".

  • Translating every step into act() — navigate with page.goto and cache repeatable steps via observe→act; don't spend an LLM call on every action.

  • Defaulting everything to agent() — that just reproduces browser-use's non-determinism in a new framework. Decompose where the flow is known.

  • Silently dropping allowed_domains — Stagehand has no domain firewall; flag it for review.

  • Inventing Browserbase/Stagehand options — if unsure of a field, check https://docs.stagehand.dev/v3 / https://docs.browserbase.com rather than guessing.

Files

references/guide.md

Migrating from browser-use to Stagehand on Browserbase

A guide for teams moving browser automation from browser-use (Python) to Stagehand (TypeScript) running on Browserbase. It focuses on mapping features and choosing the right level of determinism — not a rigid line-by-line transpile, because real migrations differ by team, tooling, and how much autonomy each workflow actually needs.

If you want an agent to do the rewriting for you, pair this guide with the /browser-use-to-stagehand skill, which applies the mappings and determinism framework below to your actual scripts.


TL;DR

  • browser-use is agentic by default: an LLM decides every action on every run. Great for exploration; harder to make deterministic, cheap, and debuggable.

  • Stagehand gives you a spectrum: cached observe→act for stable, repeatable steps, act/extract for the parts that vary, and a full agent() only when the path is open-ended.

  • Browserbase is the cloud runtime under both. Stagehand in env: "BROWSERBASE" manages the session for you, and unlocks Contexts (persistent auth), proxies, stealth, and observability.

  • The migration is a refactor with judgment: decide, per step, how much AI you actually need. The payoff is determinism, lower cost, and debuggability — at the price of more upfront authoring.


1. The philosophy shift

browser-useStagehand
Default control modelFully agentic — LLM picks each actionYou choose, per step, how much AI to use
Unit of workA natural-language taskPrimitives: act, extract, observe, agent
Page perceptionIndexed DOM/accessibility tree (vision optional)Targeted per-call; DOM by default, vision in agent hybrid/cua modes
DeterminismHard — every run re-reasonsA dial: from cached, replayable actions to full agent
Best atOpen-ended exploration, prototypingProduction flows you want repeatable and cheap
LanguagePythonTypeScript (also Python)

Teams usually migrate because an agentic script that was perfect for a demo becomes expensive, flaky, or impossible to debug in production. The fix isn't "a better agent" — it's using AI only where the page is actually unpredictable and making everything else deterministic.


2. Mental model: the loop vs. the primitives

browser-use runs a perceive → decide → act loop. Each step it snapshots the page into an indexed element tree, asks the LLM which action to take, executes it, and repeats until the task is done or max_steps is hit. One Agent(task="…") can hide a dozen decisions.

Stagehand exposes four primitives you compose yourself:

  • act(instruction) — perform one action ("click the login button"). One LLM call.

  • extract(instruction, schema) — pull structured (zod-typed) data off the page.

  • observe(instruction) — resolve an instruction into concrete actions without doing them; replay them later with no LLM call (the determinism trick).

  • agent().execute(instruction) — the full autonomous loop, when you genuinely need it.

Navigation to known URLs is just page.goto() on the Stagehand page (no AI), and you lock in repeatable steps by caching an observe() result and replaying it with act(). The migration is mostly deciding which primitive each browser-use step becomes.


3. Feature mapping

The most-used mappings (see the skill's api-mapping.md for the exhaustive table and before/after code):

browser-useStagehand v3 / Browserbase
Agent(task=…) + agent.run()Decompose into act/extract/observe when the flow is known; else stagehand.agent().execute(…)
llm=ChatAnthropic(model="claude-sonnet-4-6")new Stagehand({ model: "anthropic/claude-sonnet-4-6" })
output_model_schema=PydanticModelextract("…", zodSchema)
history.final_result() / .structured_outputextract(...) return / result.output
Browser() (local)new Stagehand({ env: "LOCAL", localBrowserLaunchOptions })
Browser(cdp_url=session.connect_url)new Stagehand({ env: "BROWSERBASE" })
sensitive_data={…}act("…%key%…", { variables: { key } })
storage_state / user_data_dirBrowserbase Context (browserSettings.context: { id, persist: true })
proxies / stealth / captchabrowserbaseSessionCreateParams (proxies, browserSettings.advancedStealth, solveCaptchas)
@tools.action custom actionplain TS (deterministic), or agent({ tools }) (agent capability)
max_stepsagent().execute({ maxSteps })
allowed_domains⚠️ no direct equivalent — see Gotchas

4. The determinism spectrum (the important part)

Every browser-use step lands somewhere on this spectrum. Choosing well is the whole game. (The skill's determinism.md has the decision tree.)

LevelStagehandUse whenCost / determinism
1. Autonomousagent().execute("…")Path is open-ended / unknownHighest cost, lowest determinism (≈ browser-use)
2. Per-step AIact("…"), extract("…", schema)Steps known, markup variesOne call/step; inspectable
3. Observe → actobserve() then act(action)Known steps you want to replayact(action) makes no LLM call
4. Self-heal + cacheselfHeal, cacheDir, serverCacheProduction replay that tolerates DOM driftCheapest steady state
5. Navigation (no AI)page.goto(url), page.url()Loading a known URL (element interactions are Levels 2–4)No AI, no cost

A healthy rewrite is a mix: page.goto for navigation + cached observe→act for the repeatable skeleton + per-step act/extract for the variable bits + a Context for auth. Reserve agent() for the one genuinely open-ended stretch, if any.

Example decomposition. browser-use: Agent(task="Go to the store, search 'wireless mouse', add the cheapest to cart, checkout with my saved card") becomes: page.goto(store) (L5) → act("search 'wireless mouse'") (L2) → extract prices, pick the min in code, act("add to cart") (L2 + code) → checkout via a Browserbase Context so the card/session is already authenticated (L3/4). One opaque agent run → an inspectable pipeline.


You don't have to rewrite everything at once. A low-risk sequence:

  1. Lift-and-shift onto Browserbase (no rewrite). Point the existing browser-use script at a Browserbase session via cdp_url=session.connect_url. You immediately gain observability, proxies, and stealth, and you establish a behavior baseline. (Optional but recommended first step — it de-risks everything after.)

  2. Observe (optional, for opaque scripts). Run it once on Browserbase and read the Session Logs API (sessions.logs.list) to see the real navigations and network calls. Use the video recording for human QA. See the skill's trace-assisted.md.

  3. Rewrite incrementally in Stagehand. Translate the skeleton — navigation to page.goto, repeatable steps to cached observe→act — the variable steps to act/extract, and only the open-ended parts to agent(). Move auth to a Context, secrets to variables.

  4. Validate against the baseline. Run the Stagehand version on Browserbase and compare logs and end state to step 1. Reuse the same Context so you're comparing like with like.

  5. Harden for production. Turn on selfHeal + caching, pin models, scope extracts with selector, lock the viewport, wait for domcontentloaded (never networkidle) before AI snapshots.


6. A worked example

Before — browser-use (Python)

from pydantic import BaseModel
from browser_use import Agent, ChatOpenAI

class Story(BaseModel):
    title: str
    points: int

class Stories(BaseModel):
    stories: list[Story]

agent = Agent(
    task="Go to Hacker News and return the top 5 stories with their title and points",
    llm=ChatOpenAI(model="gpt-5"),
    output_model_schema=Stories,
)
history = await agent.run()
print(history.structured_output)

After — Stagehand v3 (TypeScript) on Browserbase

import "dotenv/config";
import { Stagehand } from "@browserbasehq/stagehand";
import { z } from "zod";

async function main() {
  const stagehand = new Stagehand({ env: "BROWSERBASE", model: "openai/gpt-5" });
  await stagehand.init();
  try {
    const page = stagehand.context.pages()[0];
    await page.goto("https://news.ycombinator.com");          // deterministic

    const stories = await stagehand.extract(                   // structured read
      "extract the top 5 stories with their title and points",
      z.array(z.object({ title: z.string(), points: z.number() })),
    );
    console.log(stories);
  } finally {
    await stagehand.close();
  }
}

main().catch((err) => { console.error(err); process.exit(1); });

The navigation that the agent used to "decide" is now an explicit page.goto, and the data read is a single typed extract instead of a full agent loop — faster, cheaper, and deterministic. See EXAMPLES.md for more before/after pairs (simple task, structured extraction, login).


7. What you gain from Browserbase

These are often the real reason to migrate, beyond determinism:

  • Contexts — persist auth/cookies across runs; log in once, reuse everywhere. The biggest reliability win in most migrations (no more brittle login flows every run).

  • Proxies — residential/datacenter, with geo and domain rules.

  • Stealth & Verified Sessions — maintained fingerprints for sites that fight automation.

  • Captcha solving — on by default.

  • Observability — Session Replay (video), Session Inspector, and the Logs API (CDP network/console/lifecycle events) for debugging and validation.

All reachable from Stagehand via browserbaseSessionCreateParams.


8. Gotchas & version notes

  • Stagehand v3 is a rewrite. AI methods are on the instance (stagehand.act/extract/observe), not the page; get the page via stagehand.context.pages()[0]; models are "provider/model" strings. Most examples online are v2 (page.act(), stagehand.page) — don't copy them. See the v2→v3 migration guide.

  • browser-use has three API shapes — legacy (pre-0.12), stable (0.12.x), and the 0.13 Rust beta (browser_use.beta). Identify which the source uses before translating; class names differ (Browser ≡ BrowserSession, Controller → Tools).

  • allowed_domains has no Stagehand equivalent. browser-use can hard-block off-domain navigation (and pairs it with sensitive_data as a security boundary). In Stagehand, constrain the agent via systemPrompt, use Browserbase proxy domain rules, or check page.url() in code — and treat it as a deliberate review item, not an automatic drop.

  • Don't translate everything into agent(). That just reproduces browser-use's non-determinism. Decompose where the flow is known.

  • Secrets — use variables + environment variables; never hardcode. Prefer Contexts for auth.


9. Resources

Stagehand

Browserbase

browser-use

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an MCP server, Needs an API key

The author says: The skill itself uses only Read/Write/Edit/Grep/Bash — no install step. The Stagehand code it generates needs Node 18+, `@browserbasehq/stagehand` (v3) and `zod`, plus `BROWSERBASE_API_KEY` / `BROWSERBASE_PROJECT_ID` and a model-provider key (e.g. `ANTHROPIC_API_KEY`) to run. The optional trace-assisted path uses the Browserbase SDK or the sibling `browser-trace` skill.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Browserbase
Publisher
Browserbase (Official)
Source
browserbase/skills · skills/browser-use-to-stagehand · commit 6811ca3
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
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