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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
197 lines
7.3 KiB
Plaintext
197 lines
7.3 KiB
Plaintext
---
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title: Selenium Scraper
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description: The `SeleniumScrapingTool` is designed to extract and read the content of a specified website using Selenium.
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icon: clipboard-user
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mode: "wide"
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---
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# `SeleniumScrapingTool`
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<Note>
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This tool is currently in development. As we refine its capabilities, users may encounter unexpected behavior.
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Your feedback is invaluable to us for making improvements.
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</Note>
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## Description
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The `SeleniumScrapingTool` is crafted for high-efficiency web scraping tasks.
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It allows for precise extraction of content from web pages by using CSS selectors to target specific elements.
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Its design caters to a wide range of scraping needs, offering flexibility to work with any provided website URL.
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## Installation
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To use this tool, you need to install the CrewAI tools package and Selenium:
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```shell
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pip install 'crewai[tools]'
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uv add selenium webdriver-manager
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```
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You'll also need to have Chrome installed on your system, as the tool uses Chrome WebDriver for browser automation.
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## Example
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The following example demonstrates how to use the `SeleniumScrapingTool` with a CrewAI agent:
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```python Code
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from crewai import Agent, Task, Crew, Process
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from crewai_tools import SeleniumScrapingTool
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# Initialize the tool
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selenium_tool = SeleniumScrapingTool()
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# Define an agent that uses the tool
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract information from websites using Selenium",
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backstory="An expert web scraper who can extract content from dynamic websites.",
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tools=[selenium_tool],
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verbose=True,
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)
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# Example task to scrape content from a website
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scrape_task = Task(
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description="Extract the main content from the homepage of example.com. Use the CSS selector 'main' to target the main content area.",
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expected_output="The main content from example.com's homepage.",
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agent=web_scraper_agent,
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)
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# Create and run the crew
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crew = Crew(
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agents=[web_scraper_agent],
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tasks=[scrape_task],
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verbose=True,
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process=Process.sequential,
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)
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result = crew.kickoff()
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```
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You can also initialize the tool with predefined parameters:
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```python Code
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# Initialize the tool with predefined parameters
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selenium_tool = SeleniumScrapingTool(
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website_url='https://example.com',
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css_element='.main-content',
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wait_time=5
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)
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# Define an agent that uses the tool
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract information from websites using Selenium",
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backstory="An expert web scraper who can extract content from dynamic websites.",
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tools=[selenium_tool],
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verbose=True,
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)
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```
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## Parameters
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The `SeleniumScrapingTool` accepts the following parameters during initialization:
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- **website_url**: Optional. The URL of the website to scrape. If provided during initialization, the agent won't need to specify it when using the tool.
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- **css_element**: Optional. The CSS selector for the elements to extract. If provided during initialization, the agent won't need to specify it when using the tool.
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- **cookie**: Optional. A dictionary containing cookie information, useful for simulating a logged-in session to access restricted content.
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- **wait_time**: Optional. Specifies the delay (in seconds) before scraping, allowing the website and any dynamic content to fully load. Default is `3` seconds.
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- **return_html**: Optional. Whether to return the HTML content instead of just the text. Default is `False`.
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When using the tool with an agent, the agent will need to provide the following parameters (unless they were specified during initialization):
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- **website_url**: Required. The URL of the website to scrape.
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- **css_element**: Required. The CSS selector for the elements to extract.
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## Agent Integration Example
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Here's a more detailed example of how to integrate the `SeleniumScrapingTool` with a CrewAI agent:
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```python Code
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from crewai import Agent, Task, Crew, Process
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from crewai_tools import SeleniumScrapingTool
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# Initialize the tool
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selenium_tool = SeleniumScrapingTool()
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# Define an agent that uses the tool
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract and analyze information from dynamic websites",
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backstory="""You are an expert web scraper who specializes in extracting
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content from dynamic websites that require browser automation. You have
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extensive knowledge of CSS selectors and can identify the right selectors
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to target specific content on any website.""",
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tools=[selenium_tool],
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verbose=True,
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)
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# Create a task for the agent
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scrape_task = Task(
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description="""
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Extract the following information from the news website at {website_url}:
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1. The headlines of all featured articles (CSS selector: '.headline')
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2. The publication dates of these articles (CSS selector: '.pub-date')
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3. The author names where available (CSS selector: '.author')
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Compile this information into a structured format with each article's details grouped together.
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""",
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expected_output="A structured list of articles with their headlines, publication dates, and authors.",
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agent=web_scraper_agent,
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)
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# Run the task
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crew = Crew(
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agents=[web_scraper_agent],
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tasks=[scrape_task],
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verbose=True,
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process=Process.sequential,
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)
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result = crew.kickoff(inputs={"website_url": "https://news-example.com"})
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```
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## Implementation Details
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The `SeleniumScrapingTool` uses Selenium WebDriver to automate browser interactions:
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```python Code
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class SeleniumScrapingTool(BaseTool):
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name: str = "Read a website content"
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description: str = "A tool that can be used to read a website content."
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args_schema: Type[BaseModel] = SeleniumScrapingToolSchema
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def _run(self, **kwargs: Any) -> Any:
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website_url = kwargs.get("website_url", self.website_url)
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css_element = kwargs.get("css_element", self.css_element)
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return_html = kwargs.get("return_html", self.return_html)
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driver = self._create_driver(website_url, self.cookie, self.wait_time)
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content = self._get_content(driver, css_element, return_html)
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driver.close()
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return "\n".join(content)
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```
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The tool performs the following steps:
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1. Creates a headless Chrome browser instance
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2. Navigates to the specified URL
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3. Waits for the specified time to allow the page to load
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4. Adds any cookies if provided
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5. Extracts content based on the CSS selector
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6. Returns the extracted content as text or HTML
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7. Closes the browser instance
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## Handling Dynamic Content
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The `SeleniumScrapingTool` is particularly useful for scraping websites with dynamic content that is loaded via JavaScript. By using a real browser instance, it can:
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1. Execute JavaScript on the page
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2. Wait for dynamic content to load
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3. Interact with elements if needed
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4. Extract content that would not be available with simple HTTP requests
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You can adjust the `wait_time` parameter to ensure that all dynamic content has loaded before extraction.
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## Conclusion
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The `SeleniumScrapingTool` provides a powerful way to extract content from websites using browser automation. By enabling agents to interact with websites as a real user would, it facilitates scraping of dynamic content that would be difficult or impossible to extract using simpler methods. This tool is particularly useful for research, data collection, and monitoring tasks that involve modern web applications with JavaScript-rendered content.
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