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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>
140 lines
5.3 KiB
Plaintext
140 lines
5.3 KiB
Plaintext
---
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title: Scrape Element From Website Tool
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description: The `ScrapeElementFromWebsiteTool` enables CrewAI agents to extract specific elements from websites using CSS selectors.
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icon: code
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mode: "wide"
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---
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# `ScrapeElementFromWebsiteTool`
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## Description
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The `ScrapeElementFromWebsiteTool` is designed to extract specific elements from websites using CSS selectors. This tool allows CrewAI agents to scrape targeted content from web pages, making it useful for data extraction tasks where only specific parts of a webpage are needed.
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## Installation
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To use this tool, you need to install the required dependencies:
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```shell
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uv add requests beautifulsoup4
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```
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## Steps to Get Started
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To effectively use the `ScrapeElementFromWebsiteTool`, follow these steps:
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1. **Install Dependencies**: Install the required packages using the command above.
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2. **Identify CSS Selectors**: Determine the CSS selectors for the elements you want to extract from the website.
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3. **Initialize the Tool**: Create an instance of the tool with the necessary parameters.
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## Example
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The following example demonstrates how to use the `ScrapeElementFromWebsiteTool` to extract specific elements from a website:
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools import ScrapeElementFromWebsiteTool
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# Initialize the tool
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scrape_tool = ScrapeElementFromWebsiteTool()
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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 specific information from websites",
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backstory="An expert in web scraping who can extract targeted content from web pages.",
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tools=[scrape_tool],
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verbose=True,
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)
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# Example task to extract headlines from a news website
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scrape_task = Task(
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description="Extract the main headlines from the CNN homepage. Use the CSS selector '.headline' to target the headline elements.",
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expected_output="A list of the main headlines from CNN.",
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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(agents=[web_scraper_agent], tasks=[scrape_task])
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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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scrape_tool = ScrapeElementFromWebsiteTool(
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website_url="https://www.example.com",
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css_element=".main-content"
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)
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```
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## Parameters
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The `ScrapeElementFromWebsiteTool` 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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- **cookies**: Optional. A dictionary containing cookies to be sent with the request. This can be useful for websites that require authentication.
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## Usage
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When using the `ScrapeElementFromWebsiteTool` with an agent, the agent will need to provide the following parameters (unless they were specified during initialization):
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- **website_url**: The URL of the website to scrape.
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- **css_element**: The CSS selector for the elements to extract.
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The tool will return the text content of all elements matching the CSS selector, joined by newlines.
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```python Code
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# Example of using the tool with an agent
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract specific elements from websites",
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backstory="An expert in web scraping who can extract targeted content using CSS selectors.",
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tools=[scrape_tool],
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verbose=True,
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)
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# Create a task for the agent to extract specific elements
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extract_task = Task(
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description="""
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Extract all product titles from the featured products section on example.com.
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Use the CSS selector '.product-title' to target the title elements.
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""",
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expected_output="A list of product titles from the website",
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agent=web_scraper_agent,
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)
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# Run the task through a crew
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crew = Crew(agents=[web_scraper_agent], tasks=[extract_task])
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result = crew.kickoff()
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```
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## Implementation Details
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The `ScrapeElementFromWebsiteTool` uses the `requests` library to fetch the web page and `BeautifulSoup` to parse the HTML and extract the specified elements:
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```python Code
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class ScrapeElementFromWebsiteTool(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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# Implementation details...
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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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page = requests.get(
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website_url,
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headers=self.headers,
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cookies=self.cookies if self.cookies else {},
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)
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parsed = BeautifulSoup(page.content, "html.parser")
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elements = parsed.select(css_element)
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return "\n".join([element.get_text() for element in elements])
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```
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## Conclusion
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The `ScrapeElementFromWebsiteTool` provides a powerful way to extract specific elements from websites using CSS selectors. By enabling agents to target only the content they need, it makes web scraping tasks more efficient and focused. This tool is particularly useful for data extraction, content monitoring, and research tasks where specific information needs to be extracted from web pages. |