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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>
221 lines
8.3 KiB
Plaintext
221 lines
8.3 KiB
Plaintext
---
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title: Scrapfly Scrape Website Tool
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description: The `ScrapflyScrapeWebsiteTool` leverages Scrapfly's web scraping API to extract content from websites in various formats.
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icon: spider
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mode: "wide"
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---
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# `ScrapflyScrapeWebsiteTool`
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## Description
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The `ScrapflyScrapeWebsiteTool` is designed to leverage [Scrapfly](https://scrapfly.io/)'s web scraping API to extract content from websites. This tool provides advanced web scraping capabilities with headless browser support, proxies, and anti-bot bypass features. It allows for extracting web page data in various formats, including raw HTML, markdown, and plain text, making it ideal for a wide range of web scraping tasks.
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## Installation
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To use this tool, you need to install the Scrapfly SDK:
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```shell
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uv add scrapfly-sdk
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```
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You'll also need to obtain a Scrapfly API key by registering at [scrapfly.io/register](https://www.scrapfly.io/register/).
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## Steps to Get Started
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To effectively use the `ScrapflyScrapeWebsiteTool`, follow these steps:
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1. **Install Dependencies**: Install the Scrapfly SDK using the command above.
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2. **Obtain API Key**: Register at Scrapfly to get your API key.
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3. **Initialize the Tool**: Create an instance of the tool with your API key.
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4. **Configure Scraping Parameters**: Customize the scraping parameters based on your needs.
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## Example
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The following example demonstrates how to use the `ScrapflyScrapeWebsiteTool` to extract content 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 ScrapflyScrapeWebsiteTool
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# Initialize the tool
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scrape_tool = ScrapflyScrapeWebsiteTool(api_key="your_scrapfly_api_key")
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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",
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backstory="An expert in web scraping who can extract content from any website.",
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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 content from a website
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scrape_task = Task(
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description="Extract the main content from the product page at https://web-scraping.dev/products and summarize the available products.",
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expected_output="A summary of the products available on the website.",
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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 customize the scraping parameters:
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```python Code
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# Example with custom scraping parameters
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web_scraper_agent = Agent(
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role="Web Scraper",
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goal="Extract information from websites with custom parameters",
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backstory="An expert in web scraping who can extract content from any website.",
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tools=[scrape_tool],
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verbose=True,
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)
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# The agent will use the tool with parameters like:
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# url="https://web-scraping.dev/products"
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# scrape_format="markdown"
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# ignore_scrape_failures=True
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# scrape_config={
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# "asp": True, # Bypass scraping blocking solutions, like Cloudflare
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# "render_js": True, # Enable JavaScript rendering with a cloud headless browser
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# "proxy_pool": "public_residential_pool", # Select a proxy pool
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# "country": "us", # Select a proxy location
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# "auto_scroll": True, # Auto scroll the page
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# }
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scrape_task = Task(
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description="Extract the main content from the product page at https://web-scraping.dev/products using advanced scraping options including JavaScript rendering and proxy settings.",
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expected_output="A detailed summary of the products with all available information.",
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agent=web_scraper_agent,
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)
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```
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## Parameters
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The `ScrapflyScrapeWebsiteTool` accepts the following parameters:
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### Initialization Parameters
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- **api_key**: Required. Your Scrapfly API key.
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### Run Parameters
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- **url**: Required. The URL of the website to scrape.
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- **scrape_format**: Optional. The format in which to extract the web page content. Options are "raw" (HTML), "markdown", or "text". Default is "markdown".
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- **scrape_config**: Optional. A dictionary containing additional Scrapfly scraping configuration options.
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- **ignore_scrape_failures**: Optional. Whether to ignore failures during scraping. If set to `True`, the tool will return `None` instead of raising an exception when scraping fails.
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## Scrapfly Configuration Options
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The `scrape_config` parameter allows you to customize the scraping behavior with the following options:
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- **asp**: Enable anti-scraping protection bypass.
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- **render_js**: Enable JavaScript rendering with a cloud headless browser.
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- **proxy_pool**: Select a proxy pool (e.g., "public_residential_pool", "datacenter").
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- **country**: Select a proxy location (e.g., "us", "uk").
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- **auto_scroll**: Automatically scroll the page to load lazy-loaded content.
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- **js**: Execute custom JavaScript code by the headless browser.
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For a complete list of configuration options, refer to the [Scrapfly API documentation](https://scrapfly.io/docs/scrape-api/getting-started).
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## Usage
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When using the `ScrapflyScrapeWebsiteTool` with an agent, the agent will need to provide the URL of the website to scrape and can optionally specify the format and additional configuration options:
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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 information from websites",
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backstory="An expert in web scraping who can extract content from any website.",
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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
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scrape_task = Task(
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description="Extract the main content from example.com in markdown format.",
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expected_output="The main content of example.com in markdown format.",
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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(agents=[web_scraper_agent], tasks=[scrape_task])
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result = crew.kickoff()
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```
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For more advanced usage with custom configuration:
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```python Code
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# Create a task with more specific instructions
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advanced_scrape_task = Task(
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description="""
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Extract content from example.com with the following requirements:
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- Convert the content to plain text format
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- Enable JavaScript rendering
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- Use a US-based proxy
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- Handle any scraping failures gracefully
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""",
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expected_output="The extracted content from example.com",
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agent=web_scraper_agent,
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)
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```
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## Error Handling
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By default, the `ScrapflyScrapeWebsiteTool` will raise an exception if scraping fails. Agents can be instructed to handle failures gracefully by specifying the `ignore_scrape_failures` parameter:
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```python Code
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# Create a task that instructs the agent to handle errors
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error_handling_task = Task(
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description="""
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Extract content from a potentially problematic website and make sure to handle any
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scraping failures gracefully by setting ignore_scrape_failures to True.
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""",
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expected_output="Either the extracted content or a graceful error message",
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agent=web_scraper_agent,
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)
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```
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## Implementation Details
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The `ScrapflyScrapeWebsiteTool` uses the Scrapfly SDK to interact with the Scrapfly API:
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```python Code
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class ScrapflyScrapeWebsiteTool(BaseTool):
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name: str = "Scrapfly web scraping API tool"
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description: str = (
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"Scrape a webpage url using Scrapfly and return its content as markdown or text"
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)
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# Implementation details...
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def _run(
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self,
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url: str,
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scrape_format: str = "markdown",
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scrape_config: Optional[Dict[str, Any]] = None,
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ignore_scrape_failures: Optional[bool] = None,
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):
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from scrapfly import ScrapeApiResponse, ScrapeConfig
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scrape_config = scrape_config if scrape_config is not None else {}
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try:
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response: ScrapeApiResponse = self.scrapfly.scrape(
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ScrapeConfig(url, format=scrape_format, **scrape_config)
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)
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return response.scrape_result["content"]
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except Exception as e:
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if ignore_scrape_failures:
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logger.error(f"Error fetching data from {url}, exception: {e}")
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return None
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else:
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raise e
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```
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## Conclusion
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The `ScrapflyScrapeWebsiteTool` provides a powerful way to extract content from websites using Scrapfly's advanced web scraping capabilities. With features like headless browser support, proxies, and anti-bot bypass, it can handle complex websites and extract content in various formats. This tool is particularly useful for data extraction, content monitoring, and research tasks where reliable web scraping is required. |