Playwright is to support the intercept Request

[sundaqiang]

Playwright is to support the intercept Request and Response (https://playwright.dev/python/docs/api/class-request), whether we can realize the following functions.

1. Wait for a Response and then stop
2. Wait for a Response and then act on the page (e.g. glide)
3. Returns the html and Response
4. Return html
5. Return Response
6. Return the Response whitelist
7. Return the Response blacklist

[unclecode]

Hi @sundaqiang, I like your question, and I think other people might have similar questions, so I will go into very detailed answers for you and for future reference.

Crawl4AI uses Playwright under the hood, so you still have full access to Playwright’s request/response events and methods. By setting hooks at strategic points in the crawling flow, you can wait for specific responses, return them, or filter them. For example, you might use the after_goto or before_return_html hooks to register response listeners or wait for certain responses before proceeding.

Examples:

1. Wait for a Specific Response and Then Stop:
   In your custom hook, you can do something like:

   async def my_after_goto_hook(page, context):
       # Wait for a specific API response before continuing
       await page.wait_for_response(lambda r: "api/important.json" in r.url)
       # At this point, you can choose to stop the crawl or proceed with other actions.

2. Wait for a Response and Then Act on the Page:
   Once you get the response you’re interested in, simply perform additional page actions:

   async def my_after_goto_hook(page, context):
       await page.wait_for_response(lambda r: "data_done" in r.url)
       # Now that the desired response has arrived, perform your page action:
       await page.evaluate("window.scrollBy(0,1000)")  # e.g. scroll or glide

3. Return HTML and the Response:
   After waiting for the response:

   async def before_return_html_hook(page, html, context):
       response = await page.wait_for_response(lambda r: "special_resource" in r.url)
       # Now you have both the HTML and the specific response
       # You can store them or return them from your custom crawl function
       return html, response

4. Return HTML Only:
   If you just need the HTML after certain conditions:

   async def before_return_html_hook(page, html, context):
       # Possibly wait for some response or condition here
       return html  # Just return the HTML at the end

5. Return Only a Response:
   Similar to above, you can just return a waited-upon response:

   async def my_after_goto_hook(page, context):
       response = await page.wait_for_response(lambda r: "endpoint" in r.url)
       return response  # Return or store it for later use

6. Whitelist Certain Responses:
   Register a response listener to capture only certain responses:

   allowed_responses = []
   
   async def on_response(response):
       if "example.com/api" in response.url:
           allowed_responses.append(response)
   
   async def my_after_goto_hook(page, context):
       page.on("response", on_response)
       # At the end, allowed_responses will contain only responses matching the whitelist

7. Blacklist Certain Responses:
   The logic is the same as whitelist, but you filter and store responses that you don’t want, or you just ignore them at runtime.

How to Integrate Into Crawl4AI:

• Set hooks using crawler_strategy.set_hook('after_goto', my_after_goto_hook) or crawler_strategy.set_hook('before_return_html', before_return_html_hook).
• Within these hooks, you have access to the Playwright page object and can use all Playwright methods (like page.on('response', ...), page.wait_for_response(...), and page.content()).

[sundaqiang]

Hi @sundaqiang, I like your question, and I think other people might have similar questions, so I will go into very detailed answers for you and for future reference.

Crawl4AI uses Playwright under the hood, so you still have full access to Playwright’s request/response events and methods. By setting hooks at strategic points in the crawling flow, you can wait for specific responses, return them, or filter them. For example, you might use the after_goto or before_return_html hooks to register response listeners or wait for certain responses before proceeding.

Examples:

1. Wait for a Specific Response and Then Stop:
   In your custom hook, you can do something like:

   async def my_after_goto_hook(page, context):
       # Wait for a specific API response before continuing
       await page.wait_for_response(lambda r: "api/important.json" in r.url)
       # At this point, you can choose to stop the crawl or proceed with other actions.

2. Wait for a Response and Then Act on the Page:
   Once you get the response you’re interested in, simply perform additional page actions:

   async def my_after_goto_hook(page, context):
       await page.wait_for_response(lambda r: "data_done" in r.url)
       # Now that the desired response has arrived, perform your page action:
       await page.evaluate("window.scrollBy(0,1000)")  # e.g. scroll or glide

3. Return HTML and the Response:
   After waiting for the response:

   async def before_return_html_hook(page, html, context):
       response = await page.wait_for_response(lambda r: "special_resource" in r.url)
       # Now you have both the HTML and the specific response
       # You can store them or return them from your custom crawl function
       return html, response

4. Return HTML Only:
   If you just need the HTML after certain conditions:

   async def before_return_html_hook(page, html, context):
       # Possibly wait for some response or condition here
       return html  # Just return the HTML at the end

5. Return Only a Response:
   Similar to above, you can just return a waited-upon response:

   async def my_after_goto_hook(page, context):
       response = await page.wait_for_response(lambda r: "endpoint" in r.url)
       return response  # Return or store it for later use

6. Whitelist Certain Responses:
   Register a response listener to capture only certain responses:

   allowed_responses = []
   
   async def on_response(response):
       if "example.com/api" in response.url:
           allowed_responses.append(response)
   
   async def my_after_goto_hook(page, context):
       page.on("response", on_response)
       # At the end, allowed_responses will contain only responses matching the whitelist

7. Blacklist Certain Responses:
   The logic is the same as whitelist, but you filter and store responses that you don’t want, or you just ignore them at runtime.

How to Integrate Into Crawl4AI:

• Set hooks using crawler_strategy.set_hook('after_goto', my_after_goto_hook) or crawler_strategy.set_hook('before_return_html', before_return_html_hook).
• Within these hooks, you have access to the Playwright page object and can use all Playwright methods (like page.on('response', ...), page.wait_for_response(...), and page.content()).

Thank you very much for your answer. I have read the document carefully and it is true that it can be realized as you said. However, if deployed by docker, the api interface cannot implement hooks.

[sundaqiang]

The reason for this is that sometimes when crawling data, it is more efficient to fetch the API directly than to analyze the page. Why not just crawl the API, some of which require complex algorithms!

[unclecode]

@sundaqiang I very sorry, as I can't get exactly your point here. I am confused on the way you use term "api", are you referring to Crawl4ai server or Playwright library. Would you please explain more.

[sundaqiang]

@sundaqiang I very sorry, as I can't get exactly your point here. I am confused on the way you use term "api", are you referring to Crawl4ai server or Playwright library. Would you please explain more.

I'm talking about the api provided by the docker version of Crawl4ai

[unclecode]

Okay, now I understand. If you’re running Crawl4AI via Docker and want to use hooks, you need to understand that hooks are code-level features and are not directly configurable through the API. The standard Dockerized setup acts as a black-box service, so you can’t just send hook logic through its API. Instead, you have two options:

1. Run Crawl4AI as a Python library: Integrate Crawl4AI into your own Python code, set the hooks directly in your code before starting to crawl. This gives you full flexibility.

2. Create a custom Docker image with hooks:

   • Start from the official Crawl4AI Docker image as your base.
   • Copy a custom Python file (e.g. my_custom_hooks.py) into the Docker image that defines and registers your hooks.
   • Modify or replace the default startup script (e.g. main.py) so that it imports and registers these hooks before launching the API server.

   For example:

   Dockerfile:

   FROM crawl4ai:latest
   WORKDIR /app
   COPY my_custom_hooks.py .
   COPY main.py .

   my_custom_hooks.py:

   def on_execution_started(page, context=None):
       # Your custom logic
       pass
   
   def register_hooks(crawler):
       crawler.crawler_strategy.set_hook("on_execution_started", on_execution_started)

   main.py (pseudocode):

   from crawl4ai import create_crawl4ai_app
   from my_custom_hooks import register_hooks
   
   app = create_crawl4ai_app()
   register_hooks(app.crawler)  # Inject your hooks here
   
   # Then start the server as usual

   Build and run this image. Now your Dockerized Crawl4AI instance includes your custom hooks from the start. You’ll still interact with it via the API, but the internal crawl logic will follow the hooks you defined.

[unclecode]

Topic of this issue, is relevant to one of my future plan, so I document it here.

Introducing Event Streams and Interactive Hooks in Crawl4AI

In the near future, I’m planning to enhance Crawl4AI’s capabilities by introducing an event stream mechanism that will give clients deeper, real-time insights into the crawling process. Today, hooks are a powerful feature at the code level—they let developers define custom logic at key points in the crawl. However, when using Crawl4AI as a service (e.g., through a Dockerized API), there isn’t an easy way to interact with these hooks at runtime.

What’s Changing?

I’m working on a solution that will allow the crawler to emit a continuous stream of events, updating clients on the current crawling stage, encountered pages, and any decision points. This event stream could be exposed over a standardized protocol like Server-Sent Events (SSE) or WebSockets, enabling clients to “subscribe” and listen as the crawler works.

Interactivity Through Process IDs

A key part of this new design is the concept of a unique process ID for each crawl session. Imagine you’re listening to an event stream that informs you:

• The crawler just hit a certain page
• It triggered a hook and is now pausing for instructions

With the event stream in place, you can send a follow-up request back to the server—referencing the unique process ID—to provide extra data, instructions, or parameters. This might include selecting which links to follow next, adjusting extraction strategies, or providing authentication tokens for a protected API. Once the crawler receives these instructions, it resumes execution with the updated context.

sequenceDiagram
    participant Client
    participant Server
    participant Crawler

    Client->>Server: Start crawl request
    Server->>Crawler: Initiate crawl with Process ID
    Crawler-->>Server: Event: Page hit
    Server-->>Client: Stream: Page hit event
    Client->>Server: Instruction for Process ID
    Server->>Crawler: Update crawl with new instructions
    Crawler-->>Server: Event: Crawl completed
    Server-->>Client: Stream: Crawl completed

Loading

Benefits for Developers and Users

1. Fine-Grained Control: Instead of predefining all logic upfront, you can dynamically guide the crawler in response to actual data and conditions encountered mid-crawl.
2. Real-Time Insights: Monitor progress, errors, or network bottlenecks as they happen, without waiting for the entire crawl to finish.
3. Enhanced Collaboration: Different team members or automated systems can watch the same crawl events and provide input, making the crawling process more adaptive and intelligent.

Next Steps

I’m currently exploring the best APIs, technologies, and patterns to make this vision a reality. My goal is to deliver a seamless developer experience—one that integrates with existing Crawl4AI workflows while offering new flexibility and power.

Stay tuned for more updates as I continue building this feature out. In the meantime, I’d love to hear any feedback or suggestions you might have to help shape this interactive, event-driven future of web crawling with Crawl4AI.

[sundaqiang]

这个问题的主题与我未来的计划之一有关，所以我在这里记录下来。

在 Crawl4AI 中引入事件流和交互式钩子

在不久的将来，我计划通过引入事件流机制来增强 Crawl4AI 的功能，该机制将使客户能够更深入地、实时地了解爬取过程。如今，钩子是代码级别的强大功能——它们允许开发人员在爬取的关键点定义自定义逻辑。但是，当使用 Crawl4AI 作为服务时（例如，通过 Dockerized API），没有一种简单的方法可以在运行时与这些钩子进行交互。

有什么变化？

我正在研究一种解决方案，该解决方案将允许爬网程序发出连续的事件流，更新当前爬网阶段的客户端、遇到的页面和任何决策点。此事件流可以通过标准化协议（如服务器发送事件 （SSE） 或 WebSockets）公开，使客户端能够在爬网程序工作时“订阅”和侦听。

通过进程 ID 进行交互

此新设计的一个关键部分是每个爬网会话的唯一进程 ID 的概念。假设您正在侦听一个事件流，该事件流通知您：

• 爬虫刚刚点击了某个页面
• 它触发了一个钩子，现在正在暂停以获取指令

事件流就位后，您可以向服务器发送后续请求（引用唯一的进程 ID），以提供额外的数据、说明或参数。这可能包括选择接下来要跟踪的链接、调整提取策略或为受保护的 API 提供身份验证令牌。爬网程序收到这些指令后，将使用更新的上下文恢复执行。

sequenceDiagram
    participant Client
    participant Server
    participant Crawler

    Client->>Server: Start crawl request
    Server->>Crawler: Initiate crawl with Process ID
    Crawler-->>Server: Event: Page hit
    Server-->>Client: Stream: Page hit event
    Client->>Server: Instruction for Process ID
    Server->>Crawler: Update crawl with new instructions
    Crawler-->>Server: Event: Crawl completed
    Server-->>Client: Stream: Crawl completed

Loading

对开发人员和用户的好处

1. 精细控制：您可以动态引导爬网程序响应爬网过程中遇到的实际数据和条件，而不是预先预定义所有逻辑。
2. 实时洞察：在进度、错误或网络瓶颈发生时进行监控，而无需等待整个爬网完成。
3. 增强协作：不同的团队成员或自动化系统可以观看相同的爬网事件并提供输入，从而使爬网过程更具适应性和智能性。

后续步骤

我目前正在探索最好的 API、技术和模式，以实现这一愿景。我的目标是提供无缝的开发人员体验——一种与现有 Crawl4AI 工作流程集成，同时提供新的灵活性和强大功能的体验。

请继续关注更多更新，因为我将继续构建此功能。与此同时，我很想听听您可能有的任何反馈或建议，以帮助使用 Crawl4AI 塑造这个交互式、事件驱动的网络爬虫未来。

Topic of this issue, is relevant to one of my future plan, so I document it here.

Introducing Event Streams and Interactive Hooks in Crawl4AI

In the near future, I’m planning to enhance Crawl4AI’s capabilities by introducing an event stream mechanism that will give clients deeper, real-time insights into the crawling process. Today, hooks are a powerful feature at the code level—they let developers define custom logic at key points in the crawl. However, when using Crawl4AI as a service (e.g., through a Dockerized API), there isn’t an easy way to interact with these hooks at runtime.

What’s Changing?

I’m working on a solution that will allow the crawler to emit a continuous stream of events, updating clients on the current crawling stage, encountered pages, and any decision points. This event stream could be exposed over a standardized protocol like Server-Sent Events (SSE) or WebSockets, enabling clients to “subscribe” and listen as the crawler works.

Interactivity Through Process IDs

A key part of this new design is the concept of a unique process ID for each crawl session. Imagine you’re listening to an event stream that informs you:

• The crawler just hit a certain page
• It triggered a hook and is now pausing for instructions

With the event stream in place, you can send a follow-up request back to the server—referencing the unique process ID—to provide extra data, instructions, or parameters. This might include selecting which links to follow next, adjusting extraction strategies, or providing authentication tokens for a protected API. Once the crawler receives these instructions, it resumes execution with the updated context.

sequenceDiagram
    participant Client
    participant Server
    participant Crawler

    Client->>Server: Start crawl request
    Server->>Crawler: Initiate crawl with Process ID
    Crawler-->>Server: Event: Page hit
    Server-->>Client: Stream: Page hit event
    Client->>Server: Instruction for Process ID
    Server->>Crawler: Update crawl with new instructions
    Crawler-->>Server: Event: Crawl completed
    Server-->>Client: Stream: Crawl completed

Loading

Benefits for Developers and Users

1. Fine-Grained Control: Instead of predefining all logic upfront, you can dynamically guide the crawler in response to actual data and conditions encountered mid-crawl.
2. Real-Time Insights: Monitor progress, errors, or network bottlenecks as they happen, without waiting for the entire crawl to finish.
3. Enhanced Collaboration: Different team members or automated systems can watch the same crawl events and provide input, making the crawling process more adaptive and intelligent.

Next Steps

I’m currently exploring the best APIs, technologies, and patterns to make this vision a reality. My goal is to deliver a seamless developer experience—one that integrates with existing Crawl4AI workflows while offering new flexibility and power.

Stay tuned for more updates as I continue building this feature out. In the meantime, I’d love to hear any feedback or suggestions you might have to help shape this interactive, event-driven future of web crawling with Crawl4AI.

Good idea!
I mean if we can provide an official hooks middleware.
It can be used directly from the Python library and has fixed parameters exposed to the docker version of Crawl4ai.
I will continue to pay attention to the update of this project, thank you very much for your reply!

[unclecode]

@sundaqiang Please follow up. This is very useful and definitely something I'm considering. Thank you.