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Web Scraping using Apify Tools

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This notebook shows how to use Apify tools with AutoGen agents to scrape data from a website and formate the output.

First we need to install the Apify SDK and the AutoGen library.

! pip install -qqq pyautogen apify-client

Setting up the LLM configuration and the Apify API key is also required.

import os

config_list = [
{"model": "gpt-4", "api_key": os.getenv("OPENAI_API_KEY")},

apify_api_key = os.getenv("APIFY_API_KEY")

Let’s define the tool for scraping data from the website using Apify actor. Read more about tool use in this tutorial chapter.

from apify_client import ApifyClient
from typing_extensions import Annotated

def scrape_page(url: Annotated[str, "The URL of the web page to scrape"]) -> Annotated[str, "Scraped content"]:
# Initialize the ApifyClient with your API token
client = ApifyClient(token=apify_api_key)

# Prepare the Actor input
run_input = {
"startUrls": [{"url": url}],
"useSitemaps": False,
"crawlerType": "playwright:firefox",
"includeUrlGlobs": [],
"excludeUrlGlobs": [],
"ignoreCanonicalUrl": False,
"maxCrawlDepth": 0,
"maxCrawlPages": 1,
"initialConcurrency": 0,
"maxConcurrency": 200,
"initialCookies": [],
"proxyConfiguration": {"useApifyProxy": True},
"maxSessionRotations": 10,
"maxRequestRetries": 5,
"requestTimeoutSecs": 60,
"dynamicContentWaitSecs": 10,
"maxScrollHeightPixels": 5000,
"removeElementsCssSelector": """nav, footer, script, style, noscript, svg,
[role=\"region\"][aria-label*=\"skip\" i],
"removeCookieWarnings": True,
"clickElementsCssSelector": '[aria-expanded="false"]',
"htmlTransformer": "readableText",
"readableTextCharThreshold": 100,
"aggressivePrune": False,
"debugMode": True,
"debugLog": True,
"saveHtml": True,
"saveMarkdown": True,
"saveFiles": False,
"saveScreenshots": False,
"maxResults": 9999999,
"clientSideMinChangePercentage": 15,
"renderingTypeDetectionPercentage": 10,

# Run the Actor and wait for it to finish
run ="aYG0l9s7dbB7j3gbS").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
text_data = ""
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
text_data += item.get("text", "") + "\n"

average_token = 0.75
max_tokens = 20000 # slightly less than max to be safe 32k
text_data = text_data[: int(average_token * max_tokens)]
return text_data

Create the agents and register the tool.

from autogen import ConversableAgent, register_function

# Create web scrapper agent.
scraper_agent = ConversableAgent(
llm_config={"config_list": config_list},
system_message="You are a web scrapper and you can scrape any web page using the tools provided. "
"Returns 'TERMINATE' when the scraping is done.",

# Create user proxy agent.
user_proxy_agent = ConversableAgent(
llm_config=False, # No LLM for this agent.
code_execution_config=False, # No code execution for this agent.
is_termination_msg=lambda x: x.get("content", "") is not None and "terminate" in x["content"].lower(),
default_auto_reply="Please continue if not finished, otherwise return 'TERMINATE'.",

# Register the function with the agents.
description="Scrape a web page and return the content.",

Start the conversation for scraping web data. We used the reflection_with_llm option for summary method to perform the formatting of the output into a desired format. The summary method is called after the conversation is completed given the complete history of the conversation.

chat_result = user_proxy_agent.initiate_chat(
message="Can you scrape for me?",
"summary_prompt": """Summarize the scraped content and format summary EXACTLY as follows:
*Company name*:
`Acme Corp`
`Company that does things.`
`Manufacturing. Retail. E-commerce.`
`Provides shareholders with value by selling products.`
`What products do they sell? How do they make money? What is their market share?`
UserProxy (to WebScraper):

Can you scrape for me?


>>>>>>>> USING AUTO REPLY...
WebScraper (to UserProxy):

***** Suggested tool call (call_0qok2jvCxOfv7HOA0oxPWneM): scrape_page *****
"url": ""


>>>>>>>> EXECUTING FUNCTION scrape_page...
UserProxy (to WebScraper):

UserProxy (to WebScraper):

***** Response from calling tool (call_0qok2jvCxOfv7HOA0oxPWneM) *****
Take your business to the next level with our features
AI Agents Suck.
We're Fixing That.
Build compliant AI agents with observability, evals, and replay analytics. No more black boxes and prompt guessing.
New! Introducing AgentOps
Three Lines of Code. Unlimited Testing.
Instant Testing + Debugging = Compliant AI Agents That Work
# Beginning of program's code (i.e.,
ao_client = agentops.Client(<INSERT YOUR API KEY HERE>)
# (optional: record specific functions)
@ao_client.record_action('sample function being record')
def sample_function(...):
Prototype to Production
Generous free limits, upgrade only when you need it.



>>>>>>>> USING AUTO REPLY...
WebScraper (to UserProxy):

Sure, here's the information from the website

- Their main value proposition is to fix bad AI Agents and replace black boxes and prompt guessing with compliant, observable AI agents that come with evals and replay analytics.
- Their latest product is AgentOps. The simple and instant testing & debugging offered promises better-performing compliant AI agents.
- Integration is easy with just three lines of code.
- They let you record specific functions.
- They provide generous free limits and you only need to upgrade when necessary.

Here's a sample of their code:
ao_client = agentops.Client(<INSERT YOUR API KEY HERE>)

# optional: record specific functions
@ao_client.record_action('sample function being record')
def sample_function(...):

This code is for sample usage of their libraries/functions.

Let me know if you need more specific details.

UserProxy (to WebScraper):

Please continue if not finished, otherwise return 'TERMINATE'.


>>>>>>>> USING AUTO REPLY...
WebScraper (to UserProxy):



The output is stored in the summary.

*Company name*:
`Company that aims to improve AI agents. They offer observed and evaluable AI agents with replay analytics as an alternative to black box models and blind prompting.`
`Artificial Intelligence, AI agents, Observability, Analytics.`
`Their product, AgentOps, allows for easy and instant testing and debugging of AI agents. Integration is as simple as writing three lines of code. They also provide generous free limits and mandate upgrades only when necessary.`
`What differentiates AgentOps from other, similar products? How does their pricing scale with usage? What are the details of their "generous free limits"?`