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Scrape web pages and feed the extracted content into cognee using ScrapeGraphAI. The cognee-community-tasks-scrapegraph package provides two async tasks: scrape_urls for extraction and scrape_and_add for end-to-end scrape-to-graph ingestion.

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Why Use This Integration

  • Prompt-Based Extraction: Describe what you want in natural language — no CSS selectors or scraper maintenance
  • Single Function Pipeline: scrape_and_add scrapes, ingests, and builds the graph in one call
  • Structured Output: Optionally pass a Pydantic schema for domain-specific extraction
  • Source Attribution: Each scraped page is tagged with its origin URL in the knowledge graph
  • JavaScript Rendering: Handles JS-rendered pages and common bot protection

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Installation

pip install cognee-community-tasks-scrapegraph cognee
Or with uv: 
uv pip install cognee-community-tasks-scrapegraph cognee

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Requirements

You need two API keys:
VariableDescription
LLM_API_KEYOpenAI (or other LLM provider) API key used by cognee
SGAI_API_KEYScrapeGraphAI API key
export LLM_API_KEY="sk-..."
export SGAI_API_KEY="sgai-..."
See LLM Providers and Embedding Providers if you want to use a provider other than OpenAI.

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Quick Start

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1. Scrape and Inspect

Use scrape_urls to verify what ScrapeGraphAI extracts before building a graph: 
import asyncio
from cognee_community_tasks_scrapegraph import scrape_urls

async def main():
    results = await scrape_urls(
        urls=[
            "https://cognee.ai",
            "https://docs.cognee.ai",
        ],
        user_prompt="Extract the product description, key features, and target use cases",
    )

    for item in results:
        if item.get("error"):
            print(f"[!] {item['url']}: {item['error']}")
        else:
            print(f"\n=== {item['url']} ===")
            print(str(item["content"])[:500])

asyncio.run(main())
The user_prompt tells ScrapeGraphAI what to focus on when extracting content from each page.

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2. Build the Knowledge Graph

Use scrape_and_add to scrape, ingest, and build the graph in one call: 
import asyncio
import cognee
from cognee_community_tasks_scrapegraph import scrape_and_add

async def main():
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

    await scrape_and_add(
        urls=[
            "https://cognee.ai",
            "https://docs.cognee.ai",
            "https://github.com/topoteretes/cognee",
        ],
        user_prompt="Extract the product description, key features, and target use cases",
        dataset_name="cognee_research",
    )

    results = await cognee.recall(
        query_text="What is Cognee and what problems does it solve?",
        datasets=["cognee_research"],
    )
    for r in results:
        print(r)

asyncio.run(main())
The prune calls reset the local database. Skip them when building incrementally on top of an existing graph.

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Structured Extraction

When you know the shape of the data you need, pass a Pydantic schema to ScrapeGraphAI’s smartscraper directly. This bypasses the integration’s scrape_urls and gives you full control over the output structure: 
import asyncio
import os
import cognee
from pydantic import BaseModel, Field
from scrapegraph_py import Client

class ProductPage(BaseModel):
    name: str = Field(description="Product or company name")
    tagline: str = Field(description="One-line value proposition")
    features: list[str] = Field(description="List of key features or capabilities")
    pricing_model: str = Field(description="How the product is priced")
    target_audience: str = Field(description="Who the product is primarily aimed at")

async def scrape_structured(urls: list[str]) -> list[str]:
    client = Client(api_key=os.environ["SGAI_API_KEY"])
    texts = []
    try:
        for url in urls:
            response = client.smartscraper(
                website_url=url,
                user_prompt="Extract product information",
                output_schema=ProductPage,
            )
            result = response.get("result", {})
            text = f"""Source: {url}
Product: {result.get('name', 'N/A')}
Tagline: {result.get('tagline', 'N/A')}
Features: {', '.join(result.get('features', []))}
Pricing: {result.get('pricing_model', 'N/A')}
Audience: {result.get('target_audience', 'N/A')}"""
            texts.append(text)
    finally:
        client.close()
    return texts

async def main():
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

    urls = ["https://cognee.ai", "https://scrapegraphai.com", "https://firecrawl.dev"]
    texts = await scrape_structured(urls)

    combined = "\n\n".join(texts)
    await cognee.remember(combined, dataset_name="product_landscape")

    results = await cognee.recall(
        query_text="How do these products differ in their approach?",
        datasets=["product_landscape"],
    )
    for r in results:
        print(r)

asyncio.run(main())

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Querying the Graph

Once the graph is built, use cognee.recall(...) to query it.

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Use Cases

Scrape competitor product and pricing pages, build a knowledge graph, then query across all of them:
  1. Gather competitor URLs (product pages, pricing, docs)
  2. Use scrape_and_add with a prompt focused on pricing, features, and positioning
  3. Query with synthesis questions like “Which product is best for enterprise use cases?”
Scope queries to the dataset with datasets=["competitive_intel"].
Scrape news sources on a schedule and add to the existing graph incrementally:
  1. Set up a list of news/blog URLs
  2. Run scrape_and_add daily (skip the prune calls to accumulate data)
  3. Query across the full timeline: “What are the biggest trends this week?”
The graph gets better over time as entities and relationships accumulate.
Collect and correlate information from many sources:
  1. Scrape documentation, blog posts, and GitHub READMEs for a topic
  2. Build the graph with scrape_and_add
  3. Ask cross-source questions: “How does library X compare to library Y?”
Use structured extraction with Pydantic schemas for consistent input.
Map out an entire product category:
  1. Scrape product pages with a schema targeting name, features, pricing, and audience
  2. Ingest into cognee
  3. Query for patterns: “Which products target developers?” or “What pricing models are most common?”

GitHub Repository

View source code and examples

Blog Post

Read full tutorial on ScrapeGraph website