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Gemini URL Context Tool Alternative: Why ScrapeGraphAI Works Better

Gemini URL Context Tool Alternative: Why ScrapeGraphAI Works Better

Author 1

Marco Vinciguerra

Google's URL context tool for Gemini API became generally available in August 2025. It's a solid feature—you can feed URLs directly to Gemini models and let them analyze the full content of web pages, PDFs, and images. Tools like Gemini CLI already use it for web-fetch commands, and companies like Gladly.ai use it to personalize customer interactions.

But here's the thing: if you're building production AI agents that need reliable web data extraction with structured output, you might want to look elsewhere.

I've been building ScrapeGraphAI for the past couple of years, and I've seen developers hit the same walls with URL context tools. They're great for one-off analysis and research, but when you need to extract structured data at scale, handle dynamic content, or work with complex websites as part of an agent workflow, they fall short.

Let me show you why ScrapeGraphAI works better as a tool for AI agents—and when you might actually want to use both.

What Google's URL Context Tool Actually Does

According to Google's official announcement, the URL context tool lets you pass URLs directly to Gemini models as a tool. Instead of manually uploading content, you can point Gemini at a webpage and it will fetch and analyze it. That's useful for:

  • Quick analysis of a single page
  • Reading PDFs and extracting text (with table understanding)
  • Understanding images and visual content (PNG, JPEG, BMP, WebP)
  • One-off research tasks
  • Building AI agents that need web context

The tool supports PDFs, images (PNG, JPEG, BMP, WebP), web pages (HTML), structured data (JSON, XML, CSV), and text files (Plain Text, RTF, CSS, JavaScript). It's designed to work alongside Google Search grounding—use Search to discover, URL Context to analyze.

How it works as a tool:

from google import genai
from google.genai.types import Tool, GenerateContentConfig
 
client = genai.Client()
model_id = "gemini-2.5-flash"
tools = [{"url_context": {}}]
 
response = client.models.generate_content(
    model=model_id,
    contents="What are the top 3 recent announcements from https://ai.google.dev/gemini-api/docs/changelog",
    config=GenerateContentConfig(tools=tools)
)

Real-world examples include Gemini CLI using it for web-fetch commands and Gladly.ai using it to personalize customer service interactions by analyzing customer websites.

But here's where it gets tricky for production use.

The Problem with URL Context Tools

I've talked to dozens of developers who started with URL context tools and quickly hit limitations:

Rate limits tied to model choice. According to Google's documentation, rate limits are based on the specific Gemini model you choose. Your throughput depends entirely on which Gemini model you're using. Need to process hundreds of pages? You're at the mercy of Gemini's model-specific rate limits, which can be restrictive for high-volume agent workloads.

Cost structure is token-based. You pay for the added input tokens to context, based on the standard rate for the model. While Google says this makes costs "clear and predictable," it's still hard to predict costs when dealing with variable page sizes. A 10KB page costs differently than a 500KB page, and you don't know until you fetch it—making budgeting for agent workflows challenging.

No structured extraction. URL context tools are great for analysis and summarization, but they don't return structured data. If you need JSON output matching a specific schema, you're doing extra work to parse and validate responses.

Limited control over extraction. You can't specify exactly what to extract or how. The model decides what's relevant, which works for research but breaks down when you need consistent, predictable output.

Single-page focus. While you can pass multiple URLs, there's no built-in support for crawling, pagination, or handling multi-step workflows. Each URL is processed independently.

JavaScript rendering is inconsistent. Dynamic content loaded via JavaScript? Sometimes it works, sometimes it doesn't. You're rolling the dice on whether the content you need is actually in the HTML.

Why ScrapeGraphAI Works Better for Production

ScrapeGraphAI was built specifically for developers who need reliable, scalable web data extraction. Here's how it solves the problems URL context tools create:

1. Purpose-Built for Data Extraction

Unlike URL context tools that are designed for analysis, ScrapeGraphAI is optimized for extraction. You describe what you want in natural language, and it returns structured data matching your schema:

from scrapegraph_py import Client
from pydantic import BaseModel
 
class Product(BaseModel):
    name: str
    price: str
    description: str
    available: bool
 
client = Client(api_key="your-sgai-api-key")
 
# Extract structured data with schema validation
response = client.smartscraper(
    website_url="https://example-shop.com/product",
    user_prompt="Extract product name, price, description, and availability",
    output_schema=Product.model_json_schema()
)
 
# Returns validated JSON matching your schema
product = Product.model_validate(response)

2. Predictable Pricing

ScrapeGraphAI uses a credit-based system that's easy to understand and predict. One credit equals one API call, regardless of page size. You know exactly what you're paying for:

  • Free tier: 50 credits to get started
  • Starter: $20/month for 5,000 credits
  • Growth: $100/month for 40,000 credits
  • Pro: $500/month for 250,000 credits

No surprises. No variable token costs. Just straightforward pricing that scales with your usage.

3. Built-in JavaScript Rendering

ScrapeGraphAI handles dynamic content automatically. When you request a page, it renders JavaScript, waits for content to load, and extracts data from the fully rendered page. No guessing whether your content is available.

4. Multi-Page and Crawling Support

ScrapeGraphAI's SmartCrawler can discover and process entire sites, following links and handling pagination automatically:

response = client.smartcrawler_initiate(
    url="https://docs.stripe.com/api",
    prompt="Extract API endpoint names, methods, and descriptions",
    extraction_mode="ai",
    max_pages=50,
    depth=2
)

5. Schema Validation

ScrapeGraphAI validates output against your schema automatically. If extraction doesn't match your requirements, you get clear error messages instead of unpredictable JSON structures.

6. Multi-Format Support

ScrapeGraphAI handles the same formats as Gemini's URL context tool (PDFs, images, web pages) plus more:

  • Structured data (JSON, XML, CSV)
  • Text files (Plain Text, RTF, CSS, JavaScript)
  • Dynamic web content with JavaScript rendering
  • Multi-page documents and sites

When to Use Each Tool

Here's the honest breakdown:

Use Gemini's URL context tool when:

  • You're already using Gemini models and want quick, one-off analysis
  • You need Google Search grounding combined with URL analysis (Search to discover, URL Context to analyze)
  • You're building AI agents that need web context for research or exploratory work
  • You're doing analysis tasks where structured output isn't critical
  • Cost predictability isn't critical for your use case
  • You need multimodal analysis (images, PDFs with tables)

Use ScrapeGraphAI as a tool when:

  • You're building AI agents that need structured data extraction
  • You need consistent output schemas and validation for agent workflows
  • You're working with dynamic, JavaScript-heavy websites
  • You need to crawl multiple pages or entire sites as part of agent tasks
  • You want predictable, transparent pricing for agent operations
  • You need guaranteed JSON structure for agent decision-making
  • Your agent requires type-safe data with Pydantic models

Use both when:

  • You're building sophisticated AI agents that need both discovery (Gemini Search + URL Context) and extraction (ScrapeGraphAI)
  • You want to discover content with Gemini Search, then extract structured data with ScrapeGraphAI
  • You're doing research that benefits from Gemini's analysis, then production extraction that needs ScrapeGraphAI's reliability
  • Your agent workflow has both exploratory and production phases

Real-World Example: Building a Product Monitor

For monitoring competitor prices, ScrapeGraphAI provides structured output with a single call:

from scrapegraph_py import Client
from pydantic import BaseModel
 
class ProductInfo(BaseModel):
    name: str
    price: str
    in_stock: bool
 
client = Client(api_key="your-sgai-api-key")
response = client.smartscraper(
    website_url="https://www.amazon.com/dp/B08N5WRWNW",
    user_prompt="Extract product name, current price, and stock status",
    output_schema=ProductInfo.model_json_schema()
)
product_data = ProductInfo.model_validate(response)

With Gemini URL Context, you'd need to manually parse responses, handle errors, and work around model-specific rate limits.

Building an AI Agent: ScrapeGraphAI vs Gemini SDK

Let's see how each tool works when integrated into an AI agent workflow. This is where the differences really matter.

Using ScrapeGraphAI as a Tool in an AI Agent

ScrapeGraphAI works perfectly as a tool for AI agents. Here's a simple integration:

from scrapegraph_py import Client
from pydantic import BaseModel
from typing import List
import asyncio
 
class Product(BaseModel):
    name: str
    price: str
    available: bool
 
class ScrapeGraphAITool:
    def __init__(self, api_key: str):
        self.client = Client(api_key=api_key)
    
    def extract_data(self, url: str, prompt: str, schema: dict):
        return self.client.smartscraper(
            website_url=url,
            user_prompt=prompt,
            output_schema=schema
        )
 
# Usage in an agent
tool = ScrapeGraphAITool(api_key="your-sgai-api-key")
schema = Product.model_json_schema()
response = tool.extract_data(
    "https://example.com/product",
    "Extract product name, price, and availability",
    schema
)
product = Product.model_validate(response)

Key advantages: Structured output guaranteed, predictable rate limits, type-safe with Pydantic models.

Using Gemini URL Context as a Tool in an Agent

With Gemini's URL context tool, you face several challenges:

from google import genai
from google.genai.types import GenerateContentConfig
import json
 
class GeminiURLContextTool:
    def __init__(self, api_key: str):
        self.client = genai.Client(api_key=api_key)
        self.tools = [{"url_context": {}}]
    
    def analyze_url(self, url: str, prompt: str) -> str:
        response = self.client.models.generate_content(
            model="gemini-2.5-flash",
            contents=f"{prompt} from {url}",
            config=GenerateContentConfig(tools=self.tools)
        )
        return response.candidates[0].content.parts[0].text
 
# Usage requires manual parsing
tool = GeminiURLContextTool(api_key="your-sgai-api-key")
response_text = tool.analyze_url(
    "https://example.com/product",
    "Extract product info as JSON: name, price, available"
)
 
# Manual JSON extraction - structure not guaranteed
json_start = response_text.find('{')
json_end = response_text.rfind('}') + 1
if json_start != -1:
    product_data = json.loads(response_text[json_start:json_end])
    # Still need manual validation

Challenges: No guaranteed JSON structure, manual parsing required, rate limits tied to model choice, variable token costs.

Side-by-Side Comparison

Feature ScrapeGraphAI Agent Gemini SDK Agent
Structured Output Guaranteed by schema Manual parsing required
Error Handling Built-in validation Manual implementation
Rate Limits Predictable (10-200/min) Model-dependent, variable
Pricing Fixed per request Variable token costs
Concurrent Processing Native async support Requires careful rate limit handling
Type Safety Pydantic models Dict-based, no validation
JavaScript Rendering Automatic Inconsistent
Multi-page Support Built-in crawler Manual URL management

Why ScrapeGraphAI Works Better for Agents

Production agents need reliability, predictability, and performance. ScrapeGraphAI provides schema validation for consistent data formats, fixed pricing for predictable costs, built-in error handling, and rate limits designed for concurrent workloads. Gemini requires manual parsing, has variable token costs, and model-dependent rate limits that can bottleneck your agent.

The Bottom Line

Google's URL context tool is a solid addition to Gemini's capabilities, especially for AI agents that need web context. As shown in real-world examples like Gemini CLI and Gladly.ai, it's useful for research, analysis, and building context-aware agents. But if you're building AI agents that need reliable, scalable web data extraction with structured output, ScrapeGraphAI is the better choice as a tool.

For AI Agents Specifically:

ScrapeGraphAI works better as a tool for agents because it guarantees structured output, offers predictable pricing, provides production-ready reliability with built-in error handling, supports multi-page crawling, validates schemas automatically, and offers higher rate limits (10-200 requests/minute) designed for concurrent workloads.

Gemini's URL context tool is great for quick web analysis, multimodal understanding (images, PDFs), Google Search grounding integration, and exploratory research tasks.

The best part? You can try ScrapeGraphAI for free. Get 50 credits and see how it compares to URL context tools for your specific agent use case.

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