pydantic-scrape 0.2.0

Advanced web automation framework with AI-powered agents, Chawan terminal browser integration, and geographic search targeting

Pydantic Scrape

A modern web scraping framework that combines AI-powered content extraction with intelligent workflow orchestration. Built on pydantic-ai for reliable, type-safe scraping operations.

Why Pydantic Scrape?

Web scraping is complex. You need to handle dynamic content, extract meaningful information, and orchestrate multi-step workflows. Most tools force you to choose between simple scrapers or complex frameworks with steep learning curves.

Pydantic Scrape bridges this gap by providing:

• AI-powered extraction - Let AI understand and extract what you need instead of writing brittle selectors
• Type-safe workflows - Structured data with validation built-in
• Academic research focus - First-class support for papers, citations, and research workflows
• Browser automation - Handle JavaScript, authentication, and complex interactions seamlessly

Installation

1. Install Chawan Terminal Browser (Required)

Pydantic Scrape uses Chawan for advanced web automation and JavaScript-heavy sites.

macOS (Homebrew):

brew install chawan

Linux (from source):

# Install Nim compiler
curl https://nim-lang.org/choosenim/init.sh -sSf | sh
# Install Chawan
git clone https://git.sr.ht/~bptato/chawan
cd chawan && make && sudo make install

Verify installation:

cha --version

2. Install Pydantic Scrape

# Standard installation
pip install pydantic-scrape

# With development tools (if contributing)
pip install pydantic-scrape[dev]

Quick Start

Get a comprehensive research answer in under 10 lines:

import asyncio
from pydantic_scrape.graphs.search_answer import search_answer

async def research():
    result = await search_answer(
        query="latest advances in quantum computing",
        max_search_results=5
    )
    
    print(f"Found {len(result['answer']['sources'])} sources")
    print(result['answer']['answer'])

asyncio.run(research())

This searches academic sources, extracts content, and generates a structured answer with citations - all automatically.

Core Features

🔍 Smart Content Extraction

from pydantic_scrape.dependencies.fetch import fetch_url

# Automatically handles JavaScript, selects best extraction method
content = await fetch_url("https://example.com/article")
print(content.title, content.text, content.metadata)

🤖 AI-Powered Scraping

from pydantic_scrape.agents.bs4_scrape_script_agent import get_bs4_scrape_script_agent

# AI writes the scraping code for you
agent = get_bs4_scrape_script_agent()
result = await agent.run_sync("Extract product prices from this e-commerce page", 
                              html_content=page_html)

📚 Academic Research

from pydantic_scrape.dependencies.openalex import OpenAlexDependency

# Search papers by topic, author, or DOI
openalex = OpenAlexDependency()
papers = await openalex.search_papers("machine learning healthcare")

📄 Document Processing

from pydantic_scrape.dependencies.document import DocumentDependency

# Extract text from PDFs, Word docs, EPUBs
doc = DocumentDependency()
content = await doc.extract_text("research_paper.pdf")

🌐 Advanced Browser Automation

from pydantic_scrape.agents.search_and_browse import SearchAndBrowseAgent

# Intelligent search + browse with memory and geographic targeting
agent = SearchAndBrowseAgent()
result = await agent.run(
    "Find 5 cabinet refacing services in North West England with contact details"
)

# Automatically handles: cookie popups, JavaScript, geographic targeting, 
# content caching, parallel browsing, and form detection

Common Use Cases

• Literature Reviews - Automatically search, extract, and summarize academic papers
• Market Research - Monitor competitor content, pricing, and product updates
• News Monitoring - Track mentions, trends, and breaking news across sources
• Content Migration - Extract structured data from legacy systems or websites
• Research Workflows - Build custom pipelines for domain-specific content extraction

Architecture

Pydantic Scrape organizes code into three layers:

• Dependencies (pydantic_scrape.dependencies.*) - Reusable components for specific tasks
• Agents (pydantic_scrape.agents.*) - AI-powered workers that make decisions
• Graphs (pydantic_scrape.graphs.*) - Orchestrate multi-step workflows

This makes it easy to compose complex workflows from simple, tested components.

Configuration

1. Set API Keys

Create a .env file in your project root:

# AI Providers (choose one or more)
OPENAI_API_KEY=your_openai_key
GOOGLE_GENAI_API_KEY=your_google_key  
ANTHROPIC_API_KEY=your_anthropic_key

# Google Search (for enhanced search capabilities)
GOOGLE_SEARCH_API_KEY=your_google_search_key
GOOGLE_SEARCH_ENGINE_ID=your_search_engine_id

2. Chawan Configuration

The package includes an optimized Chawan configuration in .chawan/config.toml that provides:

• 7x faster web automation vs default settings
• Cookie popup handling without JavaScript overhead
• Content caching for instant subsequent operations
• Geographic search targeting for accurate local results

No additional Chawan setup required - works out of the box!

Documentation

• Installation Guide - Detailed setup instructions
• Examples - Working code samples for common tasks
• API Reference - Full documentation

Contributing

We welcome contributions! The framework is designed to be extensible - add new content sources, AI agents, or workflow patterns.

See CONTRIBUTING.md for development setup and guidelines.

License

MIT License - see LICENSE for details.

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Ready to build intelligent scraping workflows? Start with pip install pydantic-scrape and try the examples above.