search-parser 0.5.1

Parse Google, Bing, and DuckDuckGo HTML search results into JSON, Markdown, or dict — with auto-detection

search-parser

Parse Google, Bing, and DuckDuckGo HTML search results into JSON, Markdown, or Python dict — with automatic search engine detection.

search-parser takes raw HTML from Google, Bing, and DuckDuckGo — desktop or mobile — and extracts every result type — organic results, featured snippets, AI Overviews, People Also Ask, sponsored ads, shopping ads, and more — into clean, typed Python objects. It auto-detects the search engine from the HTML, so you never have to specify which parser to use.

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Quick Start

from search_parser import SearchParser

parser = SearchParser()
html = open("google_results.html").read()

# JSON string (default)
json_output = parser.parse(html)

# Markdown string — great for feeding to an LLM
md_output = parser.parse(html, output_format="markdown")

# Python dict — for programmatic access
data = parser.parse(html, output_format="dict")

# Organic results are in data["results"]
for result in data["results"]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['url']}")

# Every other result type has its own dedicated key
if data["featured_snippet"]:
    print("Featured:", data["featured_snippet"]["title"])

if data["ai_overview"]:
    print("AI Overview:", data["ai_overview"]["description"][:100])

for question in data["people_also_ask"]:
    print("PAA:", question["title"])

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Installation

With uv (recommended):

uv add search-parser

With pip:

pip install search-parser

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Supported Result Types

Result Type	Field	Google	Bing	DuckDuckGo
Organic results	results	✓	✓	✓
Featured snippet	featured_snippet	✓	✓	—
Sponsored / ads	sponsored	✓	—	—
AI Overview	ai_overview	✓	—	—
People Also Ask	people_also_ask	✓	—	—
What People Are Saying	people_saying	✓	—	—
People Also Search For	people_also_search	✓	—	—
Related Products & Services	related_products	✓	—	—
Jobs	jobs	✓	—	—
Discussions and forums	discussions	✓	—	—
Shopping ads	shopping_ads	✓	—	—
News tab articles	news	✓	—	—

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Working with Results

SearchParser.parse() with output_format="dict" returns the full SearchResults structure:

data = parser.parse(html, output_format="dict")

# Always a list (organic results only)
for r in data["results"]:
    print(r["title"], r["url"], r["description"])

# None or a single object
if data["featured_snippet"]:
    print(data["featured_snippet"]["title"])

# None or a single object with description + sources list
if data["ai_overview"]:
    overview = data["ai_overview"]
    print(overview["description"])
    for source in overview["metadata"]["sources"]:
        print(f"  - {source['title']}: {source['url']}")

# Always a list (empty when not present)
for q in data["people_also_ask"]:
    print(q["title"])

for post in data["people_saying"]:
    print(post["title"], post["url"])

for item in data["people_also_search"]:
    print(item["title"])

for ad in data["sponsored"]:
    print(ad["title"], ad["url"])

for product in data["related_products"]:
    print(product["title"])

# Jobs (title, metadata["company"], metadata["location"])
for job in data["jobs"]:
    print(job["title"], job["metadata"]["company"], job["metadata"]["location"])

# Discussions (title, url, description, metadata["source"])
for disc in data["discussions"]:
    print(disc["title"], disc["url"])
    print(disc["metadata"]["source"])

# Shopping ads (title, metadata["price"], metadata["merchant"])
for ad in data["shopping_ads"]:
    print(ad["title"], ad["metadata"]["price"], ad["metadata"]["merchant"])

# News tab articles (Google News tab, tbm=nws) — metadata["source"] and metadata["published_time"]
for article in data["news"]:
    print(article["title"], article["url"])
    print(article["metadata"]["source"], article["metadata"]["published_time"])

# Metadata
print(data["search_engine"])        # "google"
print(data["query"])                # "python web scraping"
print(data["total_results"])        # 26200000 or None
print(data["detection_confidence"]) # 0.95

Using the model directly

When you need the typed SearchResults object instead of a dict, call the engine parser directly. The model exposes to_json() and to_markdown() convenience methods:

from search_parser.parsers.google import GoogleParser

parser = GoogleParser()
results = parser.parse(html)  # returns SearchResults

# Typed access — no dict key lookups
print(results.query)
print(results.total_results)
print(len(results.results))  # organic count

if results.featured_snippet:
    print(results.featured_snippet.title)

if results.ai_overview:
    print(results.ai_overview.description)
    sources = results.ai_overview.metadata["sources"]

for q in results.people_also_ask:
    print(q.title)

for post in results.people_saying:
    print(post.title, post.url)

for ad in results.shopping_ads:
    print(ad.title, ad.metadata["price"], ad.metadata["merchant"])

for article in results.news:
    print(article.title, article.url)
    print(article.metadata["source"], article.metadata["published_time"])

# Convert to JSON or Markdown directly on the model
json_str = results.to_json()
json_str = results.to_json(indent=4)  # custom indent
md_str = results.to_markdown()

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Output Formats

JSON (output_format="json" or results.to_json())

{
  "search_engine": "google",
  "query": "python web scraping",
  "total_results": 26200000,
  "results": [
    {
      "title": "Web Scraping with Python - Real Python",
      "url": "https://realpython.com/python-web-scraping/",
      "description": "Learn how to scrape websites with Python...",
      "position": 1,
      "result_type": "organic",
      "metadata": {}
    }
  ],
  "featured_snippet": null,
  "ai_overview": {
    "title": "AI Overview",
    "url": "",
    "description": "Python is a widely used language for web scraping...",
    "position": 0,
    "result_type": "ai_overview",
    "metadata": {
      "sources": [
        {"title": "Beautiful Soup", "url": "https://www.crummy.com/software/BeautifulSoup/"},
        {"title": "Requests", "url": "https://requests.readthedocs.io/"}
      ]
    }
  },
  "people_also_ask": [
    {"title": "Is Python good for web scraping?", "url": "", "position": 0, "result_type": "people_also_ask", "metadata": {}}
  ],
  "sponsored": [],
  "people_saying": [],
  "people_also_search": [],
  "related_products": [],
  "jobs": [
    {
      "title": "Global Supply Chain Director",
      "url": "https://www.google.com/search?q=%22Supply+Chain+Director&udm=8",
      "description": null,
      "position": 0,
      "result_type": "job",
      "metadata": {
        "company": "InterSources, Inc.",
        "location": "San Jose, CA  •  via Ladders"
      }
    }
  ],
  "discussions": [
    {
      "title": "Being considered for Director of Supply Chain",
      "url": "https://www.reddit.com/r/supplychain/comments/1ib0c1a/being_considered_for_director_of_supply_chain/",
      "description": "I work for a mid-sized company as a Procurement Manager...",
      "position": 0,
      "result_type": "discussion",
      "metadata": {
        "source": "Reddit · r/supplychain · 10+ comments · 1 year ago"
      }
    }
  ],
  "shopping_ads": [
    {
      "title": "ALCON - Precision7 , 12 Pack",
      "url": "http://www.google.com/aclk?sa=L&ai=...",
      "description": null,
      "position": 0,
      "result_type": "shopping_ad",
      "metadata": {
        "price": "$51.19",
        "merchant": "Contacts Direct"
      }
    }
  ],
  "news": [],
  "detection_confidence": 0.95,
  "parsed_at": "2026-02-21T00:00:00Z",
  "metadata": {}
}

Markdown (output_format="markdown" or results.to_markdown())

# Search Results: python web scraping

**Search Engine:** Google
**Total Results:** ~26,200,000
**Parsed:** 2026-02-21 00:00:00 UTC

---

## Featured Snippet

### What is Web Scraping?
Web scraping is the process of extracting data from websites...

**Source:** [https://example.com](https://example.com)

---

## Organic Results

### 1. Web Scraping with Python - Real Python
Learn how to scrape websites with Python...

**URL:** https://realpython.com/python-web-scraping/

---

## Jobs

### Global Supply Chain Director

**Company:** InterSources, Inc.
**Location:** San Jose, CA  •  via Ladders
**URL:** https://www.google.com/search?q=%22Supply+Chain+Director&udm=8

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## Discussions and Forums

### Being considered for Director of Supply Chain

I work for a mid-sized company as a Procurement Manager...

**URL:** https://www.reddit.com/r/supplychain/comments/1ib0c1a/being_considered_for_director_of_supply_chain/

## Shopping Ads

### ALCON - Precision7 , 12 Pack

**Price:** $51.19
**Merchant:** Contacts Direct
**URL:** http://www.google.com/aclk?sa=L&ai=...

## News Results

### 1. Python web scraping tutorial released

**Source:** Real Python · 2 days ago

Learn how to scrape websites with Python using BeautifulSoup and Requests...

**URL:** https://realpython.com/python-web-scraping/

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CLI Usage

# Parse an HTML file (auto-detects search engine, outputs JSON)
search-parser parse results.html

# Markdown output
search-parser parse results.html --format markdown

# Specify engine manually
search-parser parse results.html --engine google --format json

# Read from stdin
cat results.html | search-parser parse - --format json

# Save to file
search-parser parse results.html --output results.json

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Documentation

Full documentation: https://search-parser.github.io/search-parser/

• Getting Started
• API Reference
• Adding a New Search Engine
• Examples

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Contributing

Contributions are welcome! Please read our Contributing Guide for details on the development workflow, how to add new parsers, and how to submit pull requests.

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License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.