🦆 DuckDuckGo API: Structured DuckDuckGo Search Results in Clean JSON

The most efficient, reliable, and developer-friendly way to use the DuckDuckGo API.

Actor page: apify.com/johnvc/DuckDuckGoSEOScraper Input schema: apify.com/johnvc/DuckDuckGoSEOScraper/input-schema

The DuckDuckGo API runs a DuckDuckGo search for any query and returns clean, structured JSON. Each page of results comes back as one item with organic listings (title, link, snippet, position, sitelinks), ads, knowledge graph panels, news results, inline images, inline videos, and related searches, plus per-page metadata. It supports 40+ region and language combinations, safe-search levels, date filtering, and automatic pagination. Because DuckDuckGo does not personalize results, the output is consistent across runs.

Video Walkthrough

Quick Start

Prerequisites

• Python 3.11 or higher
• An Apify account and API key (get a free key here)

1. Clone the repository

   git clone https://github.com/johnisanerd/Apify-DuckDuckGo-Search-Scraper.git
   cd Apify-DuckDuckGo-Search-Scraper
   

2. Install dependencies with UV

   # Install UV if you do not have it:
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Install project dependencies:
   uv sync
   

3. Configure your API key

   cp .env.example .env
   # Edit .env and add your Apify API key
   # Get your free API key at: https://apify.com?fpr=9n7kx3
   

4. Run the example

   uv run python duckduckgo-search-scraper.py
   

Alternative: set the API key directly

export APIFY_API_TOKEN="your_api_key_here"
uv run python duckduckgo-search-scraper.py

Why Use This DuckDuckGo API?

Full results-page coverage. One call returns the whole DuckDuckGo results page as structured data: organic listings, ads, knowledge graph panels, news, inline images, inline videos, and related searches. You get the entire page, not just the organic links.

Consistent, non-personalized snapshots. DuckDuckGo does not personalize results, so repeated runs return comparable SERPs, which is ideal for ranking analysis and monitoring over time.

Localized and filterable. Target 40+ region and language combinations, set the safe-search level, and filter by date (past day, week, month, year, or a custom range).

Predictable, pay-per-use pricing. Billing is per run plus per page processed, with no monthly rental. You control cost directly with the page limit.

Easy to automate. Call it from Python in a few lines, or load it as an MCP tool so assistants like Claude and Cursor can run DuckDuckGo searches for you on demand.

Features

Core Capabilities

• Keyword search across DuckDuckGo with full results-page extraction
• Localization across 40+ region and language combinations
• Safe-search control (strict, moderate, or off)
• Date filtering with relative periods or a custom date range
• Multi-page pagination with a configurable page cap

Data Quality

• Structured organic results with title, link, snippet, position, displayed link, date, and sitelinks
• Rich result blocks: ads, knowledge graph, news, inline images, inline videos, related searches
• Per-page metadata with result counts and pagination details
• Consistent JSON shape across every query
• Per-page billing so larger searches stay transparent

Usage Examples

Basic Example

A single-page search for one keyword. This is the cheapest way to try the API.

{
  "query": "machine learning",
  "max_pages": 1
}

Advanced Example

A UK-localized search with strict safe-search, results from the past week, and two pages.

{
  "query": "machine learning",
  "localization": "uk-en",
  "safe": "strict",
  "date_filter": "w",
  "max_pages": 2
}

Input Parameters

Output Format

A real dataset item for the query machine learning. Each page of results is one item; the first result's sitelinks are trimmed here for readability.

{
  "query": "machine learning",
  "localization": "us-en",
  "safe_search": -1,
  "max_pages": 1,
  "search_timestamp": "2026-05-29T09:56:11.848097",
  "total_results_found": 11,
  "pages_processed": 1,
  "page_number": 1,
  "search_metadata": {
    "localization": "us-en",
    "localization_name": "United States (English)",
    "safe_search": -1,
    "safe_search_description": "Moderate",
    "max_pages": 1,
    "pagination_limit_reached": true
  },
  "pagination_info": {
    "total_pages": 1,
    "max_pages_set": 1,
    "pagination_stopped_by_limit": true,
    "results_per_page": { "first_page": 11, "subsequent_pages": 50 }
  },
  "organic_results": [
    {
      "position": 1,
      "title": "Machine Learning Tutorial - GeeksforGeeks",
      "link": "https://www.geeksforgeeks.org/machine-learning/machine-learning/",
      "date": "Apr 15, 2026",
      "snippet": "Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task.",
      "favicon": "https://external-content.duckduckgo.com/ip3/www.geeksforgeeks.org.ico",
      "sitelinks": [
        {
          "title": "Gradient Descent in Linear Regression",
          "link": "https://www.geeksforgeeks.org/machine-learning/gradient-descent-in-linear-regression/"
        }
      ]
    },
    {
      "position": 2,
      "title": "Machine learning - Wikipedia",
      "link": "https://en.wikipedia.org/wiki/Machine_learning",
      "snippet": "Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data.",
      "favicon": "https://external-content.duckduckgo.com/ip3/en.wikipedia.org.ico"
    }
  ],
  "ads": [],
  "knowledge_graph": [],
  "news_results": [],
  "inline_images": [],
  "inline_videos": [],
  "related_searches": []
}

Use as an MCP tool

You can load the DuckDuckGo API as an MCP tool so assistants call it for you. The MCP server URL preloads just this one Actor:

https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper

Authenticate with OAuth in the browser when offered, or with your Apify API token (the same APIFY_API_TOKEN used by the Python example). Get a token at https://console.apify.com/settings/integrations and a free Apify account at https://apify.com?fpr=9n7kx3 .

Install in Claude Cowork Desktop

Cowork is the desktop app's automation mode. To give it the DuckDuckGo API as a tool, add the Apify MCP server as a connector.

1. Open the Claude desktop app and go to Settings → Connectors (or Settings → Developer → Edit Config to edit claude_desktop_config.json directly).
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
2. Add the Apify MCP server, preloaded with only this Actor:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper"
      ]
    }
  }
}

3. Restart the app. When Cowork first calls the tool, complete the OAuth prompt in your browser, or add your Apify API token in the connector settings to skip OAuth.
4. In a Cowork chat, confirm the tool is available and ask it to run the DuckDuckGo API.

Download the desktop app and start a free trial: https://claude.ai/referral/uIlpa7nPLg More help: https://docs.apify.com/platform/integrations/claude-desktop

Install in Claude Code

Claude Code is the command-line tool. Add the Actor's MCP server with one command:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper"

To use a token instead of browser OAuth:

claude mcp add --transport http apify \
  "https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper" \
  --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Then verify with claude mcp list, or run /mcp inside a session. Ask Claude Code to call the DuckDuckGo API.

Try Claude Code free: https://claude.ai/referral/uIlpa7nPLg Claude Code MCP docs: https://code.claude.com/docs/en/mcp

Install in Claude (website)

On claude.ai you add Apify as a connector, then enable just this Actor's tool.

1. Go to Settings → Connectors → Browse connectors and search for Apify MCP server. Install it (enable or update if prompted).
2. When connecting, authenticate with your Apify API token, and enable the tool johnvc/DuckDuckGoSEOScraper.
3. In any chat, open + → Connectors and turn on Apify.
4. Alternatively, choose Add custom connector and paste the full MCP URL https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper, using OAuth when prompted.
5. Ask Claude to run the DuckDuckGo API.

Open Claude on the web: https://claude.ai

Install in Cursor

Cursor reads MCP servers from a project file at .cursor/mcp.json.

1. In your project, create .cursor/mcp.json:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper"
    }
  }
}

2. If you prefer token auth over browser OAuth, add a header:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

3. Open Cursor → Settings → MCP and confirm the apify server is connected (green dot).
4. In Composer or Chat, ask Cursor to call the DuckDuckGo API.

New to Cursor? Get it here: https://cursor.com/referral?code=XQP4VBLI3NNX

Install in ChatGPT

ChatGPT connects to the Apify MCP server through Developer mode (available on ChatGPT Pro, Plus, Business, Enterprise, and Education plans).

1. Click your profile icon, then go to Settings > Apps. If you do not see a Create app button, open Advanced settings and enable Developer mode.
2. Click Create app and fill out the form:
   • Name: Apify
   • MCP Server URL: https://mcp.apify.com/?tools=actors,docs,johnvc/DuckDuckGoSEOScraper
   • Authentication: OAuth
3. Click Create and authorize the connection with Apify.
4. To use the app in a conversation, click + in the chat, choose Developer mode, and select Apify.

More help: https://docs.apify.com/platform/integrations/mcp

Made with care

Use the DuckDuckGo API to power your search and SEO workflows with reliable, structured results.

Last Updated: 2026.07.17