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Overview

Pro Search provides three built-in tools that the model uses automatically to answer your queries. The model decides which tools to use and when—you don’t need to configure anything. These tools are called automatically by the system; you cannot register custom tools.
All tool executions appear in the reasoning_steps array of streaming responses, giving you visibility into how the model researched your query.
Conducts web searches to find current information, statistics, and expert opinions. Example in action: 
{
  "thought": "I need current data on EV market trends",
  "type": "web_search",
  "web_search": {
    "search_keywords": [
      "EV Statistics 2023-2024",
      "electric vehicle sales data",
      "global EV market trends"
    ],
    "search_results": [
      {
        "title": "Trends in electric cars",
        "url": "https://www.iea.org/reports/global-ev-outlook-2024/trends-in-electric-cars",
        "date": "2024-03-15",
        "last_updated": null,
        "snippet": "Electric car sales neared 14 million in 2023, 95% of which were in China, Europe and the United States...",
        "source": "web"
      }
    ]
  }
}

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fetch_url_content

Retrieves full content from specific URLs to access detailed information beyond search result snippets. Example in action: 
{
  "thought": "This research paper contains detailed methodology I need to review",
  "type": "fetch_url_content",
  "fetch_url_content": {
    "contents": [
      {
        "title": "Attention Is All You Need",
        "url": "https://arxiv.org/pdf/1706.03762",
        "date": null,
        "last_updated": null,
        "snippet": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder...",
        "source": "web"
      }
    ]
  }
}

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execute_python

Runs Python code for calculations, data analysis, and computational tasks. Example in action: 
{
  "thought": "I'll calculate the compound annual growth rate",
  "type": "execute_python",
  "execute_python": {
    "code": "# Variables\ninitial = 1000\nfinal = 5000\nyears = 10\n\n# CAGR formula\ncagr = ((final / initial) ** (1/years)) - 1\ncagr_percent = cagr * 100\n\ncagr_percent",
    "result": "17.46070398016705"
  }
}
The Python environment is secure and designed for calculations and data analysis. Complex packages or external APIs are not available.

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Multi-Tool Workflows

The model automatically combines multiple tools when needed. For example, when asked to analyze solar panel ROI, it might:
  1. Use web_search to find current incentives and costs
  2. Use fetch_url_content to read detailed policy documents
  3. Use execute_python to calculate payback periods
  4. Use web_search again to verify electricity rates

Quickstart

Get started with Pro Search basics

Stream Mode Guide

Learn about streaming and real-time reasoning visibility

Context Management

Learn how to maintain conversation context across multiple API calls

API Reference

Complete API documentation