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Web search lets LLMs access real-time information from the internet when generating responses. Instead of relying solely on training data, the model can search the web to ground its answers with up-to-date facts, links, and sources.

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How It Works

Enable web search by adding web_search_options to your chat completion request. When enabled, the model will:
  1. Analyze the user’s query to determine if web search is needed.
  2. Search the web for relevant, current information.
  3. Generate a response grounded in the search results, often including source citations.

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Basic Example

import requests

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "openai/gpt-5.2",
    "messages": [
        {"role": "user", "content": "What are the latest developments in AI regulation in 2025?"}
    ],
    "web_search_options": {
        "search_context_size": "medium"
    }
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

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Supported Models

Not all models support web search. Use the List LLM Models endpoint to find models where capabilities.web_search is true.

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Parameters

ParameterTypeDescription
web_search_optionsobjectOptions for web search integration. Omit to disable web search.
web_search_options.search_context_sizestringAmount of context retrieved from the web. Options: "low", "medium", "high". Default: "medium"

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With System Messages

Combine web search with system messages to control how the model uses search results: 
import requests

url = "https://api.edenai.run/v3/llm/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "google/gemini-2.5-flash",
    "messages": [
        {
            "role": "system",
            "content": "You are a research assistant. When answering, always cite your sources with URLs."
        },
        {
            "role": "user",
            "content": "What is the current stock price of Apple?"
        }
    ],
    "web_search_options": {
        "search_context_size": "high"
    }
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])
Web search is useful when the user’s question involves:
  • Current events — news, stock prices, weather, sports scores
  • Recent releases — software versions, product launches, research papers
  • Factual lookups — statistics, regulations, schedules that change over time
  • Verification — checking claims against live sources
For questions about static knowledge (math, programming concepts, general reasoning), web search adds latency without much benefit. Only enable it when freshness matters.

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Next Steps

Topic Extraction

Extract structured topics from text with Expert Models