Using Ollama Web Search API in Python

Source: Dev.to

Getting Started

Installation
Install version 0.6.0 or higher:

pip install 'ollama>=0.6.0'

Environment setup
For managing Python environments and packages, consider using uv or a virtual environment with venv.

Create an API key from your Ollama account and set it as an environment variable:

export OLLAMA_API_KEY="your_api_key"

On Windows PowerShell:

$env:OLLAMA_API_KEY = "your_api_key"

Basic Web Search

import ollama

# Simple web search
response = ollama.web_search("What is Ollama?")
print(response)

Sample output

results = [
    {
        "title": "Ollama",
        "url": "https://ollama.com/",
        "content": "Cloud models are now available in Ollama..."
    },
    {
        "title": "What is Ollama? Features, Pricing, and Use Cases",
        "url": "https://www.walturn.com/insights/what-is-ollama",
        "content": "Our services..."
    },
    {
        "title": "Complete Ollama Guide: Installation, Usage & Code Examples",
        "url": "https://collabnix.com/complete-ollama-guide",
        "content": "Join our Discord Server..."
    }
]

Controlling Result Count

import ollama

# Get more results
response = ollama.web_search("latest AI news", max_results=10)

for result in response.results:
    print(f"📌 {result.title}")
    print(f"   {result.url}")
    print(f"   {result.content[:100]}...")
    print()

Fetching Full Page Content

web_search returns multiple search results with titles, URLs, and snippets.
web_fetch retrieves the full content of a specific URL, returning the page title, markdown content, and links.

from ollama import web_fetch

result = web_fetch('https://ollama.com')
print(result)

Sample output

WebFetchResponse(
    title='Ollama',
    content='[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama\n\n**Chat & build with open models**\n\n[Download](https://ollama.com/download) [Explore models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
    links=['https://ollama.com/', 'https://ollama.com/models', 'https://github.com/ollama/ollama']
)

For more on converting HTML to Markdown, see our guide on converting HTML to Markdown with Python.

Combining Search and Fetch

A common pattern is to search first, then fetch full content from relevant results:

from ollama import web_search, web_fetch

# Search for information
search_results = web_search("Ollama new features 2025")

# Fetch full content from the first result
if search_results.results:
    first_url = search_results.results[0].url
    full_content = web_fetch(first_url)

    print(f"Title: {full_content.title}")
    print(f"Content: {full_content.content[:500]}...")
    print(f"Links found: {len(full_content.links)}")

Building a Search Agent

Models with strong tool‑use capabilities work best, such as qwen3, gpt-oss, and cloud models like qwen3:480b-cloud and deepseek-v3.1-cloud. See the guide on LLMs with Structured Output using Ollama and Qwen3 for advanced use cases.

First, pull a capable model:

ollama pull qwen3:4b

Simple Search Agent

A basic search agent that can autonomously decide when to search:

from ollama import chat, web_fetch, web_search

available_tools = {'web_search': web_search, 'web_fetch': web_fetch}
messages = [{'role': 'user', 'content': "what is ollama's new engine"}]

while True:
    response = chat(
        model='qwen3:4b',
        messages=messages,
        tools=[web_search, web_fetch],
        think=True
    )

    if response.message.thinking:
        print('🧠 Thinking:', response.message.thinking[:200], '...')

    if response.message.content:
        print('💬 Response:', response.message.content)

    messages.append(response.message)

    if response.message.tool_calls:
        print('🔧 Tool calls:', response.message.tool_calls)
        for tool_call in response.message.tool_calls:
            function_to_call = available_tools.get(tool_call.function.name)
            if function_to_call:
                args = tool_call.function.arguments
                result = function_to_call(**args)
                print('📥 Result:', str(result)[:200], '...')
                # Truncate result for limited context lengths
                messages.append({
                    'role': 'tool',
                    'content': str(result)[:2000 * 4],
                    'tool_name': tool_call.function.name
                })
            else:
                messages.append({
                    'role': 'tool',
                    'content': f'Tool {tool_call.function.name} not found',
                    'tool_name': tool_call.function.name
                })
    else:
        break

Handling large web search results
Truncate results to fit context limits (≈ 8000 characters, i.e., 2000 tokens × 4 chars) before passing them to the model.

Advanced Search Agent with Error Handling

An enhanced version with better error handling:

from ollama import chat, web_fetch, web_search
import json

class SearchAgent:
    def __init__(self, model: str = 'qwen3:4b'):
        self.model = model
        self.tools = {'web_search': web_search, 'web_fetch': web_fetch}
        self.messages = []
        self.max_iterations = 10

    def query(self, question: str) -> str:
        self.messages = [{'role': 'user', 'content': question}]

        for iteration in range(self.max_iterations):
            try:
                response = chat(
                    model=self.model,
                    messages=self.messages,
                    tools=[web_search, web_fetch],
                    think=True
                )
            except Exception as e:
                return f"Error during chat: {e}"

            self.messages.append(response.message)

            # If no tool calls, we have a final answer
            if not response.message.tool_calls:
                return