ollama37/docs/capabilities/web-search.mdx

---
title: Web search
---

Ollama's web search API can be used to augment models with the latest information to reduce hallucinations and improve accuracy.

Web search is provided as a REST API with deeper tool integrations in the Python and JavaScript libraries. This also enables models like OpenAI’s gpt-oss models to conduct long-running research tasks.

## Authentication

For access to Ollama's web search API, create an [API key](https://ollama.com/settings/keys). A free Ollama account is required.

## Web search API

Performs a web search for a single query and returns relevant results.

### Request

`POST https://ollama.com/api/web_search`

- `query` (string, required): the search query string
- `max_results` (integer, optional): maximum results to return (default 5, max 10)

### Response

Returns an object containing:

- `results` (array): array of search result objects, each containing:
  - `title` (string): the title of the web page
  - `url` (string): the URL of the web page
  - `content` (string): relevant content snippet from the web page

### Examples

<Note>
  Ensure OLLAMA_API_KEY is set or it must be passed in the Authorization header.
</Note>

#### cURL Request

```bash
curl https://ollama.com/api/web_search \
  --header "Authorization: Bearer $OLLAMA_API_KEY" \
	-d '{
	  "query":"what is ollama?"
	}'
```

**Response**

```json
{
  "results": [
    {
      "title": "Ollama",
      "url": "https://ollama.com/",
      "content": "Cloud models are now available..."
    },
    {
      "title": "What is Ollama? Introduction to the AI model management tool",
      "url": "https://www.hostinger.com/tutorials/what-is-ollama",
      "content": "Ariffud M. 6min Read..."
    },
    {
      "title": "Ollama Explained: Transforming AI Accessibility and Language ...",
      "url": "https://www.geeksforgeeks.org/artificial-intelligence/ollama-explained-transforming-ai-accessibility-and-language-processing/",
      "content": "Data Science Data Science Projects Data Analysis..."
    }
  ]
}
```

#### Python library

```python
import ollama
response = ollama.web_search("What is Ollama?")
print(response)
```

**Example output**

```python

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

```

More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/examples/web-search.py)

#### JavaScript Library

```tsx
import { Ollama } from "ollama";

const client = new Ollama();
const results = await client.webSearch({ query: "what is ollama?" });
console.log(JSON.stringify(results, null, 2));
```

**Example output**

```json
{
  "results": [
    {
      "title": "Ollama",
      "url": "https://ollama.com/",
      "content": "Cloud models are now available..."
    },
    {
      "title": "What is Ollama? Introduction to the AI model management tool",
      "url": "https://www.hostinger.com/tutorials/what-is-ollama",
      "content": "Ollama is an open-source tool..."
    },
    {
      "title": "Ollama Explained: Transforming AI Accessibility and Language Processing",
      "url": "https://www.geeksforgeeks.org/artificial-intelligence/ollama-explained-transforming-ai-accessibility-and-language-processing/",
      "content": "Ollama is a groundbreaking..."
    }
  ]
}
```

More Ollama [JavaScript example](https://github.com/ollama/ollama-js/blob/main/examples/websearch/websearch-tools.ts)

## Web fetch API

Fetches a single web page by URL and returns its content.

### Request

`POST https://ollama.com/api/web_fetch`

- `url` (string, required): the URL to fetch

### Response

Returns an object containing:

- `title` (string): the title of the web page
- `content` (string): the main content of the web page
- `links` (array): array of links found on the page

### Examples

#### cURL Request

```python
curl --request POST \
  --url https://ollama.com/api/web_fetch \
  --header "Authorization: Bearer $OLLAMA_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
      "url": "ollama.com"
  }'
```

**Response**

```json
{
  "title": "Ollama",
  "content": "[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama...",
  "links": [
    "http://ollama.com/",
    "http://ollama.com/models",
    "https://github.com/ollama/ollama"
  ]

```

#### Python SDK

```python
from ollama import web_fetch

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

**Result**

```python
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']
)
```

#### JavaScript SDK

```tsx
import { Ollama } from "ollama";

const client = new Ollama();
const fetchResult = await client.webFetch({ url: "https://ollama.com" });
console.log(JSON.stringify(fetchResult, null, 2));
```

**Result**

```json
{
  "title": "Ollama",
  "content": "[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama...",
  "links": [
    "https://ollama.com/",
    "https://ollama.com/models",
    "https://github.com/ollama/ollama"
  ]
}
```

## Building a search agent

Use Ollama’s web search API as a tool to build a mini search agent.

This example uses Alibaba’s Qwen 3 model with 4B parameters.

```bash
ollama pull qwen3:4b
```

```python
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)
  if response.message.content:
    print('Content: ', 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]+'...')
        # Result is truncated 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
```

**Result**

```
Thinking:  Okay, the user is asking about Ollama's new engine. I need to figure out what they're referring to. Ollama is a company that develops large language models, so maybe they've released a new model or an updated version of their existing engine....

Tool calls:  [ToolCall(function=Function(name='web_search', arguments={'max_results': 3, 'query': 'Ollama new engine'}))]
Result:  results=[WebSearchResult(content='# New model scheduling\n\n## September 23, 2025\n\nOllama now includes a significantly improved model scheduling system. Ahead of running a model, Ollama’s new engine

Thinking:  Okay, the user asked about Ollama's new engine. Let me look at the search results.

First result is from September 23, 2025, talking about new model scheduling. It mentions improved memory management, reduced crashes, better GPU utilization, and multi-GPU performance. Examples show speed improvements and accurate memory reporting. Supported models include gemma3, llama4, qwen3, etc...

Content:  Ollama has introduced two key updates to its engine, both released in 2025:

1. **Enhanced Model Scheduling (September 23, 2025)**
   - **Precision Memory Management**: Exact memory allocation reduces out-of-memory crashes and optimizes GPU utilization.
   - **Performance Gains**: Examples show significant speed improvements (e.g., 85.54 tokens/s vs 52.02 tokens/s) and full GPU layer utilization.
   - **Multi-GPU Support**: Improved efficiency across multiple GPUs, with accurate memory reporting via tools like `nvidia-smi`.
   - **Supported Models**: Includes `gemma3`, `llama4`, `qwen3`, `mistral-small3.2`, and more.

2. **Multimodal Engine (May 15, 2025)**
   - **Vision Support**: First-class support for vision models, including `llama4:scout` (109B parameters), `gemma3`, `qwen2.5vl`, and `mistral-small3.1`.
   - **Multimodal Tasks**: Examples include identifying animals in multiple images, answering location-based questions from videos, and document scanning.

These updates highlight Ollama's focus on efficiency, performance, and expanded capabilities for both text and vision tasks.
```

### Context length and agents

Web search results can return thousands of tokens. It is recommended to increase the context length of the model to at least ~32000 tokens. Search agents work best with full context length. [Ollama's cloud models](https://docs.ollama.com/cloud) run at the full context length.

## MCP Server

You can enable web search in any MCP client through the [Python MCP server](https://github.com/ollama/ollama-python/blob/main/examples/web-search-mcp.py).

### Cline

Ollama's web search can be integrated with Cline easily using the MCP server configuration.

`Manage MCP Servers` > `Configure MCP Servers` > Add the following configuration:

```json
{
  "mcpServers": {
    "web_search_and_fetch": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "path/to/web-search-mcp.py"],
      "env": { "OLLAMA_API_KEY": "your_api_key_here" }
    }
  }
}
```

![Cline MCP Configuration](/images/cline-mcp.png)

### Codex

Ollama works well with OpenAI's Codex tool.

Add the following configuration to `~/.codex/config.toml`

```python
[mcp_servers.web_search]
command = "uv"
args = ["run", "path/to/web-search-mcp.py"]
env = { "OLLAMA_API_KEY" = "your_api_key_here" }
```

![Codex MCP Configuration](/images/codex-mcp.png)

### Goose

Ollama can integrate with Goose via its MCP feature.

![Goose MCP Configuration 1](/images/goose-mcp-1.png)

![Goose MCP Configuration 2](/images/goose-mcp-2.png)

### Other integrations

Ollama can be integrated into most of the tools available either through direct integration of Ollama's API, Python / JavaScript libraries, OpenAI compatible API, and MCP server integration.