Ollama 的 web search API 可用于为模型提供最新信息，从而减少幻觉并提高准确性。

Web search 以 REST API 形式提供，并在 Python 和 JavaScript 库中提供了更深层的工具集成。这也使得 OpenAI 的 gpt-oss 等模型能够执行长时间运行的研究任务。

Authentication

要访问 Ollama 的 web search API，请创建一个 API key。需要一个免费的 Ollama 账户。

Web search API

对单个查询执行 web search 并返回相关结果。

Request

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

  • query (string, required): 搜索查询字符串
  • max_results (integer, optional): 返回的最大结果数（默认 5，最大 10）

Response

返回一个包含以下内容的对象：

  • results (array): 搜索结果对象数组，每个对象包含：
    • title (string): 网页标题
    • url (string): 网页 URL
    • content (string): 来自网页的相关内容片段

Examples

确保设置了 OLLAMA_API_KEY，或者必须在 Authorization header 中传递该密钥。

cURL Request

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

Response

{
  "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

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

Example 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 - 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..."
    }
]

更多 Ollama Python example

JavaScript Library

import { Ollama } from "ollama";

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

Example output

{
  "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..."
    }
  ]
}

更多 Ollama JavaScript example

Web fetch API

通过 URL 获取单个网页并返回其内容。

Request

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

  • url (string, required): 要获取的 URL

Response

返回一个包含以下内容的对象：

  • title (string): 网页标题
  • content (string): 网页主要内容
  • links (array): 页面上发现的链接数组

Examples

cURL Request

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

{
  "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

from ollama import web_fetch

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

结果

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

import { Ollama } from "ollama";

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

结果

{
  "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"
  ]
}

构建搜索代理

使用 Ollama 的 Web 搜索 API 作为工具来构建一个迷你搜索代理。

此示例使用了 Alibaba 的 Qwen 3 模型（4B 参数）。

ollama pull qwen3:4b

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]+'...')
        # 为了限制上下文长度，结果被截断
        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

结果

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.

上下文长度与代理

Web 搜索结果可能会返回数千个 token。建议将模型的上下文长度至少增加到 ~32000 个 token。搜索代理在完整上下文长度下效果最佳。Ollama 的云端模型以完整上下文长度运行。

MCP Server

你可以通过 Python MCP server 在任何 MCP 客户端中启用 Web 搜索。

Cline

通过 MCP 服务器配置，可以轻松地将 Ollama 的网络搜索功能集成到 Cline 中。

Manage MCP Servers > Configure MCP Servers > 添加以下配置：

{
  "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 配置

Codex

Ollama 与 OpenAI 的 Codex 工具配合良好。

将以下配置添加到 ~/.codex/config.toml

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

Goose

Ollama 可以通过其 MCP 功能与 Goose 集成。

Goose MCP 配置 1Goose MCP 配置 2

其他集成

Ollama 可以通过直接集成 Ollama API、Python / JavaScript 库、OpenAI 兼容 API 以及 MCP 服务器集成等方式，集成到大多数可用工具中。