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Ollama's Web Search and Web Fetch API: Giving a Local Model Current Information

By Web Asset House Studio ·

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How we wrote this: A summary of Ollama's official Web search documentation, plus its Cloud and FAQ pages (linked below), checked October 29, 2026. Ollama's account requirements and limits can change; check the docs before you build on them.

A model running on your computer only knows what was in its training data. Ollama's docs describe its web search API as a way to "augment models with the latest information to reduce hallucinations and improve accuracy." It's a hosted service on ollama.com, separate from running models locally, so it's worth understanding what it sends where.

What you need

The docs say web search requires an API key from your ollama.com account settings, and a free Ollama account is required. Set the key in the OLLAMA_API_KEY environment variable, or send it in an Authorization: Bearer header.

The web search endpoint

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

Response: a results array. Each result has:

A minimal curl call from the docs:

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

The web fetch endpoint

Search gives snippets. To read a whole page, the docs describe a second endpoint:

Request: POST https://ollama.com/api/web_fetch with a required url.

Response: the page's title, its main content, and a links array of links found on the page.

From Python and JavaScript

Ollama's official libraries wrap both endpoints. In the docs' examples:

Setup for each library is in our Python library and JavaScript library guides.

Letting the model decide when to search

The more useful pattern is giving the model search and fetch as tools and letting it call them. The docs build a "mini search agent" with Qwen 3 (4B parameters, pulled locally with ollama pull qwen3:4b):

  1. Send the user's question to chat() with tools=[web_search, web_fetch] and thinking turned on.
  2. If the reply includes tool_calls, run each requested function with the model's arguments.
  3. Append each result as a tool message and call chat() again.
  4. Stop when the model answers without asking for a tool.

The example truncates each tool result (to about 8,000 characters) before adding it back, with a comment that this is for limited context lengths. Search results can be long, so a model with a small context window can run out of room quickly. Our guides on tool calling and context length cover both sides of that.

The privacy trade-off

Running a model locally keeps prompts on your computer; Ollama's FAQ says it doesn't see your prompts or data when you run locally. Web search is different by design: your search query goes to ollama.com, which returns results from the web. Fetched page content then goes into your local model's context.

If a machine should never reach Ollama's hosted services, the FAQ's local-only mode (disable_ollama_cloud in server.json, or OLLAMA_NO_CLOUD=1) turns off cloud models and web search. Our local-only mode guide walks through it. You can't have both at once on the same Ollama install; pick per machine.

Two practical cautions follow from how the tool loop works:

Where it fits

For questions about your own files, a local approach like chatting with your documents or embeddings-based search keeps everything on the machine. Web search fits questions about things that change: news, new releases, current documentation.

Quick checklist

Sources

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