Building an Autonomous Research Agent with OpenClaw Web Search and Fetch

One of the most time-consuming tasks in our daily workflow is online research. Whether you need to analyze competitors, gather intel on a new framework, or compile a press review, the process is always the same: search on Google, open dozens of tabs, read, extract, and synthesize.

With OpenClaw, you can completely delegate this workflow to an autonomous AI agent. In this article, I will show you how to leverage the built-in web_search and web_fetch tools to create a dedicated "research assistant".

The Native Tools: web_search and web_fetch

Unlike other frameworks that require complex setups with Puppeteer or expensive external APIs, OpenClaw natively integrates two tools designed to work in synergy:

1. web_search: Uses the Brave Search API to perform targeted queries, providing structured results with titles, URLs, and short snippets. You can filter by date (freshness), language, and region.
2. web_fetch: Takes the URL found during the search phase and extracts its readable content. It automatically converts the HTML code of the page into Markdown or plain text format, removing ads, menus, and background noise.

By combining these two tools, the agent can navigate the web smartly and lightweightly, without the overhead of a real browser (unless you need to bypass logins or complex interfaces, for which the browser tool exists).

The Autonomous Researcher Workflow

To put your research agent to work, you just need to provide a structured prompt. When I ask my agent to do some research, it implicitly follows this workflow:

1. Initial Search: Executes web_search with my query, perhaps setting freshness="week" if I only want recent news.
2. Source Selection: Analyzes the snippets of the results (usually picking the top 3 or 5 relevant URLs).
3. Content Extraction: Launches a series of parallel or sequential web_fetch calls on the selected URLs.
4. Synthesis: Parses the extracted text and generates a formatted report, cross-referencing data and citing sources.

A Practical Example

Here is a typical command you might give your agent in chat:

"Search for the latest news on the Meta Llama 4 release from the past week. Read at least three relevant articles and create a bulleted summary, indicating the original sources."

Behind the scenes, the agent will first run:

{
  "query": "Meta Llama 4 release news",
  "freshness": "week"
}

And right after, for each interesting link found:

{
  "url": "https://techcrunch.com/...",
  "extractMode": "markdown"
}

The result is a perfect report, created in a matter of seconds, with precise and contextualized information.

Limitations and Best Practices

After building several research workflows with OpenClaw, I learned a few fundamental rules to keep the agent from going off the rails:

• Watch the Length: The content extracted by web_fetch can be huge. Make sure the agent uses the maxChars parameter if it is reading many pages together, or it will clog up its own context window.
• Not Everything is Extractable: Sites with heavy anti-bot protections (like Cloudflare) or very complex Single Page Applications (SPAs) might return empty results with a quick fetch. In those rare cases, tell the agent to fall back to full browser automation.
• Cite Your Sources: Always force the agent to include original links in the final report. Hallucinations are rare when the agent has the page text at hand, but being able to verify the original source is crucial.

Conclusion

Building a research agent with OpenClaw does not require writing dozens of lines of Python code or managing complex dependencies. The web_search and web_fetch tools make information retrieval a native and seamless action.

How would you use an assistant like this in your daily work?