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Scavio AI

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How to give your OpenClaw agent Access To Real-time SERP - Google Search Data in Less Than 2 Minutes

#openclaw #ai #agents #langchain

By default your OpenClaw agent only knows what it already knows. The moment it needs to look something up — check a fact, find documentation, get current news — it has no way to reach the web.

Here's how to fix that.

1. Install the skill 

clawhub install scavio-google
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2. Set your API key

Get a free key at scavio.dev (1,000 credits/month, no card), then:

export SCAVIO_API_KEY=sk_live_your_key
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The skill won't load without this — it gates on SCAVIO_API_KEY so you get a clear error rather than a silent failure.

3. Ask your agent

That's it. Your agent can now search Google. Just ask it naturally:

Search for the latest LangChain agent tools and summarize what you find.
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Find recent news about AI funding rounds this week.
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Look up the documentation for CrewAI tool calling and explain how it works.
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The skill teaches your agent when to use Google search, how to call the API, and how to interpret the results — including news, images, maps, and knowledge graph data depending on what you're asking for.

What comes back

The agent gets structured JSON — titles, URLs, descriptions, position — not raw HTML. It can reason over the results directly without any parsing step.

For news queries it also gets source, date, and domain. For full-mode queries (2 credits) it gets knowledge graph, people also ask, and related searches.

Direct API access

If you want to call the API yourself outside of OpenClaw:

import os, requests

response = requests.post(
    "https://api.scavio.dev/api/v1/google",
    headers={"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"},
    json={"query": "langchain agent tools", "search_type": "classic"},
)
print(response.json()["results"])
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Full docs at scavio.dev/docs

(I work with the Scavio team.)

Top comments (1)

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michalis_ouroumis_d9969aa profile image
Mike Ouroumis •

Access to real-time SERP is a game-changer for agent knowledge, but the real complexity emerges in teaching the agent to effectively synthesize those results. We've found the quality of the prompt for interpreting search output, and the agent's ability to critically evaluate diverse sources, often dictates success more than just raw access.