What is Related Searches?
Related Searches is the grid of suggested queries Google shows at the bottom of a SERP. These suggestions are generated from co-occurrence patterns in real search behavior and are a high-signal source of adjacent intent. Scavio returns the list as an ordered array, each item containing the suggested query text and a deep link to the corresponding SERP. Related searches differ meaningfully from autocomplete, which fires as a user types; related searches reflect what people actually clicked on after a query. In 2026, Google has expanded this section with categorized clusters (for example, comparisons, how-to, near me), and Scavio exposes these clusters when present so you can separate informational from commercial intent.
Example Response
{
"related_searches": [
{
"query": "best ai agents framework 2026",
"link": "https://www.google.com/search?q=best+ai+agents+framework+2026",
"cluster": "comparisons"
},
{
"query": "langgraph vs crewai",
"link": "https://www.google.com/search?q=langgraph+vs+crewai",
"cluster": "comparisons"
},
{
"query": "how to build an ai agent from scratch",
"link": "https://www.google.com/search?q=how+to+build+ai+agent",
"cluster": "how-to"
},
{
"query": "open source ai agents github",
"link": "https://www.google.com/search?q=open+source+ai+agents",
"cluster": "resources"
}
]
}Use Cases
- Keyword cluster expansion for content strategy
- Seeding competitor discovery from comparison queries
- Generating internal link anchor candidates for SEO
- Mapping intent clusters for paid search campaigns
- Surfacing adjacent product categories in ecommerce search
Why Related Searches Matters
Related searches reveal the actual semantic neighborhood Google associates with a topic, which is different from what keyword tools infer from search volume alone. Scavio's clustered output makes it trivial to branch a crawl from one seed query into hundreds of high-intent adjacencies. SEO teams use this for topic modeling, and AI agents use it to generate exploratory research plans autonomously.
LangChain Example
Drop related searches data into your LangChain agent in a few lines:
from langchain_scavio import ScavioSERPTool
tool = ScavioSERPTool(api_key="your_scavio_api_key", extract="related_searches")
seed = "ai agents framework"
result = tool.invoke({"query": seed})
queue = [r["query"] for r in result["related_searches"] if r["cluster"] == "comparisons"]
print("Comparison queries to crawl next:", queue)