Google Knowledge Graph

What is Google Knowledge Graph?

The Knowledge Graph panel is the information box that appears on the right side of Google results for recognized entities such as companies, people, places, and products. Scavio's SERP API parses this panel into a normalized JSON object containing the entity title, subtitle, description, hero image, source attribution, and a factoids array of key attributes like founders, headquarters, stock symbol, or birth date. We resolve entities through Google's underlying KGID where possible so results stay stable across queries. For AI agents building entity resolution, enrichment pipelines, or grounded RAG flows, the Knowledge Graph gives you an authoritative, structured snapshot of an entity without scraping a separate source. In 2026 this is the cleanest way to get Google's own view of who or what something is.

Example Response

{
  "knowledge_graph": {
    "title": "Anthropic",
    "subtitle": "AI safety company",
    "kgmid": "/g/11h_2rqkf2",
    "description": "Anthropic is an American AI safety and research company based in San Francisco.",
    "source": {
      "name": "Wikipedia",
      "link": "https://en.wikipedia.org/wiki/Anthropic"
    },
    "image": "https://serpapi.scavio.dev/kg/anthropic-logo.png",
    "factoids": [
      { "label": "Founded", "value": "2021" },
      { "label": "Headquarters", "value": "San Francisco, California" },
      { "label": "CEO", "value": "Dario Amodei" },
      { "label": "Industry", "value": "Artificial intelligence" },
      { "label": "Products", "value": "Claude" }
    ],
    "profiles": [
      { "name": "Twitter", "link": "https://twitter.com/anthropicai" },
      { "name": "LinkedIn", "link": "https://linkedin.com/company/anthropicresearch" }
    ]
  }
}

Use Cases

• Entity enrichment for CRM and sales intelligence tools
• Grounding LLM answers with authoritative entity facts
• Automated company profile generation for investor research
• Building disambiguation UIs that show multiple candidate entities
• Populating internal knowledge bases with structured metadata

Why Google Knowledge Graph Matters

Knowledge Graph data is the fastest path to authoritative entity facts without maintaining your own wiki scraper. Scavio returns it as clean JSON with stable field names so your agent can feed it directly into prompts, vector stores, or structured outputs. Teams use it to ground generative answers, reduce hallucinations, and enrich records at scale without paying per-entity lookups to legacy data providers.

LangChain Example

Drop google knowledge graph data into your LangChain agent in a few lines:

from langchain_scavio import ScavioSERPTool
from langchain_anthropic import ChatAnthropic
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

tool = ScavioSERPTool(api_key="your_scavio_api_key", extract="knowledge_graph")

prompt = ChatPromptTemplate.from_messages([
    ("system", "Use the Scavio SERP tool to fetch Knowledge Graph facts."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

llm = ChatAnthropic(model="claude-opus-4-6")
agent = create_tool_calling_agent(llm, [tool], prompt)
executor = AgentExecutor(agent=agent, tools=[tool])

result = executor.invoke({"input": "Who founded Anthropic and when?"})
print(result["output"])