LangChain Integration

from langchain.tools import StructuredTool
from pydantic import BaseModel, Field
import requests

class AnalyzeWebsiteInput(BaseModel):
    url: str = Field(
        description="The website URL to analyze (e.g., 'https://vercel.com')"
    )

def analyze_website(url: str) -> dict:
    """Get comprehensive intelligence about any website including
    company info, tech stack, contact details, and trust signals."""
    response = requests.post(
        "https://theindex.dev/api/v1/analyze",
        json={"url": url},
        headers={"Authorization": "Bearer idx_live_YOUR_KEY"}
    )
    return response.json()

website_intel_tool = StructuredTool.from_function(
    func=analyze_website,
    name="analyze_website",
    description=(
        "Get comprehensive intelligence about any website including "
        "company info, tech stack, contact details, and trust signals. "
        "Use this when you need to research a company or website."
    ),
    args_schema=AnalyzeWebsiteInput,
)

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant."),
    ("human", "{input}"),
])

agent = create_openai_tools_agent(llm, [website_intel_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[website_intel_tool])

result = executor.invoke({
    "input": "Research vercel.com and tell me what tech stack they use"
})