LangChain

pip install langchain langchain-openai requests

import requests
from langchain.tools import tool

ENCRATA_API_KEY = "YOUR_API_KEY"
BASE_URL = "https://developer.encrata.com/api/lookup/email"
HEADERS = {
    "Authorization": f"Bearer {ENCRATA_API_KEY}",
    "Content-Type": "application/json",
}

@tool
def lookup_email(email: str) -> dict:
    """Look up detailed intelligence about a person by their email address.
    Returns name, company, job role, industry, location, and social
    profiles. Costs 1,000 credits."""
    resp = requests.post(
        f"{BASE_URL}/identity",
        headers=HEADERS,
        json={"email": email},
    )
    resp.raise_for_status()
    return resp.json()

@tool
def validate_email(email: str) -> dict:
    """Validate whether an email address is deliverable, invalid,
    or disposable. Costs 1 credit; repeat lookups within the billing window are free."""
    resp = requests.post(
        f"{BASE_URL}/validity",
        headers=HEADERS,
        json={"email": email},
    )
    resp.raise_for_status()
    return resp.json()

@tool
def check_breaches(email: str) -> dict:
    """Check if an email has been exposed in known data breaches.
    Returns breach count, affected services, and exposed data types.
    Costs 1 credit; repeat lookups within the billing window are free."""
    resp = requests.post(
        f"{BASE_URL}/breaches",
        headers=HEADERS,
        json={"email": email},
    )
    resp.raise_for_status()
    return resp.json()

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

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

tools = [lookup_email, validate_email, check_breaches]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a research assistant with access to email intelligence tools."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({
    "input": "Look up satya@microsoft.com and tell me about their role and company."
})
print(result["output"])