What is LangChain?
The most popular framework for building LLM-powered applications. Provides chains, agents, and tools for composing AI workflows.
Searching Google with LangChain
This integration lets your LangChain agent search Google in real time via the Scavio API. The agent gets back structured JSON with organic results, knowledge graph, People Also Ask, AI overview — ready for reasoning and decision-making.
Setup
Bash
pip install langchain langchain-scavio langchain-openaiCode Example
Here is a complete LangChain agent that searches Google using Scavio:
Python
from langchain_scavio import ScavioSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
# Initialize the Scavio search tool
tool = ScavioSearch(api_key="your_scavio_api_key")
# Create an agent with the tool
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful research assistant."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, [tool], prompt)
executor = AgentExecutor(agent=agent, tools=[tool])
# Search specifically on Google
result = agent.invoke({"input": "Search Google for best noise cancelling headphones 2026"})
print(result["output"])Full Working Example
A production-ready example with error handling:
Python
"""
Search Google with LangChain + Scavio.
Uses Scavio as a LangChain tool for real-time web data.
"""
from langchain_scavio import ScavioSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
tool = ScavioSearch(api_key="your_scavio_api_key")
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant with access to real-time search."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, [tool], prompt)
executor = AgentExecutor(agent=agent, tools=[tool], verbose=True)
# Search specifically on Google
result = agent.invoke({"input": "Search Google for best noise cancelling headphones 2026"})
print(result["output"])Pricing
Scavio offers a free tier with 500 credits/month (1 credit per search). No credit card required. This is enough to build and test your LangChain integration. Paid plans start at $30/month for higher volumes.