What is LangChain?
The most popular framework for building LLM-powered applications. Provides chains, agents, and tools for composing AI workflows.
Searching Walmart with LangChain
This integration lets your LangChain agent search Walmart in real time via the Scavio API. The agent gets back structured JSON with product listings, prices, ratings, review counts -- ready for reasoning and decision-making.
Setup
Bash
pip install langchain langchain-scavio langchain-openaiCode Example
Here is a complete LangChain agent that searches Walmart using Scavio:
Python
from langchain_scavio import ScavioWalmartSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
# scavio_api_key is the keyword the tool takes; SCAVIO_API_KEY also works.
tool = ScavioWalmartSearch(scavio_api_key="sk_live_your_key")
llm = ChatOpenAI(model="gpt-5.5")
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])
result = executor.invoke({"input": "Search Walmart for standing desk"})
print(result["output"])Full Working Example
A production-ready example with error handling:
Python
"""
Query Walmart with LangChain + Scavio.
ScavioWalmartSearch calls POST /api/v1/walmart/search under the hood.
"""
import os
from langchain_scavio import ScavioWalmartSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
os.environ.setdefault("SCAVIO_API_KEY", "sk_live_your_key")
tool = ScavioWalmartSearch()
llm = ChatOpenAI(model="gpt-5.5")
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)
result = executor.invoke({"input": "Search Walmart for standing desk"})
print(result["output"])Pricing
Scavio offers a free tier with 50 credits on signup (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.