Scavio + LangChain

What is LangChain?

The most popular framework for building LLM-powered applications. Provides chains, agents, and tools for composing AI workflows.

How It Works

Scavio connects to LangChain via a custom tool definition. Once connected, your LangChain agent can search Google, Amazon, YouTube, and Walmart in real time. Each search returns structured JSON -- no HTML parsing, no scraping infrastructure.

Setup

pip install langchain langchain-scavio langchain-openai

Code Example

Here is a complete LangChain integration with Scavio:

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])

    result = agent.invoke({"input": "Find the best noise cancelling headphones"})
print(result["output"])

Full Working Example

A production-ready example with error handling:

"""
Multi-platform search agent 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)

    result = agent.invoke({"input": "Find the best noise cancelling headphones"})
print(result["output"])

Available Platforms

Once connected, your LangChain agent has access to all four Scavio platforms:

• Google — Web search with knowledge graph, PAA, and AI overviews
• Amazon — Product search with prices, ratings, and reviews
• Reddit — Community, posts & threaded comments from any subreddit
• YouTube — Video search with transcripts and metadata
• Walmart — Product search with pricing and fulfillment data
• LinkedIn — Post, profile, and company discovery via search
• TikTok — Trending video, creator, and product discovery
• Shopify — Cross-store product discovery and enrichment
• X (Twitter) — Post and profile discovery via search
• Apple App Store — App discovery, ranking, and review data
• Google Play Store — Android app discovery and ranking data
• Google Reviews — Business review extraction with ratings and responses
• Google Scholar — Academic paper search with citation counts
• Google Ads Transparency — Competitor ad creative and transparency data via SERP
• Reddit Comments Tree — Deep threaded comment fetch for brand monitoring
• YouTube Shorts — Shorts-specific search with metadata
• YouTube Playlists — Playlist discovery and removal tracking
• Google Jobs — Live jobs-board search for recruiting
• Amazon Bestsellers — Bestseller rank-change feed across categories

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.