Retrieval-Augmented Generation (RAG) agents improve answer quality by grounding LLM responses in retrieved documents. Most RAG pipelines use static vector stores that go stale. By plugging Scavio into a LangChain agent as a retrieval tool, the agent can fetch live Google search results, YouTube transcripts, or Amazon product data on demand. This tutorial installs langchain-scavio, wires it into a LangChain ReAct agent, and runs a question that requires live retrieval to answer accurately.
Prerequisites
- Python 3.10 or higher
- pip install langchain langchain-scavio langchain-openai
- A Scavio API key
- An OpenAI API key (or substitute any LangChain-compatible LLM)
Walkthrough
Step 1: Install dependencies
Install LangChain, the Scavio integration package, and an LLM provider. The langchain-scavio package exposes ScavioSearch as a LangChain Tool.
pip install langchain langchain-scavio langchain-openaiStep 2: Import and configure ScavioSearch
ScavioSearch wraps the Scavio API as a LangChain BaseTool. Pass your API key and optionally restrict to a specific platform.
from langchain_scavio import ScavioSearch
search_tool = ScavioSearch(
api_key="your_scavio_api_key",
platform="google",
country_code="us",
max_results=5
)Step 3: Create the ReAct agent
Bind the search tool to a LangChain agent with an OpenAI LLM. The agent will decide when to call the search tool.
from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain import hub
llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, [search_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[search_tool], verbose=True)Step 4: Run the agent
Invoke the agent with a question that requires live information. It will call Scavio, retrieve results, and synthesize an answer.
result = executor.invoke({"input": "What are the latest Python web frameworks released in 2026?"})
print(result["output"])Python Example
import os
from langchain_scavio import ScavioSearch
from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain import hub
os.environ["OPENAI_API_KEY"] = "your_openai_key"
os.environ["SCAVIO_API_KEY"] = "your_scavio_api_key"
search_tool = ScavioSearch(
api_key=os.environ["SCAVIO_API_KEY"],
platform="google",
country_code="us",
max_results=5
)
llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, [search_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[search_tool], verbose=True)
if __name__ == "__main__":
result = executor.invoke({"input": "What are the latest AI frameworks released in 2026?"})
print(result["output"])JavaScript Example
// LangChain.js integration with Scavio via HTTP tool
const { ChatOpenAI } = require("@langchain/openai");
const { DynamicTool } = require("@langchain/core/tools");
const { AgentExecutor, createReactAgent } = require("langchain/agents");
const { pull } = require("langchain/hub");
const API_KEY = process.env.SCAVIO_API_KEY;
const searchTool = new DynamicTool({
name: "scavio_search",
description: "Search the web for current information",
func: async (query) => {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "Content-Type": "application/json" },
body: JSON.stringify({ query, country_code: "us" })
});
const data = await res.json();
return JSON.stringify(data.organic_results?.slice(0, 3) || []);
}
});
async function main() {
const llm = new ChatOpenAI({ model: "gpt-4o" });
const prompt = await pull("hwchase17/react");
const agent = await createReactAgent({ llm, tools: [searchTool], prompt });
const executor = new AgentExecutor({ agent, tools: [searchTool] });
const result = await executor.invoke({ input: "Latest AI news in 2026?" });
console.log(result.output);
}
main().catch(console.error);Expected Output
{
"input": "What are the latest AI frameworks released in 2026?",
"output": "Based on search results, several AI frameworks launched in 2026 including...",
"intermediate_steps": [
{
"action": "scavio_search",
"action_input": "AI frameworks released 2026",
"observation": "[{\"title\": \"Top AI Frameworks 2026\", ..."}]
}
]
}