What is LlamaIndex?
Data framework for building RAG pipelines and LLM applications over custom data. Connects LLMs to external data sources.
Searching Amazon Bestsellers with LlamaIndex
This integration lets your LlamaIndex agent search Amazon Bestsellers in real time via the Scavio API. The agent gets back structured JSON with products, badge, sales_volume, price -- ready for reasoning and decision-making.
Setup
Bash
pip install llama-index requestsCode Example
Here is a complete LlamaIndex agent that searches Amazon Bestsellers using Scavio:
Python
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import json
import os
import requests
# There is no bestsellers endpoint and Amazon search takes no sort parameter, so this is the marketplace's default ranking for the query, not the Best Sellers chart. Store position per ASIN on each run to see movement.
def search_amazon_bestsellers(query: str) -> str:
"""Query Amazon Bestsellers through the Scavio API (POST /api/v1/amazon/search)."""
response = requests.post(
"https://api.scavio.dev/api/v1/amazon/search",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query, "country": "us"},
timeout=60,
)
response.raise_for_status()
return json.dumps(response.json())
tool = FunctionTool.from_defaults(fn=search_amazon_bestsellers)
llm = OpenAI(model="gpt-5.5")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)
response = agent.chat("electronics bestsellers")
print(response)Full Working Example
A production-ready example with error handling:
Python
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import json
import os
import requests
# There is no bestsellers endpoint and Amazon search takes no sort parameter, so this is the marketplace's default ranking for the query, not the Best Sellers chart. Store position per ASIN on each run to see movement.
def search_amazon_bestsellers(query: str) -> str:
"""Query Amazon Bestsellers through the Scavio API (POST /api/v1/amazon/search)."""
response = requests.post(
"https://api.scavio.dev/api/v1/amazon/search",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query, "country": "us"},
timeout=60,
)
response.raise_for_status()
return json.dumps(response.json())
tool = FunctionTool.from_defaults(fn=search_amazon_bestsellers)
llm = OpenAI(model="gpt-5.5")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)
response = agent.chat("electronics bestsellers")
print(response)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 LlamaIndex integration. Paid plans start at $30/month for higher volumes.