What is AutoGen?
Microsoft's framework for building multi-agent conversational AI systems. Agents can converse, use tools, and collaborate autonomously.
Searching Amazon Bestsellers with AutoGen
This integration lets your AutoGen 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 autogen-agentchat requestsCode Example
Here is a complete AutoGen agent that searches Amazon Bestsellers using Scavio:
Python
from autogen import ConversableAgent
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 scavio_search(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())
assistant = ConversableAgent(
"assistant",
llm_config={"model": "gpt-5.5"},
system_message="You are a helpful research assistant.",
)
user = ConversableAgent("user", human_input_mode="NEVER")
assistant.register_for_llm(name="search", description="Query Amazon Bestsellers")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: electronics bestsellers")Full Working Example
A production-ready example with error handling:
Python
from autogen import ConversableAgent
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 scavio_search(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())
assistant = ConversableAgent(
"assistant",
llm_config={"model": "gpt-5.5"},
system_message="You are a helpful research assistant with real-time search.",
)
user = ConversableAgent("user", human_input_mode="NEVER", max_consecutive_auto_reply=3)
assistant.register_for_llm(name="search", description="Query Amazon Bestsellers")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: electronics bestsellers")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 AutoGen integration. Paid plans start at $30/month for higher volumes.