What is AutoGen?
Microsoft's framework for building multi-agent conversational AI systems. Agents can converse, use tools, and collaborate autonomously.
Searching Walmart with AutoGen
This integration lets your AutoGen 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 autogen-agentchat requestsCode Example
Here is a complete AutoGen agent that searches Walmart using Scavio:
Python
from autogen import ConversableAgent
import json
import os
import requests
# Product detail is a separate call: POST /api/v1/walmart/product with product_id (the id field below).
def scavio_search(query: str) -> str:
"""Query Walmart through the Scavio API (POST /api/v1/walmart/search)."""
response = requests.post(
"https://api.scavio.dev/api/v1/walmart/search",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query},
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 Walmart")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: standing desk")Full Working Example
A production-ready example with error handling:
Python
from autogen import ConversableAgent
import json
import os
import requests
# Product detail is a separate call: POST /api/v1/walmart/product with product_id (the id field below).
def scavio_search(query: str) -> str:
"""Query Walmart through the Scavio API (POST /api/v1/walmart/search)."""
response = requests.post(
"https://api.scavio.dev/api/v1/walmart/search",
headers={
"Authorization": "Bearer " + os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json",
},
json={"query": query},
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 Walmart")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: standing desk")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.