What is AutoGen?
Microsoft's framework for building multi-agent conversational AI systems. Agents can converse, use tools, and collaborate autonomously.
Searching Meta Ad Library with AutoGen
This integration lets your AutoGen agent search Meta Ad Library in real time via the Scavio API. The agent gets back structured JSON with ad creatives, page_name and page_id, start_date and end_date, publisher_platform -- ready for reasoning and decision-making.
Setup
Bash
pip install autogen-agentchat requestsCode Example
Here is a complete AutoGen agent that searches Meta Ad Library using Scavio:
Python
from autogen import ConversableAgent
import json
import os
import requests
# This page has no dedicated endpoint yet, so the sample runs a Google web search.
def scavio_search(query: str) -> str:
"""Query Meta Ad Library through the Scavio API (POST /api/v2/google)."""
response = requests.post(
"https://api.scavio.dev/api/v2/google",
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 Meta Ad Library")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: notion")Full Working Example
A production-ready example with error handling:
Python
from autogen import ConversableAgent
import json
import os
import requests
# This page has no dedicated endpoint yet, so the sample runs a Google web search.
def scavio_search(query: str) -> str:
"""Query Meta Ad Library through the Scavio API (POST /api/v2/google)."""
response = requests.post(
"https://api.scavio.dev/api/v2/google",
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 Meta Ad Library")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: notion")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 from $24/mo billed yearly for higher volumes.