What is OpenAI Assistants?
OpenAI's API for building AI assistants with function calling, code interpreter, and file search capabilities.
Searching Meta Ad Library with OpenAI Assistants
This integration lets your OpenAI Assistants 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 openai requestsCode Example
Here is a complete OpenAI Assistants agent that searches Meta Ad Library using Scavio:
Python
from openai import OpenAI
import json
import os
import requests
# This page has no dedicated endpoint yet, so the sample runs a Google web search.
client = OpenAI()
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())
tools = [{
"type": "function",
"function": {
"name": "scavio_search",
"description": "Query Meta Ad Library for real-time results",
"parameters": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"],
},
},
}]
response = client.chat.completions.create(
model="gpt-5.5",
messages=[{"role": "user", "content": "notion"}],
tools=tools,
)
for tool_call in response.choices[0].message.tool_calls or []:
result = scavio_search(json.loads(tool_call.function.arguments)["query"])
print(result[:500])Full Working Example
A production-ready example with error handling:
Python
from openai import OpenAI
import json
import os
import requests
# This page has no dedicated endpoint yet, so the sample runs a Google web search.
client = OpenAI()
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())
tools = [{
"type": "function",
"function": {
"name": "scavio_search",
"description": "Query Meta Ad Library for real-time results",
"parameters": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"],
},
},
}]
messages = [{"role": "user", "content": "notion"}]
response = client.chat.completions.create(model="gpt-5.5", messages=messages, tools=tools)
if response.choices[0].message.tool_calls:
messages.append(response.choices[0].message)
for tc in response.choices[0].message.tool_calls:
result = scavio_search(json.loads(tc.function.arguments)["query"])
messages.append({"role": "tool", "tool_call_id": tc.id, "content": result})
final = client.chat.completions.create(model="gpt-5.5", messages=messages)
print(final.choices[0].message.content)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 OpenAI Assistants integration. Paid plans start from $24/mo billed yearly for higher volumes.