What is AutoGen?
Microsoft's framework for building multi-agent conversational AI systems. Agents can converse, use tools, and collaborate autonomously.
Searching Google Scholar with AutoGen
This integration lets your AutoGen agent search Google Scholar in real time via the Scavio API. The agent gets back structured JSON with paper titles, paper URLs, SERP snippets -- ready for reasoning and decision-making.
Setup
Bash
pip install autogen-agentchat requestsCode Example
Here is a complete AutoGen agent that searches Google Scholar using Scavio:
Python
from autogen import ConversableAgent
import json
import os
import requests
# Scavio has no Google Scholar endpoint, so citation counts and author lists are not available. This runs a Google web search - narrow it with site:arxiv.org or filetype:pdf.
def scavio_search(query: str) -> str:
"""Query Google Scholar 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 Google Scholar")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: retrieval augmented generation 2024")Full Working Example
A production-ready example with error handling:
Python
from autogen import ConversableAgent
import json
import os
import requests
# Scavio has no Google Scholar endpoint, so citation counts and author lists are not available. This runs a Google web search - narrow it with site:arxiv.org or filetype:pdf.
def scavio_search(query: str) -> str:
"""Query Google Scholar 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 Google Scholar")(scavio_search)
user.register_for_execution(name="search")(scavio_search)
user.initiate_chat(assistant, message="Research: retrieval augmented generation 2024")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.