What is CrewAI?
Framework for orchestrating autonomous AI agents that collaborate to complete tasks. Supports multi-agent crews with defined roles.
Searching Google Scholar with CrewAI
This integration lets your CrewAI 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 crewai requestsCode Example
Here is a complete CrewAI agent that searches Google Scholar using Scavio:
Python
from crewai import Agent, Task, Crew
from crewai.tools import tool
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.
@tool("Scavio Search")
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())
researcher = Agent(
role="Google Scholar Research Specialist",
goal="Find accurate, up-to-date information using Google Scholar data",
backstory="You are an expert researcher with access to real-time search data.",
tools=[scavio_search],
)
task = Task(
description="Research: retrieval augmented generation 2024",
expected_output="A detailed summary with sources",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
print(result)Full Working Example
A production-ready example with error handling:
Python
from crewai import Agent, Task, Crew
from crewai.tools import tool
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.
@tool("Scavio Search")
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())
researcher = Agent(
role="Google Scholar Research Specialist",
goal="Find accurate, up-to-date information",
backstory="Expert researcher with real-time search access.",
tools=[scavio_search],
)
task = Task(
description="Research: retrieval augmented generation 2024",
expected_output="Detailed summary with key findings and sources",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task], verbose=True)
result = crew.kickoff()
print(result)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 CrewAI integration. Paid plans start at $30/month for higher volumes.