Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
from flask import Flask, request, jsonify
from crewai import Crew
from textwrap import dedent
from stock_analysis_agents import StockAnalysisAgents
from stock_analysis_tasks import StockAnalysisTasks
from dotenv import load_dotenv
load_dotenv()
app = Flask(__name__)
class FinancialCrew:
def __init__(self, company):
self.company = company
def run(self):
agents = StockAnalysisAgents()
tasks = StockAnalysisTasks()
research_analyst_agent = agents.research_analyst()
financial_analyst_agent = agents.financial_analyst()
investment_advisor_agent = agents.investment_advisor()
research_task = tasks.research(research_analyst_agent, self.company)
financial_task = tasks.financial_analysis(financial_analyst_agent)
filings_task = tasks.filings_analysis(financial_analyst_agent)
recommend_task = tasks.recommend(investment_advisor_agent)
crew = Crew(
agents=[
research_analyst_agent,
financial_analyst_agent,
investment_advisor_agent
],
tasks=[
research_task,
financial_task,
filings_task,
recommend_task
],
verbose=True
)
result = crew.kickoff()
return result
@app.route('/analyze', methods=['POST'])
def analyze():
company = request.json.get('company')
financial_crew = FinancialCrew(company)
result = financial_crew.run()
return jsonify(result)
if __name__ == "__main__":
app.run(debug=True)
The above is a scenario example: I have encapsulated multiple agents using the Flask framework. During the running of the Flask program, if I want to dynamically add agents, such as research_analyst_agent2, financial_analyst_agent2, investment_advisor_agent2, how should I proceed?
The above is a scenario example: I have encapsulated multiple agents using the Flask framework. During the running of the Flask program, if I want to dynamically add agents, such as research_analyst_agent2, financial_analyst_agent2, investment_advisor_agent2, how should I proceed?