"""
🚀 CREWAI MASTER TEMPLATE (NO OPENAI ERRORS)
--------------------------------------------
Use this template for all new projects to avoid "RateLimitError".
It implements the "3-Step Rule" to ensure your free LLM is used everywhere.
PREREQUISITES:
1. pip install crewai langchain-groq python-dotenv
2. Create a .env file with: GROQ_API_KEY=your_key_here
"""
import os
from dotenv import load_dotenv
from crewai import Agent, Crew, Task, Process, LLM
# ----------------------------------------------------------------------
# 🔧 STEP 0: SETUP
# ----------------------------------------------------------------------
load_dotenv(override=True)
# Define your LLM *ONCE* here. Switch comments to use Ollama/Gemini.
# ----------------------------------------------------------------------
# Option 1: Groq (Fast, Free Cloud)
LLM_CONFIG = LLM(
model="groq/llama-3.1-8b-instant",
api_key=os.getenv("GROQ_API_KEY")
)
# Option 2: Ollama (Local, Unlimited)
# LLM_CONFIG = LLM(
# model="ollama/llama3.1",
# base_url="http://localhost:11434"
# )
# ----------------------------------------------------------------------
# 🤖 STEP 1: DEFINE AGENTS (Assign LLM to EVERY Agent)
# ----------------------------------------------------------------------
def create_researcher():
return Agent(
role="Senior Researcher",
goal="Uncover groundbreaking information about {topic}",
backstory="Driven by curiosity, you're at the forefront of innovation.",
verbose=True,
allow_delegation=False,
# 🔑 CRITICAL FIX: Always pass the llm parameter!
llm=LLM_CONFIG
)
def create_writer():
return Agent(
role="Tech Content Strategist",
goal="Craft compelling content on {topic}",
backstory="You are a renowned Content Strategist, known for simplifying complex topics.",
verbose=True,
allow_delegation=False,
# 🔑 CRITICAL FIX: Always pass the llm parameter!
llm=LLM_CONFIG
)
# ----------------------------------------------------------------------
# 📋 STEP 2: DEFINE TASKS
# ----------------------------------------------------------------------
def create_tasks(researcher_agent, writer_agent, topic):
task1 = Task(
description=f"Conduct a comprehensive analysis on {topic}.",
expected_output="A detailed report summarizing key findings.",
agent=researcher_agent
)
task2 = Task(
description=f"Using the insights provided, write an engaging blog post about {topic}.",
expected_output="A blog post of at least 4 paragraphs.",
agent=writer_agent
)
return [task1, task2]
# ----------------------------------------------------------------------
# 🚀 STEP 3: DEFINE CREW (Assign Manager LLM)
# ----------------------------------------------------------------------
def run_crew(topic_name):
# Instantiate agents
researcher = create_researcher()
writer = create_writer()
# Instantiate tasks
tasks = create_tasks(researcher, writer, topic_name)
# Create Crew
my_crew = Crew(
agents=[researcher, writer],
tasks=tasks,
process=Process.sequential, # Sequential execution is usually safer for rate limits
verbose=True,
# 🔑 CRITICAL FIX: Always pass the manager_llm!
manager_llm=LLM_CONFIG
)
print(f"\n🚀 Starting Crew on topic: {topic_name}\n")
result = my_crew.kickoff(inputs={'topic': topic_name})
return result
# ----------------------------------------------------------------------
# 🏁 MAIN EXECUTION
# ----------------------------------------------------------------------
if __name__ == "__main__":
# Test the setup
topic = "The Future of AI Agents"
result = run_crew(topic)
print("\n\n########################")
print("## FINAL OUTPUT ##")
print("########################\n")
print(result)
