通过Cerebras和CrewAI构建多AI代理工作流

CEREBRAS_API_KEY=csk-*************************************

pip install crewai crewai_tools

💡 It's HIGHLY recommended to store your API key securely, such as in environment variables, rather than hardcoding it.

from crewai import LLM
import os

# Configure the LLM to use Cerebras
cerebras_llm = LLM(
    model="cerebras/llama3.1-70b", # Replace with your chosen Cerebras model name, e.g., "cerebras/llama3.1-8b"
    api_key=os.environ.get("CEREBRAS_API_KEY"), # Your Cerebras API key
    base_url="https://api.cerebras.ai/v1",
    temperature=0.5,
    # Optional parameters:
    # top_p=1,
    # max_completion_tokens=8192, # Max tokens for the response
    # response_format={"type": "json_object"} # Ensures the response is in JSON format
)

from crewai import Agent
from crewai_tools import SerperDevTool

# Agent definition
researcher = Agent(
    role='{topic} Senior Researcher',
    goal='Uncover groundbreaking technologies in {topic} for the year 2024',
    backstory='Driven by curiosity, you explore and share the latest innovations.',
    tools=[SerperDevTool()],
    llm=cerebras_llm
)

from crewai import Task

# Define a research task for the Senior Researcher agent
research_task = Task(
    description='Identify the next big trend in {topic} with pros and cons.',
    expected_output='A 3-paragraph report on emerging {topic} technologies.',
    agent=researcher
)

from crewai import Crew, Process

def main():
    # Forming the crew and kicking off the process
    crew = Crew(
        agents=[researcher],
        tasks=[research_task],
        process=Process.sequential,
        verbose=True  # Enables detailed logging
    )
    result = crew.kickoff(inputs={'topic': 'AI Agents'})
    print(result)

if __name__ == "__main__":
    main()

from crewai import Agent, Task, Crew, Process, LLM
from crewai_tools import SerperDevTool
import os

# Configure the LLM to use Cerebras
cerebras_llm = LLM(
    model="cerebras/llama3.1-70b",  # Replace with your chosen Cerebras model name
    api_key=os.environ.get("CEREBRAS_API_KEY"),  # Your Cerebras API key
    base_url="https://api.cerebras.ai/v1",
    temperature=0.5,
)

# Agent definition
researcher = Agent(
    role='{topic} Senior Researcher',
    goal='Uncover groundbreaking technologies in {topic} for the year 2024',
    backstory='Driven by curiosity, you explore and share the latest innovations.',
    tools=[SerperDevTool()],
    llm=cerebras_llm
)

# Define a research task for the Senior Researcher agent
research_task = Task(
    description='Identify the next big trend in {topic} with pros and cons.',
    expected_output='A 3-paragraph report on emerging {topic} technologies.',
    agent=researcher
)

def main():
    # Forming the crew and kicking off the process
    crew = Crew(
        agents=[researcher],
        tasks=[research_task],
        process=Process.sequential,
        verbose=True
    )
    result = crew.kickoff(inputs={'topic': 'AI Agents'})
    print(result)

if __name__ == "__main__":
    main()

python crewai_cerebras_integration_demo.py

# Emerging AI Agents Technologies: A 3-Paragraph Report

The year 2024 is expected to be a significant year for AI Agents technologies. 
According to various sources, including Forbes, CNBC, and PCG, AI Agents are going to revolutionize the way businesses operate. 
These agents are expected to autonomously manage supply chains, optimize inventory levels, forecast demand, and even handle complex logistics planning. 
Moreover, AI Agents will transform business processes, increase automation in workflows, improve customer service and satisfaction, and provide cost savings by reducing operational costs.

However, there are also concerns about the pros and cons of AI Agents. 
Some of the cons include issues like ethics and dependency on technology. 
Furthermore, there are risks associated with the use of AI Agents, such as new security risks and the potential for job displacement. 
Despite these concerns, many experts believe that the benefits of AI Agents outweigh the drawbacks. As Agentic AI becomes more prevalent, it is expected to change the tech stack, HR practices, and the way of getting things done.

In conclusion, AI Agents are going to play a significant role in shaping the future of businesses. 
With their ability to autonomously manage tasks and processes, they are expected to bring about increased efficiency, accuracy, and cost savings. 
However, it is essential to be aware of the potential risks and challenges associated with the use of AI Agents and to take steps to mitigate them. 
As the technology continues to evolve, it will be interesting to see how AI Agents transform various industries and revolutionize the way we work.