CrewAI: A Multi-Agent Framework Combining Diverse Strengths

import os
from crewai import Agent, Task, Crew, Process
os.environ["OPENAI_API_KEY"] = "YOUR KEY"
# You can choose to use a local model through Ollama for example.
## from langchain.llms import Ollama
# ollama_llm = Ollama(model="openhermes")
# Install duckduckgo-search for this example:
# !pip install -U duckduckgo-search
from langchain.tools import DuckDuckGoSearchRun
search_tool = DuckDuckGoSearchRun()
# Define your agents with roles and goals
researcher = Agent(  role='Senior Research Analyst',  goal='Uncover cutting-edge developments in AI and data science',  backstory="""You work at a leading tech think tank.  Your expertise lies in identifying emerging trends.  You have a knack for dissecting complex data and presenting  actionable insights.""",  verbose=True,  allow_delegation=False,  tools=[search_tool]  # You can pass an optional llm attribute specifying what mode you wanna use.  # It can be a local model through Ollama / LM Studio or a remote  # model like OpenAI, Mistral, Antrophic of others (https://python.langchain.com/docs/integrations/llms/)  #  # Examples:  # llm=ollama_llm # was defined above in the file  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7))
writer = Agent(  role='Tech Content Strategist',  goal='Craft compelling content on tech advancements',  backstory="""You are a renowned Content Strategist, known for  your insightful and engaging articles.  You transform complex concepts into compelling narratives.""",  verbose=True,  allow_delegation=True,  # (optional) llm=ollama_llm)
# Create tasks for your agents
task1 = Task(  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.  Identify key trends, breakthrough technologies, and potential industry impacts.  Your final answer MUST be a full analysis report""",  agent=researcher)
task2 = Task(  description="""Using the insights provided, develop an engaging blog  post that highlights the most significant AI advancements.  Your post should be informative yet accessible, catering to a tech-savvy audience.  Make it sound cool, avoid complex words so it doesn't sound like AI.  Your final answer MUST be the full blog post of at least 4 paragraphs.""",  agent=writer)
# Instantiate your crew with a sequential process
crew = Crew(  agents=[researcher, writer],  tasks=[task1, task2],  verbose=2,# You can set it to 1 or 2 to different logging levels  process=Process.sequential )
# Get your crew to work!
result = crew.kickoff()
print("######################")
print(result)

# pip install praw 
from langchain.tools import tool
class BrowserTool():    @tool("Scrape reddit content")    def scrape_reddit(max_comments_per_post=5):        """Useful to scrape a reddit content"""        reddit = praw.Reddit(            client_id="your-client-id",            client_secret="your-client-secret",            user_agent="your-user-agent",        )        subreddit = reddit.subreddit("LocalLLaMA")        scraped_data = []
        for post in subreddit.hot(limit=10):            post_data = {"title": post.title, "url": post.url, "comments": []}
            try:                post.comments.replace_more(limit=0)  # Load top-level comments only                comments = post.comments.list()                if max_comments_per_post is not None:                    comments = comments[:5]
                for comment in comments:                    post_data["comments"].append(comment.body)
                scraped_data.append(post_data)
            except praw.exceptions.APIException as e:                print(f"API Exception: {e}")                time.sleep(60)  # Sleep for 1 minute before retrying
        return scraped_data

# Define your agents with roles and goals
researcher = Agent(  role='Senior Research Analyst',  goal='Uncover cutting-edge developments in AI and data science in',  backstory="""You are a Senior Research Analyst at a leading tech think tank.  Your expertise lies in identifying emerging trends and technologies in AI and  data science. You have a knack for dissecting complex data and presenting  actionable insights.""",  verbose=True,  allow_delegation=False,  # Passing human tools to the agent  tools=[search_tool]+human_tools)

from langchain.llms import Ollama
ollama_openhermes = Ollama(model="openhermes")
# Pass Ollama Model to Agents: When creating your agents within the CrewAI framework, you can pass the Ollama model as an argument to the Agent constructor. For instance:
local_expert = Agent(  role='Local Expert at this city',  goal='Provide the BEST insights about the selected city',  backstory="""A knowledgeable local guide with extensive information  about the city, it's attractions and customs""",  tools=[    SearchTools.search_internet,    BrowserTools.scrape_and_summarize_website,  ],  llm=ollama_openhermes, # Ollama model passed here  verbose=True)