What do you know of CrewAI?
As of my last knowledge update in January 2022, I don’t have specific information about CrewAI. It’s possible that there have been developments or new information about CrewAI since then. If CrewAI is a recently established entity or service, or if it has gained prominence after my last update, I recommend checking the latest sources such as news articles, official websites, or reputable databases for the most current and accurate information. If you have specific questions or details you’re looking for, feel free to provide more context, and I’ll do my best to assist you.
can you please tell me how this script ran with no obvious connection to an llm: import os
from crewai import Agent, Task, Crew, Process
from langchain.tools import DuckDuckGoSearchRun
from langchain.agents import load_tools

search_tool = DuckDuckGoSearchRun()

# Loading Human Tools
human_tools = load_tools([“human”])

# 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
)
writer = Agent(
role=’Tech Content Strategist’,
goal=’Craft compelling content on tech advancements’,
backstory=”””You are a renowned Tech Content Strategist, known for your insightful
and engaging articles on technology and innovation. With a deep understanding of
the tech industry, you transform complex concepts into compelling narratives.”””,
verbose=True,
allow_delegation=True
)

# Create tasks for your agents
# Being explicit on the task to ask for human feedback.
task1 = Task(
description=”””Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.
Compile your findings in a detailed report.
Make sure to check with the human if the draft is good before returning your Final Answer.
Your final answer MUST be a full analysis report”””,
agent=researcher
)

task2 = Task(
description=”””Using the insights from the researcher’s report, 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.
Aim for a narrative that captures the essence of these breakthroughs and their
implications for the future.
Your final answer MUST be the full blog post of at least 3 paragraphs.”””,
agent=writer
)

# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=2
)

# Get your crew to work!
result = crew.kickoff()

print(“######################”)
print(result)
The provided script appears to be written in Python and is using the CrewAI library along with other tools for a simulated scenario involving agents, tasks, and a sequential process. Here’s a breakdown of the script:

1. **Import Statements:**
– The script imports necessary modules and libraries, including `os`, `crewai`, and tools from `langchain`.

2. **Agent and Task Definition:**
– Two agents, `researcher` and `writer`, are defined with specific roles, goals, backstories, and tools.
– Tasks (`task1` and `task2`) are created for each agent, specifying detailed descriptions of the tasks they need to perform.

3. **Crew Initialization:**
– A crew is instantiated with the agents (`researcher` and `writer`) and tasks (`task1` and `task2`).
– The crew is set to be verbose during its execution.

4. **Crew Execution:**
– The crew is initiated with the `kickoff()` method, which presumably triggers the agents to perform their tasks in a sequential manner.

5. **Print Results:**
– The script prints the result of the crew’s execution.

6. **Note:**
– The script appears to simulate a scenario where a Senior Research Analyst (`researcher`) is tasked with analyzing AI advancements and providing a detailed report. The Tech Content Strategist (`writer`) is then tasked with creating a blog post based on the analyst’s report.
– The `crewai` library and other tools are used to simulate this collaboration.

If you have specific questions about a particular aspect of the script or if you need further clarification on any part, feel free to ask.
Where is the LLM in this script?>
In the provided script, there is no explicit mention or instantiation of an LLM (Large Language Model). The script primarily focuses on the use of the CrewAI library, agents, tasks, and tools from `langchain`. While the script involves agents with roles related to research and content creation, it doesn’t explicitly include an LLM or utilize such a model in its current form.

If there’s a specific aspect or functionality related to an LLM that you are looking for, please provide more details or context so that I can better understand and assist you.
The script runs and provides answers but I do not know what model it is using or how?
In the provided script, it appears that the details about the underlying language model being used are not explicitly mentioned. The script mainly leverages the CrewAI library, agents, tasks, and tools for simulating a collaborative scenario involving a Senior Research Analyst and a Tech Content Strategist.

However, the specific details about the language model, including its architecture or source, are not evident from the script snippet you provided. If there is a specific language model or AI component used for tasks like analysis and content creation, it might be initialized or utilized within the functions or methods from the imported libraries (`crewai`, `langchain`, etc.).

To find more information about the underlying language model, you may need to refer to the documentation of the libraries being used in the script. Look for any mentions of default models or configurations within the library documentation or inspect the source code if available.

If you have access to the complete codebase or documentation of the libraries, that would be the ideal place to find information about the language model being used and its configuration. If you can provide more details or context, I may be able to offer further assistance.