CrewAI vs AutoGen: Build Your First AI Agent Workflow (2025)

Building Your First Autonomous Workflow: A Practical Guide to CrewAI & AutoGen

You've understood the theory of Agentic AI. You know that the future belongs to autonomous "AI teams." Now, it's time to roll up your sleeves, open your code editor, and bring one of these teams to life.

This is a hands-on, practical guide. We will walk you through building two simple but powerful multi-agent workflows using today's most popular open-source frameworks: CrewAI and AutoGen. We'll focus on code and results, so that by the end, you won't just know their differences—you'll have felt them.

This is a practical tutorial. If you need to refresh your understanding of the core concepts behind multi-agent systems, we recommend starting with our Deep Dive into Agentic AI Pillar Page.

1. Prerequisites: What You'll Need to Get Started

Before we dive in, let's make sure your workshop is ready.

• Your Environment: A Python 3.9+ environment is required.
• API Keys: You'll need an API key from an LLM provider like OpenAI. We recommend setting this as an environment variable.
• Core Libraries: Open your terminal and install the necessary packages:

pip install crewai pyautogen  

• Essential Tools (for CrewAI): We'll use DuckDuckGo for search.

pip install 'crewai[tools]'  

2. The "Assembly Line" Approach: Building a Research Crew with CrewAI

CrewAI excels at creating agents that work like a structured, efficient assembly line. Each agent has a specific role and performs a set task in a predefined order. It’s perfect for processes you already know how to solve and want to automate.

Tutorial: Create an Automated Market Research Crew

Let's build a crew to research AI trends and write a report.

Step 1: Assemble Your Agents

First, we define our two specialists: a Researcher and a Writer.

(Code Snippet: Defining CrewAI Agents)

from crewai import Agent  
from crewai_tools import SerperDevTool  
  
# Initialize the search tool  
search_tool = SerperDevTool()  
  
# Define the Researcher Agent  
researcher = Agent(  
role='Senior Market Researcher',  
goal='Uncover groundbreaking technologies and trends in AI agentics',  
backstory="""You are an expert market researcher at a top tech think tank.  
Your mastery lies in identifying emerging trends and technologies.""",  
verbose=True,  
allow_delegation=False,  
tools=[search_tool]  
)  
  
# Define the Writer Agent  
writer = Agent(  
role='Expert Tech Content Strategist',  
goal='Craft compelling content on AI agent technology',  
backstory="""You are a renowned content strategist, known for making  
complex tech topics accessible and engaging.""",  
verbose=True,  
allow_delegation=True  
)  

Step 2: Define the Tasks

Now, we give each agent a clear task. Notice how task2 uses the output of task1 as its context.
(Code Snippet: Defining CrewAI Tasks)

from crewai import Task  
# Research Task  
task1 = Task(  
description="""Conduct a comprehensive analysis of the latest advancements  
in AI agents for 2025. Identify key trends, major players,  
and emerging use cases.""",  
expected_output="A full analysis report with bullet points.",  
agent=researcher  
)  
  
# Writing Task  
task2 = Task(  
description="""Using the research analysis, write a compelling blog post  
that highlights the most significant AI agent trends.  
Your post should be informative yet accessible to a tech-savvy audience.  
Make it sound cool and futuristic.""",  
expected_output="A 4-paragraph blog post on the latest AI agent trends.",  
agent=writer  
)  

Step 3: Assemble the Crew and Start the Job

Finally, we put our agents and tasks together in a Crew and set them to work in a sequential process.
(Code Snippet: Running the Crew)

from crewai import Crew, Process  
# Instantiate your crew with a sequential process  
market_research_crew = Crew(  
agents=[researcher, writer],  
tasks=[task1, task2],  
verbose=2,  
process=Process.sequential  
)  
  
# Get your crew to work!  
result = market_research_crew.kickoff()  
print(result)  

3. The "Roundtable Discussion" Approach: Building a Coding Team with AutoGen

AutoGen, a framework from Microsoft Research, shines in scenarios where the solution isn't straightforward. It creates agents that solve problems through dynamic, back-and-forth conversation, like a team of experts in a brainstorming session.

Tutorial: Create a "Code & Execute" Group Chat

Let's build a two-agent team that can write and execute Python code to solve a problem.

Step 1: Configure Your Agents

We need an AssistantAgent to write the code and a UserProxyAgent that acts on our behalf to request the code, execute it, and report the results.

(Code Snippet: Configuring AutoGen Agents)

import autogen  
  
config_list = autogen.config_list_from_json(  
"OAI_CONFIG_LIST",  
filter_dict={"model": ["gpt-4"]},  
)  
  
# Create the Assistant Agent (the Coder)  
assistant = autogen.AssistantAgent(  
name="Assistant",  
llm_config={"config_list": config_list}  
)  
  
# Create the User Proxy Agent (the Executor)  
user_proxy = autogen.UserProxyAgent(  
name="user_proxy",  
human_input_mode="NEVER",  
max_consecutive_auto_reply=10,  
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),  
code_execution_config={"work_dir": "coding"},  
)  

Step 2: Initiate the "Meeting"

Now, the user proxy initiates the chat with a clear task. The agents will then "talk" to each other until a solution is found and executed.
(Code Snippet: Initiating AutoGen Chat)

# Start the conversation  
user_proxy.initiate_chat(  
assistant,  
message="""What is the current date? Compare the  
stock price change of NVIDIA (NVDA) and Tesla (TSLA) today."""  
)  

In the output, you'll see a fascinating back-and-forth where the assistant writes Python code to fetch the stock data, and the user proxy executes it, sometimes reporting errors that the assistant then debugs and rewrites.

4. CrewAI vs. AutoGen: A Head-to-Head Comparison

Now that you've seen both in action, let's break down the key differences. This aligns with what many experienced developers have concluded: "Use CrewAI if you know how to solve a problem and want to automate the process. Use AutoGen if you want a set of experts to figure out a solution for you."

Expert Takeaway: As Augustas Pelakauskas, an AI Frameworks Specialist at Oxylabs, notes, "CrewAI works best for tasks with defined outputs. AutoGen is better when you’re solving open-ended problems that need real-time adaptation."

5. The Privacy Shift: Embracing Local LLMs

A major trend in 2025 is the ability to run AI agents with local Large Language Models (LLMs) instead of relying solely on cloud providers like OpenAI. Both CrewAI and AutoGen have increasing support for local models (e.g., via Ollama). This is a game-changer for businesses concerned with:

• Data Privacy & Compliance: Keeping sensitive data entirely on-premise.
• Cost Reduction: Eliminating per-token API costs.
• Latency: Achieving faster response times by removing network hops.

6. Conclusion: Which Framework Is Right for You?

The choice between CrewAI and AutoGen isn't about which is "better," but which is the right tool for your specific job.

• Choose CrewAI when you have a clear blueprint and need a reliable, structured team to execute it flawlessly every time. It’s about automating a known process.
• Choose AutoGen when you have a complex problem and need a team of smart, interactive experts to brainstorm, experiment, and discover a solution. It’s about finding an unknown answer.

The most powerful realization is that they aren't mutually exclusive. Advanced systems can leverage both: using a CrewAI process to handle a structured part of a workflow, which can then trigger an AutoGen "discussion" to solve a more complex, ad-hoc problem.

You've now taken the first step from theory to practice. You've seen how to assemble and command your first autonomous AI teams.

The agents you can build with these frameworks are just the beginning. To discover the most innovative, ready-made agents being built by the community, explore the ai agents directory.