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full installation process with hello world type example for using BMAD Method and using CrewAI

February 4, 2026 5 min read

Getting Started with Autonomous Agents: BMAD and CrewAI - From Zero to “Hello World”

The world of AI is exploding, and one of the most exciting frontiers is autonomous agents. Imagine a team of AI assistants, each with specialized skills, collaborating to achieve a complex goal. That’s the promise of frameworks like CrewAI, and methodologies like BMAD (Build, Measure, Analyze, Deploy) can help you structure your agent development process.

This post will guide you through the full installation process, walking you through a simple “Hello World” type example to get you acquainted with CrewAI and thinking in terms of BMAD. Let’s dive in!

What is CrewAI?

CrewAI is a framework for orchestrating multiple AI agents, allowing them to collaborate and accomplish complex tasks. Think of it as the conductor of an AI orchestra, ensuring each instrument (agent) plays its part harmoniously. CrewAI simplifies the process of defining agents, assigning roles, and managing their interactions.

BMAD: A Practical Framework for Agent Development

Before we jump into code, let’s talk BMAD. It’s a simple yet powerful framework for iterative development, perfectly suited for AI agent projects:

  • Build: Create your initial CrewAI setup. This is our “Hello World” stage.
  • Measure: Observe the agent’s performance. Does it actually produce the expected output?
  • Analyze: Identify areas for improvement. Is the agent too slow? Is its output inaccurate?
  • Deploy: Implement the improvements and repeat the cycle.

This cyclical approach helps you refine your agents and teams over time.

Installation and Setup

Okay, let’s get our hands dirty! Here’s how to get CrewAI up and running:

  1. Install Python: Ensure you have Python 3.8 or higher installed. You can download it from python.org.

  2. Create a Virtual Environment (Recommended): Using a virtual environment keeps your project dependencies isolated.

    python3 -m venv .venv
source .venv/bin/activate  # On Linux/macOS
# .venv\Scripts\activate  # On Windows

  3. Install CrewAI: Use pip to install the CrewAI package.

    pip install crewai

  4. Install OpenAI (or your preferred LLM provider): CrewAI supports various Large Language Models (LLMs). We’ll use OpenAI for this example. You’ll also need an OpenAI API key. Get one from platform.openai.com.

    pip install openai

  5. Set your OpenAI API Key: There are several ways to do this. The simplest is to set it as an environment variable:

    export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"  # On Linux/macOS
# set OPENAI_API_KEY=YOUR_OPENAI_API_KEY  # On Windows

Hello World with CrewAI: A Simple Greeting

Let’s create a basic CrewAI setup that has a single agent whose only job is to say “Hello World!”. This will illustrate the core concepts of agent creation and task assignment.

import os
from crewai import Crew, Agent, Task

# Configure your LLM - replace with your key
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")  # Ensure your API key is set

# 1. Build: Create the Agent
greeting_agent = Agent(
    role="The Greeter",
    goal="To greet the world with enthusiasm.",
    backstory="A friendly AI assistant eager to spread positivity.",
    verbose=True,  # Set to True to see the agent's thought process
    llm="gpt-4", # Or gpt-3.5-turbo, etc.  Make sure you have access to it.
)

# 2. Build: Create the Task
greeting_task = Task(
    description="Generate a simple 'Hello World!' message.",
    agent=greeting_agent,
)

# 3. Build: Create the Crew
hello_world_crew = Crew(
    agents=[greeting_agent],
    tasks=[greeting_task],
    verbose=2, # You can set this to be higher or lower.
)

# 4. Measure & Deploy: Run the Crew and print the result!
result = hello_world_crew.kickoff()
print(result)

Explanation:

  • Agent: We define an agent with a role, goal, and backstory. This helps the LLM understand its purpose. verbose=True will show you the agent’s reasoning steps.
  • Task: We create a task that instructs the agent to generate the “Hello World!” message.
  • Crew: We assemble a crew with the agent and the task.
  • kickoff(): This starts the crew, executing the task assigned to the agent.
  • Result: The result of the task is printed to the console.

Running the Code:

Save the code as a Python file (e.g., hello_crewai.py) and run it from your terminal:

python hello_crewai.py

You should see something like:

Hello World!

Congratulations! You’ve successfully created your first CrewAI agent and run a simple task.

BMAD in Action

Let’s see how BMAD applies to our simple example:

  • Build: We built the agent, task, and crew.
  • Measure: We observed that the agent correctly produced “Hello World!”.
  • Analyze: In this simple example, there’s not much to analyze. However, we could think about how to make the agent’s greeting more personalized.
  • Deploy: We could modify the task to include the user’s name and ask the agent to generate a personalized greeting: “Hello [Your Name] World!”.

This iterative process can be applied to much more complex scenarios.

Next Steps

This “Hello World” example is just the beginning. Here are some ideas for exploring CrewAI further:

  • Multiple Agents: Create a crew with multiple agents, each with a different role (e.g., a researcher and a writer).
  • Tool Use: Equip your agents with tools that allow them to interact with the real world (e.g., search the internet, access databases).
  • More Complex Tasks: Tackle more challenging tasks that require collaboration and problem-solving.
  • Experiment with different LLMs: Try different LLMs beyond OpenAI.

CrewAI, combined with a structured approach like BMAD, offers a powerful way to build sophisticated autonomous agent systems. Start experimenting, and let the AI orchestra play!