# CrewAI Tutorial: Build a Multi-Agent Research Team

> Learn how to orchestrate autonomous AI agents using CrewAI. We'll build a team of specialized agents (Researcher, Writer, Editor) to automate content creation.

Published: 2026-03-08  
Canonical: https://aimastermindlab.com/tutorials/crewai-tutorial-multi-agent-research-team

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> **TL;DR:** CrewAI is a framework for orchestrating role-playing, autonomous AI agents. Instead of prompting a single LLM to do everything, you create specialized agents (e.g., a Researcher, a Writer, an Editor), give them tools, and let them collaborate to solve complex tasks. In this tutorial, we will build an automated research team.

While frameworks like LangChain excel at creating single agents, [CrewAI](https://www.crewai.com/) focuses entirely on **multi-agent orchestration**. It allows you to define roles, goals, and backstories for different agents, and then assign them specific tasks to execute sequentially or hierarchically.

In this tutorial, we'll build a "Content Creation Crew" consisting of three agents:
1. **The Senior Researcher**: Searches the web and gathers facts.
2. **The Tech Writer**: Turns the facts into an engaging blog post.
3. **The Editor**: Reviews the post for formatting and clarity.

## Prerequisites

```bash
pip install crewai langchain-community duckduckgo-search
```

You will need an OpenAI API key (or you can use [Ollama](/tutorials/run-llm-locally-mac-ollama) for local models). For this tutorial, we'll use OpenAI.

```bash
export OPENAI_API_KEY="your-key-here"
```

## Step 1: Define Your Tools

Agents need tools to interact with the world. We'll give our Researcher the ability to search the web using DuckDuckGo.

```python
from langchain_community.tools import DuckDuckGoSearchRun

search_tool = DuckDuckGoSearchRun()
```

## Step 2: Create the Agents

An agent in CrewAI is defined by its `role`, `goal`, and `backstory`. The backstory is crucial—it sets the persona and context for the LLM.

```python
from crewai import Agent
import os

# 1. The Researcher
researcher = Agent(
    role='Senior Technology Researcher',
    goal='Uncover the latest developments in AI and machine learning',
    backstory="""
    You are an expert researcher at a top tech magazine. Your skill lies in
    finding obscure, cutting-edge facts.
    """,
    verbose=True,
    allow_delegation=False,
    tools=[search_tool]
)

# 2. The Writer
writer = Agent(
    role='Senior Technical Writer',
    goal='Craft engaging, easy-to-understand articles on complex tech topics',
    backstory="""
    You are a renowned writer known for breaking down complex concepts for 
    software engineers. You write in a clear, authoritative, yet approachable tone.
    """,
    verbose=True,
    allow_delegation=True # The writer can ask the researcher for more info!
)

# 3. The Editor
editor = Agent(
    role='Managing Editor',
    goal='Ensure the final content is flawless, properly formatted in Markdown, and ready to publish',
    backstory="""
    You are a meticulous editor with an eagle eye for detail, grammar, and flow. 
    You ensure all articles follow proper Markdown formatting.
    """,
    verbose=True,
    allow_delegation=False
)
```

## Step 3: Define the Tasks

Tasks are specific assignments given to agents. You must explicitly state what the `expected_output` should look like.

```python
from crewai import Task

topic = "The impact of Small Language Models (SLMs) in 2026"

# Task 1: Research
research_task = Task(
    description=f"""
        Conduct comprehensive research on the topic: {topic}. 
        Find at least 3 recent developments or specific models.
    """,
    expected_output="A detailed bulleted list of facts, models, and trends regarding SLMs.",
    agent=researcher
)

# Task 2: Write
write_task = Task(
    description="""
        Using the research provided, write an engaging blog post. 
        It must have an introduction, body paragraphs, and a conclusion.
    """,
    expected_output="A draft blog post of at least 500 words.",
    agent=writer
)

# Task 3: Edit
edit_task = Task(
    description="""
        Review the drafted blog post. Fix any grammatical errors and ensure it is 
        formatted perfectly in Github-flavored Markdown.
    """,
    expected_output="A final, publication-ready Markdown document.",
    agent=editor
)
```

## Step 4: Assemble the Crew and Execute

Now, we bring the agents and tasks together into a `Crew`. By default, CrewAI executes tasks sequentially.

```python
from crewai import Crew, Process

content_crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, write_task, edit_task],
    process=Process.sequential, # Execute tasks one after another
    verbose=True
)

# Start the process
print("Starting the CrewAI execution...")
result = content_crew.kickoff()

print("######################")
print("FINAL RESULT:")
print("######################")
print(result)
```

## Why CrewAI Works So Well

When you run the script, watch the terminal output. You will see the `Researcher` searching DuckDuckGo, reading definitions, and compiling notes. Once finished, it hands its output to the `Writer`, who drafts the post. Finally, the `Editor` cleans it up.

**Key benefits over single-prompting:**
1. **Reduced Hallucinations**: Because the Writer isn't trying to remember facts—it's just re-formatting the Researcher's notes—it hallucinates far less.
2. **Separation of Concerns**: Tuning the writing style doesn't interfere with the research logic.
3. **Delegation**: If the Writer realizes the notes are incomplete, because we set `allow_delegation=True`, it can dynamically create a sub-task and ask the Researcher to find more details before proceeding.

## Using Local Models with CrewAI

You don't have to use OpenAI. You can easily plug in local Ollama models using LangChain's integration:

```python
import os
os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
os.environ["OPENAI_MODEL_NAME"] ='llama3.3'
os.environ["OPENAI_API_KEY"] ='sk-11111111111111111111111111111111'
```

Just add those lines to the top of your script, ensure Ollama is running, and your multi-agent crew is now 100% private and free.
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Source: https://aimastermindlab.com/tutorials/crewai-tutorial-multi-agent-research-team — AI Mastermind Lab (https://aimastermindlab.com)