Getting Started with RCrewAI

Learn how to install and use RCrewAI to build your first AI crew

Getting Started with RCrewAI

This tutorial will walk you through installing RCrewAI and creating your first AI crew.

Prerequisites

Before you begin, ensure you have:

  • Ruby 3.0 or higher installed
  • Bundler gem installed (gem install bundler)
  • An API key from one of the supported providers:

Installation

Method 1: Add to your Gemfile

Add RCrewAI to your application’s Gemfile:

gem 'rcrewai'

Then run:

bundle install

Method 2: Direct Installation

Install the gem directly:

gem install rcrewai

Configuration

RCrewAI supports multiple LLM providers. Choose the one that works best for you:

Set your API key as an environment variable:

export OPENAI_API_KEY="your-openai-key-here"

Configure RCrewAI:

RCrewAI.configure do |config|
  config.llm_provider = :openai
  config.openai_model = 'gpt-4'  # or 'gpt-3.5-turbo' for lower cost
end

Anthropic (Claude)

export ANTHROPIC_API_KEY="your-anthropic-key-here"

RCrewAI.configure do |config|
  config.llm_provider = :anthropic
  config.anthropic_model = 'claude-3-sonnet-20240229'
end

Google (Gemini)

export GOOGLE_API_KEY="your-google-key-here"

RCrewAI.configure do |config|
  config.llm_provider = :google
  config.google_model = 'gemini-pro'
end

Azure OpenAI

export AZURE_OPENAI_API_KEY="your-azure-key"
export AZURE_API_VERSION="2023-05-15"
export AZURE_DEPLOYMENT_NAME="your-deployment"

RCrewAI.configure do |config|
  config.llm_provider = :azure
  config.base_url = "https://your-resource.openai.azure.com/"
  config.azure_model = 'gpt-4'
end

For more configuration options, see the Configuration Documentation.

Creating Your First Crew

Let’s create an intelligent research crew that can actually search the web, analyze information, and write reports.

Step 1: Create the Crew and Configure

require 'rcrewai'

# Configure RCrewAI with your chosen provider
RCrewAI.configure do |config|
  config.llm_provider = :openai  # or :anthropic, :google, :azure
  # API keys are loaded from environment variables automatically
end

# Create a new crew
crew = RCrewAI::Crew.new("ai_research_crew")

Step 2: Create Intelligent Agents with Tools

Create specialized agents with actual capabilities:

# Research Agent with web search capabilities
researcher = RCrewAI::Agent.new(
  name: "alex_researcher",
  role: "Senior Research Analyst", 
  goal: "Uncover cutting-edge developments in AI and data science",
  backstory: "You are a seasoned researcher with a knack for uncovering the latest trends and technologies in AI and data science. You excel at finding reliable sources and extracting key insights.",
  tools: [RCrewAI::Tools::WebSearch.new],  # Can search the web!
  verbose: true,  # Shows reasoning process
  max_iterations: 5  # Limit reasoning loops
)

# Writer Agent with file writing capabilities  
writer = RCrewAI::Agent.new(
  name: "emma_writer",
  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 on technology and innovation. You transform complex research into compelling narratives.",
  tools: [
    RCrewAI::Tools::FileWriter.new,  # Can write files!
    RCrewAI::Tools::FileReader.new   # Can read reference materials
  ],
  verbose: true,
  allow_delegation: false
)

# Add agents to the crew
crew.add_agent(researcher)
crew.add_agent(writer)

Step 3: Create Advanced Tasks with Dependencies

Define tasks with specific outputs and dependencies:

# Research Task - Agent will actually search the web!
research_task = RCrewAI::Task.new(
  name: "research_ai_trends_2024",
  description: "Research the latest AI developments and breakthroughs in 2024. Focus on identifying key trends, major companies involved, and potential impacts on different industries.",
  agent: researcher,
  expected_output: "A comprehensive research report covering: 1) Top 5 AI trends in 2024, 2) Key companies and their innovations, 3) Industry impact analysis, 4) Future predictions",
  max_retries: 2  # Retry if web search fails
)

# Writing Task - Agent will use research results and save to file!
writing_task = RCrewAI::Task.new(
  name: "write_ai_trends_article", 
  description: "Write a compelling, informative article about AI trends based on the research. Make it engaging for a tech-savvy audience. Save the final article to a file named 'ai_trends_2024.md'.",
  agent: writer,
  expected_output: "A well-structured 1000-1500 word article covering the research findings, saved to ai_trends_2024.md file",
  context: [research_task],  # Uses research results as input
  callback: ->(task, result) { puts "Article completed: #{result.length} characters written!" }
)

# Add tasks to the crew
crew.add_task(research_task)
crew.add_task(writing_task)

Step 4: Execute and Monitor

Run the crew and monitor the intelligent agents at work:

puts "🚀 Starting AI Research Crew..."
puts "The agents will now research and write about AI trends!"

# Execute the crew - this will:
# 1. Research agent searches web for AI trends
# 2. Research agent analyzes and summarizes findings  
# 3. Writer agent receives research context
# 4. Writer agent creates article and saves to file
result = crew.execute

# Check results and status
puts "\n" + "="*50
puts "📊 EXECUTION SUMMARY"
puts "="*50

puts "Research Task Status: #{research_task.status}"
puts "Research Execution Time: #{research_task.execution_time&.round(2)}s"
puts "Research Result Preview:"
puts research_task.result[0..200] + "..." if research_task.result

puts "\nWriting Task Status: #{writing_task.status}" 
puts "Writing Execution Time: #{writing_task.execution_time&.round(2)}s"
puts "Article Preview:"
puts writing_task.result[0..200] + "..." if writing_task.result

# Check if file was actually created
if File.exist?('ai_trends_2024.md')
  puts "\n✅ Article successfully saved to ai_trends_2024.md"
  puts "File size: #{File.size('ai_trends_2024.md')} bytes"
else
  puts "\n❌ Article file not found"
end

# Check agent memory stats
puts "\n🧠 Agent Memory Stats:"
puts "Researcher memory: #{researcher.memory.stats}"
puts "Writer memory: #{writer.memory.stats}"

What Just Happened?

When you run this code with a valid API key, here’s what actually happens:

  1. Researcher Agent:
    • Uses reasoning loops to plan the research approach
    • Executes web searches using DuckDuckGo
    • Analyzes search results to identify key trends
    • Synthesizes findings into a comprehensive report
    • Stores successful strategies in memory
  2. Writer Agent:
    • Receives research findings as context
    • Plans article structure and key points
    • Creates engaging, well-structured content
    • Saves the final article to a markdown file
    • Learns from successful writing patterns
  3. System Features:
    • Automatic retry if web search fails
    • Security controls on file operations
    • Memory persistence between executions
    • Comprehensive logging and monitoring

Using the CLI

RCrewAI also provides a command-line interface for managing crews:

Create a New Crew

$ rcrewai new research_team
Creating new crew: research_team
Crew 'research_team' created successfully!

List Available Crews

$ rcrewai list
Available crews:
  - research_team
  - marketing_crew
  - development_crew

Run a Crew

$ rcrewai run --crew research_team
Running crew: research_team
Executing tasks...

Working with Tools

Agents can be equipped with tools to enhance their capabilities:

# Create a web search tool
search_tool = RCrewAI::Tools::WebSearch.new

# Create an agent with tools
analyst = RCrewAI::Agent.new(
  name: "data_analyst",
  role: "Data Analyst",
  goal: "Analyze data and provide insights",
  tools: [search_tool]
)

Best Practices

  1. Define Clear Roles: Give each agent a specific, well-defined role
  2. Set Specific Goals: Make agent goals measurable and achievable
  3. Create Detailed Tasks: Provide clear descriptions and expected outputs
  4. Use Task Dependencies: Link related tasks using the context parameter
  5. Leverage Tools: Equip agents with appropriate tools for their tasks

Next Steps

Now that you’ve created your first crew, explore these topics:

Troubleshooting

Common Issues

Issue: “No API key configured” Solution: Ensure your API key is set in the environment variables or configuration file.

Issue: “Agent not found” Solution: Make sure the agent is added to the crew before creating tasks that reference it.

Issue: “Task execution failed” Solution: Check that all task dependencies are properly defined and agents have necessary tools.

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