RCrewAI

gem 'rcrewai'

require 'rcrewai'

# Configure your LLM provider
RCrewAI.configure do |config|
  config.llm_provider = :openai  # or :anthropic, :google, :azure, :ollama
  config.temperature = 0.1
end

# Create intelligent agents with specialized tools
researcher = RCrewAI::Agent.new(
  name: "researcher",
  role: "Senior Research Analyst",
  goal: "Uncover cutting-edge developments in AI",
  backstory: "Expert at finding and analyzing the latest tech trends",
  tools: [RCrewAI::Tools::WebSearch.new],
  verbose: true
)

writer = RCrewAI::Agent.new(
  name: "writer", 
  role: "Tech Content Strategist",
  goal: "Create compelling technical content",
  backstory: "Skilled at transforming research into engaging articles",
  tools: [RCrewAI::Tools::FileWriter.new]
)

# Create crew with sequential process
crew = RCrewAI::Crew.new("ai_research_crew")
crew.add_agent(researcher)
crew.add_agent(writer)

# Define tasks with dependencies
research_task = RCrewAI::Task.new(
  name: "research_ai_trends",
  description: "Research the latest developments in AI for 2024",
  agent: researcher,
  expected_output: "Comprehensive report on AI trends with key insights"
)

writing_task = RCrewAI::Task.new(
  name: "write_article",
  description: "Write an engaging 1000-word article about AI trends",
  agent: writer,
  context: [research_task],  # Uses research results as context
  expected_output: "Publication-ready article saved as ai_trends.md"
)

crew.add_task(research_task)
crew.add_task(writing_task)

# Execute - agents will reason, search, and produce real results!
results = crew.execute
puts "✅ Crew completed #{results[:completed_tasks]}/#{results[:total_tasks]} tasks"

# OpenAI (GPT-4, GPT-3.5, etc.)
RCrewAI.configure do |config|
  config.llm_provider = :openai
  config.openai_api_key = ENV['OPENAI_API_KEY']
  config.model = 'gpt-4'
end

# Anthropic Claude
RCrewAI.configure do |config|
  config.llm_provider = :anthropic
  config.anthropic_api_key = ENV['ANTHROPIC_API_KEY'] 
  config.model = 'claude-3-sonnet-20240229'
end

# Google Gemini
RCrewAI.configure do |config|
  config.llm_provider = :google
  config.google_api_key = ENV['GOOGLE_API_KEY']
  config.model = 'gemini-pro'
end

# Azure OpenAI
RCrewAI.configure do |config|
  config.llm_provider = :azure
  config.azure_api_key = ENV['AZURE_OPENAI_API_KEY']
  config.azure_endpoint = ENV['AZURE_OPENAI_ENDPOINT']
  config.model = 'gpt-4'
end

# Local Ollama
RCrewAI.configure do |config|
  config.llm_provider = :ollama
  config.ollama_url = 'http://localhost:11434'
  config.model = 'llama2'
end

# Create a hierarchical crew with manager coordination
crew = RCrewAI::Crew.new("enterprise_team", process: :hierarchical)

# Manager agent coordinates the team
manager = RCrewAI::Agent.new(
  name: "project_manager",
  role: "Senior Project Manager", 
  goal: "Coordinate team execution efficiently",
  manager: true,
  allow_delegation: true
)

# Specialist agents with human-in-the-loop capabilities
data_analyst = RCrewAI::Agent.new(
  name: "data_analyst",
  role: "Senior Data Analyst",
  goal: "Analyze data with human validation",
  tools: [RCrewAI::Tools::SqlDatabase.new],
  human_input: true,                      # Enable human interaction
  require_approval_for_tools: true,       # Human approves SQL queries
  require_approval_for_final_answer: true # Human validates analysis
)

crew.add_agent(manager)
crew.add_agent(data_analyst)

# Execute with async/hierarchical coordination
results = crew.execute(async: true, max_concurrency: 2)

# Tasks that can run in parallel
research_task = RCrewAI::Task.new(
  name: "market_research",
  description: "Research market trends",
  async: true
)

analysis_task = RCrewAI::Task.new(
  name: "competitive_analysis", 
  description: "Analyze competitors",
  async: true
)

crew.add_task(research_task)
crew.add_task(analysis_task)

# Execute with parallel processing
results = crew.execute(
  async: true,
  max_concurrency: 4,
  timeout: 300
)