uv pip install 'crewai[tools]'

from crewai import Agent, Task, Crew
from crewai_tools import InvokeCrewAIAutomationTool

# Initialize the tool
automation_tool = InvokeCrewAIAutomationTool(
    crew_api_url="https://data-analysis-crew-[...].crewai.com",
    crew_bearer_token="your_bearer_token_here",
    crew_name="Data Analysis Crew",
    crew_description="Analyzes data and generates insights"
)

# Create a CrewAI agent that uses the tool
automation_coordinator = Agent(
    role='Automation Coordinator',
    goal='Coordinate and execute automated crew tasks',
    backstory='I am an expert at leveraging automation tools to execute complex workflows.',
    tools=[automation_tool],
    verbose=True
)

# Create a task for the agent
analysis_task = Task(
    description="Execute data analysis automation and provide insights",
    agent=automation_coordinator,
    expected_output="Comprehensive data analysis report"
)

# Create a crew with the agent
crew = Crew(
    agents=[automation_coordinator],
    tasks=[analysis_task],
    verbose=2
)

# Run the crew
result = crew.kickoff()
print(result)

CREWAI_API_URL=https://your-crew-automation.crewai.com  # Alternative to passing crew_api_url
CREWAI_BEARER_TOKEN=your_bearer_token_here              # Alternative to passing crew_bearer_token

from crewai import Agent, Task, Crew
from crewai_tools import InvokeCrewAIAutomationTool
from pydantic import Field

# Define custom input schema
custom_inputs = {
    "year": Field(..., description="Year to retrieve the report for (integer)"),
    "region": Field(default="global", description="Geographic region for analysis"),
    "format": Field(default="summary", description="Report format (summary, detailed, raw)")
}

# Create tool with custom inputs
market_research_tool = InvokeCrewAIAutomationTool(
    crew_api_url="https://state-of-ai-report-crew-[...].crewai.com",
    crew_bearer_token="your_bearer_token_here",
    crew_name="State of AI Report",
    crew_description="Retrieves a comprehensive report on state of AI for a given year and region",
    crew_inputs=custom_inputs,
    max_polling_time=15 * 60  # 15 minutes timeout
)

# Create an agent with the tool
research_agent = Agent(
    role="Research Coordinator",
    goal="Coordinate and execute market research tasks",
    backstory="You are an expert at coordinating research tasks and leveraging automation tools.",
    tools=[market_research_tool],
    verbose=True
)

# Create and execute a task with custom parameters
research_task = Task(
    description="Conduct market research on AI tools market for 2024 in North America with detailed format",
    agent=research_agent,
    expected_output="Comprehensive market research report"
)

crew = Crew(
    agents=[research_agent],
    tasks=[research_task]
)

result = crew.kickoff()

from crewai import Agent, Task, Crew, Process
from crewai_tools import InvokeCrewAIAutomationTool

# Initialize different automation tools
data_collection_tool = InvokeCrewAIAutomationTool(
    crew_api_url="https://data-collection-crew-[...].crewai.com",
    crew_bearer_token="your_bearer_token_here",
    crew_name="Data Collection Automation",
    crew_description="Collects and preprocesses raw data"
)

analysis_tool = InvokeCrewAIAutomationTool(
    crew_api_url="https://analysis-crew-[...].crewai.com",
    crew_bearer_token="your_bearer_token_here",
    crew_name="Analysis Automation",
    crew_description="Performs advanced data analysis and modeling"
)

reporting_tool = InvokeCrewAIAutomationTool(
    crew_api_url="https://reporting-crew-[...].crewai.com",
    crew_bearer_token="your_bearer_token_here",
    crew_name="Reporting Automation",
    crew_description="Generates comprehensive reports and visualizations"
)

# Create specialized agents
data_collector = Agent(
    role='Data Collection Specialist',
    goal='Gather and preprocess data from various sources',
    backstory='I specialize in collecting and cleaning data from multiple sources.',
    tools=[data_collection_tool]
)

data_analyst = Agent(
    role='Data Analysis Expert',
    goal='Perform advanced analysis on collected data',
    backstory='I am an expert in statistical analysis and machine learning.',
    tools=[analysis_tool]
)

report_generator = Agent(
    role='Report Generation Specialist',
    goal='Create comprehensive reports and visualizations',
    backstory='I excel at creating clear, actionable reports from complex data.',
    tools=[reporting_tool]
)

# Create sequential tasks
collection_task = Task(
    description="Collect market data for Q4 2024 analysis",
    agent=data_collector
)

analysis_task = Task(
    description="Analyze collected data to identify trends and patterns",
    agent=data_analyst
)

reporting_task = Task(
    description="Generate executive summary report with key insights and recommendations",
    agent=report_generator
)

# Create a crew with sequential processing
crew = Crew(
    agents=[data_collector, data_analyst, report_generator],
    tasks=[collection_task, analysis_task, reporting_task],
    process=Process.sequential,
    verbose=2
)

result = crew.kickoff()

from pydantic import Field

crew_inputs = {
    "required_param": Field(..., description="This parameter is required"),
    "optional_param": Field(default="default_value", description="This parameter is optional"),
    "typed_param": Field(..., description="Integer parameter", ge=1, le=100)  # With validation
}