Tools

import os
from crewai import Agent, Task, Crew
# Importing crewAI tools
from crewai_tools import (
    DirectoryReadTool,
    FileReadTool,
    SerperDevTool,
    WebsiteSearchTool
)

# Set up API keys
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
os.environ["OPENAI_API_KEY"] = "Your Key"

# Instantiate tools
docs_tool = DirectoryReadTool(directory='./blog-posts')
file_tool = FileReadTool()
search_tool = SerperDevTool()
web_rag_tool = WebsiteSearchTool()

# Create agents
researcher = Agent(
    role='Market Research Analyst',
    goal='Provide up-to-date market analysis of the AI industry',
    backstory='An expert analyst with a keen eye for market trends.',
    tools=[search_tool, web_rag_tool],
    verbose=True
)

writer = Agent(
    role='Content Writer',
    goal='Craft engaging blog posts about the AI industry',
    backstory='A skilled writer with a passion for technology.',
    tools=[docs_tool, file_tool],
    verbose=True
)

# Define tasks
research = Task(
    description='Research the latest trends in the AI industry and provide a summary.',
    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
    agent=researcher
)

write = Task(
    description='Write an engaging blog post about the AI industry, based on the research analyst's summary. Draw inspiration from the latest blog posts in the directory.',
    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
    agent=writer,
    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
)

# Assemble a crew with planning enabled
crew = Crew(
    agents=[researcher, writer],
    tasks=[research, write],
    verbose=True,
    planning=True,  # Enable planning feature
)

# Execute tasks
crew.kickoff()

from crewai.tools import BaseTool
from pydantic import BaseModel, Field

class MyToolInput(BaseModel):
    """Input schema for MyCustomTool."""
    argument: str = Field(..., description="Description of the argument.")

class MyCustomTool(BaseTool):
    name: str = "Name of my tool"
    description: str = "What this tool does. It's vital for effective utilization."
    args_schema: Type[BaseModel] = MyToolInput

    def _run(self, argument: str) -> str:
        # Your tool's logic here
        return "Tool's result"

from crewai.tools import BaseTool
from pydantic import BaseModel

class InventoryResult(BaseModel):
    sku: str
    quantity: int
    needs_reorder: bool

class InventoryTool(BaseTool):
    name: str = "Inventory Check"
    description: str = "Checks current stock for a product SKU."

    def _run(self, sku: str) -> InventoryResult:
        quantity = {"SKU-123": 14, "SKU-456": 0}.get(sku, 0)
        return InventoryResult(sku=sku, quantity=quantity, needs_reorder=quantity < 5)

tool = InventoryTool()

# Direct calls receive the raw Pydantic object.
result = tool.run(sku="SKU-123")
print(result.quantity)

class InventoryTool(BaseTool):
    name: str = "Inventory Check"
    description: str = "Checks current stock for a product SKU."

    def _run(self, sku: str) -> InventoryResult:
        quantity = {"SKU-123": 14, "SKU-456": 0}.get(sku, 0)
        return InventoryResult(sku=sku, quantity=quantity, needs_reorder=quantity < 5)

    def format_output_for_agent(self, raw_result: object) -> str:
        result = InventoryResult.model_validate(raw_result)
        status = "reorder needed" if result.needs_reorder else "stock is healthy"
        return f"{result.sku}: {result.quantity} units. {status}."

from crewai.tools import tool

@tool("fetch_data_async")
async def fetch_data_async(query: str) -> str:
    """Asynchronously fetch data based on the query."""
    # Simulate async operation
    await asyncio.sleep(1)
    return f"Data retrieved for {query}"

from crewai.tools import BaseTool

class AsyncCustomTool(BaseTool):
    name: str = "async_custom_tool"
    description: str = "An asynchronous custom tool"

    async def _run(self, query: str = "") -> str:
        """Asynchronously run the tool"""
        # Your async implementation here
        await asyncio.sleep(1)
        return f"Processed {query} asynchronously"

# In standard Crew
agent = Agent(role="researcher", tools=[async_custom_tool])

# In Flow
class MyFlow(Flow):
    @start()
    async def begin(self):
        crew = Crew(agents=[agent])
        result = await crew.kickoff_async()
        return result

from crewai.tools import tool
@tool("Name of my tool")
def my_tool(question: str) -> str:
    """Clear description for what this tool is useful for, your agent will need this information to use it."""
    # Function logic here
    return "Result from your custom tool"

from crewai.tools import tool

@tool
def multiplication_tool(first_number: int, second_number: int) -> str:
    """Useful for when you need to multiply two numbers together."""
    return first_number * second_number

def cache_func(args, result):
    # In this case, we only cache the result if it's a multiple of 2
    cache = result % 2 == 0
    return cache

multiplication_tool.cache_function = cache_func

writer1 = Agent(
        role="Writer",
        goal="You write lessons of math for kids.",
        backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
        tools=[multiplication_tool],
        allow_delegation=False,
    )
    #...