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* move base_tool to main package and consolidate tool desscription generation * update import path * update tests * update doc * add base_tool test * migrate agent delegation tools to use BaseTool * update tests * update import path for tool * fix lint * update param signature * add from_langchain to BaseTool for backwards support of langchain tools * fix the case where StructuredTool doesn't have func --------- Co-authored-by: c0dez <li@vitablehealth.com> Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
70 lines
2.6 KiB
Plaintext
70 lines
2.6 KiB
Plaintext
---
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title: Create Custom Tools
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description: Comprehensive guide on crafting, using, and managing custom tools within the CrewAI framework, including new functionalities and error handling.
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icon: hammer
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---
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## Creating and Utilizing Tools in CrewAI
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This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
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incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
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enabling agents to perform a wide range of actions.
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### Subclassing `BaseTool`
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To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, including the `args_schema` for input validation, and the `_run` method.
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```python Code
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from typing import Type
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from crewai.tools import BaseTool
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from pydantic import BaseModel, Field
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class MyToolInput(BaseModel):
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"""Input schema for MyCustomTool."""
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argument: str = Field(..., description="Description of the argument.")
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class MyCustomTool(BaseTool):
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name: str = "Name of my tool"
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description: str = "What this tool does. It's vital for effective utilization."
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args_schema: Type[BaseModel] = MyToolInput
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def _run(self, argument: str) -> str:
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# Your tool's logic here
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return "Tool's result"
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```
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### Using the `tool` Decorator
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Alternatively, you can use the tool decorator `@tool`. This approach allows you to define the tool's attributes and functionality directly within a function,
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offering a concise and efficient way to create specialized tools tailored to your needs.
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```python Code
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from crewai.tools import tool
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@tool("Tool Name")
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def my_simple_tool(question: str) -> str:
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"""Tool description for clarity."""
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# Tool logic here
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return "Tool output"
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```
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### Defining a Cache Function for the Tool
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To optimize tool performance with caching, define custom caching strategies using the `cache_function` attribute.
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```python Code
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@tool("Tool with Caching")
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def cached_tool(argument: str) -> str:
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"""Tool functionality description."""
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return "Cacheable result"
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def my_cache_strategy(arguments: dict, result: str) -> bool:
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# Define custom caching logic
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return True if some_condition else False
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cached_tool.cache_function = my_cache_strategy
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```
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By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
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you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.
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