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* Minor fixes and updates * minor fixes across docs --------- Co-authored-by: theCyberTech <mattrapidb@gmail.com>
64 lines
2.4 KiB
Markdown
64 lines
2.4 KiB
Markdown
---
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title: Creating and Utilizing Tools in crewAI
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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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---
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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, incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, enabling agents to perform a wide range of actions.
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### Prerequisites
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Before creating your own tools, ensure you have the crewAI extra tools package installed:
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```bash
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pip install 'crewai[tools]'
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```
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### Subclassing `BaseTool`
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To create a personalized tool, inherit from `BaseTool` and define the necessary attributes and the `_run` method.
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```python
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from crewai_tools import BaseTool
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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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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, offering a concise and efficient way to create specialized tools tailored to your needs.
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```python
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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
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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, you can leverage the full capabilities of the crewAI framework, enhancing both the development experience and the efficiency of your AI agents. |