adding cache_function to base_tool

src/crewai_tools/tools/base_tool.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Callable, cast, Optional, Type
+from typing import Any, Callable, Optional, Type
 
 from pydantic import BaseModel, model_validator
 from pydantic.v1 import BaseModel as V1BaseModel
@@ -14,6 +14,9 @@ class BaseTool(BaseModel, ABC):
     args_schema: Optional[Type[V1BaseModel]] = None
     """The schema for the arguments that the tool accepts."""
     description_updated: bool = False
+    """Flag to check if the description has been updated."""
+    cache_function: Optional[Callable] = lambda: True
+    """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
 
     @model_validator(mode="after")
     def _check_args_schema(self):

tests/base_tool_test.py

@@ -1,6 +1,4 @@
-import json
+from typing import Callable
-import pydantic_core
-import pytest
 from crewai_tools import BaseTool, tool
 
 def test_creating_a_tool_using_annotation():
@@ -11,14 +9,14 @@ def test_creating_a_tool_using_annotation():
 
 	# Assert all the right attributes were defined
 	assert my_tool.name == "Name of my tool"
-	assert my_tool.description == "Clear description for what this tool is useful for, you agent will need this information to use it."
+	assert my_tool.description == "Name of my tool(question: 'string') - Clear description for what this tool is useful for, you agent will need this information to use it."
 	assert my_tool.args_schema.schema()["properties"] == {'question': {'title': 'Question', 'type': 'string'}}
 	assert my_tool.func("What is the meaning of life?") == "What is the meaning of life?"
 
 	# Assert the langchain tool conversion worked as expected
 	converted_tool = my_tool.to_langchain()
 	assert converted_tool.name == "Name of my tool"
-	assert converted_tool.description == "Clear description for what this tool is useful for, you agent will need this information to use it."
+	assert converted_tool.description == "Name of my tool(question: 'string') - Clear description for what this tool is useful for, you agent will need this information to use it."
 	assert converted_tool.args_schema.schema()["properties"] == {'question': {'title': 'Question', 'type': 'string'}}
 	assert converted_tool.func("What is the meaning of life?") == "What is the meaning of life?"
 
@@ -33,14 +31,38 @@ def test_creating_a_tool_using_baseclass():
 	my_tool = MyCustomTool()
 	# Assert all the right attributes were defined
 	assert my_tool.name == "Name of my tool"
-	assert my_tool.description == "Clear description for what this tool is useful for, you agent will need this information to use it."
+	assert my_tool.description == "Name of my tool(question: 'string') - Clear description for what this tool is useful for, you agent will need this information to use it."
 	assert my_tool.args_schema.schema()["properties"] == {'question': {'title': 'Question', 'type': 'string'}}
 	assert my_tool.run("What is the meaning of life?") == "What is the meaning of life?"
 
 	# Assert the langchain tool conversion worked as expected
 	converted_tool = my_tool.to_langchain()
 	assert converted_tool.name == "Name of my tool"
-	assert converted_tool.description == "Clear description for what this tool is useful for, you agent will need this information to use it."
+	assert converted_tool.description == "Name of my tool(question: 'string') - Clear description for what this tool is useful for, you agent will need this information to use it."
 	assert converted_tool.args_schema.schema()["properties"] == {'question': {'title': 'Question', 'type': 'string'}}
 	assert converted_tool.run("What is the meaning of life?") == "What is the meaning of life?"
 
+def test_setting_cache_function():
+	class MyCustomTool(BaseTool):
+		name: str = "Name of my tool"
+		description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+		cache_function: Callable = lambda: False
+
+		def _run(self, question: str) -> str:
+			return question
+
+	my_tool = MyCustomTool()
+	# Assert all the right attributes were defined
+	assert my_tool.cache_function() == False
+
+def test_default_cache_function_is_true():
+	class MyCustomTool(BaseTool):
+		name: str = "Name of my tool"
+		description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+
+		def _run(self, question: str) -> str:
+			return question
+
+	my_tool = MyCustomTool()
+	# Assert all the right attributes were defined
+	assert my_tool.cache_function() == True