Adding custom caching

docs/how-to/Create-Custom-Tools.md

@@ -5,43 +5,43 @@ description: Guide on how to create and use custom tools within the crewAI frame
 
 ## Creating your own Tools
 !!! example "Custom Tool Creation"
-    Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
+    Developers can craft custom tools tailored to their agent’s needs or utilize pre-built options.
 
-To create your own crewAI tools you will need to install our extra tools package:
+To create your own crewAI tools, you will need to install our extra tools package:
 
 ```bash
 pip install 'crewai[tools]'
 ```
 
-Once you do that there are two main ways for one to create a crewAI tool:
+Once installed, there are two primary methods for creating a crewAI tool:
 
 ### Subclassing `BaseTool`
 
+To define a custom tool, create a new class that inherits from `BaseTool`. Specify the `name`, `description`, and implement the `_run` method to outline its operational logic.
+
 ```python
 from crewai_tools import BaseTool
 
 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."
+    description: str = "Clear description for what this tool is useful for. Your agent will need this information to utilize it effectively."
 
     def _run(self, argument: str) -> str:
-        # Implementation goes here
+        # Implementation details go here
         return "Result from custom tool"
 ```
 
-Define a new class inheriting from `BaseTool`, specifying `name`, `description`, and the `_run` method for operational logic.
-
-
 ### Utilizing the `tool` Decorator
 
-For a simpler approach, create a `Tool` object directly with the required attributes and a functional logic.
+For a more straightforward approach, employ the `tool` decorator to create a `Tool` object directly. This method requires specifying the required attributes and functional logic within a decorated function.
 
 ```python
 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, you agent will need this information to use it."""
+    """Provide a clear description of what this tool is useful for. Your agent will need this information to use it."""
-    # Function logic here
+    # Implement function logic here
 ```
 
 ```python
@@ -49,43 +49,30 @@ import json
 import requests
 from crewai import Agent
 from crewai.tools import tool
-from unstructured.partition.html import partition_html
 
-    # Annotate the function with the tool decorator from crewAI
+# Decorate the function with the tool decorator from crewAI
-@tool("Integration with a given API")
+@tool("Integration with a Given API")
-def integtation_tool(argument: str) -> str:
+def integration_tool(argument: str) -> str:
-    """Integration with a given API"""
+    """Details the integration process with a given API."""
-    # Code here
+    # Implementation details
-    return resutls # string to be sent back to the agent
+    return "Results to be sent back to the agent"
-
-# Assign the scraping tool to an agent
-agent = Agent(
-    role='Research Analyst',
-    goal='Provide up-to-date market analysis',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[integtation_tool]
-)
 ```
 
+### Defining a Cache Function for the Tool
 
-
+By default, all tools have caching enabled, meaning that if a tool is called with the same arguments by any agent in the crew, it will return the same result. However, specific scenarios may require more tailored caching strategies. For these cases, use the `cache_function` attribute to assign a function that determines whether the result should be cached.
-### Using the `Tool` function from langchain
-
-For another simple approach, create a function in python directly with the required attributes and a functional logic.
 
 ```python
-def combine(a, b):
+@tool("Integration with a Given API")
-    return a + b
+def integration_tool(argument: str) -> str:
-```
+    """Integration with a given API."""
+    # Implementation details
+    return "Results to be sent back to the agent"
 
-Then you can add that function into the your tool by using 'func' variable in the Tool function.
+def cache_strategy(arguments: dict, result: str) -> bool:
+    if result == "some_value":
+        return True
+    return False
 
-```python
+integration_tool.cache_function = cache_strategy
-from langchain.agents import Tool
+```
-
-math_tool = Tool(
-            name="Math tool",
-            func=math_tool,
-            description="Useful for adding two numbers together, in other words combining them."
-        )
-```

src/crewai/agent.py

@@ -193,13 +193,15 @@ class Agent(BaseModel):
                 task=task_prompt, context=context
             )
 
-        tools = self._parse_tools(tools or self.tools)
+        tools = tools or self.tools
+        parsed_tools = self._parse_tools(tools)
+
         self.create_agent_executor(tools=tools)
-        self.agent_executor.tools = tools
+        self.agent_executor.tools = parsed_tools
         self.agent_executor.task = task
 
-        self.agent_executor.tools_description = render_text_description(tools)
+        self.agent_executor.tools_description = render_text_description(parsed_tools)
-        self.agent_executor.tools_names = self.__tools_names(tools)
+        self.agent_executor.tools_names = self.__tools_names(parsed_tools)
 
         result = self.agent_executor.invoke(
             {
@@ -220,10 +222,11 @@ class Agent(BaseModel):
         Args:
             cache_handler: An instance of the CacheHandler class.
         """
+        self.tools_handler = ToolsHandler()
         if self.cache:
             self.cache_handler = cache_handler
-            self.tools_handler = ToolsHandler(cache=self.cache_handler)
+            self.tools_handler.cache = cache_handler
-            self.create_agent_executor()
+        self.create_agent_executor()
 
     def set_rpm_controller(self, rpm_controller: RPMController) -> None:
         """Set the rpm controller for the agent.
@@ -257,6 +260,7 @@ class Agent(BaseModel):
             "i18n": self.i18n,
             "tools": self._parse_tools(tools),
             "verbose": self.verbose,
+            "original_tools": tools,
             "handle_parsing_errors": True,
             "max_iterations": self.max_iter,
             "step_callback": self.step_callback,

src/crewai/agents/executor.py

@@ -24,6 +24,7 @@ class CrewAgentExecutor(AgentExecutor):
     task: Any = None
     tools_description: str = ""
     tools_names: str = ""
+    original_tools: List[Any] = []
     function_calling_llm: Any = None
     request_within_rpm_limit: Any = None
     tools_handler: InstanceOf[ToolsHandler] = None
@@ -202,6 +203,7 @@ class CrewAgentExecutor(AgentExecutor):
             tool_usage = ToolUsage(
                 tools_handler=self.tools_handler,
                 tools=self.tools,
+                original_tools=self.original_tools,
                 tools_description=self.tools_description,
                 tools_names=self.tools_names,
                 function_calling_llm=self.function_calling_llm,

src/crewai/agents/tools_handler.py

@@ -1,7 +1,7 @@
-from typing import Any
+from typing import Any, Optional, Union
 
 from ..tools.cache_tools import CacheTools
-from ..tools.tool_calling import ToolCalling
+from ..tools.tool_calling import InstructorToolCalling, ToolCalling
 from .cache.cache_handler import CacheHandler
 
 
@@ -11,15 +11,20 @@ class ToolsHandler:
     last_used_tool: ToolCalling = {}
     cache: CacheHandler
 
-    def __init__(self, cache: CacheHandler):
+    def __init__(self, cache: Optional[CacheHandler] = None):
         """Initialize the callback handler."""
         self.cache = cache
         self.last_used_tool = {}
 
-    def on_tool_use(self, calling: ToolCalling, output: str) -> Any:
+    def on_tool_use(
+        self,
+        calling: Union[ToolCalling, InstructorToolCalling],
+        output: str,
+        should_cache: bool = True,
+    ) -> Any:
         """Run when tool ends running."""
         self.last_used_tool = calling
-        if calling.tool_name != CacheTools().name:
+        if self.cache and should_cache and calling.tool_name != CacheTools().name:
             self.cache.add(
                 tool=calling.tool_name,
                 input=calling.arguments,

src/crewai/tools/tool_usage.py

@@ -29,6 +29,7 @@ class ToolUsage:
         task: Task being executed.
         tools_handler: Tools handler that will manage the tool usage.
         tools: List of tools available for the agent.
+        original_tools: Original tools available for the agent before being converted to BaseTool.
         tools_description: Description of the tools available for the agent.
         tools_names: Names of the tools available for the agent.
         function_calling_llm: Language model to be used for the tool usage.
@@ -38,6 +39,7 @@ class ToolUsage:
         self,
         tools_handler: ToolsHandler,
         tools: List[BaseTool],
+        original_tools: List[Any],
         tools_description: str,
         tools_names: str,
         task: Any,
@@ -53,6 +55,7 @@ class ToolUsage:
         self.tools_description = tools_description
         self.tools_names = tools_names
         self.tools_handler = tools_handler
+        self.original_tools = original_tools
         self.tools = tools
         self.task = task
         self.action = action
@@ -111,7 +114,7 @@ class ToolUsage:
 
         result = None
 
-        if self.tools_handler:
+        if self.tools_handler.cache:
             result = self.tools_handler.cache.read(
                 tool=calling.tool_name, input=calling.arguments
             )
@@ -159,7 +162,25 @@ class ToolUsage:
                 return self.use(calling=calling, tool_string=tool_string)
 
             if self.tools_handler:
-                self.tools_handler.on_tool_use(calling=calling, output=result)
+                should_cache = True
+                print("FORA")
+                print(tool)
+                original_tool = next(
+                    (ot for ot in self.original_tools if ot.name == tool.name), None
+                )
+                if (
+                    hasattr(original_tool, "cache_function")
+                    and original_tool.cache_function
+                ):
+                    print("CARALHOOOO")
+                    print(original_tool.cache_function)
+                    should_cache = original_tool.cache_function(
+                        calling.arguments, result
+                    )
+
+                self.tools_handler.on_tool_use(
+                    calling=calling, output=result, should_cache=should_cache
+                )
 
         self._printer.print(content=f"\n\n{result}\n", color="yellow")
         self._telemetry.tool_usage(
@@ -248,12 +269,12 @@ class ToolUsage:
                     model=model,
                     instructions=dedent(
                         """\
-                                            The schema should have the following structure, only two keys:
+                            The schema should have the following structure, only two keys:
-                                            - tool_name: str
+                            - tool_name: str
-                                            - arguments: dict (with all arguments being passed)
+                            - arguments: dict (with all arguments being passed)
 
-                                            Example:
+                            Example:
-                                            {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
+                            {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
                     ),
                     max_attemps=1,
                 )

tests/agent_test.py

@@ -349,14 +349,14 @@ def test_agent_repeated_tool_usage(capsys):
         goal="test goal",
         backstory="test backstory",
         max_iter=4,
-        llm=ChatOpenAI(model="gpt-4-0125-preview"),
+        llm=ChatOpenAI(model="gpt-4"),
         allow_delegation=False,
         verbose=True,
     )
 
     task = Task(
         description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
-        expected_output="The final answer",
+        expected_output="The final answer, don't give it until I tell you so",
     )
     # force cleaning cache
     agent.tools_handler.cache = CacheHandler()
@@ -367,7 +367,47 @@ def test_agent_repeated_tool_usage(capsys):
 
     captured = capsys.readouterr()
 
-    assert "The final answer is 42." in captured.out
+    assert (
+        "I tried reusing the same input, I must stop using this action input. I'll try something else instead."
+        in captured.out
+    )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
+    @tool
+    def get_final_answer(anything: str) -> float:
+        """Get the final answer but don't give it yet, just re-use this
+        tool non-stop."""
+        return 42
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_iter=4,
+        llm=ChatOpenAI(model="gpt-4"),
+        allow_delegation=False,
+        verbose=True,
+        cache=False,
+    )
+
+    task = Task(
+        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
+        expected_output="The final answer, don't give it until I tell you so",
+    )
+
+    agent.execute_task(
+        task=task,
+        tools=[get_final_answer],
+    )
+
+    captured = capsys.readouterr()
+    print(captured.out)
+    assert (
+        "I tried reusing the same input, I must stop using this action input. I'll try something else instead."
+        in captured.out
+    )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

tests/cassettes/test_agent_repeated_tool_usage_check_even_with_disabled_cache.yaml

@@ -0,0 +1,887 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": "You are test role. test backstory\nYour
+      personal goal is: test goal\n\nYou ONLY have access to the following tools,
+      and should NEVER make up tools that are not listed here:\n\nget_final_answer:
+      get_final_answer(anything: str) -> float - Get the final answer but don''t give
+      it yet, just re-use this\n        tool non-stop.\n\nUse the following format:\n\nThought:
+      you should always think about what to do\nAction: the action to take, only one
+      name of [get_final_answer], just the name, exactly as it''s written.\nAction
+      Input: the input to the action, just a simple a python dictionary using \" to
+      wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary
+      information is gathered:\n\nThought: I now know the final answer\nFinal Answer:
+      the final answer to the original input question\n\n\nCurrent Task: The final
+      answer is 42. But don''t give it until I tell you so, instead keep using the
+      `get_final_answer` tool.\n\nThis is the expect criteria for your final answer:
+      The final answer, don''t give it until I tell you so \n you MUST return the
+      actual complete content as the final answer, not a summary.\n\nBegin! This is
+      VERY important to you, use the tools available and give your best Final Answer,
+      your job depends on it!\n\nThought: \n"}], "model": "gpt-4", "n": 1, "stop":
+      ["\nObservation"], "stream": true, "temperature": 0.7}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, br
+      connection:
+      - keep-alive
+      content-length:
+      - '1398'
+      content-type:
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+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.13.3
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.13.3
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.7
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: 'data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
+
+
+        data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"The"},"logprobs":null,"finish_reason":null}]}
+
+
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+
+
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+
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+        to"},"logprobs":null,"finish_reason":null}]}
+
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+        tool"},"logprobs":null,"finish_reason":null}]}
+
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+
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+        The"},"logprobs":null,"finish_reason":null}]}
+
+
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+        final"},"logprobs":null,"finish_reason":null}]}
+
+
+        data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
+        answer"},"logprobs":null,"finish_reason":null}]}
+
+
+        data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
+        is"},"logprobs":null,"finish_reason":null}]}
+
+
+        data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
+        already"},"logprobs":null,"finish_reason":null}]}
+
+
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+        given"},"logprobs":null,"finish_reason":null}]}
+
+
+        data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
+        as"},"logprobs":null,"finish_reason":null}]}
+
+
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+        "},"logprobs":null,"finish_reason":null}]}
+
+
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+
+
+        data: {"id":"chatcmpl-94Y7nHp7Rqq1aPh9cyfo16KJBehh1","object":"chat.completion.chunk","created":1710871515,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":","},"logprobs":null,"finish_reason":null}]}
+
+
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+
+
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+        the"},"logprobs":null,"finish_reason":null}]}
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+
+
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+
+
+        data: [DONE]
+
+
+        '
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
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+      - 866f63e0bf0fa559-GRU
+      Cache-Control:
+      - no-cache, must-revalidate
+      Connection:
+      - keep-alive
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+      - text/event-stream
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+      - Tue, 19 Mar 2024 18:05:22 GMT
+      Server:
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+      - chunked
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+      - '*'
+      alt-svc:
+      - h3=":443"; ma=86400
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+      - gpt-4-0613
+      openai-organization:
+      - crewai-iuxna1
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+      - '256'
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+      x-ratelimit-remaining-requests:
+      - '9999'
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+      - '299436'
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+      - 6ms
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+      - 112ms
+      x-request-id:
+      - req_0f47e77546d773004e0dfe7e64949091
+    status:
+      code: 200
+      message: OK
+version: 1

tests/crew_test.py

@@ -733,3 +733,78 @@ def test_crew_inputs_interpolate_both_agents_and_tasks():
                 crew.kickoff(inputs={"topic": "AI", "points": 5})
                 interpolate_agent_inputs.assert_called()
                 interpolate_task_inputs.assert_called()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_tools_with_custom_caching():
+    from unittest.mock import patch
+
+    from crewai_tools import tool
+
+    @tool
+    def multiplcation_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):
+        cache = result % 2 == 0
+        print(f"cache?: {cache}")
+        return cache
+
+    multiplcation_tool.cache_function = cache_func
+
+    writer1 = Agent(
+        role="Writer",
+        goal="You write lesssons of math for kids.",
+        backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
+        tools=[multiplcation_tool],
+        allow_delegation=False,
+    )
+
+    writer2 = Agent(
+        role="Writer",
+        goal="You write lesssons of math for kids.",
+        backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
+        tools=[multiplcation_tool],
+        allow_delegation=False,
+    )
+
+    task1 = Task(
+        description="What is 2 times 6? Return only the number after using the multiplication tool.",
+        expected_output="the result of multiplication",
+        agent=writer1,
+    )
+
+    task2 = Task(
+        description="What is 3 times 1? Return only the number after using the multiplication tool.",
+        expected_output="the result of multiplication",
+        agent=writer1,
+    )
+
+    task3 = Task(
+        description="What is 2 times 6? Return only the number after using the multiplication tool.",
+        expected_output="the result of multiplication",
+        agent=writer2,
+    )
+
+    task4 = Task(
+        description="What is 3 times 1? Return only the number after using the multiplication tool.",
+        expected_output="the result of multiplication",
+        agent=writer2,
+    )
+
+    crew = Crew(agents=[writer1, writer2], tasks=[task1, task2, task3, task4])
+
+    with patch.object(
+        CacheHandler, "add", wraps=crew._cache_handler.add
+    ) as add_to_cache:
+        with patch.object(
+            CacheHandler, "read", wraps=crew._cache_handler.read
+        ) as read_from_cache:
+            result = crew.kickoff()
+            add_to_cache.assert_called_once_with(
+                tool="multiplcation_tool",
+                input={"first_number": 2, "second_number": 6},
+                output=12,
+            )
+            assert result == "3"