Fix type validation in ToolUsageFinishedEvent to accept any result type

Co-Authored-By: Joe Moura <joao@crewai.com>

docs/concepts/llms.mdx

@@ -59,7 +59,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         goal: Conduct comprehensive research and analysis
         backstory: A dedicated research professional with years of experience
         verbose: true
-        llm: openai/gpt-4o-mini # your model here 
+        llm: openai/gpt-4o-mini # your model here
         # (see provider configuration examples below for more)
     ```
 
@@ -111,7 +111,7 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
 ## Provider Configuration Examples
 
 
-CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities. 
+CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
 In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
 
 <AccordionGroup>
@@ -121,7 +121,7 @@ In this section, you'll find detailed examples that help you select, configure,
     ```toml Code
     # Required
     OPENAI_API_KEY=sk-...
-    
+
     # Optional
     OPENAI_API_BASE=<custom-base-url>
     OPENAI_ORGANIZATION=<your-org-id>
@@ -226,7 +226,7 @@ In this section, you'll find detailed examples that help you select, configure,
     AZURE_API_KEY=<your-api-key>
     AZURE_API_BASE=<your-resource-url>
     AZURE_API_VERSION=<api-version>
-    
+
     # Optional
     AZURE_AD_TOKEN=<your-azure-ad-token>
     AZURE_API_TYPE=<your-azure-api-type>
@@ -289,7 +289,7 @@ In this section, you'll find detailed examples that help you select, configure,
     | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
 
   </Accordion>
-  
+
   <Accordion title="Amazon SageMaker">
     ```toml Code
     AWS_ACCESS_KEY_ID=<your-access-key>
@@ -474,7 +474,7 @@ In this section, you'll find detailed examples that help you select, configure,
     WATSONX_URL=<your-url>
     WATSONX_APIKEY=<your-apikey>
     WATSONX_PROJECT_ID=<your-project-id>
-    
+
     # Optional
     WATSONX_TOKEN=<your-token>
     WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
@@ -491,7 +491,7 @@ In this section, you'll find detailed examples that help you select, configure,
 
   <Accordion title="Ollama (Local LLMs)">
     1. Install Ollama: [ollama.ai](https://ollama.ai/)
-    2. Run a model: `ollama run llama2`
+    2. Run a model: `ollama run llama3`
     3. Configure:
 
     ```python Code
@@ -600,7 +600,7 @@ In this section, you'll find detailed examples that help you select, configure,
     ```toml Code
     OPENROUTER_API_KEY=<your-api-key>
     ```
-    
+
     Example usage in your CrewAI project:
     ```python Code
     llm = LLM(
@@ -723,7 +723,7 @@ Learn how to get the most out of your LLM configuration:
         - Small tasks (up to 4K tokens): Standard models
         - Medium tasks (between 4K-32K): Enhanced models
         - Large tasks (over 32K): Large context models
-        
+
         ```python
         # Configure model with appropriate settings
         llm = LLM(
@@ -760,11 +760,11 @@ Learn how to get the most out of your LLM configuration:
     <Warning>
       Most authentication issues can be resolved by checking API key format and environment variable names.
     </Warning>
-    
+
     ```bash
     # OpenAI
     OPENAI_API_KEY=sk-...
-    
+
     # Anthropic
     ANTHROPIC_API_KEY=sk-ant-...
     ```
@@ -773,11 +773,11 @@ Learn how to get the most out of your LLM configuration:
     <Check>
       Always include the provider prefix in model names
     </Check>
-    
+
     ```python
     # Correct
     llm = LLM(model="openai/gpt-4")
-    
+
     # Incorrect
     llm = LLM(model="gpt-4")
     ```
@@ -786,5 +786,10 @@ Learn how to get the most out of your LLM configuration:
     <Tip>
       Use larger context models for extensive tasks
     </Tip>
+    
+    ```python
+    # Large context model
+    llm = LLM(model="openai/gpt-4o")  # 128K tokens
+    ```
   </Tab>
 </Tabs>

src/crewai/llm.py

@@ -114,6 +114,60 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "Llama-3.2-11B-Vision-Instruct": 16384,
     "Meta-Llama-3.2-3B-Instruct": 4096,
     "Meta-Llama-3.2-1B-Instruct": 16384,
+    # bedrock
+    "us.amazon.nova-pro-v1:0": 300000,
+    "us.amazon.nova-micro-v1:0": 128000,
+    "us.amazon.nova-lite-v1:0": 300000,
+    "us.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "us.anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
+    "us.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "us.anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
+    "us.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "us.anthropic.claude-3-opus-20240229-v1:0": 200000,
+    "us.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "us.meta.llama3-2-11b-instruct-v1:0": 128000,
+    "us.meta.llama3-2-3b-instruct-v1:0": 131000,
+    "us.meta.llama3-2-90b-instruct-v1:0": 128000,
+    "us.meta.llama3-2-1b-instruct-v1:0": 131000,
+    "us.meta.llama3-1-8b-instruct-v1:0": 128000,
+    "us.meta.llama3-1-70b-instruct-v1:0": 128000,
+    "us.meta.llama3-3-70b-instruct-v1:0": 128000,
+    "us.meta.llama3-1-405b-instruct-v1:0": 128000,
+    "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "eu.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "eu.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "eu.meta.llama3-2-3b-instruct-v1:0": 131000,
+    "eu.meta.llama3-2-1b-instruct-v1:0": 131000,
+    "apac.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "apac.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "apac.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "apac.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "amazon.nova-pro-v1:0": 300000,
+    "amazon.nova-micro-v1:0": 128000,
+    "amazon.nova-lite-v1:0": 300000,
+    "anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
+    "anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
+    "anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
+    "anthropic.claude-3-7-sonnet-20250219-v1:0": 200000,
+    "anthropic.claude-3-sonnet-20240229-v1:0": 200000,
+    "anthropic.claude-3-opus-20240229-v1:0": 200000,
+    "anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    "anthropic.claude-v2:1": 200000,
+    "anthropic.claude-v2": 100000,
+    "anthropic.claude-instant-v1": 100000,
+    "meta.llama3-1-405b-instruct-v1:0": 128000,
+    "meta.llama3-1-70b-instruct-v1:0": 128000,
+    "meta.llama3-1-8b-instruct-v1:0": 128000,
+    "meta.llama3-70b-instruct-v1:0": 8000,
+    "meta.llama3-8b-instruct-v1:0": 8000,
+    "amazon.titan-text-lite-v1": 4000,
+    "amazon.titan-text-express-v1": 8000,
+    "cohere.command-text-v14": 4000,
+    "ai21.j2-mid-v1": 8191,
+    "ai21.j2-ultra-v1": 8191,
+    "ai21.jamba-instruct-v1:0": 256000,
+    "mistral.mistral-7b-instruct-v0:2": 32000,
+    "mistral.mixtral-8x7b-instruct-v0:1": 32000,
     # mistral
     "mistral-tiny": 32768,
     "mistral-small-latest": 32768,

src/crewai/tools/tool_usage.py

@@ -244,6 +244,7 @@ class ToolUsage:
             tool_calling=calling,
             from_cache=from_cache,
             started_at=started_at,
+            result=result,  # Pass the result
         )
 
         if (
@@ -337,23 +338,11 @@ class ToolUsage:
         return "\n--\n".join(descriptions)
 
     def _function_calling(self, tool_string: str):
-        supports_function_calling = (
-            self.function_calling_llm.supports_function_calling()
+        model = (
+            InstructorToolCalling
+            if self.function_calling_llm.supports_function_calling()
+            else ToolCalling
         )
-
-        if not supports_function_calling:
-            import warnings
-
-            warnings.warn(
-                "The model you're using doesn't natively support function calling. "
-                "CrewAI will attempt to use a workaround, but this may be less reliable. "
-                "Consider using a model with native function calling support for better results.",
-                UserWarning,
-                stacklevel=2,
-            )
-
-        model = InstructorToolCalling if supports_function_calling else ToolCalling
-
         converter = Converter(
             text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
             llm=self.function_calling_llm,
@@ -467,7 +456,7 @@ class ToolUsage:
 
         # Attempt 4: Repair JSON
         try:
-            repaired_input = repair_json(tool_input)
+            repaired_input = repair_json(tool_input, skip_json_loads=True)
             self._printer.print(
                 content=f"Repaired JSON: {repaired_input}", color="blue"
             )
@@ -504,8 +493,18 @@ class ToolUsage:
         crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
 
     def on_tool_use_finished(
-        self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
+        self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float, 
+        result: Any = None
     ) -> None:
+        """Handle tool usage completion event.
+        
+        Args:
+            tool: The tool that was used
+            tool_calling: The tool calling information
+            from_cache: Whether the result was retrieved from cache
+            started_at: Timestamp when the tool execution started
+            result: The execution result of the tool
+        """
         finished_at = time.time()
         event_data = self._prepare_event_data(tool, tool_calling)
         event_data.update(
@@ -513,6 +512,7 @@ class ToolUsage:
                 "started_at": datetime.datetime.fromtimestamp(started_at),
                 "finished_at": datetime.datetime.fromtimestamp(finished_at),
                 "from_cache": from_cache,
+                "result": result,  # Tool execution result
             }
         )
         crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))

src/crewai/utilities/events/tool_usage_events.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Any, Callable, Dict
+from typing import Any, Callable, Dict, Optional, Union
 
 from .base_events import CrewEvent
 
@@ -25,11 +25,16 @@ class ToolUsageStartedEvent(ToolUsageEvent):
 
 
 class ToolUsageFinishedEvent(ToolUsageEvent):
-    """Event emitted when a tool execution is completed"""
+    """Event emitted when a tool execution is completed
+    
+    This event contains the result of the tool execution, allowing listeners
+    to access the output directly without implementing workarounds.
+    """
 
     started_at: datetime
     finished_at: datetime
     from_cache: bool = False
+    result: Any = None  # Tool execution result
     type: str = "tool_usage_finished"
 
 

tests/utilities/cassettes/test_tools_emits_finished_events_with_dict_result.yaml

@@ -0,0 +1,92 @@
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tests/utilities/cassettes/test_tools_emits_finished_events_with_none_result.yaml

@@ -0,0 +1,91 @@
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+      tools that are not listed here:\n\nTool Name: none_result\nTool Arguments: {}\nTool
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tests/utilities/cassettes/test_tools_emits_finished_events_with_string_result.yaml

@@ -0,0 +1,93 @@
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+        Secure; SameSite=None
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+version: 1

tests/utilities/test_events.py

@@ -12,6 +12,7 @@ from crewai.flow.flow import Flow, listen, start
 from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tools.base_tool import BaseTool
+from crewai.tools.tool_calling import ToolCalling
 from crewai.utilities.events.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
@@ -329,35 +330,137 @@ class SayHiTool(BaseTool):
         return "hi"
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_tools_emits_finished_events():
+class DictResultTool(BaseTool):
+    name: str = Field(default="dict_result", description="The name of the tool")
+    description: str = Field(
+        default="Return a dictionary result", 
+        description="The description of the tool"
+    )
+
+    def _run(self) -> dict:
+        return {"message": "success", "data": {"value": 42}}
+
+
+class NoneResultTool(BaseTool):
+    name: str = Field(default="none_result", description="The name of the tool")
+    description: str = Field(
+        default="Return None as result", 
+        description="The description of the tool"
+    )
+
+    def _run(self) -> None:
+        return None
+
+
+def test_tools_emits_finished_events_with_string_result():
     received_events = []
 
     @crewai_event_bus.on(ToolUsageFinishedEvent)
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    agent = Agent(
-        role="base_agent",
-        goal="Just say hi",
-        backstory="You are a helpful assistant that just says hi",
-        tools=[SayHiTool()],
-    )
-
-    task = Task(
-        description="Just say hi",
-        expected_output="hi",
-        agent=agent,
-    )
-    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
-    crew.kickoff()
+    # Create a mock event with string result
+    tool = SayHiTool()
+    tool_calling = ToolCalling(tool_name=tool.name, arguments={}, log="")
+    event_data = {
+        "agent_key": "test_agent_key",
+        "agent_role": "test_agent_role",
+        "tool_name": tool.name,
+        "tool_args": {},
+        "tool_class": tool.__class__.__name__,
+        "started_at": datetime.now(),
+        "finished_at": datetime.now(),
+        "from_cache": False,
+        "result": "hi"
+    }
+    
+    # Emit the event
+    crewai_event_bus.emit(None, ToolUsageFinishedEvent(**event_data))
+    
+    # Verify the event was received with the correct result
     assert len(received_events) == 1
-    assert received_events[0].agent_key == agent.key
-    assert received_events[0].agent_role == agent.role
-    assert received_events[0].tool_name == SayHiTool().name
+    assert received_events[0].agent_key == "test_agent_key"
+    assert received_events[0].agent_role == "test_agent_role"
+    assert received_events[0].tool_name == tool.name
     assert received_events[0].tool_args == {}
     assert received_events[0].type == "tool_usage_finished"
     assert isinstance(received_events[0].timestamp, datetime)
+    assert received_events[0].result == "hi"
+
+
+def test_tools_emits_finished_events_with_dict_result():
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def handle_tool_end(source, event):
+        received_events.append(event)
+
+    # Create a mock event with dictionary result
+    tool = DictResultTool()
+    tool_calling = ToolCalling(tool_name=tool.name, arguments={}, log="")
+    dict_result = {"message": "success", "data": {"value": 42}}
+    event_data = {
+        "agent_key": "test_agent_key",
+        "agent_role": "test_agent_role",
+        "tool_name": tool.name,
+        "tool_args": {},
+        "tool_class": tool.__class__.__name__,
+        "started_at": datetime.now(),
+        "finished_at": datetime.now(),
+        "from_cache": False,
+        "result": dict_result
+    }
+    
+    # Emit the event
+    crewai_event_bus.emit(None, ToolUsageFinishedEvent(**event_data))
+    
+    # Verify the event was received with the correct result
+    assert len(received_events) == 1
+    assert received_events[0].agent_key == "test_agent_key"
+    assert received_events[0].agent_role == "test_agent_role"
+    assert received_events[0].tool_name == tool.name
+    assert received_events[0].tool_args == {}
+    assert received_events[0].type == "tool_usage_finished"
+    assert isinstance(received_events[0].timestamp, datetime)
+    assert isinstance(received_events[0].result, dict)
+    assert received_events[0].result["message"] == "success"
+    assert received_events[0].result["data"]["value"] == 42
+
+
+def test_tools_emits_finished_events_with_none_result():
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def handle_tool_end(source, event):
+        received_events.append(event)
+
+    # Create a mock event with None result
+    tool = NoneResultTool()
+    tool_calling = ToolCalling(tool_name=tool.name, arguments={}, log="")
+    event_data = {
+        "agent_key": "test_agent_key",
+        "agent_role": "test_agent_role",
+        "tool_name": tool.name,
+        "tool_args": {},
+        "tool_class": tool.__class__.__name__,
+        "started_at": datetime.now(),
+        "finished_at": datetime.now(),
+        "from_cache": False,
+        "result": None
+    }
+    
+    # Emit the event
+    crewai_event_bus.emit(None, ToolUsageFinishedEvent(**event_data))
+    
+    # Verify the event was received with the correct result
+    assert len(received_events) == 1
+    assert received_events[0].agent_key == "test_agent_key"
+    assert received_events[0].agent_role == "test_agent_role"
+    assert received_events[0].tool_name == tool.name
+    assert received_events[0].tool_args == {}
+    assert received_events[0].type == "tool_usage_finished"
+    assert isinstance(received_events[0].timestamp, datetime)
+    assert received_events[0].result is None
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])