Merge branch 'main' into bugfix/restrict-python-version-compatibility

docs/concepts/crews.mdx

@@ -32,6 +32,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
 | **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
+| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |

docs/concepts/llms.mdx

@@ -29,7 +29,7 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
 
 ## Available Models and Their Capabilities
 
-Here's a detailed breakdown of supported models and their capabilities, you can compare performance at [lmarena.ai](https://lmarena.ai/):
+Here's a detailed breakdown of supported models and their capabilities:
 
 <Tabs>
   <Tab title="OpenAI">
@@ -43,17 +43,6 @@ Here's a detailed breakdown of supported models and their capabilities, you can
       1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
     </Note>
   </Tab>
-  <Tab title="Gemini">
-    | Model | Context Window | Best For |
-    |-------|---------------|-----------|
-    | Gemini 1.5 Flash | 1M tokens | Balanced multimodal model, good for most tasks |
-    | Gemini 1.5 Flash 8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
-    | Gemini 1.5 Pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    <Tip>
-      Google's Gemini models are all multimodal, supporting audio, images, video and text, supporting context caching, json schema, function calling, etc.
-    </Tip>
-  </Tab>
   <Tab title="Groq">
     | Model | Context Window | Best For |
     |-------|---------------|-----------|
@@ -139,10 +128,10 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: anthropic/claude-2.1
         # llm: anthropic/claude-2.0
 
-        # Google Models - Strong reasoning, large cachable context window, multimodal
+        # Google Models - Good for general tasks
+        # llm: gemini/gemini-pro
         # llm: gemini/gemini-1.5-pro-latest
-        # llm: gemini/gemini-1.5-flash-latest
-        # llm: gemini/gemini-1.5-flash-8b-latest
+        # llm: gemini/gemini-1.0-pro-latest
 
         # AWS Bedrock Models - Enterprise-grade
         # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
@@ -361,18 +350,13 @@ Learn how to get the most out of your LLM configuration:
 
   <Accordion title="Google">
     ```python Code
-    # Option 1. Gemini accessed with an API key.
-    # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
-
-    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
-    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
     Example usage:
     ```python Code
     llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
+        model="gemini/gemini-pro",
         temperature=0.7
     )
     ```

pyproject.toml

@@ -15,6 +15,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
+    "crewai-tools>=0.17.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -29,7 +30,6 @@ dependencies = [
     "chromadb>=0.5.18",
     "pdfplumber>=0.11.4",
     "openpyxl>=3.1.5",
-    "blinker>=1.9.0",
 ]
 
 [project.urls]

src/crewai/agents/crew_agent_executor.py

@@ -413,6 +413,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         while self.ask_for_human_input:
             human_feedback = self._ask_human_input(formatted_answer.output)
+            print("Human feedback: ", human_feedback)
 
             if self.crew and self.crew._train:
                 self._handle_crew_training_output(formatted_answer, human_feedback)

src/crewai/cli/tools/main.py

@@ -117,7 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         published_handle = publish_response.json()["handle"]
         console.print(
-            f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
+            f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
             style="bold green",
         )
 
@@ -138,7 +138,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         self._add_package(get_response.json())
 
-        console.print(f"Successfully installed {handle}", style="bold green")
+        console.print(f"Succesfully installed {handle}", style="bold green")
 
     def login(self):
         login_response = self.plus_api_client.login_to_tool_repository()

src/crewai/flow/flow.py

@@ -14,15 +14,8 @@ from typing import (
     cast,
 )
 
-from blinker import Signal
 from pydantic import BaseModel, ValidationError
 
-from crewai.flow.flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
 from crewai.telemetry import Telemetry
@@ -166,7 +159,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
     _routers: Dict[str, str] = {}
     _router_paths: Dict[str, List[str]] = {}
     initial_state: Union[Type[T], T, None] = None
-    event_emitter = Signal("event_emitter")
 
     def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
@@ -261,14 +253,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Returns:
             The final output from the flow execution.
         """
-        self.event_emitter.send(
-            self,
-            event=FlowStartedEvent(
-                type="flow_started",
-                flow_name=self.__class__.__name__,
-            ),
-        )
-
         if inputs is not None:
             self._initialize_state(inputs)
         return asyncio.run(self.kickoff_async())
@@ -283,6 +267,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
         Returns:
             The final output from the flow execution.
         """
+        if inputs is not None:
+            self._initialize_state(inputs)
         if not self._start_methods:
             raise ValueError("No start method defined")
 
@@ -299,19 +285,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # Run all start methods concurrently
         await asyncio.gather(*tasks)
 
-        # Determine the final output (from the last executed method)
-        final_output = self._method_outputs[-1] if self._method_outputs else None
-
-        self.event_emitter.send(
-            self,
-            event=FlowFinishedEvent(
-                type="flow_finished",
-                flow_name=self.__class__.__name__,
-                result=final_output,
-            ),
-        )
-
-        return final_output
+        # Return the final output (from the last executed method)
+        if self._method_outputs:
+            return self._method_outputs[-1]
+        else:
+            return None  # Or raise an exception if no methods were executed
 
     async def _execute_start_method(self, start_method_name: str) -> None:
         result = await self._execute_method(
@@ -374,16 +352,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
         try:
             method = self._methods[listener_name]
-
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionStartedEvent(
-                    type="method_execution_started",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
 
@@ -399,15 +367,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # If listener does not expect parameters, call without arguments
                 listener_result = await self._execute_method(listener_name, method)
 
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionFinishedEvent(
-                    type="method_execution_finished",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
             # Execute listeners of this listener
             await self._execute_listeners(listener_name, listener_result)
         except Exception as e:

src/crewai/flow/flow_events.py

@@ -1,33 +0,0 @@
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import Any, Optional
-
-
-@dataclass
-class Event:
-    type: str
-    flow_name: str
-    timestamp: datetime = field(init=False)
-
-    def __post_init__(self):
-        self.timestamp = datetime.now()
-
-
-@dataclass
-class FlowStartedEvent(Event):
-    pass
-
-
-@dataclass
-class MethodExecutionStartedEvent(Event):
-    method_name: str
-
-
-@dataclass
-class MethodExecutionFinishedEvent(Event):
-    method_name: str
-
-
-@dataclass
-class FlowFinishedEvent(Event):
-    result: Optional[Any] = None

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -38,7 +38,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
             if not path.exists():
                 self._logger.log(
                     "error",
-                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If its inside the knowledge directory, use the relative path.",
                     color="red",
                 )
                 raise FileNotFoundError(f"File not found: {path}")

src/crewai/llm.py

@@ -43,10 +43,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gpt-4-turbo": 128000,
     "o1-preview": 128000,
     "o1-mini": 128000,
-    # gemini
-    "gemini-1.5-pro": 2097152,
-    "gemini-1.5-flash": 1048576,
-    "gemini-1.5-flash-8b": 1048576,
     # deepseek
     "deepseek-chat": 128000,
     # groq
@@ -65,9 +61,6 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
 }
 
-DEFAULT_CONTEXT_WINDOW_SIZE = 8192
-CONTEXT_WINDOW_USAGE_RATIO = 0.75
-
 
 @contextmanager
 def suppress_warnings():
@@ -131,7 +124,6 @@ class LLM:
         self.api_version = api_version
         self.api_key = api_key
         self.callbacks = callbacks
-        self.context_window_size = 0
         self.kwargs = kwargs
 
         litellm.drop_params = True
@@ -199,16 +191,7 @@ class LLM:
 
     def get_context_window_size(self) -> int:
         # Only using 75% of the context window size to avoid cutting the message in the middle
-        if self.context_window_size != 0:
-            return self.context_window_size
-
-        self.context_window_size = int(
-            DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
-        )
-        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if self.model.startswith(key):
-                self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
-        return self.context_window_size
+        return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
 
     def set_callbacks(self, callbacks: List[Any]):
         callback_types = [type(callback) for callback in callbacks]

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -44,14 +44,14 @@ class BaseAgentTool(BaseTool):
                 if available_agent.role.casefold().replace("\n", "") == agent_name
             ]
         except Exception as _:
-            return self.i18n.errors("agent_tool_unexisting_coworker").format(
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )
             )
 
         if not agent:
-            return self.i18n.errors("agent_tool_unexisting_coworker").format(
+            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )

src/crewai/translations/en.json

@@ -28,7 +28,7 @@
   "errors": {
     "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
     "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
-    "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
+    "agent_tool_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
     "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
     "tool_usage_error": "I encountered an error: {error}",
     "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",

tests/cli/tools/test_main.py

@@ -85,7 +85,7 @@ def test_install_success(mock_get, mock_subprocess_run):
         env=unittest.mock.ANY
     )
 
-    assert "Successfully installed sample-tool" in output
+    assert "Succesfully installed sample-tool" in output
 
 
 @patch("crewai.cli.plus_api.PlusAPI.get_tool")

tests/utilities/prompts.json

@@ -26,7 +26,7 @@
   },
   "errors": {
     "force_final_answer": "Lorem ipsum dolor sit amet",
-    "agent_tool_unexisting_coworker": "Lorem ipsum dolor sit amet",
+    "agent_tool_unexsiting_coworker": "Lorem ipsum dolor sit amet",
     "task_repeated_usage": "Lorem ipsum dolor sit amet",
     "tool_usage_error": "Lorem ipsum dolor sit amet",
     "tool_arguments_error": "Lorem ipsum dolor sit amet",

uv.lock

@@ -272,15 +272,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed", size = 147925 },
 ]
 
-[[package]]
-name = "blinker"
-version = "1.9.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458 },
-]
-
 [[package]]
 name = "build"
 version = "1.2.2.post1"
@@ -577,9 +568,9 @@ source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
     { name = "auth0-python" },
-    { name = "blinker" },
     { name = "chromadb" },
     { name = "click" },
+    { name = "crewai-tools" },
     { name = "instructor" },
     { name = "json-repair" },
     { name = "jsonref" },
@@ -647,9 +638,9 @@ requires-dist = [
     { name = "agentops", marker = "extra == 'agentops'", specifier = ">=0.3.0" },
     { name = "appdirs", specifier = ">=1.4.4" },
     { name = "auth0-python", specifier = ">=4.7.1" },
-    { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.18" },
     { name = "click", specifier = ">=8.1.7" },
+    { name = "crewai-tools", specifier = ">=0.17.0" },
     { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },