Include embedding type fix

* add docs for ApifyActorsTool

* improve readme, add link to template

* format

* improve tool docs

* improve readme

* Update apifyactorstool.mdx (#1)

* Update apifyactorstool.mdx

* Update apifyactorstool.mdx

* dans suggestions

* custom apify icon

* update descripton

* Update apifyactorstool.mdx

---------

Co-authored-by: Jan Čurn <jan.curn@gmail.com>
Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>

docs/concepts/tools.mdx

@@ -106,6 +106,7 @@ Here is a list of the available tools and their descriptions:
 
 | Tool                             | Description                                                                                    |
 | :------------------------------- | :--------------------------------------------------------------------------------------------- |
+| **ApifyActorsTool**              | A tool that integrates Apify Actors with your workflows for web scraping and automation tasks. |
 | **BrowserbaseLoadTool**          | A tool for interacting with and extracting data from web browsers.                             |
 | **CodeDocsSearchTool**           | A RAG tool optimized for searching through code documentation and related technical documents. |
 | **CodeInterpreterTool**          | A tool for interpreting python code.                                                           |

docs/mint.json

@@ -115,6 +115,7 @@
         "concepts/testing",
         "concepts/cli",
         "concepts/tools",
+        "concepts/event-listener",
         "concepts/langchain-tools",
         "concepts/llamaindex-tools"
       ]
@@ -154,6 +155,7 @@
       "group": "Tools",
       "pages": [
         "tools/aimindtool",
+        "tools/apifyactorstool",
         "tools/bravesearchtool",
         "tools/browserbaseloadtool",
         "tools/codedocssearchtool",
@@ -220,4 +222,4 @@
     "linkedin": "https://www.linkedin.com/company/crewai-inc",
     "youtube": "https://youtube.com/@crewAIInc"
   }
-}
+}

docs/tools/apifyactorstool.mdx

@@ -0,0 +1,99 @@
+---
+title: Apify Actors
+description: "`ApifyActorsTool` lets you call Apify Actors to provide your CrewAI workflows with web scraping, crawling, data extraction, and web automation capabilities."
+# hack to use custom Apify icon
+icon: "); -webkit-mask-image: url('https://upload.wikimedia.org/wikipedia/commons/a/ae/Apify.svg');/*"
+---
+
+# `ApifyActorsTool`
+
+Integrate [Apify Actors](https://apify.com/actors) into your CrewAI workflows.
+
+## Description
+
+The `ApifyActorsTool` connects [Apify Actors](https://apify.com/actors), cloud-based programs for web scraping and automation, to your CrewAI workflows.
+Use any of the 4,000+ Actors on [Apify Store](https://apify.com/store) for use cases such as extracting data from social media, search engines, online maps, e-commerce sites, travel portals, or general websites.
+
+For details, see the [Apify CrewAI integration](https://docs.apify.com/platform/integrations/crewai) in Apify documentation.
+
+## Steps to get started
+
+<Steps>
+    <Step title="Install dependencies">
+        Install `crewai[tools]` and `langchain-apify` using pip: `pip install 'crewai[tools]' langchain-apify`.
+    </Step>
+    <Step title="Obtain an Apify API token">
+        Sign up to [Apify Console](https://console.apify.com/) and get your [Apify API token](https://console.apify.com/settings/integrations)..
+    </Step>
+    <Step title="Configure environment">
+        Set your Apify API token as the `APIFY_API_TOKEN` environment variable to enable the tool's functionality.
+    </Step>
+</Steps>
+
+## Usage example
+
+Use the `ApifyActorsTool` manually to run the [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) to perform a web search:
+
+```python
+from crewai_tools import ApifyActorsTool
+
+# Initialize the tool with an Apify Actor
+tool = ApifyActorsTool(actor_name="apify/rag-web-browser")
+
+# Run the tool with input parameters
+results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5})
+
+# Process the results
+for result in results:
+    print(f"URL: {result['metadata']['url']}")
+    print(f"Content: {result.get('markdown', 'N/A')[:100]}...")
+```
+
+### Expected output
+
+Here is the output from running the code above:
+
+```text
+URL: https://www.example.com/crewai-intro
+Content: CrewAI is a framework for building AI-powered workflows...
+URL: https://docs.crewai.com/
+Content: Official documentation for CrewAI...
+```
+
+The `ApifyActorsTool` automatically fetches the Actor definition and input schema from Apify using the provided `actor_name` and then constructs the tool description and argument schema. This means you need to specify only a valid `actor_name`, and the tool handles the rest when used with agents—no need to specify the `run_input`. Here's how it works:
+
+```python
+from crewai import Agent
+from crewai_tools import ApifyActorsTool
+
+rag_browser = ApifyActorsTool(actor_name="apify/rag-web-browser")
+
+agent = Agent(
+    role="Research Analyst",
+    goal="Find and summarize information about specific topics",
+    backstory="You are an experienced researcher with attention to detail",
+    tools=[rag_browser],
+)
+```
+
+You can run other Actors from [Apify Store](https://apify.com/store) simply by changing the `actor_name` and, when using it manually, adjusting the `run_input` based on the Actor input schema.
+
+For an example of usage with agents, see the [CrewAI Actor template](https://apify.com/templates/python-crewai).
+
+## Configuration
+
+The `ApifyActorsTool` requires these inputs to work:
+
+- **`actor_name`**
+  The ID of the Apify Actor to run, e.g., `"apify/rag-web-browser"`. Browse all Actors on [Apify Store](https://apify.com/store).
+- **`run_input`**
+  A dictionary of input parameters for the Actor when running the tool manually.
+  - For example, for the `apify/rag-web-browser` Actor: `{"query": "search term", "maxResults": 5}`
+  - See the Actor's [input schema](https://apify.com/apify/rag-web-browser/input-schema) for the list of input parameters.
+
+## Resources
+
+- **[Apify](https://apify.com/)**: Explore the Apify platform.
+- **[How to build an AI agent on Apify](https://blog.apify.com/how-to-build-an-ai-agent/)** - A complete step-by-step guide to creating, publishing, and monetizing AI agents on the Apify platform.
+- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: A popular Actor for web search for LLMs.
+- **[CrewAI Integration Guide](https://docs.apify.com/platform/integrations/crewai)**: Follow the official guide for integrating Apify and CrewAI.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.105.0"
+version = "0.108.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.105.0"
+__version__ = "0.108.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -20,6 +20,7 @@ from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
+from crewai.utilities.embedding_configurator import EmbeddingConfig
 from crewai.utilities.events.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
@@ -108,7 +109,7 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    embedder: Optional[Dict[str, Any]] = Field(
+    embedder: Optional[EmbeddingConfig] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
@@ -134,7 +135,7 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def set_knowledge(self, crew_embedder: Optional[EmbeddingConfig] = None):
         try:
             if self.embedder is None and crew_embedder:
                 self.embedder = crew_embedder

src/crewai/agents/agent_builder/base_agent.py

@@ -25,6 +25,7 @@ from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter
+from crewai.utilities.embedding_configurator import EmbeddingConfig
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -362,5 +363,5 @@ class BaseAgent(ABC, BaseModel):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+    def set_knowledge(self, crew_embedder: Optional[EmbeddingConfig] = None):
         pass

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0,<1.0.0"
+    "crewai[tools]>=0.108.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0,<1.0.0",
+    "crewai[tools]>=0.108.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0"
+    "crewai[tools]>=0.108.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -41,6 +41,7 @@ from crewai.tools.base_tool import Tool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
+from crewai.utilities.embedding_configurator import EmbeddingConfig
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.events.crew_events import (
@@ -145,7 +146,7 @@ class Crew(BaseModel):
         default=None,
         description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
     )
-    embedder: Optional[dict] = Field(
+    embedder: Optional[EmbeddingConfig] = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )

src/crewai/knowledge/knowledge.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.utilities.embedding_configurator import EmbeddingConfig
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
 
@@ -21,14 +22,14 @@ class Knowledge(BaseModel):
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: Optional[KnowledgeStorage] = Field(default=None)
-    embedder: Optional[Dict[str, Any]] = None
+    embedder: Optional[EmbeddingConfig] = None
     collection_name: Optional[str] = None
 
     def __init__(
         self,
         collection_name: str,
         sources: List[BaseKnowledgeSource],
-        embedder: Optional[Dict[str, Any]] = None,
+        embedder: Optional[EmbeddingConfig] = None,
         storage: Optional[KnowledgeStorage] = None,
         **data,
     ):

src/crewai/knowledge/storage/knowledge_storage.py

@@ -15,6 +15,7 @@ from chromadb.config import Settings
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.utilities import EmbeddingConfigurator
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+from crewai.utilities.embedding_configurator import EmbeddingConfig
 from crewai.utilities.logger import Logger
 from crewai.utilities.paths import db_storage_path
 
@@ -48,7 +49,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
 
     def __init__(
         self,
-        embedder: Optional[Dict[str, Any]] = None,
+        embedder: Optional[EmbeddingConfig] = None,
         collection_name: Optional[str] = None,
     ):
         self.collection_name = collection_name
@@ -187,7 +188,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )
 
-    def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
+    def _set_embedder_config(self, embedder: Optional[EmbeddingConfig] = None) -> None:
         """Set the embedding configuration for the knowledge storage.
 
         Args:

src/crewai/utilities/embedding_configurator.py

@@ -1,8 +1,84 @@
 import os
-from typing import Any, Dict, Optional, cast
+from typing import Any, Callable, Literal, cast
 
 from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
+from pydantic import BaseModel
+
+
+class EmbeddingProviderConfig(BaseModel):
+    """Configuration model for embedding providers.
+
+    Attributes:
+        # Core Model Configuration
+        model (str | None): The model identifier for embeddings, used across multiple providers
+            like OpenAI, Azure, Watson, etc.
+        embedder (str | Callable | None): Custom embedding function or callable for custom
+            embedding implementations.
+
+        # API Authentication & Configuration
+        api_key (str | None): Authentication key for various providers (OpenAI, VertexAI,
+            Google, Cohere, VoyageAI, Watson).
+        api_base (str | None): Base API URL override for OpenAI and Azure services.
+        api_type (str | None): API type specification, particularly used for Azure configuration.
+        api_version (str | None): API version for OpenAI and Azure services.
+        api_url (str | None): API endpoint URL, used by HuggingFace and Watson services.
+        url (str | None): Base URL for the embedding service, primarily used for Ollama and
+            HuggingFace endpoints.
+
+        # Service-Specific Configuration
+        project_id (str | None): Project identifier used by VertexAI and Watson services.
+        organization_id (str | None): Organization identifier for OpenAI and Azure services.
+        deployment_id (str | None): Deployment identifier for OpenAI and Azure services.
+        region (str | None): Geographic region for VertexAI services.
+        session (str | None): Session configuration for Amazon Bedrock embeddings.
+
+        # Request Configuration
+        task_type (str | None): Specifies the task type for Google Generative AI embeddings.
+        default_headers (str | None): Custom headers for OpenAI and Azure API requests.
+        dimensions (str | None): Output dimensions specification for OpenAI and Azure embeddings.
+    """
+
+    # Core Model Configuration
+    model: str | None = None
+    embedder: str | Callable | None = None
+
+    # API Authentication & Configuration
+    api_key: str | None = None
+    api_base: str | None = None
+    api_type: str | None = None
+    api_version: str | None = None
+    api_url: str | None = None
+    url: str | None = None
+
+    # Service-Specific Configuration
+    project_id: str | None = None
+    organization_id: str | None = None
+    deployment_id: str | None = None
+    region: str | None = None
+    session: str | None = None
+
+    # Request Configuration
+    task_type: str | None = None
+    default_headers: str | None = None
+    dimensions: str | None = None
+
+
+class EmbeddingConfig(BaseModel):
+    provider: Literal[
+        "openai",
+        "azure",
+        "ollama",
+        "vertexai",
+        "google",
+        "cohere",
+        "voyageai",
+        "bedrock",
+        "huggingface",
+        "watson",
+        "custom",
+    ]
+    config: EmbeddingProviderConfig | None = None
 
 
 class EmbeddingConfigurator:
@@ -23,15 +99,19 @@ class EmbeddingConfigurator:
 
     def configure_embedder(
         self,
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder_config: EmbeddingConfig | None = None,
     ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
         if embedder_config is None:
             return self._create_default_embedding_function()
 
-        provider = embedder_config.get("provider")
-        config = embedder_config.get("config", {})
-        model_name = config.get("model") if provider != "custom" else None
+        provider = embedder_config.provider
+        config = (
+            embedder_config.config
+            if embedder_config.config
+            else EmbeddingProviderConfig()
+        )
+        model_name = config.model if provider != "custom" else None
 
         if provider not in self.embedding_functions:
             raise Exception(
@@ -56,123 +136,123 @@ class EmbeddingConfigurator:
         )
 
     @staticmethod
-    def _configure_openai(config, model_name):
+    def _configure_openai(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.openai_embedding_function import (
             OpenAIEmbeddingFunction,
         )
 
         return OpenAIEmbeddingFunction(
-            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+            api_key=config.api_key or os.getenv("OPENAI_API_KEY"),
             model_name=model_name,
-            api_base=config.get("api_base", None),
-            api_type=config.get("api_type", None),
-            api_version=config.get("api_version", None),
-            default_headers=config.get("default_headers", None),
-            dimensions=config.get("dimensions", None),
-            deployment_id=config.get("deployment_id", None),
-            organization_id=config.get("organization_id", None),
+            api_base=config.api_base,
+            api_type=config.api_type,
+            api_version=config.api_version,
+            default_headers=config.default_headers,
+            dimensions=config.dimensions,
+            deployment_id=config.deployment_id,
+            organization_id=config.organization_id,
         )
 
     @staticmethod
-    def _configure_azure(config, model_name):
+    def _configure_azure(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.openai_embedding_function import (
             OpenAIEmbeddingFunction,
         )
 
         return OpenAIEmbeddingFunction(
-            api_key=config.get("api_key"),
-            api_base=config.get("api_base"),
-            api_type=config.get("api_type", "azure"),
-            api_version=config.get("api_version"),
+            api_key=config.api_key,
+            api_base=config.api_base,
+            api_type=config.api_type if config.api_type else "azure",
+            api_version=config.api_version,
             model_name=model_name,
-            default_headers=config.get("default_headers"),
-            dimensions=config.get("dimensions"),
-            deployment_id=config.get("deployment_id"),
-            organization_id=config.get("organization_id"),
+            default_headers=config.default_headers,
+            dimensions=config.dimensions,
+            deployment_id=config.deployment_id,
+            organization_id=config.organization_id,
         )
 
     @staticmethod
-    def _configure_ollama(config, model_name):
+    def _configure_ollama(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.ollama_embedding_function import (
             OllamaEmbeddingFunction,
         )
 
         return OllamaEmbeddingFunction(
-            url=config.get("url", "http://localhost:11434/api/embeddings"),
+            url=config.url if config.url else "http://localhost:11434/api/embeddings",
             model_name=model_name,
         )
 
     @staticmethod
-    def _configure_vertexai(config, model_name):
+    def _configure_vertexai(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.google_embedding_function import (
             GoogleVertexEmbeddingFunction,
         )
 
         return GoogleVertexEmbeddingFunction(
             model_name=model_name,
-            api_key=config.get("api_key"),
-            project_id=config.get("project_id"),
-            region=config.get("region"),
+            api_key=config.api_key,
+            project_id=config.project_id,
+            region=config.region,
         )
 
     @staticmethod
-    def _configure_google(config, model_name):
+    def _configure_google(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.google_embedding_function import (
             GoogleGenerativeAiEmbeddingFunction,
         )
 
         return GoogleGenerativeAiEmbeddingFunction(
             model_name=model_name,
-            api_key=config.get("api_key"),
-            task_type=config.get("task_type"),
+            api_key=config.api_key,
+            task_type=config.task_type,
         )
 
     @staticmethod
-    def _configure_cohere(config, model_name):
+    def _configure_cohere(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.cohere_embedding_function import (
             CohereEmbeddingFunction,
         )
 
         return CohereEmbeddingFunction(
             model_name=model_name,
-            api_key=config.get("api_key"),
+            api_key=config.api_key,
         )
 
     @staticmethod
-    def _configure_voyageai(config, model_name):
+    def _configure_voyageai(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.voyageai_embedding_function import (
             VoyageAIEmbeddingFunction,
         )
 
         return VoyageAIEmbeddingFunction(
             model_name=model_name,
-            api_key=config.get("api_key"),
+            api_key=config.api_key,
         )
 
     @staticmethod
-    def _configure_bedrock(config, model_name):
+    def _configure_bedrock(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
             AmazonBedrockEmbeddingFunction,
         )
 
         # Allow custom model_name override with backwards compatibility
-        kwargs = {"session": config.get("session")}
+        kwargs = {"session": config.session}
         if model_name is not None:
             kwargs["model_name"] = model_name
         return AmazonBedrockEmbeddingFunction(**kwargs)
 
     @staticmethod
-    def _configure_huggingface(config, model_name):
+    def _configure_huggingface(config: EmbeddingProviderConfig, model_name: str):
         from chromadb.utils.embedding_functions.huggingface_embedding_function import (
             HuggingFaceEmbeddingServer,
         )
 
         return HuggingFaceEmbeddingServer(
-            url=config.get("api_url"),
+            url=config.api_url,
         )
 
     @staticmethod
-    def _configure_watson(config, model_name):
+    def _configure_watson(config: EmbeddingProviderConfig, model_name: str):
         try:
             import ibm_watsonx_ai.foundation_models as watson_models
             from ibm_watsonx_ai import Credentials
@@ -193,12 +273,10 @@ class EmbeddingConfigurator:
                 }
 
                 embedding = watson_models.Embeddings(
-                    model_id=config.get("model"),
+                    model_id=config.model,
                     params=embed_params,
-                    credentials=Credentials(
-                        api_key=config.get("api_key"), url=config.get("api_url")
-                    ),
-                    project_id=config.get("project_id"),
+                    credentials=Credentials(api_key=config.api_key, url=config.api_url),
+                    project_id=config.project_id,
                 )
 
                 try:
@@ -211,8 +289,8 @@ class EmbeddingConfigurator:
         return WatsonEmbeddingFunction()
 
     @staticmethod
-    def _configure_custom(config):
-        custom_embedder = config.get("embedder")
+    def _configure_custom(config: EmbeddingProviderConfig):
+        custom_embedder = config.embedder
         if isinstance(custom_embedder, EmbeddingFunction):
             try:
                 validate_embedding_function(custom_embedder)

src/crewai/utilities/events/flow_events.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict, Optional, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from .base_events import CrewEvent
 
@@ -52,9 +52,11 @@ class MethodExecutionFailedEvent(FlowEvent):
 
     flow_name: str
     method_name: str
-    error: Any
+    error: Exception
     type: str = "method_execution_failed"
 
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
 
 class FlowFinishedEvent(FlowEvent):
     """Event emitted when a flow completes execution"""

uv.lock

@@ -619,7 +619,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.105.0"
+version = "0.108.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },