Fix import sorting in test_span_creation function

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

docs/concepts/knowledge.mdx

@@ -150,8 +150,6 @@ result = crew.kickoff(
 
 Here are examples of how to use different types of knowledge sources:
 
-Note: Please ensure that you create the ./knowldge folder. All source files (e.g., .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management.
-
 ### Text File Knowledge Source
 ```python
 from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
@@ -462,12 +460,12 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
             data = response.json()
             articles = data.get('results', [])
 
-            formatted_data = self.validate_content(articles)
+            formatted_data = self._format_articles(articles)
             return {self.api_endpoint: formatted_data}
         except Exception as e:
             raise ValueError(f"Failed to fetch space news: {str(e)}")
 
-    def validate_content(self, articles: list) -> str:
+    def _format_articles(self, articles: list) -> str:
         """Format articles into readable text."""
         formatted = "Space News Articles:\n\n"
         for article in articles:

docs/concepts/llms.mdx

@@ -158,11 +158,7 @@ In this section, you'll find detailed examples that help you select, configure,
 
   <Accordion title="Anthropic">
     ```toml Code
-    # Required
     ANTHROPIC_API_KEY=sk-ant-...
-
-    # Optional
-    ANTHROPIC_API_BASE=<custom-base-url>
     ```
 
     Example usage in your CrewAI project:
@@ -254,40 +250,6 @@ In this section, you'll find detailed examples that help you select, configure,
         model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
     )
     ```
-
-    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
-
-    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
-
-    | Model                   | Context Window       | Best For                                                          |
-    |-------------------------|----------------------|-------------------------------------------------------------------|
-    | Amazon Nova Pro         | Up to 300k tokens    | High-performance, model balancing accuracy, speed, and cost-effectiveness across diverse tasks. |
-    | Amazon Nova Micro       | Up to 128k tokens    | High-performance, cost-effective text-only model optimized for lowest latency responses. |
-    | Amazon Nova Lite        | Up to 300k tokens    | High-performance, affordable multimodal processing for images, video, and text with real-time capabilities. |
-    | Claude 3.7 Sonnet       | Up to 128k tokens    | High-performance, best for complex reasoning, coding & AI agents |
-    | Claude 3.5 Sonnet v2    | Up to 200k tokens    | State-of-the-art model specialized in software engineering, agentic capabilities, and computer interaction at optimized cost. |
-    | Claude 3.5 Sonnet       | Up to 200k tokens    | High-performance model delivering superior intelligence and reasoning across diverse tasks with optimal speed-cost balance. |
-    | Claude 3.5 Haiku        | Up to 200k tokens    | Fast, compact multimodal model optimized for quick responses and seamless human-like interactions |
-    | Claude 3 Sonnet         | Up to 200k tokens    | Multimodal model balancing intelligence and speed for high-volume deployments. |
-    | Claude 3 Haiku          | Up to 200k tokens    | Compact, high-speed multimodal model optimized for quick responses and natural conversational interactions |
-    | Claude 3 Opus           | Up to 200k tokens    | Most advanced multimodal model excelling at complex tasks with human-like reasoning and superior contextual understanding. |
-    | Claude 2.1              | Up to 200k tokens    | Enhanced version with expanded context window, improved reliability, and reduced hallucinations for long-form and RAG applications |
-    | Claude                  | Up to 100k tokens    | Versatile model excelling in sophisticated dialogue, creative content, and precise instruction following. |
-    | Claude Instant          | Up to 100k tokens    | Fast, cost-effective model for everyday tasks like dialogue, analysis, summarization, and document Q&A |
-    | Llama 3.1 405B Instruct | Up to 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
-    | Llama 3.1 70B Instruct  | Up to 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | Llama 3.1 8B Instruct   | Up to 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
-    | Llama 3 70B Instruct    | Up to 8k tokens      | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | Llama 3 8B Instruct     | Up to 8k tokens      | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | Titan Text G1 - Lite    | Up to 4k tokens      | Lightweight, cost-effective model optimized for English tasks and fine-tuning with focus on summarization and content generation. |
-    | Titan Text G1 - Express | Up to 8k tokens      | Versatile model for general language tasks, chat, and RAG applications with support for English and 100+ languages. |
-    | Cohere Command          | Up to 4k tokens      | Model specialized in following user commands and delivering practical enterprise solutions. |
-    | Jurassic-2 Mid          | Up to 8,191 tokens   | Cost-effective model balancing quality and affordability for diverse language tasks like Q&A, summarization, and content generation. |
-    | Jurassic-2 Ultra        | Up to 8,191 tokens   | Model for advanced text generation and comprehension, excelling in complex tasks like analysis and content creation. |
-    | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
-    | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
-    | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
-
   </Accordion>
   
   <Accordion title="Amazon SageMaker">

docs/concepts/memory.mdx

@@ -60,8 +60,7 @@ my_crew = Crew(
 ```python Code
 from crewai import Crew, Process
 from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
-from crewai.memory.storage.rag_storage import RAGStorage
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
 from typing import List, Optional
 
 # Assemble your crew with memory capabilities
@@ -120,7 +119,7 @@ Example using environment variables:
 import os
 from crewai import Crew
 from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage import LTMSQLiteStorage
 
 # Configure storage path using environment variable
 storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
@@ -149,7 +148,7 @@ crew = Crew(memory=True)  # Uses default storage locations
 ```python
 from crewai import Crew
 from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage import LTMSQLiteStorage
 
 # Configure custom storage paths
 crew = Crew(

docs/concepts/tools.mdx

@@ -106,7 +106,6 @@ 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/guides/advanced/customizing-prompts.mdx

@@ -1,5 +1,4 @@
----
-title: Customizing Prompts
+---title: Customizing Prompts
 description: Dive deeper into low-level prompt customization for CrewAI, enabling super custom and complex use cases for different models and languages.
 icon: message-pen
 ---

docs/mint.json

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

docs/tools/apifyactorstool.mdx

@@ -1,99 +0,0 @@
----
-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.

docs/tools/jsonsearchtool.mdx

@@ -7,10 +7,8 @@ icon: file-code
 # `JSONSearchTool`
 
 <Note>
-  The JSONSearchTool is currently in an experimental phase. This means the tool
-  is under active development, and users might encounter unexpected behavior or
-  changes. We highly encourage feedback on any issues or suggestions for
-  improvements.
+    The JSONSearchTool is currently in an experimental phase. This means the tool is under active development, and users might encounter unexpected behavior or changes. 
+    We highly encourage feedback on any issues or suggestions for improvements.
 </Note>
 
 ## Description
@@ -62,7 +60,7 @@ tool = JSONSearchTool(
                 # stream=true,
             },
         },
-        "embedding_model": {
+        "embedder": {
             "provider": "google", # or openai, ollama, ...
             "config": {
                 "model": "models/embedding-001",
@@ -72,4 +70,4 @@ tool = JSONSearchTool(
         },
     }
 )
-```
+```

docs/tools/ragtool.mdx

@@ -8,8 +8,8 @@ icon: vector-square
 
 ## Description
 
-The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
-It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
+The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain. 
+It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources. 
 This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
 
 ## Example
@@ -138,7 +138,7 @@ config = {
             "model": "gpt-4",
         }
     },
-    "embedding_model": {
+    "embedder": {
         "provider": "openai",
         "config": {
             "model": "text-embedding-ada-002"
@@ -151,4 +151,4 @@ rag_tool = RagTool(config=config, summarize=True)
 
 ## Conclusion
 
-The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.
+The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses. 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.108.0"
+version = "0.105.0"
 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"
@@ -17,9 +17,11 @@ dependencies = [
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
     # Telemetry and Monitoring
-    "opentelemetry-api>=1.22.0",
-    "opentelemetry-sdk>=1.22.0",
-    "opentelemetry-exporter-otlp-proto-http>=1.22.0",
+    # Version constraints ensure compatibility with various OpenTelemetry versions
+    # while preventing installation of incompatible versions (issue #2372)
+    "opentelemetry-api>=1.22.0,<1.32.0",
+    "opentelemetry-sdk>=1.22.0,<1.32.0",
+    "opentelemetry-exporter-otlp-proto-http>=1.22.0,<1.32.0",
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",

src/crewai/__init__.py

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

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]>=0.108.0,<1.0.0"
+    "crewai[tools]>=0.105.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,12 +5,11 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.108.0,<1.0.0",
+    "crewai[tools]>=0.105.0,<1.0.0",
 ]
 
 [project.scripts]
 kickoff = "{{folder_name}}.main:kickoff"
-run_crew = "{{folder_name}}.main:kickoff"
 plot = "{{folder_name}}.main:plot"
 
 [build-system]

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]>=0.108.0"
+    "crewai[tools]>=0.105.0"
 ]
 
 [tool.crewai]

src/crewai/task.py

@@ -19,8 +19,6 @@ from typing import (
     Tuple,
     Type,
     Union,
-    get_args,
-    get_origin,
 )
 
 from pydantic import (
@@ -180,29 +178,15 @@ class Task(BaseModel):
         """
         if v is not None:
             sig = inspect.signature(v)
-            positional_args = [
-                param
-                for param in sig.parameters.values()
-                if param.default is inspect.Parameter.empty
-            ]
-            if len(positional_args) != 1:
+            if len(sig.parameters) != 1:
                 raise ValueError("Guardrail function must accept exactly one parameter")
 
             # Check return annotation if present, but don't require it
             return_annotation = sig.return_annotation
             if return_annotation != inspect.Signature.empty:
-
-                return_annotation_args = get_args(return_annotation)
                 if not (
-                    get_origin(return_annotation) is tuple
-                    and len(return_annotation_args) == 2
-                    and return_annotation_args[0] is bool
-                    and (
-                        return_annotation_args[1] is Any
-                        or return_annotation_args[1] is str
-                        or return_annotation_args[1] is TaskOutput
-                        or return_annotation_args[1] == Union[str, TaskOutput]
-                    )
+                    return_annotation == Tuple[bool, Any]
+                    or str(return_annotation) == "Tuple[bool, Any]"
                 ):
                     raise ValueError(
                         "If return type is annotated, it must be Tuple[bool, Any]"

src/crewai/utilities/events/crewai_event_bus.py

@@ -67,13 +67,16 @@ class CrewAIEventsBus:
             source: The object emitting the event
             event: The event instance to emit
         """
-        for event_type, handlers in self._handlers.items():
-            if isinstance(event, event_type):
-                for handler in handlers:
-                    handler(source, event)
-
+        event_type = type(event)
+        if event_type in self._handlers:
+            for handler in self._handlers[event_type]:
+                handler(source, event)
         self._signal.send(source, event=event)
 
+    def clear_handlers(self) -> None:
+        """Clear all registered event handlers - useful for testing"""
+        self._handlers.clear()
+
     def register_handler(
         self, event_type: Type[EventTypes], handler: Callable[[Any, EventTypes], None]
     ) -> None:

src/crewai/utilities/events/flow_events.py

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

tests/task_test.py

@@ -3,8 +3,6 @@
 import hashlib
 import json
 import os
-from functools import partial
-from typing import Tuple, Union
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -217,75 +215,6 @@ def test_multiple_output_type_error():
         )
 
 
-def test_guardrail_type_error():
-    desc = "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting."
-    expected_output = "Bullet point list of 5 interesting ideas."
-    # Lambda function
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=lambda x: (True, x),
-    )
-
-    # Function
-    def guardrail_fn(x: TaskOutput) -> tuple[bool, TaskOutput]:
-        return (True, x)
-
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=guardrail_fn,
-    )
-
-    class Object:
-        def guardrail_fn(self, x: TaskOutput) -> tuple[bool, TaskOutput]:
-            return (True, x)
-
-        @classmethod
-        def guardrail_class_fn(cls, x: TaskOutput) -> tuple[bool, str]:
-            return (True, x)
-
-        @staticmethod
-        def guardrail_static_fn(x: TaskOutput) -> tuple[bool, Union[str, TaskOutput]]:
-            return (True, x)
-
-    obj = Object()
-    # Method
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=obj.guardrail_fn,
-    )
-    # Class method
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=Object.guardrail_class_fn,
-    )
-    # Static method
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=Object.guardrail_static_fn,
-    )
-
-    def error_fn(x: TaskOutput, y: bool) -> Tuple[bool, TaskOutput]:
-        return (y, x)
-
-    Task(
-        description=desc,
-        expected_output=expected_output,
-        guardrail=partial(error_fn, y=True),
-    )
-
-    with pytest.raises(ValidationError):
-        Task(
-            description=desc,
-            expected_output=expected_output,
-            guardrail=error_fn,
-        )
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_output_pydantic_sequential():
     class ScoreOutput(BaseModel):

tests/telemetry/test_opentelemetry_compatibility.py

@@ -0,0 +1,116 @@
+from importlib import import_module
+
+import pytest
+
+
+def test_opentelemetry_imports():
+    """Test that all required OpenTelemetry modules can be imported.
+    
+    This test verifies that all necessary OpenTelemetry modules can be imported
+    correctly with the current version constraints, ensuring compatibility
+    between different OpenTelemetry packages.
+    """
+    try:
+        # Test basic imports
+        from opentelemetry import trace
+        from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+            OTLPSpanExporter,
+        )
+        from opentelemetry.sdk.resources import SERVICE_NAME, Resource
+        from opentelemetry.sdk.trace import TracerProvider
+        from opentelemetry.sdk.trace.export import BatchSpanProcessor
+        from opentelemetry.trace import Span, Status, StatusCode
+        
+        # Verify that the imports are from the expected modules
+        assert trace.__name__ == 'opentelemetry.trace'
+        assert OTLPSpanExporter.__module__ == 'opentelemetry.exporter.otlp.proto.http.trace_exporter'
+        assert Resource.__module__ == 'opentelemetry.sdk.resources'
+        assert TracerProvider.__module__ == 'opentelemetry.sdk.trace'
+        assert BatchSpanProcessor.__module__ == 'opentelemetry.sdk.trace.export'
+        assert Span.__module__ == 'opentelemetry.trace.span'
+        assert Status.__module__ == 'opentelemetry.trace.status'
+        assert StatusCode.__module__ == 'opentelemetry.trace.status'
+    except ImportError as e:
+        pytest.fail(f"Failed to import OpenTelemetry modules: {e}")
+
+
+def test_telemetry_class_initialization():
+    """Test that the Telemetry class can be initialized with current OpenTelemetry versions.
+    
+    This test verifies that the Telemetry class can be properly initialized and
+    that its core attributes are set correctly. It also tests the set_tracer method
+    to ensure it works as expected with the current OpenTelemetry versions.
+    """
+    try:
+        from src.crewai.telemetry.telemetry import Telemetry
+        
+        # Create an instance of the Telemetry class
+        telemetry = Telemetry()
+        
+        # Check if the telemetry instance is initialized correctly
+        assert hasattr(telemetry, 'ready')
+        assert hasattr(telemetry, 'trace_set')
+        assert telemetry.trace_set is False  # Should be False initially
+        
+        # Try to set the tracer
+        telemetry.set_tracer()
+        
+        # After setting the tracer, trace_set should be True if ready is True
+        if telemetry.ready:
+            assert telemetry.trace_set is True
+    except ImportError as e:
+        pytest.fail(f"Failed to import Telemetry class: {e}")
+
+
+def test_telemetry_configuration():
+    """Test that the Telemetry class can be configured with different options.
+    
+    This test verifies that the Telemetry class respects environment variables
+    for disabling telemetry collection.
+    """
+    import os
+
+    from src.crewai.telemetry.telemetry import Telemetry
+    
+    # Test with telemetry disabled via environment variable
+    os.environ["OTEL_SDK_DISABLED"] = "true"
+    telemetry = Telemetry()
+    assert telemetry.ready is False
+    
+    # Reset environment variable
+    os.environ.pop("OTEL_SDK_DISABLED", None)
+
+
+def test_span_creation():
+    """Test that spans can be created with the current OpenTelemetry versions.
+    
+    This test verifies that the Telemetry class can create spans using the
+    OpenTelemetry tracer, which is a core functionality for telemetry.
+    """
+    import os
+
+    from opentelemetry import trace
+
+    from src.crewai.telemetry.telemetry import Telemetry
+    
+    # Ensure telemetry is enabled for this test
+    if "OTEL_SDK_DISABLED" in os.environ:
+        old_value = os.environ.pop("OTEL_SDK_DISABLED")
+    
+    try:
+        telemetry = Telemetry()
+        telemetry.set_tracer()
+        
+        # Only test span creation if telemetry is ready
+        if telemetry.ready and telemetry.trace_set:
+            tracer = trace.get_tracer("crewai.telemetry.test")
+            if tracer:
+                with tracer.start_as_current_span("test_operation") as span:
+                    assert span is not None
+                    assert span.is_recording()
+    except Exception as e:
+        pytest.fail(f"Failed to create span: {e}")
+    finally:
+        # Restore environment variable if it was set
+        if "old_value" in locals():
+            os.environ["OTEL_SDK_DISABLED"] = old_value

tests/utilities/events/test_crewai_event_bus.py

@@ -1,34 +0,0 @@
-from unittest.mock import Mock
-
-from crewai.utilities.events.base_events import CrewEvent
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-
-
-class TestEvent(CrewEvent):
-    pass
-
-
-def test_specific_event_handler():
-    mock_handler = Mock()
-
-    @crewai_event_bus.on(TestEvent)
-    def handler(source, event):
-        mock_handler(source, event)
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    mock_handler.assert_called_once_with("source_object", event)
-
-
-def test_wildcard_event_handler():
-    mock_handler = Mock()
-
-    @crewai_event_bus.on(CrewEvent)
-    def handler(source, event):
-        mock_handler(source, event)
-
-    event = TestEvent(type="test_event")
-    crewai_event_bus.emit("source_object", event)
-
-    mock_handler.assert_called_once_with("source_object", event)

uv.lock

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