Added is_auto_end flag in agentops.end session in crew.py (#1320)

When using agentops, we have the option to pass the `skip_auto_end_session` parameter, which is supposed to not end the session if the `end_session` function is called by Crew.

Now the way it works is, the `agentops.end_session` accepts `is_auto_end` flag and crewai should have passed it as `True` (its `False` by default). 

I have changed the code to pass is_auto_end=True

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -65,7 +65,6 @@ body:
         - '3.10'
         - '3.11'
         - '3.12'
-        - '3.13'
     validations:
       required: true
   - type: input
@@ -113,4 +112,4 @@ body:
       label: Additional context
       description: Add any other context about the problem here.
     validations:
-      required: true
+      required: true

.github/workflows/linter.yml

@@ -13,4 +13,4 @@ jobs:
           pip install ruff
 
       - name: Run Ruff Linter
-        run: ruff check --exclude "templates","__init__.py"
+        run: ruff check

.github/workflows/tests.yml

@@ -23,7 +23,7 @@ jobs:
 
 
       - name: Set up Python
-        run: uv python install 3.11.9
+        run: uv python install 3.12.8
 
       - name: Install the project
         run: uv sync --dev --all-extras

.pre-commit-config.yaml

@@ -1,9 +1,7 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.8.2
     hooks:
       - id: ruff
         args: ["--fix"]
-        exclude: "templates"
       - id: ruff-format
-        exclude: "templates"

.ruff.toml

@@ -0,0 +1,9 @@
+exclude = [
+    "templates",
+    "__init__.py",
+]
+
+[lint]
+select = [
+    "I", # isort rules
+]

README.md

@@ -44,7 +44,7 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.12 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 

docs/concepts/crews.mdx

@@ -32,7 +32,6 @@ 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/knowledge.mdx

@@ -8,8 +8,8 @@ icon: book
 
 ## What is Knowledge?
 
-Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. 
+Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
-Think of it as giving your agents a reference library they can consult while working. 
+Think of it as giving your agents a reference library they can consult while working.
 
 <Info>
   Key benefits of using Knowledge:
@@ -47,8 +47,7 @@ from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSourc
 # Create a knowledge source
 content = "Users name is John. He is 30 years old and lives in San Francisco."
 string_source = StringKnowledgeSource(
-    content=content, 
+    content=content,
-    metadata={"preference": "personal"}
 )
 
 # Create an LLM with a temperature of 0 to ensure deterministic outputs
@@ -74,10 +73,7 @@ crew = Crew(
     tasks=[task],
     verbose=True,
     process=Process.sequential,
-    knowledge={
+    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
-      "sources": [string_source], 
-      "metadata": {"preference": "personal"}
-    }, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
 )
 
 result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
@@ -85,17 +81,6 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 
 ## Knowledge Configuration
 
-### Metadata and Filtering
-
-Knowledge sources support metadata for better organization and filtering. Metadata is used to filter the knowledge sources when querying the knowledge store.
-
-```python Code
-knowledge_source = StringKnowledgeSource(
-    content="Users name is John. He is 30 years old and lives in San Francisco.",
-    metadata={"preference": "personal"} # Metadata is used to filter the knowledge sources
-)
-```
-
 ### Chunking Configuration
 
 Control how content is split for processing by setting the chunk size and overlap.
@@ -116,21 +101,27 @@ You can also configure the embedder for the knowledge store. This is useful if y
 ...
 string_source = StringKnowledgeSource(
     content="Users name is John. He is 30 years old and lives in San Francisco.",
-    metadata={"preference": "personal"}
 )
 crew = Crew(
     ...
-    knowledge={
+    knowledge_sources=[string_source],
-        "sources": [string_source],
+    embedder={
-        "metadata": {"preference": "personal"},
+        "provider": "openai",
-        "embedder_config": {
+        "config": {"model": "text-embedding-3-small"},
-          "provider": "openai", # Default embedder provider; can be "ollama", "gemini", e.t.c.
-          "config": {"model": "text-embedding-3-small"} # Default embedder model; can be "mxbai-embed-large", "nomic-embed-tex", e.t.c.
-        },
     },
 )
 ```
 
+## Clearing Knowledge
+
+If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
+
+```bash Command
+crewai reset-memories --knowledge
+```
+
+This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
+
 ## Custom Knowledge Sources
 
 CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.
@@ -149,10 +140,10 @@ from pydantic import BaseModel, Field
 
 class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
     """Knowledge source that fetches data from Space News API."""
-    
+
     api_endpoint: str = Field(description="API endpoint URL")
     limit: int = Field(default=10, description="Number of articles to fetch")
-    
+
     def load_content(self) -> Dict[Any, str]:
         """Fetch and format space news articles."""
         try:
@@ -160,26 +151,26 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
                 f"{self.api_endpoint}?limit={self.limit}"
             )
             response.raise_for_status()
-            
+
             data = response.json()
             articles = data.get('results', [])
-            
+
             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 _format_articles(self, articles: list) -> str:
         """Format articles into readable text."""
         formatted = "Space News Articles:\n\n"
         for article in articles:
             formatted += f"""
-Title: {article['title']}
+                Title: {article['title']}
-Published: {article['published_at']}
+                Published: {article['published_at']}
-Summary: {article['summary']}
+                Summary: {article['summary']}
-News Site: {article['news_site']}
+                News Site: {article['news_site']}
-URL: {article['url']}
+                URL: {article['url']}
--------------------"""
+                -------------------"""
         return formatted
 
     def add(self) -> None:
@@ -188,25 +179,20 @@ URL: {article['url']}
         for _, text in content.items():
             chunks = self._chunk_text(text)
             self.chunks.extend(chunks)
-        
+
-        self.save_documents(metadata={
+        self._save_documents()
-            "source": "space_news_api",
-            "timestamp": datetime.now().isoformat(),
-            "article_count": self.limit
-        })
 
 # Create knowledge source
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
     limit=10,
-    metadata={"category": "recent_news", "source": "spaceflight_news"}
 )
 
 # Create specialized agent
 space_analyst = Agent(
     role="Space News Analyst",
     goal="Answer questions about space news accurately and comprehensively",
-    backstory="""You are a space industry analyst with expertise in space exploration, 
+    backstory="""You are a space industry analyst with expertise in space exploration,
     satellite technology, and space industry trends. You excel at answering questions
     about space news and providing detailed, accurate information.""",
     knowledge_sources=[recent_news],
@@ -233,13 +219,14 @@ result = crew.kickoff(
     inputs={"user_question": "What are the latest developments in space exploration?"}
 )
 ```
+
 ```output Output
 # Agent: Space News Analyst
 ## Task: Answer this question about space news: What are the latest developments in space exploration?
 
 
 # Agent: Space News Analyst
-## Final Answer: 
+## Final Answer:
 The latest developments in space exploration, based on recent space news articles, include the following:
 
 1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)
@@ -255,23 +242,27 @@ The latest developments in space exploration, based on recent space news article
 6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)
 
 7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
-``` 
+```
+
 </CodeGroup>
 #### Key Components Explained
 
 1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**:
+
    - Extends `BaseKnowledgeSource` for integration with CrewAI
    - Configurable API endpoint and article limit
    - Implements three key methods:
      - `load_content()`: Fetches articles from the API
      - `_format_articles()`: Structures the articles into readable text
-     - `add()`: Processes and stores the content with metadata
+     - `add()`: Processes and stores the content
 
 2. **Agent Configuration**:
+
    - Specialized role as a Space News Analyst
    - Uses the knowledge source to access space news
 
 3. **Task Setup**:
+
    - Takes a user question as input through `{user_question}`
    - Designed to provide detailed answers based on the knowledge source
 
@@ -280,6 +271,7 @@ The latest developments in space exploration, based on recent space news article
    - Handles input/output through the kickoff method
 
 This example demonstrates how to:
+
 - Create a custom knowledge source that fetches real-time data
 - Process and format external data for AI consumption
 - Use the knowledge source to answer specific user questions
@@ -287,26 +279,26 @@ This example demonstrates how to:
 
 #### About the Spaceflight News API
 
-The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/documentation), which:
+The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
+
 - Provides free access to space-related news articles
 - Requires no authentication
 - Returns structured data about space news
 - Supports pagination and filtering
 
 You can customize the API query by modifying the endpoint URL:
+
 ```python
 # Fetch more articles
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
     limit=20,  # Increase the number of articles
-    metadata={"category": "recent_news"}
 )
 
 # Add search parameters
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
     limit=10,
-    metadata={"category": "nasa_news"}
 )
 ```
 
@@ -314,16 +306,14 @@ recent_news = SpaceNewsKnowledgeSource(
 
 <AccordionGroup>
   <Accordion title="Content Organization">
-    - Use descriptive metadata for better filtering
     - Keep chunk sizes appropriate for your content type
     - Consider content overlap for context preservation
     - Organize related information into separate knowledge sources
   </Accordion>
-  
+
   <Accordion title="Performance Tips">
-    - Use metadata filtering to narrow search scope
+    - Adjust chunk sizes based on content complexity 
-    - Adjust chunk sizes based on content complexity
     - Configure appropriate embedding models
     - Consider using local embedding providers for faster processing
   </Accordion>
-</AccordionGroup>
+</AccordionGroup>

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:
+Here's a detailed breakdown of supported models and their capabilities, you can compare performance at [lmarena.ai](https://lmarena.ai/):
 
 <Tabs>
   <Tab title="OpenAI">
@@ -43,6 +43,17 @@ Here's a detailed breakdown of supported models and their capabilities:
       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 |
     |-------|---------------|-----------|
@@ -128,10 +139,10 @@ 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 - Good for general tasks
+        # Google Models - Strong reasoning, large cachable context window, multimodal
-        # llm: gemini/gemini-pro
         # llm: gemini/gemini-1.5-pro-latest
-        # llm: gemini/gemini-1.0-pro-latest
+        # llm: gemini/gemini-1.5-flash-latest
+        # llm: gemini/gemini-1.5-flash-8b-latest
 
         # AWS Bedrock Models - Enterprise-grade
         # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
@@ -350,13 +361,18 @@ 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-pro",
+        model="gemini/gemini-1.5-pro-latest",
         temperature=0.7
     )
     ```

docs/concepts/tools.mdx

@@ -172,6 +172,48 @@ def my_tool(question: str) -> str:
     return "Result from your custom tool"
 ```
 
+### Structured Tools
+
+The `StructuredTool` class wraps functions as tools, providing flexibility and validation while reducing boilerplate. It supports custom schemas and dynamic logic for seamless integration of complex functionalities.
+
+#### Example:
+Using `StructuredTool.from_function`, you can wrap a function that interacts with an external API or system, providing a structured interface. This enables robust validation and consistent execution, making it easier to integrate complex functionalities into your applications as demonstrated in the following example:
+
+```python
+from crewai.tools.structured_tool import CrewStructuredTool
+from pydantic import BaseModel
+
+# Define the schema for the tool's input using Pydantic
+class APICallInput(BaseModel):
+    endpoint: str
+    parameters: dict
+
+# Wrapper function to execute the API call
+def tool_wrapper(*args, **kwargs):
+    # Here, you would typically call the API using the parameters
+    # For demonstration, we'll return a placeholder string
+    return f"Call the API at {kwargs['endpoint']} with parameters {kwargs['parameters']}"
+
+# Create and return the structured tool
+def create_structured_tool():
+    return CrewStructuredTool.from_function(
+        name='Wrapper API',
+        description="A tool to wrap API calls with structured input.",
+        args_schema=APICallInput,
+        func=tool_wrapper,
+    )
+
+# Example usage
+structured_tool = create_structured_tool()
+
+# Execute the tool with structured input
+result = structured_tool._run(**{
+    "endpoint": "https://example.com/api",
+    "parameters": {"key1": "value1", "key2": "value2"}
+})
+print(result)  # Output: Call the API at https://example.com/api with parameters {'key1': 'value1', 'key2': 'value2'}
+```
+
 ### Custom Caching Mechanism
 
 <Tip>

docs/how-to/agentops-observability.mdx

@@ -57,7 +57,7 @@ This feature is useful for debugging and understanding how agents interact with
    <Step title="Install AgentOps">
       Install AgentOps with:
       ```bash
-      pip install crewai[agentops]
+      pip install 'crewai[agentops]'
       ```
       or
       ```bash

docs/installation.mdx

@@ -7,7 +7,7 @@ icon: wrench
 <Note>
   **Python Version Requirements**
   
-  CrewAI requires `Python >=3.10 and <=3.13`. Here's how to check your version:
+  CrewAI requires `Python >=3.10 and <=3.12`. Here's how to check your version:
   ```bash
   python3 --version
   ```

pyproject.toml

@@ -3,7 +3,7 @@ name = "crewai"
 version = "0.86.0"
 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"
+requires-python = ">=3.10,<=3.12"
 authors = [
     { name = "Joao Moura", email = "joao@crewai.com" }
 ]
@@ -15,7 +15,6 @@ 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",
@@ -30,6 +29,7 @@ dependencies = [
     "chromadb>=0.5.18",
     "pdfplumber>=0.11.4",
     "openpyxl>=3.1.5",
+    "blinker>=1.9.0",
 ]
 
 [project.urls]
@@ -54,7 +54,7 @@ mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]
 dev-dependencies = [
-    "ruff>=0.4.10",
+    "ruff>=0.8.2",
     "mypy>=1.10.0",
     "pre-commit>=3.6.0",
     "mkdocs>=1.4.3",

src/crewai/agents/crew_agent_executor.py

@@ -143,6 +143,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         tool_result = self._execute_tool_and_check_finality(
                             formatted_answer
                         )
+                        if self.step_callback:
+                            self.step_callback(tool_result)
+
                         formatted_answer.text += f"\nObservation: {tool_result.result}"
                         formatted_answer.result = tool_result.result
                         if tool_result.result_as_answer:
@@ -299,7 +302,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         self._i18n.slice("summarizer_system_message"), role="system"
                     ),
                     self._format_msg(
-                        self._i18n.slice("sumamrize_instruction").format(group=group),
+                        self._i18n.slice("summarize_instruction").format(group=group),
                     ),
                 ],
                 callbacks=self.callbacks,
@@ -410,7 +413,6 @@ 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/agents/parser.py

@@ -1,5 +1,6 @@
 import re
 from typing import Any, Union
+
 from json_repair import repair_json
 
 from crewai.utilities import I18N

src/crewai/cli/authentication/main.py

@@ -5,9 +5,10 @@ from typing import Any, Dict
 import requests
 from rich.console import Console
 
+from crewai.cli.tools.main import ToolCommand
+
 from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
 from .utils import TokenManager, validate_token
-from crewai.cli.tools.main import ToolCommand
 
 console = Console()
 
@@ -79,7 +80,9 @@ class AuthenticationCommand:
                         style="yellow",
                     )
 
-                console.print("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
+                console.print(
+                    "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
+                )
                 return
 
             if token_data["error"] not in ("authorization_pending", "slow_down"):

src/crewai/cli/authentication/token.py

@@ -1,10 +1,9 @@
 from .utils import TokenManager
 
+
 def get_auth_token() -> str:
     """Get the authentication token."""
     access_token = TokenManager().get_token()
     if not access_token:
         raise Exception()
     return access_token
-
-

src/crewai/cli/command.py

@@ -1,8 +1,9 @@
 import requests
 from requests.exceptions import JSONDecodeError
 from rich.console import Console
-from crewai.cli.plus_api import PlusAPI
+
 from crewai.cli.authentication.token import get_auth_token
+from crewai.cli.plus_api import PlusAPI
 from crewai.telemetry.telemetry import Telemetry
 
 console = Console()

src/crewai/cli/config.py

@@ -1,13 +1,19 @@
 import json
 from pathlib import Path
-from pydantic import BaseModel, Field
 from typing import Optional
 
+from pydantic import BaseModel, Field
+
 DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
 
+
 class Settings(BaseModel):
-    tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
+    tool_repository_username: Optional[str] = Field(
-    tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
+        None, description="Username for interacting with the Tool Repository"
+    )
+    tool_repository_password: Optional[str] = Field(
+        None, description="Password for interacting with the Tool Repository"
+    )
     config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
 
     def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):

src/crewai/cli/plus_api.py

@@ -1,9 +1,11 @@
-from typing import Optional
-import requests
 from os import getenv
-from crewai.cli.version import get_crewai_version
+from typing import Optional
 from urllib.parse import urljoin
 
+import requests
+
+from crewai.cli.version import get_crewai_version
+
 
 class PlusAPI:
     """

src/crewai/cli/reset_memories_command.py

@@ -1,11 +1,12 @@
 import subprocess
+
 import click
 
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 
 
 def reset_memories_command(

src/crewai/cli/templates/crew/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

src/crewai/cli/templates/crew/config/tasks.yaml

@@ -12,6 +12,6 @@ reporting_task:
     Review the context you got and expand each topic into a full section for a report.
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
-    A fully fledge reports with the mains topics, each with a full section of information.
+    A fully fledged report with the main topics, each with a full section of information.
     Formatted as markdown without '```'
   agent: reporting_analyst

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

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<=3.12"
 dependencies = [
     "crewai[tools]>=0.86.0,<1.0.0"
 ]

src/crewai/cli/templates/flow/README.md

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
 
 ## Installation
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, if you haven't already, install uv:
 

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

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<=3.12"
 dependencies = [
     "crewai[tools]>=0.86.0,<1.0.0",
 ]

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

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.86.0,<1.0.0" }
-asyncio = "*"
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/tool/README.md

@@ -5,7 +5,7 @@ custom tools to power up your crews.
 
 ## Installing
 
-Ensure you have Python >=3.10 <=3.13 installed on your system. This project
+Ensure you have Python >=3.10 <=3.12 installed on your system. This project
 uses [UV](https://docs.astral.sh/uv/) for dependency management and package
 handling, offering a seamless setup and execution experience.
 

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

@@ -3,7 +3,7 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.10,<=3.12"
 dependencies = [
     "crewai[tools]>=0.86.0"
 ]

src/crewai/cli/tools/main.py

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

src/crewai/cli/utils.py

@@ -33,26 +33,6 @@ def copy_template(src, dst, name, class_name, folder_name):
     click.secho(f"  - Created {dst}", fg="green")
 
 
-# Drop the simple_toml_parser when we move to python3.11
-def simple_toml_parser(content):
-    result = {}
-    current_section = result
-    for line in content.split("\n"):
-        line = line.strip()
-        if line.startswith("[") and line.endswith("]"):
-            # New section
-            section = line[1:-1].split(".")
-            current_section = result
-            for key in section:
-                current_section = current_section.setdefault(key, {})
-        elif "=" in line:
-            key, value = line.split("=", 1)
-            key = key.strip()
-            value = value.strip().strip('"')
-            current_section[key] = value
-    return result
-
-
 def read_toml(file_path: str = "pyproject.toml"):
     """Read the content of a TOML file and return it as a dictionary."""
     with open(file_path, "rb") as f:
@@ -63,7 +43,7 @@ def read_toml(file_path: str = "pyproject.toml"):
 def parse_toml(content):
     if sys.version_info >= (3, 11):
         return tomllib.loads(content)
-    return simple_toml_parser(content)
+    return tomli.loads(content)
 
 
 def get_project_name(

src/crewai/cli/version.py

@@ -1,6 +1,6 @@
 import importlib.metadata
 
+
 def get_crewai_version() -> str:
     """Get the version number of CrewAI running the CLI"""
     return importlib.metadata.version("crewai")
-

src/crewai/crew.py

@@ -1032,6 +1032,7 @@ class Crew(BaseModel):
             agentops.end_session(
                 end_state="Success",
                 end_state_reason="Finished Execution",
+                is_auto_end=True,
             )
         self._telemetry.end_crew(self, final_string_output)
 

src/crewai/flow/flow.py

@@ -14,8 +14,15 @@ 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
@@ -159,6 +166,7 @@ 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
@@ -253,6 +261,14 @@ 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())
@@ -267,8 +283,6 @@ 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")
 
@@ -285,11 +299,19 @@ class Flow(Generic[T], metaclass=FlowMeta):
         # Run all start methods concurrently
         await asyncio.gather(*tasks)
 
-        # Return the final output (from the last executed method)
+        # Determine the final output (from the last executed method)
-        if self._method_outputs:
+        final_output = self._method_outputs[-1] if self._method_outputs else None
-            return self._method_outputs[-1]
+
-        else:
+        self.event_emitter.send(
-            return None  # Or raise an exception if no methods were executed
+            self,
+            event=FlowFinishedEvent(
+                type="flow_finished",
+                flow_name=self.__class__.__name__,
+                result=final_output,
+            ),
+        )
+
+        return final_output
 
     async def _execute_start_method(self, start_method_name: str) -> None:
         result = await self._execute_method(
@@ -352,6 +374,16 @@ 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())
 
@@ -367,6 +399,15 @@ 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

@@ -0,0 +1,33 @@
+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/knowledge.py

@@ -1,11 +1,10 @@
 import os
+from typing import Any, Dict, List, Optional
 
-from typing import List, Optional, Dict, Any
 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.constants import DEFAULT_SCORE_THRESHOLD
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
 
@@ -46,9 +45,7 @@ class Knowledge(BaseModel):
             source.storage = self.storage
             source.add()
 
-    def query(
+    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
-        self, query: List[str], limit: int = 3, preference: Optional[str] = None
-    ) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
@@ -57,8 +54,6 @@ class Knowledge(BaseModel):
         results = self.storage.search(
             query,
             limit,
-            filter={"preference": preference} if preference else None,
-            score_threshold=DEFAULT_SCORE_THRESHOLD,
         )
         return results
 

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -1,13 +1,13 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Union, List, Dict, Any
+from typing import Dict, List, Union
 
 from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.utilities.logger import Logger
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+from crewai.utilities.logger import Logger
 
 
 class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
@@ -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 its inside the knowledge directory, use the relative path.",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
                     color="red",
                 )
                 raise FileNotFoundError(f"File not found: {path}")
@@ -49,10 +49,9 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
                     color="red",
                 )
 
-    def save_documents(self, metadata: Dict[str, Any]):
+    def _save_documents(self):
         """Save the documents to the storage."""
-        chunk_metadatas = [metadata.copy() for _ in self.chunks]
+        self.storage.save(self.chunks)
-        self.storage.save(self.chunks, chunk_metadatas)
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""

src/crewai/knowledge/source/base_knowledge_source.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Dict, Any, Optional
+from typing import Any, Dict, List, Optional
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
@@ -17,7 +17,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
     collection_name: Optional[str] = Field(default=None)
 
     @abstractmethod
@@ -41,9 +41,9 @@ class BaseKnowledgeSource(BaseModel, ABC):
             for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
         ]
 
-    def save_documents(self, metadata: Dict[str, Any]):
+    def _save_documents(self):
         """
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks, metadata)
+        self.storage.save(self.chunks)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -1,6 +1,6 @@
 import csv
-from typing import Dict, List
 from pathlib import Path
+from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -30,7 +30,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,5 +1,6 @@
-from typing import Dict, List
 from pathlib import Path
+from typing import Dict, List
+
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
 
@@ -44,7 +45,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
 
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/json_knowledge_source.py

@@ -1,6 +1,6 @@
 import json
-from typing import Any, Dict, List
 from pathlib import Path
+from typing import Any, Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -42,7 +42,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -1,5 +1,5 @@
-from typing import List, Dict
 from pathlib import Path
+from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -43,7 +43,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/string_knowledge_source.py

@@ -24,7 +24,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
         """Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
         new_chunks = self._chunk_text(self.content)
         self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -1,5 +1,5 @@
-from typing import Dict, List
 from pathlib import Path
+from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -24,7 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self.save_documents(metadata=self.metadata)
+        self._save_documents()
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/storage/base_knowledge_storage.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict, Any, List, Optional
+from typing import Any, Dict, List, Optional
 
 
 class BaseKnowledgeStorage(ABC):

src/crewai/knowledge/storage/knowledge_storage.py

@@ -1,18 +1,22 @@
 import contextlib
+import hashlib
 import io
 import logging
-import chromadb
 import os
+import shutil
+from typing import Any, Dict, List, Optional, Union, cast
 
+import chromadb
 import chromadb.errors
-from crewai.utilities.paths import db_storage_path
-from typing import Optional, List, Dict, Any, Union
-from crewai.utilities import EmbeddingConfigurator
-from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
-import hashlib
-from chromadb.config import Settings
 from chromadb.api import ClientAPI
+from chromadb.api.types import OneOrMany
+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.logger import Logger
+from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -103,24 +107,31 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             raise Exception("Failed to create or get collection")
 
     def reset(self):
-        if self.app:
+        base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
-            self.app.reset()
+        if not self.app:
-        else:
-            base_path = os.path.join(db_storage_path(), "knowledge")
             self.app = chromadb.PersistentClient(
                 path=base_path,
                 settings=Settings(allow_reset=True),
             )
-            self.app.reset()
+
+        self.app.reset()
+        shutil.rmtree(base_path)
+        self.app = None
+        self.collection = None
 
     def save(
         self,
         documents: List[str],
-        metadata: Union[Dict[str, Any], List[Dict[str, Any]]],
+        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
         if self.collection:
             try:
-                metadatas = [metadata] if isinstance(metadata, dict) else metadata
+                if metadata is None:
+                    metadatas: Optional[OneOrMany[chromadb.Metadata]] = None
+                elif isinstance(metadata, list):
+                    metadatas = [cast(chromadb.Metadata, m) for m in metadata]
+                else:
+                    metadatas = cast(chromadb.Metadata, metadata)
 
                 ids = [
                     hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents

src/crewai/llm.py

@@ -1,4 +1,5 @@
 import logging
+import os
 import sys
 import threading
 import warnings
@@ -42,6 +43,10 @@ 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
@@ -60,6 +65,9 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
 }
 
+DEFAULT_CONTEXT_WINDOW_SIZE = 8192
+CONTEXT_WINDOW_USAGE_RATIO = 0.75
+
 
 @contextmanager
 def suppress_warnings():
@@ -123,11 +131,13 @@ 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
         litellm.set_verbose = False
         self.set_callbacks(callbacks)
+        self.set_env_callbacks()
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
@@ -189,7 +199,16 @@ 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
-        return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
+        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
 
     def set_callbacks(self, callbacks: List[Any]):
         callback_types = [type(callback) for callback in callbacks]
@@ -202,3 +221,39 @@ class LLM:
                 litellm._async_success_callback.remove(callback)
 
         litellm.callbacks = callbacks
+
+    def set_env_callbacks(self):
+        """
+        Sets the success and failure callbacks for the LiteLLM library from environment variables.
+
+        This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
+        environment variables, which should contain comma-separated lists of callback names.
+        It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
+        respectively.
+
+        If the environment variables are not set or are empty, the corresponding callback lists
+        will be set to empty lists.
+
+        Example:
+            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
+            LITELLM_FAILURE_CALLBACKS="langfuse"
+
+        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
+        `litellm.failure_callback` to ["langfuse"].
+        """
+        success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+        success_callbacks = []
+        if success_callbacks_str:
+            success_callbacks = [
+                callback.strip() for callback in success_callbacks_str.split(",")
+            ]
+
+        failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+        failure_callbacks = []
+        if failure_callbacks_str:
+            failure_callbacks = [
+                callback.strip() for callback in failure_callbacks_str.split(",")
+            ]
+
+        litellm.success_callback = success_callbacks
+        litellm.failure_callback = failure_callbacks

src/crewai/memory/contextual/contextual_memory.py

@@ -1,4 +1,4 @@
-from typing import Optional, Dict, Any
+from typing import Any, Dict, Optional
 
 from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 

src/crewai/memory/memory.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, List
+from typing import Any, Dict, List, Optional
 
 from crewai.memory.storage.rag_storage import RAGStorage
 

src/crewai/memory/short_term/short_term_memory.py

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -32,7 +33,10 @@ class ShortTermMemory(Memory):
                 storage
                 if storage
                 else RAGStorage(
-                    type="short_term", embedder_config=embedder_config, crew=crew, path=path
+                    type="short_term",
+                    embedder_config=embedder_config,
+                    crew=crew,
+                    path=path,
                 )
             )
         super().__init__(storage)

src/crewai/memory/storage/mem0_storage.py

@@ -2,6 +2,7 @@ import os
 from typing import Any, Dict, List
 
 from mem0 import MemoryClient
+
 from crewai.memory.storage.interface import Storage
 
 

src/crewai/memory/storage/rag_storage.py

@@ -150,9 +150,11 @@ class RAGStorage(BaseRAGStorage):
 
     def reset(self) -> None:
         try:
-            shutil.rmtree(f"{db_storage_path()}/{self.type}")
             if self.app:
                 self.app.reset()
+                shutil.rmtree(f"{db_storage_path()}/{self.type}")
+                self.app = None
+                self.collection = None
         except Exception as e:
             if "attempt to write a readonly database" in str(e):
                 # Ignore this specific error

src/crewai/memory/user/user_memory.py

@@ -37,7 +37,7 @@ class UserMemory(Memory):
         limit: int = 3,
         score_threshold: float = 0.35,
     ):
-        results = super().search(
+        results = self.storage.search(
             query=query,
             limit=limit,
             score_threshold=score_threshold,

src/crewai/project/annotations.py

@@ -66,6 +66,8 @@ def cache_handler(func):
 
 
 def crew(func) -> Callable[..., Crew]:
+
+    @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:
         instantiated_tasks = []
         instantiated_agents = []

src/crewai/project/crew_base.py

@@ -213,4 +213,8 @@ def CrewBase(cls: T) -> T:
                     callback_functions[callback]() for callback in callbacks
                 ]
 
+    # Include base class (qual)name in the wrapper class (qual)name.
+    WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
+    WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
+  
     return cast(T, WrappedClass)

src/crewai/project/utils.py

@@ -1,11 +1,14 @@
+from functools import wraps
+
+
 def memoize(func):
     cache = {}
 
+    @wraps(func)
     def memoized_func(*args, **kwargs):
         key = (args, tuple(kwargs.items()))
         if key not in cache:
             cache[key] = func(*args, **kwargs)
         return cache[key]
 
-    memoized_func.__dict__.update(func.__dict__)
     return memoized_func

src/crewai/task.py

@@ -1,11 +1,11 @@
 import datetime
 import json
-import os
 import threading
 import uuid
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
 
 from opentelemetry.trace import Span
@@ -393,12 +393,13 @@ class Task(BaseModel):
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
-        directory = os.path.dirname(self.output_file)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
+        resolved_path = Path(self.output_file).expanduser().resolve()
+        directory = resolved_path.parent
 
-        if directory and not os.path.exists(directory):
+        if not directory.exists():
-            os.makedirs(directory)
+            directory.mkdir(parents=True, exist_ok=True)
 
-        with open(self.output_file, "w", encoding="utf-8") as file:
+        with resolved_path.open("w", encoding="utf-8") as file:
             if isinstance(result, dict):
                 import json
 

src/crewai/telemetry/telemetry.py

@@ -21,7 +21,9 @@ with suppress_warnings():
 
 
 from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,  # noqa: E402
+)
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
 from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402

src/crewai/tools/agent_tools/agent_tools.py

@@ -1,9 +1,9 @@
-from crewai.tools.base_tool import BaseTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tools.base_tool import BaseTool
 from crewai.utilities import I18N
 
-from .delegate_work_tool import DelegateWorkTool
 from .ask_question_tool import AskQuestionTool
+from .delegate_work_tool import DelegateWorkTool
 
 
 class AgentTools:

src/crewai/tools/agent_tools/ask_question_tool.py

@@ -1,7 +1,9 @@
-from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 from typing import Optional
+
 from pydantic import BaseModel, Field
 
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+
 
 class AskQuestionToolSchema(BaseModel):
     question: str = Field(..., description="The question to ask")

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,9 +1,10 @@
 from typing import Optional, Union
+
 from pydantic import Field
 
-from crewai.tools.base_tool import BaseTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
+from crewai.tools.base_tool import BaseTool
 from crewai.utilities import I18N
 
 
@@ -44,14 +45,14 @@ class BaseAgentTool(BaseTool):
                 if available_agent.role.casefold().replace("\n", "") == agent_name
             ]
         except Exception as _:
-            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+            return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )
             )
 
         if not agent:
-            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
+            return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
                     [f"- {agent.role.casefold()}" for agent in self.agents]
                 )

src/crewai/tools/agent_tools/delegate_work_tool.py

@@ -1,8 +1,9 @@
-from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
 from typing import Optional
 
 from pydantic import BaseModel, Field
 
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+
 
 class DelegateWorkToolSchema(BaseModel):
     task: str = Field(..., description="The task to delegate")

src/crewai/tools/tool_usage_events.py

@@ -1,6 +1,7 @@
-from typing import Any, Dict
-from pydantic import BaseModel
 from datetime import datetime
+from typing import Any, Dict
+
+from pydantic import BaseModel
 
 
 class ToolUsageEvent(BaseModel):

src/crewai/translations/en.json

@@ -19,7 +19,7 @@
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",
-    "sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
+    "summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
@@ -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_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
+    "agent_tool_unexisting_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.",

src/crewai/utilities/crew_json_encoder.py

@@ -1,8 +1,9 @@
-from datetime import datetime, date
 import json
-from uuid import UUID
+from datetime import date, datetime
-from pydantic import BaseModel
 from decimal import Decimal
+from uuid import UUID
+
+from pydantic import BaseModel
 
 
 class CrewJSONEncoder(json.JSONEncoder):

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -1,10 +1,11 @@
 import json
-import regex
 from typing import Any, Type
 
-from crewai.agents.parser import OutputParserException
+import regex
 from pydantic import BaseModel, ValidationError
 
+from crewai.agents.parser import OutputParserException
+
 
 class CrewPydanticOutputParser:
     """Parses the text into pydantic models"""

src/crewai/utilities/embedding_configurator.py

@@ -1,6 +1,7 @@
 import os
 from typing import Any, Dict, cast
-from chromadb import EmbeddingFunction, Documents, Embeddings
+
+from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
 
 

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,13 +1,14 @@
 from collections import defaultdict
 
+from pydantic import BaseModel, Field
+from rich.box import HEAVY_EDGE
+from rich.console import Console
+from rich.table import Table
+
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
-from pydantic import BaseModel, Field
-from rich.box import HEAVY_EDGE
-from rich.console import Console
-from rich.table import Table
 
 
 class TaskEvaluationPydanticOutput(BaseModel):

src/crewai/utilities/events.py

@@ -1,7 +1,7 @@
-from typing import Any, Callable, Generic, List, Dict, Type, TypeVar
 from functools import wraps
-from pydantic import BaseModel
+from typing import Any, Callable, Dict, Generic, List, Type, TypeVar
 
+from pydantic import BaseModel
 
 T = TypeVar("T")
 EVT = TypeVar("EVT", bound=BaseModel)

src/crewai/utilities/planning_handler.py

@@ -1,4 +1,5 @@
 from typing import Any, List, Optional
+
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent

src/crewai/utilities/prompts.py

@@ -1,5 +1,7 @@
-from pydantic import BaseModel, Field
 from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
 from crewai.utilities import I18N
 
 

src/crewai/utilities/pydantic_schema_parser.py

@@ -1,4 +1,4 @@
-from typing import Type, get_args, get_origin, Union
+from typing import Type, Union, get_args, get_origin
 
 from pydantic import BaseModel
 

src/crewai/utilities/task_output_storage_handler.py

@@ -1,6 +1,8 @@
-from pydantic import BaseModel, Field
 from datetime import datetime
-from typing import Dict, Any, Optional, List
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )

src/crewai/utilities/token_counter_callback.py

@@ -1,5 +1,6 @@
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
+
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 
 
@@ -11,7 +12,7 @@ class TokenCalcHandler(CustomLogger):
         if self.token_cost_process is None:
             return
 
-        usage : Usage = response_obj["usage"]
+        usage: Usage = response_obj["usage"]
         self.token_cost_process.sum_successful_requests(1)
         self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
         self.token_cost_process.sum_completion_tokens(usage.completion_tokens)

tests/agents/agent_builder/base_agent_test.py

@@ -1,9 +1,10 @@
 import hashlib
 from typing import Any, List, Optional
 
+from pydantic import BaseModel
+
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tools.base_tool import BaseTool
-from pydantic import BaseModel
 
 
 class TestAgent(BaseAgent):

tests/agents/test_crew_agent_parser.py

@@ -1,10 +1,11 @@
 import pytest
-from crewai.agents.parser import CrewAgentParser
+
 from crewai.agents.crew_agent_executor import (
     AgentAction,
     AgentFinish,
     OutputParserException,
 )
+from crewai.agents.parser import CrewAgentParser
 
 
 @pytest.fixture

tests/cassettes/test_crew_kickoff_usage_metrics.yaml

@@ -28,13 +28,13 @@ interactions:
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       - OpenAI/Python 1.52.1
       x-stainless-arch:
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+      - arm64
       x-stainless-async:
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       x-stainless-os:
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+      - MacOS
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+      - crewai-iuxna1
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+      - '150000000'
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tests/cli/authentication/test_auth_main.py

@@ -2,6 +2,7 @@ import unittest
 from unittest.mock import MagicMock, patch
 
 import requests
+
 from crewai.cli.authentication.main import AuthenticationCommand
 
 
@@ -47,7 +48,9 @@ class TestAuthenticationCommand(unittest.TestCase):
     @patch("crewai.cli.authentication.main.requests.post")
     @patch("crewai.cli.authentication.main.validate_token")
     @patch("crewai.cli.authentication.main.console.print")
-    def test_poll_for_token_success(self, mock_print, mock_validate_token, mock_post, mock_tool):
+    def test_poll_for_token_success(
+        self, mock_print, mock_validate_token, mock_post, mock_tool
+    ):
         mock_response = MagicMock()
         mock_response.status_code = 200
         mock_response.json.return_value = {
@@ -62,7 +65,9 @@ class TestAuthenticationCommand(unittest.TestCase):
         self.auth_command._poll_for_token({"device_code": "123456"})
 
         mock_validate_token.assert_called_once_with("TOKEN")
-        mock_print.assert_called_once_with("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
+        mock_print.assert_called_once_with(
+            "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
+        )
 
     @patch("crewai.cli.authentication.main.requests.post")
     @patch("crewai.cli.authentication.main.console.print")

tests/cli/authentication/test_utils.py

@@ -3,9 +3,10 @@ import unittest
 from datetime import datetime, timedelta
 from unittest.mock import MagicMock, patch
 
-from crewai.cli.authentication.utils import TokenManager, validate_token
 from cryptography.fernet import Fernet
 
+from crewai.cli.authentication.utils import TokenManager, validate_token
+
 
 class TestValidateToken(unittest.TestCase):
     @patch("crewai.cli.authentication.utils.AsymmetricSignatureVerifier")

tests/cli/config_test.py

@@ -1,10 +1,12 @@
-import unittest
 import json
-import tempfile
 import shutil
+import tempfile
+import unittest
 from pathlib import Path
+
 from crewai.cli.config import Settings
 
+
 class TestSettings(unittest.TestCase):
     def setUp(self):
         self.test_dir = Path(tempfile.mkdtemp())
@@ -20,8 +22,7 @@ class TestSettings(unittest.TestCase):
 
     def test_initialization_with_data(self):
         settings = Settings(
-            config_path=self.config_path,
+            config_path=self.config_path, tool_repository_username="user1"
-            tool_repository_username="user1"
         )
         self.assertEqual(settings.tool_repository_username, "user1")
         self.assertIsNone(settings.tool_repository_password)
@@ -37,22 +38,23 @@ class TestSettings(unittest.TestCase):
     def test_merge_file_and_input_data(self):
         self.config_path.parent.mkdir(parents=True, exist_ok=True)
         with self.config_path.open("w") as f:
-            json.dump({
+            json.dump(
-                "tool_repository_username": "file_user",
+                {
-                "tool_repository_password": "file_pass"
+                    "tool_repository_username": "file_user",
-            }, f)
+                    "tool_repository_password": "file_pass",
+                },
+                f,
+            )
 
         settings = Settings(
-            config_path=self.config_path,
+            config_path=self.config_path, tool_repository_username="new_user"
-            tool_repository_username="new_user"
         )
         self.assertEqual(settings.tool_repository_username, "new_user")
         self.assertEqual(settings.tool_repository_password, "file_pass")
 
     def test_dump_new_settings(self):
         settings = Settings(
-            config_path=self.config_path,
+            config_path=self.config_path, tool_repository_username="user1"
-            tool_repository_username="user1"
         )
         settings.dump()
 
@@ -67,8 +69,7 @@ class TestSettings(unittest.TestCase):
             json.dump({"existing_setting": "value"}, f)
 
         settings = Settings(
-            config_path=self.config_path,
+            config_path=self.config_path, tool_repository_username="user1"
-            tool_repository_username="user1"
         )
         settings.dump()
 
@@ -79,10 +80,7 @@ class TestSettings(unittest.TestCase):
         self.assertEqual(saved_data["tool_repository_username"], "user1")
 
     def test_none_values(self):
-        settings = Settings(
+        settings = Settings(config_path=self.config_path, tool_repository_username=None)
-            config_path=self.config_path,
-            tool_repository_username=None
-        )
         settings.dump()
 
         with self.config_path.open("r") as f:

tests/cli/deploy/test_deploy_main.py

@@ -228,17 +228,18 @@ class TestDeployCommand(unittest.TestCase):
         "builtins.open",
         new_callable=unittest.mock.mock_open,
         read_data="""
-    [project]
+        [project]
-    name = "test_project"
+        name = "test_project"
-    version = "0.1.0"
+        version = "0.1.0"
-    requires-python = ">=3.10,<=3.13"
+        requires-python = ">=3.10,<=3.12"
-    dependencies = ["crewai"]
+        dependencies = ["crewai"]
-    """,
+        """,
     )
     def test_get_project_name_python_310(self, mock_open):
         from crewai.cli.utils import get_project_name
 
         project_name = get_project_name()
+        print("project_name", project_name)
         self.assertEqual(project_name, "test_project")
 
     @unittest.skipIf(sys.version_info < (3, 11), "Requires Python 3.11+")
@@ -249,7 +250,7 @@ class TestDeployCommand(unittest.TestCase):
     [project]
     name = "test_project"
     version = "0.1.0"
-    requires-python = ">=3.10,<=3.13"
+    requires-python = ">=3.10,<=3.12"
     dependencies = ["crewai"]
     """,
     )

tests/cli/test_git.py

@@ -1,6 +1,7 @@
-from crewai.cli.git import Repository
 import pytest
 
+from crewai.cli.git import Repository
+
 
 @pytest.fixture()
 def repository(fp):

tests/cli/test_plus_api.py

@@ -1,6 +1,7 @@
 import os
 import unittest
 from unittest.mock import MagicMock, patch
+
 from crewai.cli.plus_api import PlusAPI
 
 

tests/cli/test_utils.py

@@ -1,7 +1,9 @@
-import pytest
+import os
 import shutil
 import tempfile
-import os
+
+import pytest
+
 from crewai.cli import utils
 
 

tests/cli/tools/test_main.py

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

tests/crew_test.py

@@ -1285,7 +1285,7 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
         prompt_tokens=1562,
         completion_tokens=111,
         successful_requests=3,
-        cached_prompt_tokens=0
+        cached_prompt_tokens=0,
     )
 
 
@@ -1785,16 +1785,18 @@ def test_crew_train_success(
     crew_training_handler.assert_any_call("trained_agents_data.pkl")
     crew_training_handler().load.assert_called()
 
-    crew_training_handler().save_trained_data.assert_has_calls([
+    crew_training_handler().save_trained_data.assert_has_calls(
-        mock.call(
+        [
-            agent_id="Researcher",
+            mock.call(
-            trained_data=task_evaluator().evaluate_training_data().model_dump(),
+                agent_id="Researcher",
-        ),
+                trained_data=task_evaluator().evaluate_training_data().model_dump(),
-        mock.call(
+            ),
-            agent_id="Senior Writer",
+            mock.call(
-            trained_data=task_evaluator().evaluate_training_data().model_dump(),
+                agent_id="Senior Writer",
-        )
+                trained_data=task_evaluator().evaluate_training_data().model_dump(),
-    ])
+            ),
+        ]
+    )
 
 
 def test_crew_train_error():

tests/flow_test.py

@@ -3,6 +3,7 @@
 import asyncio
 
 import pytest
+
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 
 

tests/knowledge/knowledge_test.py

@@ -3,6 +3,8 @@
 from pathlib import Path
 from unittest.mock import patch
 
+import pytest
+
 from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
 from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
 from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
@@ -10,8 +12,6 @@ from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
 
-import pytest
-
 
 @pytest.fixture(autouse=True)
 def mock_vector_db():

tests/memory/short_term_memory_test.py

@@ -1,5 +1,7 @@
-import pytest
 from unittest.mock import patch
+
+import pytest
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.memory.short_term.short_term_memory import ShortTermMemory

tests/task_test.py

@@ -6,12 +6,13 @@ import os
 from unittest.mock import MagicMock, patch
 
 import pytest
+from pydantic import BaseModel
+from pydantic_core import ValidationError
+
 from crewai import Agent, Crew, Process, Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.converter import Converter
-from pydantic import BaseModel
-from pydantic_core import ValidationError
 
 
 def test_task_tool_reflect_agent_tools():

tests/tools/test_tool_usage.py

@@ -6,8 +6,8 @@ import pytest
 from pydantic import BaseModel, Field
 
 from crewai import Agent, Task
-from crewai.tools.tool_usage import ToolUsage
 from crewai.tools import BaseTool
+from crewai.tools.tool_usage import ToolUsage
 
 
 class RandomNumberToolInput(BaseModel):

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -1,6 +1,7 @@
 from unittest import mock
 
 import pytest
+
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.task import Task

tests/utilities/prompts.json

@@ -26,7 +26,7 @@
   },
   "errors": {
     "force_final_answer": "Lorem ipsum dolor sit amet",
-    "agent_tool_unexsiting_coworker": "Lorem ipsum dolor sit amet",
+    "agent_tool_unexisting_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

@@ -1,13 +1,9 @@
 version = 1
-requires-python = ">=3.10, <=3.13"
+requires-python = ">=3.10, <=3.12"
 resolution-markers = [
-    "python_full_version < '3.11' and platform_python_implementation == 'PyPy'",
+    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy'",
+    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11' and platform_python_implementation != 'PyPy'",
+    "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
-    "python_full_version >= '3.12.4' and python_full_version < '3.13'",
-    "python_full_version >= '3.13'",
 ]
 
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 ]
 
+[[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 = [
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+]
+
 [[package]]
 name = "build"
 version = "1.2.2.post1"
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