Update pyproject.toml and uv.lock to drop crewai-tools as a default requirement in crewai repo

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -65,6 +65,7 @@ body:
         - '3.10'
         - '3.11'
         - '3.12'
+        - '3.13'
     validations:
       required: true
   - type: input

.github/workflows/tests.yml

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

README.md

@@ -44,7 +44,7 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-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.
+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.
 
 First, install CrewAI:
 

docs/concepts/crews.mdx

@@ -41,155 +41,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
 </Tip>
 
-## Creating Crews
-
-There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
-
-### YAML Configuration (Recommended)
-
-Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
-
-After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
-
-#### Example Crew Class with Decorators
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
-
-
-@CrewBase
-class YourCrewName:
-    """Description of your crew"""
-
-    # Paths to your YAML configuration files
-    # To see an example agent and task defined in YAML, checkout the following:
-    # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml' 
-    tasks_config = 'config/tasks.yaml' 
-
-    @before_kickoff
-    def prepare_inputs(self, inputs):
-        # Modify inputs before the crew starts
-        inputs['additional_data'] = "Some extra information"
-        return inputs
-
-    @after_kickoff
-    def process_output(self, output):
-        # Modify output after the crew finishes
-        output.raw += "\nProcessed after kickoff."
-        return output
-
-    @agent
-    def agent_one(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_one'],
-            verbose=True
-        )
-
-    @agent
-    def agent_two(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_two'],
-            verbose=True
-        )
-
-    @task
-    def task_one(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_one']
-        )
-
-    @task
-    def task_two(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_two']
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator. 
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-<Note>
-Tasks will be executed in the order they are defined.
-</Note>
-
-The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
-
-#### Decorators overview from `annotations.py`
-
-CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
-
-- `@CrewBase`: Marks the class as a crew base class.
-- `@agent`: Denotes a method that returns an `Agent` object.
-- `@task`: Denotes a method that returns a `Task` object.
-- `@crew`: Denotes the method that returns the `Crew` object.
-- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
-- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
-
-These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
-
-### Direct Code Definition (Alternative)
-
-Alternatively, you can define the crew directly in code without using YAML configuration files.
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai_tools import YourCustomTool
-
-class YourCrewName:
-    def agent_one(self) -> Agent:
-        return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
-            verbose=True,
-            tools=[YourCustomTool()]
-        )
-
-    def agent_two(self) -> Agent:
-        return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
-            verbose=True
-        )
-
-    def task_one(self) -> Task:
-        return Task(
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
-            agent=self.agent_one()
-        )
-
-    def task_two(self) -> Task:
-        return Task(
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
-            agent=self.agent_two()
-        )
-
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.agent_one(), self.agent_two()],
-            tasks=[self.task_one(), self.task_two()],
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-In this example:
-
-- Agents and tasks are defined directly within the class without decorators.
-- We manually create and manage the list of agents and tasks.
-- This approach provides more control but can be less maintainable for larger projects.
 
 ## Crew Output
 

docs/concepts/knowledge.mdx

@@ -48,6 +48,7 @@ from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSourc
 content = "Users name is John. He is 30 years old and lives in San Francisco."
 string_source = StringKnowledgeSource(
     content=content, 
+    metadata={"preference": "personal"}
 )
 
 # Create an LLM with a temperature of 0 to ensure deterministic outputs
@@ -73,7 +74,10 @@ crew = Crew(
     tasks=[task],
     verbose=True,
     process=Process.sequential,
-    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+    knowledge={
+      "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?"})
@@ -81,6 +85,17 @@ 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.
@@ -101,27 +116,21 @@ 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_sources=[string_source],
+    knowledge={
-    embedder={
+        "sources": [string_source],
-        "provider": "openai",
+        "metadata": {"preference": "personal"},
-        "config": {"model": "text-embedding-3-small"},
+        "embedder_config": {
+          "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.
@@ -180,12 +189,17 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
             chunks = self._chunk_text(text)
             self.chunks.extend(chunks)
         
-        self._save_documents()
+        self.save_documents(metadata={
+            "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
@@ -219,7 +233,6 @@ 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?
@@ -243,26 +256,22 @@ The latest developments in space exploration, based on recent space news article
 
 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
+     - `add()`: Processes and stores the content with metadata
 
 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
 
@@ -271,7 +280,6 @@ 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
@@ -279,26 +287,26 @@ This example demonstrates how to:
 
 #### About the Spaceflight News API
 
-The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
+The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/documentation), 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"}
 )
 ```
 
@@ -306,12 +314,14 @@ 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
     - Configure appropriate embedding models
     - Consider using local embedding providers for faster processing

docs/concepts/tools.mdx

@@ -172,48 +172,6 @@ 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 <=3.12`. Here's how to check your version:
+  CrewAI requires `Python >=3.10 and <=3.13`. Here's how to check your version:
   ```bash
   python3 --version
   ```

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "crewai"
-version = "0.86.0"
+version = "0.85.0"
 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,<=3.12"
+requires-python = ">=3.10,<=3.13"
 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",

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.86.0"
+__version__ = "0.85.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -8,7 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
+from crewai.cli.constants import ENV_VARS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -181,11 +181,20 @@ class Agent(BaseAgent):
                         if key_name and key_name not in unaccepted_attributes:
                             env_value = os.environ.get(key_name)
                             if env_value:
-                                key_name = key_name.lower()
+                                # Map key names containing "API_KEY" to "api_key"
-                                for pattern in LITELLM_PARAMS:
+                                key_name = (
-                                    if pattern in key_name:
+                                    "api_key" if "API_KEY" in key_name else key_name
-                                        key_name = pattern
+                                )
-                                        break
+                                # Map key names containing "API_BASE" to "api_base"
+                                key_name = (
+                                    "api_base" if "API_BASE" in key_name else key_name
+                                )
+                                # Map key names containing "API_VERSION" to "api_version"
+                                key_name = (
+                                    "api_version"
+                                    if "API_VERSION" in key_name
+                                    else key_name
+                                )
                                 llm_params[key_name] = env_value
                         # Check for default values if the environment variable is not set
                         elif env_var.get("default", False):

src/crewai/agents/crew_agent_executor.py

@@ -143,9 +143,6 @@ 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:
@@ -302,7 +299,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         self._i18n.slice("summarizer_system_message"), role="system"
                     ),
                     self._format_msg(
-                        self._i18n.slice("summarize_instruction").format(group=group),
+                        self._i18n.slice("sumamrize_instruction").format(group=group),
                     ),
                 ],
                 callbacks=self.callbacks,

src/crewai/cli/cli.py

@@ -25,7 +25,6 @@ from .update_crew import update_crew
 
 
 @click.group()
-@click.version_option(pkg_resources.get_distribution("crewai").version)
 def crewai():
     """Top-level command group for crewai."""
 
@@ -51,10 +50,7 @@ def create(type, name, provider, skip_provider=False):
 )
 def version(tools):
     """Show the installed version of crewai."""
-    try:
     crewai_version = pkg_resources.get_distribution("crewai").version
-    except Exception:
-        crewai_version = "unknown version"
     click.echo(f"crewai version: {crewai_version}")
 
     if tools:

src/crewai/cli/constants.py

@@ -159,6 +159,3 @@ MODELS = {
 }
 
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
-
-
-LITELLM_PARAMS = ["api_key", "api_base", "api_version"]

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 <=3.12 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.
+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.
 
 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 fledged report with the main topics, each with a full section of information.
+    A fully fledge reports with the mains topics, each with a full section of information.
     Formatted as markdown without '```'
   agent: reporting_analyst

src/crewai/cli/templates/crew/crew.py

@@ -1,26 +1,37 @@
 from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
+from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
+# Uncomment the following line to use an example of a custom tool
+# from {{folder_name}}.tools.custom_tool import MyCustomTool
+# Uncomment the following line to use an example of a knowledge source
+# from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
 
-# If you want to run a snippet of code before or after the crew starts, 
+# Check our tools documentations for more information on how to use them
-# you can use the @before_kickoff and @after_kickoff decorators
+# from crewai_tools import SerperDevTool
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
 
 @CrewBase
 class {{crew_name}}():
 	"""{{crew_name}} crew"""
 
-	# Learn more about YAML configuration files here:
-	# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-	# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
 	agents_config = 'config/agents.yaml'
 	tasks_config = 'config/tasks.yaml'
 
-	# If you would like to add tools to your agents, you can learn more about it here:
+	@before_kickoff # Optional hook to be executed before the crew starts
-	# https://docs.crewai.com/concepts/agents#agent-tools
+	def pull_data_example(self, inputs):
+		# Example of pulling data from an external API, dynamically changing the inputs
+		inputs['extra_data'] = "This is extra data"
+		return inputs
+
+	@after_kickoff # Optional hook to be executed after the crew has finished
+	def log_results(self, output):
+		# Example of logging results, dynamically changing the output
+		print(f"Results: {output}")
+		return output
+
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(
 			config=self.agents_config['researcher'],
+			# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
 			verbose=True
 		)
 
@@ -31,9 +42,6 @@ class {{crew_name}}():
 			verbose=True
 		)
 
-	# To learn more about structured task outputs, 
-	# task dependencies, and task callbacks, check out the documentation:
-	# https://docs.crewai.com/concepts/tasks#overview-of-a-task
 	@task
 	def research_task(self) -> Task:
 		return Task(
@@ -50,8 +58,14 @@ class {{crew_name}}():
 	@crew
 	def crew(self) -> Crew:
 		"""Creates the {{crew_name}} crew"""
-		# To learn how to add knowledge sources to your crew, check out the documentation:
+		# You can add knowledge sources here
-		# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
+		# knowledge_path = "user_preference.txt"
+		# sources = [
+		# 	TextFileKnowledgeSource(
+		# 		file_path="knowledge/user_preference.txt",
+		# 		metadata={"preference": "personal"}
+		# 	),
+		# ]
 
 		return Crew(
 			agents=self.agents, # Automatically created by the @agent decorator
@@ -59,4 +73,5 @@ class {{crew_name}}():
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
+			# knowledge_sources=sources, # In the case you want to add knowledge sources
 		)

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0,<1.0.0"
+    "crewai[tools]>=0.85.0,<1.0.0"
 ]
 
 [project.scripts]

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 <=3.12 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.
+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.
 
 First, if you haven't already, install uv:
 

src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py

@@ -1,44 +1,28 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 
-# If you want to run a snippet of code before or after the crew starts,
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
-
-
 @CrewBase
-class PoemCrew:
+class PoemCrew():
 	"""Poem Crew"""
 
-    # Learn more about YAML configuration files here:
+	agents_config = 'config/agents.yaml'
-    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
+	tasks_config = 'config/tasks.yaml'
-    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
 
-    # If you would lik to add tools to your crew, you can learn more about it here:
-    # https://docs.crewai.com/concepts/agents#agent-tools
 	@agent
 	def poem_writer(self) -> Agent:
 		return Agent(
-            config=self.agents_config["poem_writer"],
+			config=self.agents_config['poem_writer'],
 		)
 
-    # To learn more about structured task outputs,
-    # task dependencies, and task callbacks, check out the documentation:
-    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
 	@task
 	def write_poem(self) -> Task:
 		return Task(
-            config=self.tasks_config["write_poem"],
+			config=self.tasks_config['write_poem'],
 		)
 
 	@crew
 	def crew(self) -> Crew:
 		"""Creates the Research Crew"""
-        # To learn how to add knowledge sources to your crew, check out the documentation:
-        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-
 		return Crew(
 			agents=self.agents, # Automatically created by the @agent decorator
 			tasks=self.tasks, # Automatically created by the @task decorator

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

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0,<1.0.0",
+    "crewai[tools]>=0.85.0,<1.0.0",
 ]
 
 [project.scripts]

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 <=3.12 installed on your system. This project
+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.
 

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

@@ -3,8 +3,8 @@ name = "{{folder_name}}"
 version = "0.1.0"
 description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
-requires-python = ">=3.10,<=3.12"
+requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0"
+    "crewai[tools]>=0.85.0"
 ]
 

src/crewai/cli/utils.py

@@ -33,6 +33,26 @@ 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:
@@ -43,7 +63,7 @@ def read_toml(file_path: str = "pyproject.toml"):
 def parse_toml(content):
     if sys.version_info >= (3, 11):
         return tomllib.loads(content)
-    return tomli.loads(content)
+    return simple_toml_parser(content)
 
 
 def get_project_name(

src/crewai/knowledge/knowledge.py

@@ -1,10 +1,11 @@
 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
 
@@ -45,7 +46,9 @@ class Knowledge(BaseModel):
             source.storage = self.storage
             source.add()
 
-    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
+    def query(
+        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.
@@ -54,6 +57,8 @@ 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 Dict, List, Union
+from typing import Union, List, Dict, Any
 
 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):
@@ -49,9 +49,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
                     color="red",
                 )
 
-    def _save_documents(self):
+    def save_documents(self, metadata: Dict[str, Any]):
         """Save the documents to the storage."""
-        self.storage.save(self.chunks)
+        chunk_metadatas = [metadata.copy() for _ in 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 Any, Dict, List, Optional
+from typing import List, Dict, Any, 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)  # Currently unused
+    metadata: Dict[str, Any] = Field(default_factory=dict)
     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):
+    def save_documents(self, metadata: Dict[str, Any]):
         """
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks)
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -1,6 +1,6 @@
 import csv
-from pathlib import Path
 from typing import Dict, List
+from pathlib import Path
 
 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()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/excel_knowledge_source.py

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

src/crewai/knowledge/storage/knowledge_storage.py

@@ -1,22 +1,18 @@
 import contextlib
-import hashlib
 import io
 import logging
-import os
-import shutil
-from typing import Any, Dict, List, Optional, Union, cast
-
 import chromadb
-import chromadb.errors
+import os
-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
+import chromadb.errors
-from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.logger import Logger
 from crewai.utilities.paths import db_storage_path
-from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+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 crewai.utilities.logger import Logger
 
 
 @contextlib.contextmanager
@@ -107,31 +103,24 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             raise Exception("Failed to create or get collection")
 
     def reset(self):
-        base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
+        if self.app:
-        if not self.app:
+            self.app.reset()
+        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()
-        shutil.rmtree(base_path)
-        self.app = None
-        self.collection = None
 
     def save(
         self,
         documents: List[str],
-        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
+        metadata: Union[Dict[str, Any], List[Dict[str, Any]]],
     ):
         if self.collection:
             try:
-                if metadata is None:
+                metadatas = [metadata] if isinstance(metadata, dict) else metadata
-                    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

@@ -1,5 +1,4 @@
 import logging
-import os
 import sys
 import threading
 import warnings
@@ -129,7 +128,6 @@ class LLM:
         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():
@@ -204,39 +202,3 @@ 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/storage/rag_storage.py

@@ -4,14 +4,12 @@ import logging
 import os
 import shutil
 import uuid
+
 from typing import Any, Dict, List, Optional
-
 from chromadb.api import ClientAPI
-
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
+from crewai.utilities import EmbeddingConfigurator
 
 
 @contextlib.contextmanager
@@ -39,15 +37,12 @@ class RAGStorage(BaseRAGStorage):
 
     app: ClientAPI | None = None
 
-    def __init__(
+    def __init__(self, type, allow_reset=True, embedder_config=None, crew=None, path=None):
-        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
-    ):
         super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
         self.agents = agents
-        self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
 
@@ -65,7 +60,7 @@ class RAGStorage(BaseRAGStorage):
 
         self._set_embedder_config()
         chroma_client = chromadb.PersistentClient(
-            path=self.path if self.path else self.storage_file_name,
+            path=self.path if self.path else f"{db_storage_path()}/{self.type}/{self.agents}",
             settings=Settings(allow_reset=self.allow_reset),
         )
 
@@ -86,20 +81,6 @@ class RAGStorage(BaseRAGStorage):
         """
         return role.replace("\n", "").replace(" ", "_").replace("/", "_")
 
-    def _build_storage_file_name(self, type: str, file_name: str) -> str:
-        """
-        Ensures file name does not exceed max allowed by OS
-        """
-        base_path = f"{db_storage_path()}/{type}"
-
-        if len(file_name) > MAX_FILE_NAME_LENGTH:
-            logging.warning(
-                f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters."
-            )
-            file_name = file_name[:MAX_FILE_NAME_LENGTH]
-
-        return f"{base_path}/{file_name}"
-
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
@@ -150,11 +131,9 @@ 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 = self.storage.search(
+        results = super().search(
             query=query,
             limit=limit,
             score_threshold=score_threshold,

src/crewai/project/annotations.py

@@ -66,8 +66,6 @@ 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

@@ -213,8 +213,4 @@ 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

@@ -1,13 +1,11 @@
-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

@@ -1,6 +1,6 @@
 import datetime
 import json
-from pathlib import Path
+import os
 import threading
 import uuid
 from concurrent.futures import Future
@@ -393,13 +393,12 @@ class Task(BaseModel):
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
-        resolved_path = Path(self.output_file).expanduser().resolve()
+        directory = os.path.dirname(self.output_file)  # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
-        directory = resolved_path.parent
 
-        if not directory.exists():
+        if directory and not os.path.exists(directory):
-            directory.mkdir(parents=True, exist_ok=True)
+            os.makedirs(directory)
 
-        with resolved_path.open("w", encoding="utf-8") as file:
+        with open(self.output_file, "w", encoding="utf-8") as file:
             if isinstance(result, dict):
                 import json
 

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.",
-    "summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
+    "sumamrize_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.",

src/crewai/utilities/constants.py

@@ -3,4 +3,3 @@ TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
 DEFAULT_SCORE_THRESHOLD = 0.35
 KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
-MAX_FILE_NAME_LENGTH = 255

tests/cassettes/test_crew_kickoff_usage_metrics.yaml

@@ -28,13 +28,13 @@ interactions:
       user-agent:
       - OpenAI/Python 1.52.1
       x-stainless-arch:
-      - arm64
+      - x64
       x-stainless-async:
       - 'false'
       x-stainless-lang:
       - python
       x-stainless-os:
-      - MacOS
+      - Linux
       x-stainless-package-version:
       - 1.52.1
       x-stainless-raw-response:
@@ -44,27 +44,26 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.12.7
+      - 3.11.9
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-AcdAr57gPSeXoBUvpM7ihR5ocSg8K\",\n  \"object\":
+    body:
-      \"chat.completion\",\n  \"created\": 1733770413,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      string: !!binary |
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
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-      - '149999979'
+      - '199978'
       x-ratelimit-reset-requests:
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+      - 8.64s
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+      - 6ms
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+      - req_037288753767e763a51a04eae757ca84
-    http_version: HTTP/1.1
+    status:
-    status_code: 200
+      code: 200
+      message: OK
 - request:
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+      - x64
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       x-stainless-lang:
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       x-stainless-os:
-      - MacOS
+      - Linux
       x-stainless-package-version:
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@@ -140,26 +140,25 @@ interactions:
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       x-stainless-runtime-version:
-      - 3.12.7
+      - 3.11.9
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     uri: https://api.openai.com/v1/chat/completions
   response:
-    content: "{\n  \"id\": \"chatcmpl-AcdBWMAembczwWDLdjIRYwtbMLONh\",\n  \"object\":
+    body:
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-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
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+      - 8e185b31398a0303-GRU
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       Content-Encoding:
@@ -167,7 +166,7 @@ interactions:
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+      - Tue, 12 Nov 2024 17:49:02 GMT
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       Transfer-Encoding:
@@ -179,27 +178,28 @@ interactions:
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-      - crewai-iuxna1
+      - user-tqfegqsiobpvvjmn0giaipdq
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-      - '659'
+      - '889'
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       x-ratelimit-limit-requests:
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tests/cli/cli_test.py

@@ -131,13 +131,6 @@ def test_reset_no_memory_flags(runner):
     )
 
 
-def test_version_flag(runner):
-    result = runner.invoke(version)
-
-    assert result.exit_code == 0
-    assert "crewai version:" in result.output
-
-
 def test_version_command(runner):
     result = runner.invoke(version)
 

tests/cli/deploy/test_deploy_main.py

@@ -231,7 +231,7 @@ class TestDeployCommand(unittest.TestCase):
     [project]
     name = "test_project"
     version = "0.1.0"
-        requires-python = ">=3.10,<=3.12"
+    requires-python = ">=3.10,<=3.13"
     dependencies = ["crewai"]
     """,
     )
@@ -239,7 +239,6 @@ class TestDeployCommand(unittest.TestCase):
         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+")
@@ -250,7 +249,7 @@ class TestDeployCommand(unittest.TestCase):
     [project]
     name = "test_project"
     version = "0.1.0"
-    requires-python = ">=3.10,<=3.12"
+    requires-python = ">=3.10,<=3.13"
     dependencies = ["crewai"]
     """,
     )

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
     )
 
 
@@ -1785,8 +1785,7 @@ 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",
             trained_data=task_evaluator().evaluate_training_data().model_dump(),
@@ -1794,9 +1793,8 @@ def test_crew_train_success(
         mock.call(
             agent_id="Senior Writer",
             trained_data=task_evaluator().evaluate_training_data().model_dump(),
-            ),
-        ]
         )
+    ])
 
 
 def test_crew_train_error():

uv.lock

@@ -1,9 +1,13 @@
 version = 1
-requires-python = ">=3.10, <=3.12"
+requires-python = ">=3.10, <=3.13"
 resolution-markers = [
-    "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 == '3.11.*' 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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