docs: modernize type annotations to Python 3.10+ built-in generics

docs/en/guides/concepts/evaluating-use-cases.mdx

@@ -174,7 +174,6 @@ Flows are ideal when:
 # Example: Customer Support Flow with structured processing
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
-from typing import List, Dict
 
 # Define structured state
 class SupportTicketState(BaseModel):
@@ -262,7 +261,6 @@ The most sophisticated applications often benefit from combining Crews and Flows
 from crewai.flow.flow import Flow, listen, start
 from crewai import Agent, Crew, Process, Task
 from pydantic import BaseModel
-from typing import List, Dict
 
 class ContentState(BaseModel):
     topic: str = ""

docs/en/guides/crews/first-crew.mdx

@@ -58,7 +58,6 @@ This will generate a project with the basic structure needed for your crew. The
   <img src="/images/crews.png" alt="CrewAI Framework Overview" />
 </Frame>
 
-
 ## Step 2: Explore the Project Structure
 
 Let's take a moment to understand the project structure created by the CLI. CrewAI follows best practices for Python projects, making it easy to maintain and extend your code as your crews become more complex.
@@ -187,14 +186,13 @@ from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai_tools import SerperDevTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
 
 @CrewBase
 class ResearchCrew():
     """Research crew for comprehensive topic analysis and reporting"""
 
-    agents: List[BaseAgent]
-    tasks: List[Task]
+    agents: list[BaseAgent]
+    tasks: list[Task]
 
     @agent
     def researcher(self) -> Agent:

docs/en/guides/flows/first-flow.mdx

@@ -206,14 +206,13 @@ These task definitions provide detailed instructions to our agents, ensuring the
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
 
 @CrewBase
 class ContentCrew():
     """Content writing crew"""
 
-    agents: List[BaseAgent]
-    tasks: List[Task]
+    agents: list[BaseAgent]
+    tasks: list[Task]
 
     @agent
     def content_writer(self) -> Agent:
@@ -271,7 +270,7 @@ Let's create our flow in the `main.py` file:
 #!/usr/bin/env python
 import json
 import os
-from typing import List, Dict
+
 from pydantic import BaseModel, Field
 from crewai import LLM
 from crewai.flow.flow import Flow, listen, start
@@ -286,15 +285,15 @@ class GuideOutline(BaseModel):
     title: str = Field(description="Title of the guide")
     introduction: str = Field(description="Introduction to the topic")
     target_audience: str = Field(description="Description of the target audience")
-    sections: List[Section] = Field(description="List of sections in the guide")
+    sections: list[Section] = Field(description="List of sections in the guide")
     conclusion: str = Field(description="Conclusion or summary of the guide")
 
 # Define our flow state
 class GuideCreatorState(BaseModel):
     topic: str = ""
     audience_level: str = ""
     guide_outline: GuideOutline = None
-    sections_content: Dict[str, str] = {}
+    sections_content: dict[str, str] = {}
 
 class GuideCreatorFlow(Flow[GuideCreatorState]):
     """Flow for creating a comprehensive guide on any topic"""
@@ -590,7 +589,7 @@ class GuideCreatorState(BaseModel):
     topic: str = ""
     audience_level: str = ""
     guide_outline: GuideOutline = None
-    sections_content: Dict[str, str] = {}
+    sections_content: dict[str, str] = {}
 ```
 
 This provides a type-safe way to track and transform data throughout your flow.

docs/en/guides/flows/mastering-flow-state.mdx

@@ -149,7 +149,6 @@ Here's how to implement structured state management:
 ```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel, Field
-from typing import List, Dict, Optional
 
 # Define your state model
 class UserPreferences(BaseModel):
@@ -159,7 +158,7 @@ class UserPreferences(BaseModel):
 class AppState(BaseModel):
     user_name: str = ""
     preferences: UserPreferences = UserPreferences()
-    items: List[str] = []
+    items: list[str] = []
     processed: bool = False
     completion_percentage: float = 0.0
 
@@ -388,7 +387,6 @@ Behavior notes:
 - `restore_from_state_id=None` (default) is byte-identical to a kickoff without the parameter.
 - Pinning `inputs["id"]` while forking means the new run shares a persistence key with another flow — usually you want only `restore_from_state_id`.
 
-
 ## Advanced State Patterns
 
 ### Conditional starts and resumable execution
@@ -469,15 +467,14 @@ For complex state transformations, you can create dedicated methods:
 ```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
-from typing import List, Dict
 
 class UserData(BaseModel):
     name: str
     active: bool = True
     login_count: int = 0
 
 class ComplexState(BaseModel):
-    users: Dict[str, UserData] = {}
+    users: dict[str, UserData] = {}
     active_user_count: int = 0
 
 class TransformationFlow(Flow[ComplexState]):
@@ -641,8 +638,8 @@ class BloatedState(BaseModel):
 # Better: Focused state
 class FocusedState(BaseModel):
     user_id: str
-    preferences: Dict[str, str]
-    completion_status: Dict[str, bool]
+    preferences: dict[str, str]
+    completion_status: dict[str, bool]
 ```
 
 ### 2. Use Structured State for Complex Flows
@@ -663,7 +660,7 @@ class UserRegistrationState(BaseModel):
     email: str
     verification_status: bool = False
     registration_date: datetime = Field(default_factory=datetime.now)
-    last_login: Optional[datetime] = None
+    last_login: datetime | None = None
 
 class RegistrationFlow(Flow[UserRegistrationState]):
     # Methods with strongly-typed state access
@@ -748,10 +745,9 @@ self.state.items.append(new_item)  # Mutable operation
 
 # Consider creating new state:
 from pydantic import BaseModel
-from typing import List
 
 class ItemState(BaseModel):
-    items: List[str] = []
+    items: list[str] = []
 
 class ImmutableFlow(Flow[ItemState]):
     @start()

docs/en/learn/bring-your-own-agent.mdx

@@ -7,15 +7,13 @@ mode: "wide"
 
 Interoperability is a core concept in CrewAI. This guide will show you how to bring your own agents that work within a Crew.
 
-
 ## Adapter Guide for Bringing your own agents (Langgraph Agents, OpenAI Agents, etc...)
 We require 3 adapters to turn any agent from different frameworks to work within crew.
 
 1. BaseAgentAdapter
 2. BaseToolAdapter
 3. BaseConverter
 
-
 ## BaseAgentAdapter
 This abstract class defines the common interface and functionality that all
 agent adapters must implement. It extends BaseAgent to maintain compatibility
@@ -35,7 +33,7 @@ Here's how you implement your custom adapter:
     ```python
     from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter
     from crewai.tools import BaseTool
-    from typing import List, Optional, Any, Dict
+    from typing import Any
 
     class MyCustomAgentAdapter(BaseAgentAdapter):
         # ... implementation details ...
@@ -45,7 +43,7 @@ Here's how you implement your custom adapter:
     The constructor should call the parent class constructor `super().__init__(**kwargs)` and perform any initialization specific to your external agent. You can use the optional `agent_config` dictionary passed during CrewAI's `Agent` initialization to configure your adapter and the underlying agent.
 
     ```python
-    def __init__(self, agent_config: Optional[Dict[str, Any]] = None, **kwargs: Any):
+    def __init__(self, agent_config: dict[str, Any] | None = None, **kwargs: Any):
         super().__init__(agent_config=agent_config, **kwargs)
         # Initialize your external agent here, possibly using agent_config
         # Example: self.external_agent = initialize_my_agent(agent_config)
@@ -56,7 +54,7 @@ Here's how you implement your custom adapter:
     This abstract method is crucial. It receives a list of CrewAI `BaseTool` instances. Your implementation must convert or adapt these tools into the format expected by your external agent framework. This might involve wrapping them, extracting specific attributes, or registering them with the external agent instance.
 
     ```python
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def configure_tools(self, tools: list[BaseTool] | None = None) -> None:
         if tools:
             adapted_tools = []
             for tool in tools:
@@ -96,7 +94,7 @@ Here's how you implement your custom tool adapter:
     ```python
     from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
     from crewai.tools import BaseTool
-    from typing import List, Any
+    from typing import Any
 
     class MyCustomToolAdapter(BaseToolAdapter):
         # ... implementation details ...
@@ -106,7 +104,7 @@ Here's how you implement your custom tool adapter:
     This is the core abstract method you must implement. It receives a list of CrewAI `BaseTool` instances provided to the agent. Your task is to iterate through this list, adapt each `BaseTool` into the format expected by your external framework, and store the converted tools in the `self.converted_tools` list (which is initialized in the base class constructor).
 
     ```python
-    def configure_tools(self, tools: List[BaseTool]) -> None:
+    def configure_tools(self, tools: list[BaseTool]) -> None:
         """Configure and convert CrewAI tools for the specific implementation."""
         self.converted_tools = [] # Reset in case it's called multiple times
         for tool in tools:
@@ -175,7 +173,7 @@ Here's how you implement your custom tool adapter:
 
     ```python
     # Inside MyCustomAgentAdapter.configure_tools
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
+    def configure_tools(self, tools: list[BaseTool] | None = None) -> None:
         if tools:
             tool_adapter = MyCustomToolAdapter() # Instantiate your tool adapter
             tool_adapter.configure_tools(tools)  # Convert the tools
@@ -328,7 +326,6 @@ We provide out of the box adapters for the following frameworks:
 ```python
 import json
 import os
-from typing import List
 
 from crewai_tools import SerperDevTool
 from src.crewai import Agent, Crew, Task
@@ -368,11 +365,9 @@ reporter_agent = LangGraphAgentAdapter(
     verbose=True,
 )
 
-
 class Code(BaseModel):
     code: str
 
-
 task = Task(
     description="Give an answer to the coding question: {task}",
     expected_output="A thorough answer to the coding question: {task}",
@@ -385,11 +380,9 @@ task2 = Task(
     agent=link_finder_agent,
 )
 
-
 class Report(BaseModel):
     code: str
-    links: List[str]
-
+    links: list[str]
 
 task3 = Task(
     description="Report the results of the tasks.",

docs/en/learn/conditional-tasks.mdx

@@ -13,7 +13,7 @@ This powerful feature enables crews to make decisions and execute tasks selectiv
 ## Example Usage
 
 ```python Code
-from typing import List
+
 from pydantic import BaseModel
 from crewai import Agent, Crew
 from crewai.tasks.conditional_task import ConditionalTask
@@ -50,7 +50,7 @@ summary_generator_agent = Agent(
 )
 
 class EventOutput(BaseModel):
-    events: List[str]
+    events: list[str]
 
 task1 = Task(
     description="Fetch data about events in San Francisco using Serper tool",

docs/en/learn/custom-llm.mdx

@@ -15,11 +15,11 @@ Here's a minimal custom LLM implementation:
 
 ```python
 from crewai import BaseLLM
-from typing import Any, Dict, List, Optional, Union
+from typing import Any
 import requests
 
 class CustomLLM(BaseLLM):
-    def __init__(self, model: str, api_key: str, endpoint: str, temperature: Optional[float] = None):
+    def __init__(self, model: str, api_key: str, endpoint: str, temperature: float | None = None):
         # IMPORTANT: Call super().__init__() with required parameters
         super().__init__(model=model, temperature=temperature)
 
@@ -28,11 +28,11 @@ class CustomLLM(BaseLLM):
 
     def call(
         self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
+        messages: str | list[dict[str, str]],
+        tools: list[dict] | None = None,
+        callbacks: list[Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
+    ) -> str | Any:
         """Call the LLM with the given messages."""
         # Convert string to message format if needed
         if isinstance(messages, str):
@@ -113,7 +113,7 @@ result = crew.kickoff()
 **Critical**: You must call `super().__init__(model, temperature)` with the required parameters:
 
 ```python
-def __init__(self, model: str, api_key: str, temperature: Optional[float] = None):
+def __init__(self, model: str, api_key: str, temperature: float | None = None):
     # REQUIRED: Call parent constructor with model and temperature
     super().__init__(model=model, temperature=temperature)
 
@@ -176,10 +176,9 @@ def call(self, messages, tools=None, callbacks=None, available_functions=None):
 
 ```python
 from crewai import BaseLLM
-from typing import Optional
 
 class CustomAuthLLM(BaseLLM):
-    def __init__(self, model: str, auth_token: str, endpoint: str, temperature: Optional[float] = None):
+    def __init__(self, model: str, auth_token: str, endpoint: str, temperature: float | None = None):
         super().__init__(model=model, temperature=temperature)
         self.auth_token = auth_token
         self.endpoint = endpoint
@@ -293,7 +292,7 @@ def __init__(self, api_key: str):
     super().__init__()
 
 # ✅ Correct
-def __init__(self, model: str, api_key: str, temperature: Optional[float] = None):
+def __init__(self, model: str, api_key: str, temperature: float | None = None):
     super().__init__(model=model, temperature=temperature)
 ```
 

docs/en/observability/braintrust.mdx

@@ -47,7 +47,7 @@ Initialize the Braintrust OpenTelemetry instrumentation to start capturing trace
 
 ```python
 import os
-from typing import Any, Dict
+from typing import Any
 
 from braintrust.otel import BraintrustSpanProcessor
 from crewai import Agent, Crew, Task
@@ -70,7 +70,6 @@ def setup_tracing() -> None:
     CrewAIInstrumentor().instrument(tracer_provider=provider)
     OpenAIInstrumentor().instrument(tracer_provider=provider)
 
-
 setup_tracing()
 ```