Merge branch 'main' into gl/feat/pydantic-refactor-llms

.github/dependabot.yml

@@ -1,11 +0,0 @@
-# To get started with Dependabot version updates, you'll need to specify which 
-# package ecosystems to update and where the package manifests are located.
-# Please see the documentation for all configuration options:
-# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
-
-version: 2
-updates:
-  - package-ecosystem: uv # See documentation for possible values
-    directory: "/" # Location of package manifests
-    schedule:
-      interval: "weekly"

.github/workflows/docs-broken-links.yml

@@ -1,35 +0,0 @@
-name: Check Documentation Broken Links
-
-on:
-  pull_request:
-    paths:
-      - "docs/**"
-      - "docs.json"
-  push:
-    branches:
-      - main
-    paths:
-      - "docs/**"
-      - "docs.json"
-  workflow_dispatch:
-
-jobs:
-  check-links:
-    name: Check broken links
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Node
-        uses: actions/setup-node@v4
-        with:
-          node-version: "latest"
-
-      - name: Install Mintlify CLI
-        run: npm i -g mintlify
-
-      - name: Run broken link checker
-        run: |
-          # Auto-answer the prompt with yes command
-          yes "" | mintlify broken-links || test $? -eq 141
-        working-directory: ./docs

docs/docs.json

@@ -313,10 +313,7 @@
                   "en/learn/multimodal-agents",
                   "en/learn/replay-tasks-from-latest-crew-kickoff",
                   "en/learn/sequential-process",
-                  "en/learn/using-annotations",
-                  "en/learn/execution-hooks",
-                  "en/learn/llm-hooks",
-                  "en/learn/tool-hooks"
+                  "en/learn/using-annotations"
                 ]
               },
               {
@@ -740,10 +737,7 @@
                   "pt-BR/learn/multimodal-agents",
                   "pt-BR/learn/replay-tasks-from-latest-crew-kickoff",
                   "pt-BR/learn/sequential-process",
-                  "pt-BR/learn/using-annotations",
-                  "pt-BR/learn/execution-hooks",
-                  "pt-BR/learn/llm-hooks",
-                  "pt-BR/learn/tool-hooks"
+                  "pt-BR/learn/using-annotations"
                 ]
               },
               {
@@ -1176,10 +1170,7 @@
                   "ko/learn/multimodal-agents",
                   "ko/learn/replay-tasks-from-latest-crew-kickoff",
                   "ko/learn/sequential-process",
-                  "ko/learn/using-annotations",
-                  "ko/learn/execution-hooks",
-                  "ko/learn/llm-hooks",
-                  "ko/learn/tool-hooks"
+                  "ko/learn/using-annotations"
                 ]
               },
               {

docs/en/concepts/knowledge.mdx

@@ -739,7 +739,7 @@ class KnowledgeMonitorListener(BaseEventListener):
 knowledge_monitor = KnowledgeMonitorListener()
 ```
 
-For more information on using events, see the [Event Listeners](/en/concepts/event-listener) documentation.
+For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
 
 ### Custom Knowledge Sources
 

docs/en/concepts/llms.mdx

@@ -1035,7 +1035,7 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     ```
 
     <Tip>
-      [Click here](/en/concepts/event-listener#event-listeners) for more details
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
     </Tip>
   </Tab>
 

docs/en/concepts/tasks.mdx

@@ -60,7 +60,6 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
-| **Guardrails** _(optional)_       | `guardrails`       | `Optional[List[Callable] | List[str]]` | List of guardrails to validate task output before proceeding to next task.                                      |
 | **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
 <Note type="warning" title="Deprecated: max_retries">
@@ -224,7 +223,6 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
 | **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
 | **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
-| **Messages**      | `messages`      | `list[LLMMessage]`         | The messages from the last task execution.                                                           |
 
 ### Task Methods and Properties
 
@@ -343,11 +341,7 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-CrewAI supports two types of guardrails:
-
-1. **Function-based guardrails**: Python functions with custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
-
-2. **LLM-based guardrails**: String descriptions that use the agent's LLM to validate outputs based on natural language criteria. These are ideal for complex or subjective validation requirements.
+Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
 
 ### Function-Based Guardrails
 
@@ -361,12 +355,12 @@ def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate blog content meets requirements."""
     try:
         # Check word count
-        word_count = len(result.raw.split())
+        word_count = len(result.split())
         if word_count > 200:
             return (False, "Blog content exceeds 200 words")
 
         # Additional validation logic here
-        return (True, result.raw.strip())
+        return (True, result.strip())
     except Exception as e:
         return (False, "Unexpected error during validation")
 
@@ -378,147 +372,6 @@ blog_task = Task(
 )
 ```
 
-### LLM-Based Guardrails (String Descriptions)
-
-Instead of writing custom validation functions, you can use string descriptions that leverage LLM-based validation. When you provide a string to the `guardrail` or `guardrails` parameter, CrewAI automatically creates an `LLMGuardrail` that uses the agent's LLM to validate the output based on your description.
-
-**Requirements**:
-- The task must have an `agent` assigned (the guardrail uses the agent's LLM)
-- Provide a clear, descriptive string explaining the validation criteria
-
-```python Code
-from crewai import Task
-
-# Single LLM-based guardrail
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A blog post under 200 words",
-    agent=blog_agent,
-    guardrail="The blog post must be under 200 words and contain no technical jargon"
-)
-```
-
-LLM-based guardrails are particularly useful for:
-- **Complex validation logic** that's difficult to express programmatically
-- **Subjective criteria** like tone, style, or quality assessments
-- **Natural language requirements** that are easier to describe than code
-
-The LLM guardrail will:
-1. Analyze the task output against your description
-2. Return `(True, output)` if the output complies with the criteria
-3. Return `(False, feedback)` with specific feedback if validation fails
-
-**Example with detailed validation criteria**:
-
-```python Code
-research_task = Task(
-    description="Research the latest developments in quantum computing",
-    expected_output="A comprehensive research report",
-    agent=researcher_agent,
-    guardrail="""
-    The research report must:
-    - Be at least 1000 words long
-    - Include at least 5 credible sources
-    - Cover both technical and practical applications
-    - Be written in a professional, academic tone
-    - Avoid speculation or unverified claims
-    """
-)
-```
-
-### Multiple Guardrails
-
-You can apply multiple guardrails to a task using the `guardrails` parameter. Multiple guardrails are executed sequentially, with each guardrail receiving the output from the previous one. This allows you to chain validation and transformation steps.
-
-The `guardrails` parameter accepts:
-- A list of guardrail functions or string descriptions
-- A single guardrail function or string (same as `guardrail`)
-
-**Note**: If `guardrails` is provided, it takes precedence over `guardrail`. The `guardrail` parameter will be ignored when `guardrails` is set.
-
-```python Code
-from typing import Tuple, Any
-from crewai import TaskOutput, Task
-
-def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
-    """Validate word count is within limits."""
-    word_count = len(result.raw.split())
-    if word_count < 100:
-        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
-    if word_count > 500:
-        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
-    return (True, result.raw)
-
-def validate_no_profanity(result: TaskOutput) -> Tuple[bool, Any]:
-    """Check for inappropriate language."""
-    profanity_words = ["badword1", "badword2"]  # Example list
-    content_lower = result.raw.lower()
-    for word in profanity_words:
-        if word in content_lower:
-            return (False, f"Inappropriate language detected: {word}")
-    return (True, result.raw)
-
-def format_output(result: TaskOutput) -> Tuple[bool, Any]:
-    """Format and clean the output."""
-    formatted = result.raw.strip()
-    # Capitalize first letter
-    formatted = formatted[0].upper() + formatted[1:] if formatted else formatted
-    return (True, formatted)
-
-# Apply multiple guardrails sequentially
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A well-formatted blog post between 100-500 words",
-    agent=blog_agent,
-    guardrails=[
-        validate_word_count,      # First: validate length
-        validate_no_profanity,    # Second: check content
-        format_output             # Third: format the result
-    ],
-    guardrail_max_retries=3
-)
-```
-
-In this example, the guardrails execute in order:
-1. `validate_word_count` checks the word count
-2. `validate_no_profanity` checks for inappropriate language (using the output from step 1)
-3. `format_output` formats the final result (using the output from step 2)
-
-If any guardrail fails, the error is sent back to the agent, and the task is retried up to `guardrail_max_retries` times.
-
-**Mixing function-based and LLM-based guardrails**:
-
-You can combine both function-based and string-based guardrails in the same list:
-
-```python Code
-from typing import Tuple, Any
-from crewai import TaskOutput, Task
-
-def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
-    """Validate word count is within limits."""
-    word_count = len(result.raw.split())
-    if word_count < 100:
-        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
-    if word_count > 500:
-        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
-    return (True, result.raw)
-
-# Mix function-based and LLM-based guardrails
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A well-formatted blog post between 100-500 words",
-    agent=blog_agent,
-    guardrails=[
-        validate_word_count,  # Function-based: precise word count check
-        "The content must be engaging and suitable for a general audience",  # LLM-based: subjective quality check
-        "The writing style should be clear, concise, and free of technical jargon"  # LLM-based: style validation
-    ],
-    guardrail_max_retries=3
-)
-```
-
-This approach combines the precision of programmatic validation with the flexibility of LLM-based assessment for subjective criteria.
-
 ### Guardrail Function Requirements
 
 1. **Function Signature**:

docs/en/enterprise/features/marketplace.mdx

@@ -37,7 +37,7 @@ you can use them locally or refine them to your needs.
   <Card title="Tools & Integrations" href="/en/enterprise/features/tools-and-integrations" icon="wrench">
     Connect external apps and manage internal tools your agents can use.
   </Card>
-  <Card title="Tool Repository" href="/en/enterprise/guides/tool-repository#tool-repository" icon="toolbox">
+  <Card title="Tool Repository" href="/en/enterprise/features/tool-repository" icon="toolbox">
     Publish and install tools to enhance your crews' capabilities.
   </Card>
   <Card title="Agents Repository" href="/en/enterprise/features/agent-repositories" icon="people-group">

docs/en/enterprise/features/tools-and-integrations.mdx

@@ -241,7 +241,7 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Tool Repository" href="/en/enterprise/guides/tool-repository#tool-repository" icon="toolbox">
+  <Card title="Tool Repository" href="/en/enterprise/features/tool-repository" icon="toolbox">
     Create, publish, and version custom tools for your organization.
   </Card>
   <Card title="Webhook Automation" href="/en/enterprise/guides/webhook-automation" icon="bolt">

docs/en/enterprise/guides/tool-repository.mdx

@@ -21,7 +21,7 @@ The repository is not a version control system. Use Git to track code changes an
 Before using the Tool Repository, ensure you have:
 
 - A [CrewAI AMP](https://app.crewai.com) account
-- [CrewAI CLI](/en/concepts/cli#cli) installed
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
 - uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
 - [Git](https://git-scm.com) installed and configured
 - Access permissions to publish or install tools in your CrewAI AMP organization
@@ -112,7 +112,7 @@ By default, tools are published as private. To make a tool public:
 crewai tool publish --public
 ```
 
-For more details on how to build tools, see [Creating your own tools](/en/concepts/tools#creating-your-own-tools).
+For more details on how to build tools, see [Creating your own tools](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
 
 ## Updating Tools
 

docs/en/enterprise/resources/frequently-asked-questions.mdx

@@ -49,7 +49,7 @@ mode: "wide"
 
         To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
 
-        For detailed implementation guidance, see our [Human-in-the-Loop guide](/en/enterprise/guides/human-in-the-loop).
+        For detailed implementation guidance, see our [Human-in-the-Loop guide](/en/how-to/human-in-the-loop).
     </Accordion>
 
     <Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
@@ -142,7 +142,7 @@ mode: "wide"
     <Accordion title="How can I create custom tools for my CrewAI agents?">
         You can create custom tools by subclassing the `BaseTool` class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from `BaseTool`, specifying the name, description, and the `_run` method for operational logic. The tool decorator allows you to create a `Tool` object directly with the required attributes and a functional logic.
 
-        <Card href="/en/learn/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
+        <Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
     </Accordion>
 
     <Accordion title="How can you control the maximum number of requests per minute that the entire crew can perform?">

docs/en/learn/a2a-agent-delegation.mdx

@@ -83,10 +83,6 @@ The `A2AConfig` class accepts the following parameters:
   Whether to raise an error immediately if agent connection fails. When `False`, the agent continues with available agents and informs the LLM about unavailable ones.
 </ParamField>
 
-<ParamField path="trust_remote_completion_status" type="bool" default="False">
-  When `True`, returns the A2A agent's result directly when it signals completion. When `False`, allows the server agent to review the result and potentially continue the conversation.
-</ParamField>
-
 ## Authentication
 
 For A2A agents that require authentication, use one of the provided auth schemes:

docs/en/learn/execution-hooks.mdx

@@ -1,522 +0,0 @@
----
-title: Execution Hooks Overview
-description: Understanding and using execution hooks in CrewAI for fine-grained control over agent operations
-mode: "wide"
----
-
-Execution Hooks provide fine-grained control over the runtime behavior of your CrewAI agents. Unlike kickoff hooks that run before and after crew execution, execution hooks intercept specific operations during agent execution, allowing you to modify behavior, implement safety checks, and add comprehensive monitoring.
-
-## Types of Execution Hooks
-
-CrewAI provides two main categories of execution hooks:
-
-### 1. [LLM Call Hooks](/learn/llm-hooks)
-
-Control and monitor language model interactions:
-- **Before LLM Call**: Modify prompts, validate inputs, implement approval gates
-- **After LLM Call**: Transform responses, sanitize outputs, update conversation history
-
-**Use Cases:**
-- Iteration limiting
-- Cost tracking and token usage monitoring
-- Response sanitization and content filtering
-- Human-in-the-loop approval for LLM calls
-- Adding safety guidelines or context
-- Debug logging and request/response inspection
-
-[View LLM Hooks Documentation →](/learn/llm-hooks)
-
-### 2. [Tool Call Hooks](/learn/tool-hooks)
-
-Control and monitor tool execution:
-- **Before Tool Call**: Modify inputs, validate parameters, block dangerous operations
-- **After Tool Call**: Transform results, sanitize outputs, log execution details
-
-**Use Cases:**
-- Safety guardrails for destructive operations
-- Human approval for sensitive actions
-- Input validation and sanitization
-- Result caching and rate limiting
-- Tool usage analytics
-- Debug logging and monitoring
-
-[View Tool Hooks Documentation →](/learn/tool-hooks)
-
-## Hook Registration Methods
-
-### 1. Decorator-Based Hooks (Recommended)
-
-The cleanest and most Pythonic way to register hooks:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call, before_tool_call, after_tool_call
-
-@before_llm_call
-def limit_iterations(context):
-    """Prevent infinite loops by limiting iterations."""
-    if context.iterations > 10:
-        return False  # Block execution
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    """Remove sensitive data from LLM responses."""
-    if "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[REDACTED]")
-    return None
-
-@before_tool_call
-def block_dangerous_tools(context):
-    """Block destructive operations."""
-    if context.tool_name == "delete_database":
-        return False  # Block execution
-    return None
-
-@after_tool_call
-def log_tool_result(context):
-    """Log tool execution."""
-    print(f"Tool {context.tool_name} completed")
-    return None
-```
-
-### 2. Crew-Scoped Hooks
-
-Apply hooks only to specific crew instances:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_llm_call_crew, after_tool_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # Only applies to this crew
-        print(f"LLM call in {self.__class__.__name__}")
-        return None
-
-    @after_tool_call_crew
-    def log_results(self, context):
-        # Crew-specific logging
-        print(f"Tool result: {context.tool_result[:50]}...")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential
-        )
-```
-
-## Hook Execution Flow
-
-### LLM Call Flow
-
-```
-Agent needs to call LLM
-    ↓
-[Before LLM Call Hooks Execute]
-    ├→ Hook 1: Validate iteration count
-    ├→ Hook 2: Add safety context
-    └→ Hook 3: Log request
-    ↓
-If any hook returns False:
-    ├→ Block LLM call
-    └→ Raise ValueError
-    ↓
-If all hooks return True/None:
-    ├→ LLM call proceeds
-    └→ Response generated
-    ↓
-[After LLM Call Hooks Execute]
-    ├→ Hook 1: Sanitize response
-    ├→ Hook 2: Log response
-    └→ Hook 3: Update metrics
-    ↓
-Final response returned
-```
-
-### Tool Call Flow
-
-```
-Agent needs to execute tool
-    ↓
-[Before Tool Call Hooks Execute]
-    ├→ Hook 1: Check if tool is allowed
-    ├→ Hook 2: Validate inputs
-    └→ Hook 3: Request approval if needed
-    ↓
-If any hook returns False:
-    ├→ Block tool execution
-    └→ Return error message
-    ↓
-If all hooks return True/None:
-    ├→ Tool execution proceeds
-    └→ Result generated
-    ↓
-[After Tool Call Hooks Execute]
-    ├→ Hook 1: Sanitize result
-    ├→ Hook 2: Cache result
-    └→ Hook 3: Log metrics
-    ↓
-Final result returned
-```
-
-## Hook Context Objects
-
-### LLMCallHookContext
-
-Provides access to LLM execution state:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # Full executor access
-    messages: list               # Mutable message list
-    agent: Agent                 # Current agent
-    task: Task                   # Current task
-    crew: Crew                   # Crew instance
-    llm: BaseLLM                 # LLM instance
-    iterations: int              # Current iteration
-    response: str | None         # LLM response (after hooks)
-```
-
-### ToolCallHookContext
-
-Provides access to tool execution state:
-
-```python
-class ToolCallHookContext:
-    tool_name: str               # Tool being called
-    tool_input: dict             # Mutable input parameters
-    tool: CrewStructuredTool     # Tool instance
-    agent: Agent | None          # Agent executing
-    task: Task | None            # Current task
-    crew: Crew | None            # Crew instance
-    tool_result: str | None      # Tool result (after hooks)
-```
-
-## Common Patterns
-
-### Safety and Validation
-
-```python
-@before_tool_call
-def safety_check(context):
-    """Block destructive operations."""
-    dangerous = ['delete_file', 'drop_table', 'system_shutdown']
-    if context.tool_name in dangerous:
-        print(f"🛑 Blocked: {context.tool_name}")
-        return False
-    return None
-
-@before_llm_call
-def iteration_limit(context):
-    """Prevent infinite loops."""
-    if context.iterations > 15:
-        print("⛔ Maximum iterations exceeded")
-        return False
-    return None
-```
-
-### Human-in-the-Loop
-
-```python
-@before_tool_call
-def require_approval(context):
-    """Require approval for sensitive operations."""
-    sensitive = ['send_email', 'make_payment', 'post_message']
-
-    if context.tool_name in sensitive:
-        response = context.request_human_input(
-            prompt=f"Approve {context.tool_name}?",
-            default_message="Type 'yes' to approve:"
-        )
-
-        if response.lower() != 'yes':
-            return False
-
-    return None
-```
-
-### Monitoring and Analytics
-
-```python
-from collections import defaultdict
-import time
-
-metrics = defaultdict(lambda: {'count': 0, 'total_time': 0})
-
-@before_tool_call
-def start_timer(context):
-    context.tool_input['_start'] = time.time()
-    return None
-
-@after_tool_call
-def track_metrics(context):
-    start = context.tool_input.get('_start', time.time())
-    duration = time.time() - start
-
-    metrics[context.tool_name]['count'] += 1
-    metrics[context.tool_name]['total_time'] += duration
-
-    return None
-
-# View metrics
-def print_metrics():
-    for tool, data in metrics.items():
-        avg = data['total_time'] / data['count']
-        print(f"{tool}: {data['count']} calls, {avg:.2f}s avg")
-```
-
-### Response Sanitization
-
-```python
-import re
-
-@after_llm_call
-def sanitize_llm_response(context):
-    """Remove sensitive data from LLM responses."""
-    if not context.response:
-        return None
-
-    result = context.response
-    result = re.sub(r'(api[_-]?key)["\']?\s*[:=]\s*["\']?[\w-]+',
-                   r'\1: [REDACTED]', result, flags=re.IGNORECASE)
-    return result
-
-@after_tool_call
-def sanitize_tool_result(context):
-    """Remove sensitive data from tool results."""
-    if not context.tool_result:
-        return None
-
-    result = context.tool_result
-    result = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
-                   '[EMAIL-REDACTED]', result)
-    return result
-```
-
-## Hook Management
-
-### Clearing All Hooks
-
-```python
-from crewai.hooks import clear_all_global_hooks
-
-# Clear all hooks at once
-result = clear_all_global_hooks()
-print(f"Cleared {result['total']} hooks")
-# Output: {'llm_hooks': (2, 1), 'tool_hooks': (1, 2), 'total': (3, 3)}
-```
-
-### Clearing Specific Hook Types
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks
-)
-
-# Clear specific types
-llm_before_count = clear_before_llm_call_hooks()
-tool_after_count = clear_after_tool_call_hooks()
-```
-
-### Unregistering Individual Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_llm_call_hook,
-    unregister_after_tool_call_hook
-)
-
-def my_hook(context):
-    ...
-
-# Register
-register_before_llm_call_hook(my_hook)
-
-# Later, unregister
-success = unregister_before_llm_call_hook(my_hook)
-print(f"Unregistered: {success}")
-```
-
-## Best Practices
-
-### 1. Keep Hooks Focused
-Each hook should have a single, clear responsibility:
-
-```python
-# ✅ Good - focused responsibility
-@before_tool_call
-def validate_file_path(context):
-    if context.tool_name == 'read_file':
-        if '..' in context.tool_input.get('path', ''):
-            return False
-    return None
-
-# ❌ Bad - too many responsibilities
-@before_tool_call
-def do_everything(context):
-    # Validation + logging + metrics + approval...
-    ...
-```
-
-### 2. Handle Errors Gracefully
-
-```python
-@before_llm_call
-def safe_hook(context):
-    try:
-        # Your logic
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"Hook error: {e}")
-        return None  # Allow execution despite error
-```
-
-### 3. Modify Context In-Place
-
-```python
-# ✅ Correct - modify in-place
-@before_llm_call
-def add_context(context):
-    context.messages.append({"role": "system", "content": "Be concise"})
-
-# ❌ Wrong - replaces reference
-@before_llm_call
-def wrong_approach(context):
-    context.messages = [{"role": "system", "content": "Be concise"}]
-```
-
-### 4. Use Type Hints
-
-```python
-from crewai.hooks import LLMCallHookContext, ToolCallHookContext
-
-def my_llm_hook(context: LLMCallHookContext) -> bool | None:
-    # IDE autocomplete and type checking
-    return None
-
-def my_tool_hook(context: ToolCallHookContext) -> str | None:
-    return None
-```
-
-### 5. Clean Up in Tests
-
-```python
-import pytest
-from crewai.hooks import clear_all_global_hooks
-
-@pytest.fixture(autouse=True)
-def clean_hooks():
-    """Reset hooks before each test."""
-    yield
-    clear_all_global_hooks()
-```
-
-## When to Use Which Hook
-
-### Use LLM Hooks When:
-- Implementing iteration limits
-- Adding context or safety guidelines to prompts
-- Tracking token usage and costs
-- Sanitizing or transforming responses
-- Implementing approval gates for LLM calls
-- Debugging prompt/response interactions
-
-### Use Tool Hooks When:
-- Blocking dangerous or destructive operations
-- Validating tool inputs before execution
-- Implementing approval gates for sensitive actions
-- Caching tool results
-- Tracking tool usage and performance
-- Sanitizing tool outputs
-- Rate limiting tool calls
-
-### Use Both When:
-Building comprehensive observability, safety, or approval systems that need to monitor all agent operations.
-
-## Alternative Registration Methods
-
-### Programmatic Registration (Advanced)
-
-For dynamic hook registration or when you need to register hooks programmatically:
-
-```python
-from crewai.hooks import (
-    register_before_llm_call_hook,
-    register_after_tool_call_hook
-)
-
-def my_hook(context):
-    return None
-
-# Register programmatically
-register_before_llm_call_hook(my_hook)
-
-# Useful for:
-# - Loading hooks from configuration
-# - Conditional hook registration
-# - Plugin systems
-```
-
-**Note:** For most use cases, decorators are cleaner and more maintainable.
-
-## Performance Considerations
-
-1. **Keep Hooks Fast**: Hooks execute on every call - avoid heavy computation
-2. **Cache When Possible**: Store expensive validations or lookups
-3. **Be Selective**: Use crew-scoped hooks when global hooks aren't needed
-4. **Monitor Hook Overhead**: Profile hook execution time in production
-5. **Lazy Import**: Import heavy dependencies only when needed
-
-## Debugging Hooks
-
-### Enable Debug Logging
-
-```python
-import logging
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-@before_llm_call
-def debug_hook(context):
-    logger.debug(f"LLM call: {context.agent.role}, iteration {context.iterations}")
-    return None
-```
-
-### Hook Execution Order
-
-Hooks execute in registration order. If a before hook returns `False`, subsequent hooks don't execute:
-
-```python
-# Register order matters!
-register_before_tool_call_hook(hook1)  # Executes first
-register_before_tool_call_hook(hook2)  # Executes second
-register_before_tool_call_hook(hook3)  # Executes third
-
-# If hook2 returns False:
-# - hook1 executed
-# - hook2 executed and returned False
-# - hook3 NOT executed
-# - Tool call blocked
-```
-
-## Related Documentation
-
-- [LLM Call Hooks →](/learn/llm-hooks) - Detailed LLM hook documentation
-- [Tool Call Hooks →](/learn/tool-hooks) - Detailed tool hook documentation
-- [Before and After Kickoff Hooks →](/learn/before-and-after-kickoff-hooks) - Crew lifecycle hooks
-- [Human-in-the-Loop →](/learn/human-in-the-loop) - Human input patterns
-
-## Conclusion
-
-Execution hooks provide powerful control over agent runtime behavior. Use them to implement safety guardrails, approval workflows, comprehensive monitoring, and custom business logic. Combined with proper error handling, type safety, and performance considerations, hooks enable production-ready, secure, and observable agent systems.

docs/en/learn/hierarchical-process.mdx

@@ -97,7 +97,7 @@ project_crew = Crew(
 ```
 
 <Tip>
-    For more details on creating and customizing a manager agent, check out the [Custom Manager Agent documentation](/en/learn/custom-manager-agent).
+    For more details on creating and customizing a manager agent, check out the [Custom Manager Agent documentation](https://docs.crewai.com/how-to/custom-manager-agent#custom-manager-agent).
 </Tip>
 
 

docs/en/learn/llm-hooks.mdx

@@ -1,427 +0,0 @@
----
-title: LLM Call Hooks
-description: Learn how to use LLM call hooks to intercept, modify, and control language model interactions in CrewAI
-mode: "wide"
----
-
-LLM Call Hooks provide fine-grained control over language model interactions during agent execution. These hooks allow you to intercept LLM calls, modify prompts, transform responses, implement approval gates, and add custom logging or monitoring.
-
-## Overview
-
-LLM hooks are executed at two critical points:
-- **Before LLM Call**: Modify messages, validate inputs, or block execution
-- **After LLM Call**: Transform responses, sanitize outputs, or modify conversation history
-
-## Hook Types
-
-### Before LLM Call Hooks
-
-Executed before every LLM call, these hooks can:
-- Inspect and modify messages sent to the LLM
-- Block LLM execution based on conditions
-- Implement rate limiting or approval gates
-- Add context or system messages
-- Log request details
-
-**Signature:**
-```python
-def before_hook(context: LLMCallHookContext) -> bool | None:
-    # Return False to block execution
-    # Return True or None to allow execution
-    ...
-```
-
-### After LLM Call Hooks
-
-Executed after every LLM call, these hooks can:
-- Modify or sanitize LLM responses
-- Add metadata or formatting
-- Log response details
-- Update conversation history
-- Implement content filtering
-
-**Signature:**
-```python
-def after_hook(context: LLMCallHookContext) -> str | None:
-    # Return modified response string
-    # Return None to keep original response
-    ...
-```
-
-## LLM Hook Context
-
-The `LLMCallHookContext` object provides comprehensive access to execution state:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # Full executor reference
-    messages: list               # Mutable message list
-    agent: Agent                 # Current agent
-    task: Task                   # Current task
-    crew: Crew                   # Crew instance
-    llm: BaseLLM                 # LLM instance
-    iterations: int              # Current iteration count
-    response: str | None         # LLM response (after hooks only)
-```
-
-### Modifying Messages
-
-**Important:** Always modify messages in-place:
-
-```python
-# ✅ Correct - modify in-place
-def add_context(context: LLMCallHookContext) -> None:
-    context.messages.append({"role": "system", "content": "Be concise"})
-
-# ❌ Wrong - replaces list reference
-def wrong_approach(context: LLMCallHookContext) -> None:
-    context.messages = [{"role": "system", "content": "Be concise"}]
-```
-
-## Registration Methods
-
-### 1. Global Hook Registration
-
-Register hooks that apply to all LLM calls across all crews:
-
-```python
-from crewai.hooks import register_before_llm_call_hook, register_after_llm_call_hook
-
-def log_llm_call(context):
-    print(f"LLM call by {context.agent.role} at iteration {context.iterations}")
-    return None  # Allow execution
-
-register_before_llm_call_hook(log_llm_call)
-```
-
-### 2. Decorator-Based Registration
-
-Use decorators for cleaner syntax:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call
-
-@before_llm_call
-def validate_iteration_count(context):
-    if context.iterations > 10:
-        print("⚠️ Exceeded maximum iterations")
-        return False  # Block execution
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    if context.response and "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[REDACTED]")
-    return None
-```
-
-### 3. Crew-Scoped Hooks
-
-Register hooks for a specific crew instance:
-
-```python
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # Only applies to this crew
-        if context.iterations == 0:
-            print(f"Starting task: {context.task.description}")
-        return None
-
-    @after_llm_call_crew
-    def log_responses(self, context):
-        # Crew-specific response logging
-        print(f"Response length: {len(context.response)}")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## Common Use Cases
-
-### 1. Iteration Limiting
-
-```python
-@before_llm_call
-def limit_iterations(context: LLMCallHookContext) -> bool | None:
-    max_iterations = 15
-    if context.iterations > max_iterations:
-        print(f"⛔ Blocked: Exceeded {max_iterations} iterations")
-        return False  # Block execution
-    return None
-```
-
-### 2. Human Approval Gate
-
-```python
-@before_llm_call
-def require_approval(context: LLMCallHookContext) -> bool | None:
-    if context.iterations > 5:
-        response = context.request_human_input(
-            prompt=f"Iteration {context.iterations}: Approve LLM call?",
-            default_message="Press Enter to approve, or type 'no' to block:"
-        )
-        if response.lower() == "no":
-            print("🚫 LLM call blocked by user")
-            return False
-    return None
-```
-
-### 3. Adding System Context
-
-```python
-@before_llm_call
-def add_guardrails(context: LLMCallHookContext) -> None:
-    # Add safety guidelines to every LLM call
-    context.messages.append({
-        "role": "system",
-        "content": "Ensure responses are factual and cite sources when possible."
-    })
-    return None
-```
-
-### 4. Response Sanitization
-
-```python
-@after_llm_call
-def sanitize_sensitive_data(context: LLMCallHookContext) -> str | None:
-    if not context.response:
-        return None
-
-    # Remove sensitive patterns
-    import re
-    sanitized = context.response
-    sanitized = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[SSN-REDACTED]', sanitized)
-    sanitized = re.sub(r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b', '[CARD-REDACTED]', sanitized)
-
-    return sanitized
-```
-
-### 5. Cost Tracking
-
-```python
-import tiktoken
-
-@before_llm_call
-def track_token_usage(context: LLMCallHookContext) -> None:
-    encoding = tiktoken.get_encoding("cl100k_base")
-    total_tokens = sum(
-        len(encoding.encode(msg.get("content", "")))
-        for msg in context.messages
-    )
-    print(f"📊 Input tokens: ~{total_tokens}")
-    return None
-
-@after_llm_call
-def track_response_tokens(context: LLMCallHookContext) -> None:
-    if context.response:
-        encoding = tiktoken.get_encoding("cl100k_base")
-        tokens = len(encoding.encode(context.response))
-        print(f"📊 Response tokens: ~{tokens}")
-    return None
-```
-
-### 6. Debug Logging
-
-```python
-@before_llm_call
-def debug_request(context: LLMCallHookContext) -> None:
-    print(f"""
-    🔍 LLM Call Debug:
-    - Agent: {context.agent.role}
-    - Task: {context.task.description[:50]}...
-    - Iteration: {context.iterations}
-    - Message Count: {len(context.messages)}
-    - Last Message: {context.messages[-1] if context.messages else 'None'}
-    """)
-    return None
-
-@after_llm_call
-def debug_response(context: LLMCallHookContext) -> None:
-    if context.response:
-        print(f"✅ Response Preview: {context.response[:100]}...")
-    return None
-```
-
-## Hook Management
-
-### Unregistering Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_llm_call_hook,
-    unregister_after_llm_call_hook
-)
-
-# Unregister specific hook
-def my_hook(context):
-    ...
-
-register_before_llm_call_hook(my_hook)
-# Later...
-unregister_before_llm_call_hook(my_hook)  # Returns True if found
-```
-
-### Clearing Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_all_llm_call_hooks
-)
-
-# Clear specific hook type
-count = clear_before_llm_call_hooks()
-print(f"Cleared {count} before hooks")
-
-# Clear all LLM hooks
-before_count, after_count = clear_all_llm_call_hooks()
-print(f"Cleared {before_count} before and {after_count} after hooks")
-```
-
-### Listing Registered Hooks
-
-```python
-from crewai.hooks import (
-    get_before_llm_call_hooks,
-    get_after_llm_call_hooks
-)
-
-# Get current hooks
-before_hooks = get_before_llm_call_hooks()
-after_hooks = get_after_llm_call_hooks()
-
-print(f"Registered: {len(before_hooks)} before, {len(after_hooks)} after")
-```
-
-## Advanced Patterns
-
-### Conditional Hook Execution
-
-```python
-@before_llm_call
-def conditional_blocking(context: LLMCallHookContext) -> bool | None:
-    # Only block for specific agents
-    if context.agent.role == "researcher" and context.iterations > 10:
-        return False
-
-    # Only block for specific tasks
-    if "sensitive" in context.task.description.lower() and context.iterations > 5:
-        return False
-
-    return None
-```
-
-### Context-Aware Modifications
-
-```python
-@before_llm_call
-def adaptive_prompting(context: LLMCallHookContext) -> None:
-    # Add different context based on iteration
-    if context.iterations == 0:
-        context.messages.append({
-            "role": "system",
-            "content": "Start with a high-level overview."
-        })
-    elif context.iterations > 3:
-        context.messages.append({
-            "role": "system",
-            "content": "Focus on specific details and provide examples."
-        })
-    return None
-```
-
-### Chaining Hooks
-
-```python
-# Multiple hooks execute in registration order
-
-@before_llm_call
-def first_hook(context):
-    print("1. First hook executed")
-    return None
-
-@before_llm_call
-def second_hook(context):
-    print("2. Second hook executed")
-    return None
-
-@before_llm_call
-def blocking_hook(context):
-    if context.iterations > 10:
-        print("3. Blocking hook - execution stopped")
-        return False  # Subsequent hooks won't execute
-    print("3. Blocking hook - execution allowed")
-    return None
-```
-
-## Best Practices
-
-1. **Keep Hooks Focused**: Each hook should have a single responsibility
-2. **Avoid Heavy Computation**: Hooks execute on every LLM call
-3. **Handle Errors Gracefully**: Use try-except to prevent hook failures from breaking execution
-4. **Use Type Hints**: Leverage `LLMCallHookContext` for better IDE support
-5. **Document Hook Behavior**: Especially for blocking conditions
-6. **Test Hooks Independently**: Unit test hooks before using in production
-7. **Clear Hooks in Tests**: Use `clear_all_llm_call_hooks()` between test runs
-8. **Modify In-Place**: Always modify `context.messages` in-place, never replace
-
-## Error Handling
-
-```python
-@before_llm_call
-def safe_hook(context: LLMCallHookContext) -> bool | None:
-    try:
-        # Your hook logic
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"⚠️ Hook error: {e}")
-        # Decide: allow or block on error
-        return None  # Allow execution despite error
-```
-
-## Type Safety
-
-```python
-from crewai.hooks import LLMCallHookContext, BeforeLLMCallHookType, AfterLLMCallHookType
-
-# Explicit type annotations
-def my_before_hook(context: LLMCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: LLMCallHookContext) -> str | None:
-    return None
-
-# Type-safe registration
-register_before_llm_call_hook(my_before_hook)
-register_after_llm_call_hook(my_after_hook)
-```
-
-## Troubleshooting
-
-### Hook Not Executing
-- Verify hook is registered before crew execution
-- Check if previous hook returned `False` (blocks subsequent hooks)
-- Ensure hook signature matches expected type
-
-### Message Modifications Not Persisting
-- Use in-place modifications: `context.messages.append()`
-- Don't replace the list: `context.messages = []`
-
-### Response Modifications Not Working
-- Return the modified string from after hooks
-- Returning `None` keeps the original response
-
-## Conclusion
-
-LLM Call Hooks provide powerful capabilities for controlling and monitoring language model interactions in CrewAI. Use them to implement safety guardrails, approval gates, logging, cost tracking, and response sanitization. Combined with proper error handling and type safety, hooks enable robust and production-ready agent systems.

docs/en/learn/tool-hooks.mdx

@@ -1,600 +0,0 @@
----
-title: Tool Call Hooks
-description: Learn how to use tool call hooks to intercept, modify, and control tool execution in CrewAI
-mode: "wide"
----
-
-Tool Call Hooks provide fine-grained control over tool execution during agent operations. These hooks allow you to intercept tool calls, modify inputs, transform outputs, implement safety checks, and add comprehensive logging or monitoring.
-
-## Overview
-
-Tool hooks are executed at two critical points:
-- **Before Tool Call**: Modify inputs, validate parameters, or block execution
-- **After Tool Call**: Transform results, sanitize outputs, or log execution details
-
-## Hook Types
-
-### Before Tool Call Hooks
-
-Executed before every tool execution, these hooks can:
-- Inspect and modify tool inputs
-- Block tool execution based on conditions
-- Implement approval gates for dangerous operations
-- Validate parameters
-- Log tool invocations
-
-**Signature:**
-```python
-def before_hook(context: ToolCallHookContext) -> bool | None:
-    # Return False to block execution
-    # Return True or None to allow execution
-    ...
-```
-
-### After Tool Call Hooks
-
-Executed after every tool execution, these hooks can:
-- Modify or sanitize tool results
-- Add metadata or formatting
-- Log execution results
-- Implement result validation
-- Transform output formats
-
-**Signature:**
-```python
-def after_hook(context: ToolCallHookContext) -> str | None:
-    # Return modified result string
-    # Return None to keep original result
-    ...
-```
-
-## Tool Hook Context
-
-The `ToolCallHookContext` object provides comprehensive access to tool execution state:
-
-```python
-class ToolCallHookContext:
-    tool_name: str                    # Name of the tool being called
-    tool_input: dict[str, Any]        # Mutable tool input parameters
-    tool: CrewStructuredTool          # Tool instance reference
-    agent: Agent | BaseAgent | None   # Agent executing the tool
-    task: Task | None                 # Current task
-    crew: Crew | None                 # Crew instance
-    tool_result: str | None           # Tool result (after hooks only)
-```
-
-### Modifying Tool Inputs
-
-**Important:** Always modify tool inputs in-place:
-
-```python
-# ✅ Correct - modify in-place
-def sanitize_input(context: ToolCallHookContext) -> None:
-    context.tool_input['query'] = context.tool_input['query'].lower()
-
-# ❌ Wrong - replaces dict reference
-def wrong_approach(context: ToolCallHookContext) -> None:
-    context.tool_input = {'query': 'new query'}
-```
-
-## Registration Methods
-
-### 1. Global Hook Registration
-
-Register hooks that apply to all tool calls across all crews:
-
-```python
-from crewai.hooks import register_before_tool_call_hook, register_after_tool_call_hook
-
-def log_tool_call(context):
-    print(f"Tool: {context.tool_name}")
-    print(f"Input: {context.tool_input}")
-    return None  # Allow execution
-
-register_before_tool_call_hook(log_tool_call)
-```
-
-### 2. Decorator-Based Registration
-
-Use decorators for cleaner syntax:
-
-```python
-from crewai.hooks import before_tool_call, after_tool_call
-
-@before_tool_call
-def block_dangerous_tools(context):
-    dangerous_tools = ['delete_database', 'drop_table', 'rm_rf']
-    if context.tool_name in dangerous_tools:
-        print(f"⛔ Blocked dangerous tool: {context.tool_name}")
-        return False  # Block execution
-    return None
-
-@after_tool_call
-def sanitize_results(context):
-    if context.tool_result and "password" in context.tool_result.lower():
-        return context.tool_result.replace("password", "[REDACTED]")
-    return None
-```
-
-### 3. Crew-Scoped Hooks
-
-Register hooks for a specific crew instance:
-
-```python
-@CrewBase
-class MyProjCrew:
-    @before_tool_call_crew
-    def validate_tool_inputs(self, context):
-        # Only applies to this crew
-        if context.tool_name == "web_search":
-            if not context.tool_input.get('query'):
-                print("❌ Invalid search query")
-                return False
-        return None
-
-    @after_tool_call_crew
-    def log_tool_results(self, context):
-        # Crew-specific tool logging
-        print(f"✅ {context.tool_name} completed")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## Common Use Cases
-
-### 1. Safety Guardrails
-
-```python
-@before_tool_call
-def safety_check(context: ToolCallHookContext) -> bool | None:
-    # Block tools that could cause harm
-    destructive_tools = [
-        'delete_file',
-        'drop_table',
-        'remove_user',
-        'system_shutdown'
-    ]
-
-    if context.tool_name in destructive_tools:
-        print(f"🛑 Blocked destructive tool: {context.tool_name}")
-        return False
-
-    # Warn on sensitive operations
-    sensitive_tools = ['send_email', 'post_to_social_media', 'charge_payment']
-    if context.tool_name in sensitive_tools:
-        print(f"⚠️  Executing sensitive tool: {context.tool_name}")
-
-    return None
-```
-
-### 2. Human Approval Gate
-
-```python
-@before_tool_call
-def require_approval_for_actions(context: ToolCallHookContext) -> bool | None:
-    approval_required = [
-        'send_email',
-        'make_purchase',
-        'delete_file',
-        'post_message'
-    ]
-
-    if context.tool_name in approval_required:
-        response = context.request_human_input(
-            prompt=f"Approve {context.tool_name}?",
-            default_message=f"Input: {context.tool_input}\nType 'yes' to approve:"
-        )
-
-        if response.lower() != 'yes':
-            print(f"❌ Tool execution denied: {context.tool_name}")
-            return False
-
-    return None
-```
-
-### 3. Input Validation and Sanitization
-
-```python
-@before_tool_call
-def validate_and_sanitize_inputs(context: ToolCallHookContext) -> bool | None:
-    # Validate search queries
-    if context.tool_name == 'web_search':
-        query = context.tool_input.get('query', '')
-        if len(query) < 3:
-            print("❌ Search query too short")
-            return False
-
-        # Sanitize query
-        context.tool_input['query'] = query.strip().lower()
-
-    # Validate file paths
-    if context.tool_name == 'read_file':
-        path = context.tool_input.get('path', '')
-        if '..' in path or path.startswith('/'):
-            print("❌ Invalid file path")
-            return False
-
-    return None
-```
-
-### 4. Result Sanitization
-
-```python
-@after_tool_call
-def sanitize_sensitive_data(context: ToolCallHookContext) -> str | None:
-    if not context.tool_result:
-        return None
-
-    import re
-    result = context.tool_result
-
-    # Remove API keys
-    result = re.sub(
-        r'(api[_-]?key|token)["\']?\s*[:=]\s*["\']?[\w-]+',
-        r'\1: [REDACTED]',
-        result,
-        flags=re.IGNORECASE
-    )
-
-    # Remove email addresses
-    result = re.sub(
-        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
-        '[EMAIL-REDACTED]',
-        result
-    )
-
-    # Remove credit card numbers
-    result = re.sub(
-        r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b',
-        '[CARD-REDACTED]',
-        result
-    )
-
-    return result
-```
-
-### 5. Tool Usage Analytics
-
-```python
-import time
-from collections import defaultdict
-
-tool_stats = defaultdict(lambda: {'count': 0, 'total_time': 0, 'failures': 0})
-
-@before_tool_call
-def start_timer(context: ToolCallHookContext) -> None:
-    context.tool_input['_start_time'] = time.time()
-    return None
-
-@after_tool_call
-def track_tool_usage(context: ToolCallHookContext) -> None:
-    start_time = context.tool_input.get('_start_time', time.time())
-    duration = time.time() - start_time
-
-    tool_stats[context.tool_name]['count'] += 1
-    tool_stats[context.tool_name]['total_time'] += duration
-
-    if not context.tool_result or 'error' in context.tool_result.lower():
-        tool_stats[context.tool_name]['failures'] += 1
-
-    print(f"""
-    📊 Tool Stats for {context.tool_name}:
-    - Executions: {tool_stats[context.tool_name]['count']}
-    - Avg Time: {tool_stats[context.tool_name]['total_time'] / tool_stats[context.tool_name]['count']:.2f}s
-    - Failures: {tool_stats[context.tool_name]['failures']}
-    """)
-
-    return None
-```
-
-### 6. Rate Limiting
-
-```python
-from collections import defaultdict
-from datetime import datetime, timedelta
-
-tool_call_history = defaultdict(list)
-
-@before_tool_call
-def rate_limit_tools(context: ToolCallHookContext) -> bool | None:
-    tool_name = context.tool_name
-    now = datetime.now()
-
-    # Clean old entries (older than 1 minute)
-    tool_call_history[tool_name] = [
-        call_time for call_time in tool_call_history[tool_name]
-        if now - call_time < timedelta(minutes=1)
-    ]
-
-    # Check rate limit (max 10 calls per minute)
-    if len(tool_call_history[tool_name]) >= 10:
-        print(f"🚫 Rate limit exceeded for {tool_name}")
-        return False
-
-    # Record this call
-    tool_call_history[tool_name].append(now)
-    return None
-```
-
-### 7. Caching Tool Results
-
-```python
-import hashlib
-import json
-
-tool_cache = {}
-
-def cache_key(tool_name: str, tool_input: dict) -> str:
-    """Generate cache key from tool name and input."""
-    input_str = json.dumps(tool_input, sort_keys=True)
-    return hashlib.md5(f"{tool_name}:{input_str}".encode()).hexdigest()
-
-@before_tool_call
-def check_cache(context: ToolCallHookContext) -> bool | None:
-    key = cache_key(context.tool_name, context.tool_input)
-    if key in tool_cache:
-        print(f"💾 Cache hit for {context.tool_name}")
-        # Note: Can't return cached result from before hook
-        # Would need to implement this differently
-    return None
-
-@after_tool_call
-def cache_result(context: ToolCallHookContext) -> None:
-    if context.tool_result:
-        key = cache_key(context.tool_name, context.tool_input)
-        tool_cache[key] = context.tool_result
-        print(f"💾 Cached result for {context.tool_name}")
-    return None
-```
-
-### 8. Debug Logging
-
-```python
-@before_tool_call
-def debug_tool_call(context: ToolCallHookContext) -> None:
-    print(f"""
-    🔍 Tool Call Debug:
-    - Tool: {context.tool_name}
-    - Agent: {context.agent.role if context.agent else 'Unknown'}
-    - Task: {context.task.description[:50] if context.task else 'Unknown'}...
-    - Input: {context.tool_input}
-    """)
-    return None
-
-@after_tool_call
-def debug_tool_result(context: ToolCallHookContext) -> None:
-    if context.tool_result:
-        result_preview = context.tool_result[:200]
-        print(f"✅ Result Preview: {result_preview}...")
-    else:
-        print("⚠️  No result returned")
-    return None
-```
-
-## Hook Management
-
-### Unregistering Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_tool_call_hook,
-    unregister_after_tool_call_hook
-)
-
-# Unregister specific hook
-def my_hook(context):
-    ...
-
-register_before_tool_call_hook(my_hook)
-# Later...
-success = unregister_before_tool_call_hook(my_hook)
-print(f"Unregistered: {success}")
-```
-
-### Clearing Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks,
-    clear_all_tool_call_hooks
-)
-
-# Clear specific hook type
-count = clear_before_tool_call_hooks()
-print(f"Cleared {count} before hooks")
-
-# Clear all tool hooks
-before_count, after_count = clear_all_tool_call_hooks()
-print(f"Cleared {before_count} before and {after_count} after hooks")
-```
-
-### Listing Registered Hooks
-
-```python
-from crewai.hooks import (
-    get_before_tool_call_hooks,
-    get_after_tool_call_hooks
-)
-
-# Get current hooks
-before_hooks = get_before_tool_call_hooks()
-after_hooks = get_after_tool_call_hooks()
-
-print(f"Registered: {len(before_hooks)} before, {len(after_hooks)} after")
-```
-
-## Advanced Patterns
-
-### Conditional Hook Execution
-
-```python
-@before_tool_call
-def conditional_blocking(context: ToolCallHookContext) -> bool | None:
-    # Only block for specific agents
-    if context.agent and context.agent.role == "junior_agent":
-        if context.tool_name in ['delete_file', 'send_email']:
-            print(f"❌ Junior agents cannot use {context.tool_name}")
-            return False
-
-    # Only block during specific tasks
-    if context.task and "sensitive" in context.task.description.lower():
-        if context.tool_name == 'web_search':
-            print("❌ Web search blocked for sensitive tasks")
-            return False
-
-    return None
-```
-
-### Context-Aware Input Modification
-
-```python
-@before_tool_call
-def enhance_tool_inputs(context: ToolCallHookContext) -> None:
-    # Add context based on agent role
-    if context.agent and context.agent.role == "researcher":
-        if context.tool_name == 'web_search':
-            # Add domain restrictions for researchers
-            context.tool_input['domains'] = ['edu', 'gov', 'org']
-
-    # Add context based on task
-    if context.task and "urgent" in context.task.description.lower():
-        if context.tool_name == 'send_email':
-            context.tool_input['priority'] = 'high'
-
-    return None
-```
-
-### Tool Chain Monitoring
-
-```python
-tool_call_chain = []
-
-@before_tool_call
-def track_tool_chain(context: ToolCallHookContext) -> None:
-    tool_call_chain.append({
-        'tool': context.tool_name,
-        'timestamp': time.time(),
-        'agent': context.agent.role if context.agent else 'Unknown'
-    })
-
-    # Detect potential infinite loops
-    recent_calls = tool_call_chain[-5:]
-    if len(recent_calls) == 5 and all(c['tool'] == context.tool_name for c in recent_calls):
-        print(f"⚠️  Warning: {context.tool_name} called 5 times in a row")
-
-    return None
-```
-
-## Best Practices
-
-1. **Keep Hooks Focused**: Each hook should have a single responsibility
-2. **Avoid Heavy Computation**: Hooks execute on every tool call
-3. **Handle Errors Gracefully**: Use try-except to prevent hook failures
-4. **Use Type Hints**: Leverage `ToolCallHookContext` for better IDE support
-5. **Document Blocking Conditions**: Make it clear when/why tools are blocked
-6. **Test Hooks Independently**: Unit test hooks before using in production
-7. **Clear Hooks in Tests**: Use `clear_all_tool_call_hooks()` between test runs
-8. **Modify In-Place**: Always modify `context.tool_input` in-place, never replace
-9. **Log Important Decisions**: Especially when blocking tool execution
-10. **Consider Performance**: Cache expensive validations when possible
-
-## Error Handling
-
-```python
-@before_tool_call
-def safe_validation(context: ToolCallHookContext) -> bool | None:
-    try:
-        # Your validation logic
-        if not validate_input(context.tool_input):
-            return False
-    except Exception as e:
-        print(f"⚠️ Hook error: {e}")
-        # Decide: allow or block on error
-        return None  # Allow execution despite error
-```
-
-## Type Safety
-
-```python
-from crewai.hooks import ToolCallHookContext, BeforeToolCallHookType, AfterToolCallHookType
-
-# Explicit type annotations
-def my_before_hook(context: ToolCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: ToolCallHookContext) -> str | None:
-    return None
-
-# Type-safe registration
-register_before_tool_call_hook(my_before_hook)
-register_after_tool_call_hook(my_after_hook)
-```
-
-## Integration with Existing Tools
-
-### Wrapping Existing Validation
-
-```python
-def existing_validator(tool_name: str, inputs: dict) -> bool:
-    """Your existing validation function."""
-    # Your validation logic
-    return True
-
-@before_tool_call
-def integrate_validator(context: ToolCallHookContext) -> bool | None:
-    if not existing_validator(context.tool_name, context.tool_input):
-        print(f"❌ Validation failed for {context.tool_name}")
-        return False
-    return None
-```
-
-### Logging to External Systems
-
-```python
-import logging
-
-logger = logging.getLogger(__name__)
-
-@before_tool_call
-def log_to_external_system(context: ToolCallHookContext) -> None:
-    logger.info(f"Tool call: {context.tool_name}", extra={
-        'tool_name': context.tool_name,
-        'tool_input': context.tool_input,
-        'agent': context.agent.role if context.agent else None
-    })
-    return None
-```
-
-## Troubleshooting
-
-### Hook Not Executing
-- Verify hook is registered before crew execution
-- Check if previous hook returned `False` (blocks execution and subsequent hooks)
-- Ensure hook signature matches expected type
-
-### Input Modifications Not Working
-- Use in-place modifications: `context.tool_input['key'] = value`
-- Don't replace the dict: `context.tool_input = {}`
-
-### Result Modifications Not Working
-- Return the modified string from after hooks
-- Returning `None` keeps the original result
-- Ensure the tool actually returned a result
-
-### Tool Blocked Unexpectedly
-- Check all before hooks for blocking conditions
-- Verify hook execution order
-- Add debug logging to identify which hook is blocking
-
-## Conclusion
-
-Tool Call Hooks provide powerful capabilities for controlling and monitoring tool execution in CrewAI. Use them to implement safety guardrails, approval gates, input validation, result sanitization, logging, and analytics. Combined with proper error handling and type safety, hooks enable secure and production-ready agent systems with comprehensive observability.

docs/en/observability/portkey.mdx

@@ -733,7 +733,9 @@ Here's a basic configuration to route requests to OpenAI, specifically using GPT
     - Collect relevant metadata to filter logs
     - Enforce access permissions
 
-    Create API keys through the [Portkey App](https://app.portkey.ai/)
+    Create API keys through:
+    - [Portkey App](https://app.portkey.ai/)
+    - [API Key Management API](/en/api-reference/admin-api/control-plane/api-keys/create-api-key)
 
     Example using Python SDK:
     ```python
@@ -756,7 +758,7 @@ Here's a basic configuration to route requests to OpenAI, specifically using GPT
     )
     ```
 
-    For detailed key management instructions, see the [Portkey documentation](https://portkey.ai/docs).
+    For detailed key management instructions, see our [API Keys documentation](/en/api-reference/admin-api/control-plane/api-keys/create-api-key).
   </Accordion>
 
   <Accordion title="Step 4: Deploy & Monitor">

docs/en/tools/cloud-storage/overview.mdx

@@ -18,7 +18,7 @@ These tools enable your agents to interact with cloud services, access cloud sto
     Write and upload files to Amazon S3 storage.
   </Card>
 
-  <Card title="Bedrock Invoke Agent" icon="aws" href="/en/tools/integration/bedrockinvokeagenttool">
+  <Card title="Bedrock Invoke Agent" icon="aws" href="/en/tools/cloud-storage/bedrockinvokeagenttool">
     Invoke Amazon Bedrock agents for AI-powered tasks.
   </Card>
 

docs/ko/changelog.mdx

@@ -632,11 +632,11 @@ mode: "wide"
 
   ## 기여
 
-  기여를 원하시면, [기여 가이드](https://github.com/crewAIInc/crewAI/blob/main/CONTRIBUTING.md)를 참조하세요.
+  기여를 원하시면, [기여 가이드](CONTRIBUTING.md)를 참조하세요.
 
   ## 라이센스
 
-  이 프로젝트는 MIT 라이센스 하에 배포됩니다. 자세한 내용은 [LICENSE](https://github.com/crewAIInc/crewAI/blob/main/LICENSE) 파일을 확인하세요.
+  이 프로젝트는 MIT 라이센스 하에 배포됩니다. 자세한 내용은 [LICENSE](LICENSE) 파일을 확인하세요.
 </Update>
 
 <Update label="2025년 5월 22일">

docs/ko/concepts/knowledge.mdx

@@ -706,7 +706,7 @@ class KnowledgeMonitorListener(BaseEventListener):
 knowledge_monitor = KnowledgeMonitorListener()
 ```
 
-이벤트 사용에 대한 자세한 내용은 [이벤트 리스너](/ko/concepts/event-listener) 문서를 참고하세요.
+이벤트 사용에 대한 자세한 내용은 [이벤트 리스너](https://docs.crewai.com/concepts/event-listener) 문서를 참고하세요.
 
 ### 맞춤형 지식 소스
 

docs/ko/concepts/llms.mdx

@@ -748,7 +748,7 @@ CrewAI는 LLM의 스트리밍 응답을 지원하여, 애플리케이션이 출
     ```
 
     <Tip>
-      [자세한 내용은 여기를 클릭하세요](/ko/concepts/event-listener#event-listeners)
+      [자세한 내용은 여기를 클릭하세요](https://docs.crewai.com/concepts/event-listener#event-listeners)
     </Tip>
   </Tab>
 

docs/ko/enterprise/features/marketplace.mdx

@@ -36,7 +36,7 @@ mode: "wide"
   <Card title="도구 & 통합" href="/ko/enterprise/features/tools-and-integrations" icon="wrench">
     에이전트가 사용할 외부 앱 연결 및 내부 도구 관리.
   </Card>
-  <Card title="도구 저장소" href="/ko/enterprise/guides/tool-repository" icon="toolbox">
+  <Card title="도구 저장소" href="/ko/enterprise/features/tool-repository" icon="toolbox">
     크루 기능을 확장할 수 있도록 도구를 게시하고 설치.
   </Card>
   <Card title="에이전트 저장소" href="/ko/enterprise/features/agent-repositories" icon="people-group">

docs/ko/enterprise/features/tools-and-integrations.mdx

@@ -231,7 +231,7 @@ mode: "wide"
 ## 관련 문서
 
 <CardGroup cols={2}>
-  <Card title="도구 저장소" href="/ko/enterprise/guides/tool-repository" icon="toolbox">
+  <Card title="도구 저장소" href="/ko/enterprise/features/tool-repository" icon="toolbox">
     크루 기능을 확장할 수 있도록 도구를 게시하고 설치하세요.
   </Card>
   <Card title="Webhook 자동화" href="/ko/enterprise/guides/webhook-automation" icon="bolt">

docs/ko/enterprise/guides/tool-repository.mdx

@@ -21,7 +21,7 @@ Tool Repository는 CrewAI 도구를 위한 패키지 관리자입니다. 사용
 Tool Repository를 사용하기 전에 다음이 준비되어 있어야 합니다:
 
 - [CrewAI AMP](https://app.crewai.com) 계정
-- [CrewAI CLI](/ko/concepts/cli#cli) 설치됨
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) 설치됨
 - uv>=0.5.0 이 설치되어 있어야 합니다. [업그레이드 방법](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)을 참고하세요.
 - [Git](https://git-scm.com) 설치 및 구성 완료
 - CrewAI AMP 조직에서 도구를 게시하거나 설치할 수 있는 액세스 권한
@@ -66,7 +66,7 @@ crewai tool publish
 crewai tool publish --public
 ```
 
-도구 빌드에 대한 자세한 내용은 [나만의 도구 만들기](/ko/concepts/tools#creating-your-own-tools)를 참고하세요.
+도구 빌드에 대한 자세한 내용은 [나만의 도구 만들기](https://docs.crewai.com/concepts/tools#creating-your-own-tools)를 참고하세요.
 
 ## 도구 업데이트
 

docs/ko/enterprise/resources/frequently-asked-questions.mdx

@@ -49,7 +49,7 @@ mode: "wide"
 
         에이전트 실행에 인간 입력을 통합하려면 작업 정의에서 `human_input` 플래그를 설정하세요. 활성화하면, 에이전트가 최종 답변을 제공하기 전에 사용자에게 입력을 요청합니다. 이 입력은 추가 맥락을 제공하거나, 애매함을 해소하거나, 에이전트의 출력을 검증해야 할 때 활용될 수 있습니다.
 
-        자세한 구현 방법은 [Human-in-the-Loop 가이드](/ko/enterprise/guides/human-in-the-loop)를 참고해 주세요.
+        자세한 구현 방법은 [Human-in-the-Loop 가이드](/ko/how-to/human-in-the-loop)를 참고해 주세요.
     </Accordion>
 
     <Accordion title="CrewAI에서 에이전트의 행동과 역량을 맞춤화하고 향상시키기 위한 고급 커스터마이징 옵션에는 어떤 것이 있나요?">
@@ -142,7 +142,7 @@ mode: "wide"
     <Accordion title="CrewAI 에이전트를 위한 커스텀 도구는 어떻게 만들 수 있습니까?">
         CrewAI에서 제공하는 `BaseTool` 클래스를 상속받아 커스텀 도구를 직접 만들거나, tool 데코레이터를 활용할 수 있습니다. 상속 방식은 `BaseTool`을 상속하는 새로운 클래스를 정의해 이름, 설명, 그리고 실제 논리를 처리하는 `_run` 메서드를 작성합니다. tool 데코레이터를 사용하면 필수 속성과 운영 로직만 정의해 바로 `Tool` 객체를 만들 수 있습니다.
 
-        <Card href="/ko/learn/create-custom-tools" icon="code">CrewAI 도구 가이드</Card>
+        <Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI 도구 가이드</Card>
     </Accordion>
 
     <Accordion title="전체 crew가 수행할 수 있는 분당 최대 요청 수는 어떻게 제한할 수 있나요?">

docs/ko/learn/execution-hooks.mdx

@@ -1,379 +0,0 @@
----
-title: 실행 훅 개요
-description: 에이전트 작업에 대한 세밀한 제어를 위한 CrewAI 실행 훅 이해 및 사용
-mode: "wide"
----
-
-실행 훅(Execution Hooks)은 CrewAI 에이전트의 런타임 동작을 세밀하게 제어할 수 있게 해줍니다. 크루 실행 전후에 실행되는 킥오프 훅과 달리, 실행 훅은 에이전트 실행 중 특정 작업을 가로채서 동작을 수정하고, 안전성 검사를 구현하며, 포괄적인 모니터링을 추가할 수 있습니다.
-
-## 실행 훅의 유형
-
-CrewAI는 두 가지 주요 범주의 실행 훅을 제공합니다:
-
-### 1. [LLM 호출 훅](/learn/llm-hooks)
-
-언어 모델 상호작용을 제어하고 모니터링합니다:
-- **LLM 호출 전**: 프롬프트 수정, 입력 검증, 승인 게이트 구현
-- **LLM 호출 후**: 응답 변환, 출력 정제, 대화 기록 업데이트
-
-**사용 사례:**
-- 반복 제한
-- 비용 추적 및 토큰 사용량 모니터링
-- 응답 정제 및 콘텐츠 필터링
-- LLM 호출에 대한 사람의 승인
-- 안전 가이드라인 또는 컨텍스트 추가
-- 디버그 로깅 및 요청/응답 검사
-
-[LLM 훅 문서 보기 →](/learn/llm-hooks)
-
-### 2. [도구 호출 훅](/learn/tool-hooks)
-
-도구 실행을 제어하고 모니터링합니다:
-- **도구 호출 전**: 입력 수정, 매개변수 검증, 위험한 작업 차단
-- **도구 호출 후**: 결과 변환, 출력 정제, 실행 세부사항 로깅
-
-**사용 사례:**
-- 파괴적인 작업에 대한 안전 가드레일
-- 민감한 작업에 대한 사람의 승인
-- 입력 검증 및 정제
-- 결과 캐싱 및 속도 제한
-- 도구 사용 분석
-- 디버그 로깅 및 모니터링
-
-[도구 훅 문서 보기 →](/learn/tool-hooks)
-
-## 훅 등록 방법
-
-### 1. 데코레이터 기반 훅 (권장)
-
-훅을 등록하는 가장 깔끔하고 파이썬스러운 방법:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call, before_tool_call, after_tool_call
-
-@before_llm_call
-def limit_iterations(context):
-    """반복 횟수를 제한하여 무한 루프를 방지합니다."""
-    if context.iterations > 10:
-        return False  # 실행 차단
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    """LLM 응답에서 민감한 데이터를 제거합니다."""
-    if "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[수정됨]")
-    return None
-
-@before_tool_call
-def block_dangerous_tools(context):
-    """파괴적인 작업을 차단합니다."""
-    if context.tool_name == "delete_database":
-        return False  # 실행 차단
-    return None
-
-@after_tool_call
-def log_tool_result(context):
-    """도구 실행을 로깅합니다."""
-    print(f"도구 {context.tool_name} 완료")
-    return None
-```
-
-### 2. 크루 범위 훅
-
-특정 크루 인스턴스에만 훅을 적용합니다:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_llm_call_crew, after_tool_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # 이 크루에만 적용됩니다
-        print(f"{self.__class__.__name__}에서 LLM 호출")
-        return None
-
-    @after_tool_call_crew
-    def log_results(self, context):
-        # 크루별 로깅
-        print(f"도구 결과: {context.tool_result[:50]}...")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential
-        )
-```
-
-## 훅 실행 흐름
-
-### LLM 호출 흐름
-
-```
-에이전트가 LLM을 호출해야 함
-    ↓
-[LLM 호출 전 훅 실행]
-    ├→ 훅 1: 반복 횟수 검증
-    ├→ 훅 2: 안전 컨텍스트 추가
-    └→ 훅 3: 요청 로깅
-    ↓
-훅이 False를 반환하는 경우:
-    ├→ LLM 호출 차단
-    └→ ValueError 발생
-    ↓
-모든 훅이 True/None을 반환하는 경우:
-    ├→ LLM 호출 진행
-    └→ 응답 생성
-    ↓
-[LLM 호출 후 훅 실행]
-    ├→ 훅 1: 응답 정제
-    ├→ 훅 2: 응답 로깅
-    └→ 훅 3: 메트릭 업데이트
-    ↓
-최종 응답 반환
-```
-
-### 도구 호출 흐름
-
-```
-에이전트가 도구를 실행해야 함
-    ↓
-[도구 호출 전 훅 실행]
-    ├→ 훅 1: 도구 허용 여부 확인
-    ├→ 훅 2: 입력 검증
-    └→ 훅 3: 필요시 승인 요청
-    ↓
-훅이 False를 반환하는 경우:
-    ├→ 도구 실행 차단
-    └→ 오류 메시지 반환
-    ↓
-모든 훅이 True/None을 반환하는 경우:
-    ├→ 도구 실행 진행
-    └→ 결과 생성
-    ↓
-[도구 호출 후 훅 실행]
-    ├→ 훅 1: 결과 정제
-    ├→ 훅 2: 결과 캐싱
-    └→ 훅 3: 메트릭 로깅
-    ↓
-최종 결과 반환
-```
-
-## 훅 컨텍스트 객체
-
-### LLMCallHookContext
-
-LLM 실행 상태에 대한 액세스를 제공합니다:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # 전체 실행자 액세스
-    messages: list               # 변경 가능한 메시지 목록
-    agent: Agent                 # 현재 에이전트
-    task: Task                   # 현재 작업
-    crew: Crew                   # 크루 인스턴스
-    llm: BaseLLM                 # LLM 인스턴스
-    iterations: int              # 현재 반복 횟수
-    response: str | None         # LLM 응답 (후 훅용)
-```
-
-### ToolCallHookContext
-
-도구 실행 상태에 대한 액세스를 제공합니다:
-
-```python
-class ToolCallHookContext:
-    tool_name: str               # 호출되는 도구
-    tool_input: dict             # 변경 가능한 입력 매개변수
-    tool: CrewStructuredTool     # 도구 인스턴스
-    agent: Agent | None          # 실행 중인 에이전트
-    task: Task | None            # 현재 작업
-    crew: Crew | None            # 크루 인스턴스
-    tool_result: str | None      # 도구 결과 (후 훅용)
-```
-
-## 일반적인 패턴
-
-### 안전 및 검증
-
-```python
-@before_tool_call
-def safety_check(context):
-    """파괴적인 작업을 차단합니다."""
-    dangerous = ['delete_file', 'drop_table', 'system_shutdown']
-    if context.tool_name in dangerous:
-        print(f"🛑 차단됨: {context.tool_name}")
-        return False
-    return None
-
-@before_llm_call
-def iteration_limit(context):
-    """무한 루프를 방지합니다."""
-    if context.iterations > 15:
-        print("⛔ 최대 반복 횟수 초과")
-        return False
-    return None
-```
-
-### 사람의 개입
-
-```python
-@before_tool_call
-def require_approval(context):
-    """민감한 작업에 대한 승인을 요구합니다."""
-    sensitive = ['send_email', 'make_payment', 'post_message']
-
-    if context.tool_name in sensitive:
-        response = context.request_human_input(
-            prompt=f"{context.tool_name} 승인하시겠습니까?",
-            default_message="승인하려면 'yes'를 입력하세요:"
-        )
-
-        if response.lower() != 'yes':
-            return False
-
-    return None
-```
-
-### 모니터링 및 분석
-
-```python
-from collections import defaultdict
-import time
-
-metrics = defaultdict(lambda: {'count': 0, 'total_time': 0})
-
-@before_tool_call
-def start_timer(context):
-    context.tool_input['_start'] = time.time()
-    return None
-
-@after_tool_call
-def track_metrics(context):
-    start = context.tool_input.get('_start', time.time())
-    duration = time.time() - start
-
-    metrics[context.tool_name]['count'] += 1
-    metrics[context.tool_name]['total_time'] += duration
-
-    return None
-```
-
-## 훅 관리
-
-### 모든 훅 지우기
-
-```python
-from crewai.hooks import clear_all_global_hooks
-
-# 모든 훅을 한 번에 지웁니다
-result = clear_all_global_hooks()
-print(f"{result['total']} 훅이 지워졌습니다")
-```
-
-### 특정 훅 유형 지우기
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks
-)
-
-# 특정 유형 지우기
-llm_before_count = clear_before_llm_call_hooks()
-tool_after_count = clear_after_tool_call_hooks()
-```
-
-## 모범 사례
-
-### 1. 훅을 집중적으로 유지
-각 훅은 단일하고 명확한 책임을 가져야 합니다.
-
-### 2. 오류를 우아하게 처리
-```python
-@before_llm_call
-def safe_hook(context):
-    try:
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"훅 오류: {e}")
-        return None  # 오류에도 불구하고 실행 허용
-```
-
-### 3. 컨텍스트를 제자리에서 수정
-```python
-# ✅ 올바름 - 제자리에서 수정
-@before_llm_call
-def add_context(context):
-    context.messages.append({"role": "system", "content": "간결하게"})
-
-# ❌ 잘못됨 - 참조를 교체
-@before_llm_call
-def wrong_approach(context):
-    context.messages = [{"role": "system", "content": "간결하게"}]
-```
-
-### 4. 타입 힌트 사용
-```python
-from crewai.hooks import LLMCallHookContext, ToolCallHookContext
-
-def my_llm_hook(context: LLMCallHookContext) -> bool | None:
-    return None
-
-def my_tool_hook(context: ToolCallHookContext) -> str | None:
-    return None
-```
-
-### 5. 테스트에서 정리
-```python
-import pytest
-from crewai.hooks import clear_all_global_hooks
-
-@pytest.fixture(autouse=True)
-def clean_hooks():
-    """각 테스트 전에 훅을 재설정합니다."""
-    yield
-    clear_all_global_hooks()
-```
-
-## 어떤 훅을 사용해야 할까요
-
-### LLM 훅을 사용하는 경우:
-- 반복 제한 구현
-- 프롬프트에 컨텍스트 또는 안전 가이드라인 추가
-- 토큰 사용량 및 비용 추적
-- 응답 정제 또는 변환
-- LLM 호출에 대한 승인 게이트 구현
-- 프롬프트/응답 상호작용 디버깅
-
-### 도구 훅을 사용하는 경우:
-- 위험하거나 파괴적인 작업 차단
-- 실행 전 도구 입력 검증
-- 민감한 작업에 대한 승인 게이트 구현
-- 도구 결과 캐싱
-- 도구 사용 및 성능 추적
-- 도구 출력 정제
-- 도구 호출 속도 제한
-
-### 둘 다 사용하는 경우:
-모든 에이전트 작업을 모니터링해야 하는 포괄적인 관찰성, 안전 또는 승인 시스템을 구축하는 경우.
-
-## 관련 문서
-
-- [LLM 호출 훅 →](/learn/llm-hooks) - 상세한 LLM 훅 문서
-- [도구 호출 훅 →](/learn/tool-hooks) - 상세한 도구 훅 문서
-- [킥오프 전후 훅 →](/learn/before-and-after-kickoff-hooks) - 크루 생명주기 훅
-- [사람의 개입 →](/learn/human-in-the-loop) - 사람 입력 패턴
-
-## 결론
-
-실행 훅은 에이전트 런타임 동작에 대한 강력한 제어를 제공합니다. 이를 사용하여 안전 가드레일, 승인 워크플로우, 포괄적인 모니터링 및 사용자 정의 비즈니스 로직을 구현하세요. 적절한 오류 처리, 타입 안전성 및 성능 고려사항과 결합하면, 훅을 통해 프로덕션 준비가 된 안전하고 관찰 가능한 에이전트 시스템을 구축할 수 있습니다.

docs/ko/learn/hierarchical-process.mdx

@@ -95,7 +95,7 @@ project_crew = Crew(
 ```
 
 <Tip>
-    매니저 에이전트 생성 및 맞춤화에 대한 자세한 내용은 [커스텀 매니저 에이전트 문서](/ko/learn/custom-manager-agent)를 참고하세요.
+    매니저 에이전트 생성 및 맞춤화에 대한 자세한 내용은 [커스텀 매니저 에이전트 문서](https://docs.crewai.com/how-to/custom-manager-agent#custom-manager-agent)를 참고하세요.
 </Tip>
 
 ### 워크플로우 실행

docs/ko/learn/llm-hooks.mdx

@@ -1,412 +0,0 @@
----
-title: LLM 호출 훅
-description: CrewAI에서 언어 모델 상호작용을 가로채고, 수정하고, 제어하는 LLM 호출 훅 사용 방법 배우기
-mode: "wide"
----
-
-LLM 호출 훅(LLM Call Hooks)은 에이전트 실행 중 언어 모델 상호작용에 대한 세밀한 제어를 제공합니다. 이러한 훅을 사용하면 LLM 호출을 가로채고, 프롬프트를 수정하고, 응답을 변환하고, 승인 게이트를 구현하고, 사용자 정의 로깅 또는 모니터링을 추가할 수 있습니다.
-
-## 개요
-
-LLM 훅은 두 가지 중요한 시점에 실행됩니다:
-- **LLM 호출 전**: 메시지 수정, 입력 검증 또는 실행 차단
-- **LLM 호출 후**: 응답 변환, 출력 정제 또는 대화 기록 수정
-
-## 훅 타입
-
-### LLM 호출 전 훅
-
-모든 LLM 호출 전에 실행되며, 다음을 수행할 수 있습니다:
-- LLM에 전송되는 메시지 검사 및 수정
-- 조건에 따라 LLM 실행 차단
-- 속도 제한 또는 승인 게이트 구현
-- 컨텍스트 또는 시스템 메시지 추가
-- 요청 세부사항 로깅
-
-**시그니처:**
-```python
-def before_hook(context: LLMCallHookContext) -> bool | None:
-    # 실행을 차단하려면 False 반환
-    # 실행을 허용하려면 True 또는 None 반환
-    ...
-```
-
-### LLM 호출 후 훅
-
-모든 LLM 호출 후에 실행되며, 다음을 수행할 수 있습니다:
-- LLM 응답 수정 또는 정제
-- 메타데이터 또는 서식 추가
-- 응답 세부사항 로깅
-- 대화 기록 업데이트
-- 콘텐츠 필터링 구현
-
-**시그니처:**
-```python
-def after_hook(context: LLMCallHookContext) -> str | None:
-    # 수정된 응답 문자열 반환
-    # 원본 응답을 유지하려면 None 반환
-    ...
-```
-
-## LLM 훅 컨텍스트
-
-`LLMCallHookContext` 객체는 실행 상태에 대한 포괄적인 액세스를 제공합니다:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # 전체 실행자 참조
-    messages: list               # 변경 가능한 메시지 목록
-    agent: Agent                 # 현재 에이전트
-    task: Task                   # 현재 작업
-    crew: Crew                   # 크루 인스턴스
-    llm: BaseLLM                 # LLM 인스턴스
-    iterations: int              # 현재 반복 횟수
-    response: str | None         # LLM 응답 (후 훅용)
-```
-
-### 메시지 수정
-
-**중요:** 항상 메시지를 제자리에서 수정하세요:
-
-```python
-# ✅ 올바름 - 제자리에서 수정
-def add_context(context: LLMCallHookContext) -> None:
-    context.messages.append({"role": "system", "content": "간결하게 작성하세요"})
-
-# ❌ 잘못됨 - 리스트 참조를 교체
-def wrong_approach(context: LLMCallHookContext) -> None:
-    context.messages = [{"role": "system", "content": "간결하게 작성하세요"}]
-```
-
-## 등록 방법
-
-### 1. 데코레이터 기반 등록 (권장)
-
-더 깔끔한 구문을 위해 데코레이터를 사용합니다:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call
-
-@before_llm_call
-def validate_iteration_count(context):
-    """반복 횟수를 검증합니다."""
-    if context.iterations > 10:
-        print("⚠️ 최대 반복 횟수 초과")
-        return False  # 실행 차단
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    """민감한 데이터를 제거합니다."""
-    if context.response and "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[수정됨]")
-    return None
-```
-
-### 2. 크루 범위 훅
-
-특정 크루 인스턴스에 대한 훅을 등록합니다:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_llm_call_crew, after_llm_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # 이 크루에만 적용됩니다
-        if context.iterations == 0:
-            print(f"작업 시작: {context.task.description}")
-        return None
-    
-    @after_llm_call_crew
-    def log_responses(self, context):
-        # 크루별 응답 로깅
-        print(f"응답 길이: {len(context.response)}")
-        return None
-    
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## 일반적인 사용 사례
-
-### 1. 반복 제한
-
-```python
-@before_llm_call
-def limit_iterations(context: LLMCallHookContext) -> bool | None:
-    """무한 루프를 방지하기 위해 반복을 제한합니다."""
-    max_iterations = 15
-    if context.iterations > max_iterations:
-        print(f"⛔ 차단됨: {max_iterations}회 반복 초과")
-        return False  # 실행 차단
-    return None
-```
-
-### 2. 사람의 승인 게이트
-
-```python
-@before_llm_call
-def require_approval(context: LLMCallHookContext) -> bool | None:
-    """특정 반복 후 승인을 요구합니다."""
-    if context.iterations > 5:
-        response = context.request_human_input(
-            prompt=f"반복 {context.iterations}: LLM 호출을 승인하시겠습니까?",
-            default_message="승인하려면 Enter를 누르고, 차단하려면 'no'를 입력하세요:"
-        )
-        if response.lower() == "no":
-            print("🚫 사용자에 의해 LLM 호출이 차단되었습니다")
-            return False
-    return None
-```
-
-### 3. 시스템 컨텍스트 추가
-
-```python
-@before_llm_call
-def add_guardrails(context: LLMCallHookContext) -> None:
-    """모든 LLM 호출에 안전 가이드라인을 추가합니다."""
-    context.messages.append({
-        "role": "system",
-        "content": "응답이 사실에 기반하고 가능한 경우 출처를 인용하도록 하세요."
-    })
-    return None
-```
-
-### 4. 응답 정제
-
-```python
-@after_llm_call
-def sanitize_sensitive_data(context: LLMCallHookContext) -> str | None:
-    """민감한 데이터 패턴을 제거합니다."""
-    if not context.response:
-        return None
-    
-    import re
-    sanitized = context.response
-    sanitized = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[주민번호-수정됨]', sanitized)
-    sanitized = re.sub(r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b', '[카드번호-수정됨]', sanitized)
-    
-    return sanitized
-```
-
-### 5. 비용 추적
-
-```python
-import tiktoken
-
-@before_llm_call
-def track_token_usage(context: LLMCallHookContext) -> None:
-    """입력 토큰을 추적합니다."""
-    encoding = tiktoken.get_encoding("cl100k_base")
-    total_tokens = sum(
-        len(encoding.encode(msg.get("content", ""))) 
-        for msg in context.messages
-    )
-    print(f"📊 입력 토큰: ~{total_tokens}")
-    return None
-
-@after_llm_call
-def track_response_tokens(context: LLMCallHookContext) -> None:
-    """응답 토큰을 추적합니다."""
-    if context.response:
-        encoding = tiktoken.get_encoding("cl100k_base")
-        tokens = len(encoding.encode(context.response))
-        print(f"📊 응답 토큰: ~{tokens}")
-    return None
-```
-
-### 6. 디버그 로깅
-
-```python
-@before_llm_call
-def debug_request(context: LLMCallHookContext) -> None:
-    """LLM 요청을 디버그합니다."""
-    print(f"""
-    🔍 LLM 호출 디버그:
-    - 에이전트: {context.agent.role}
-    - 작업: {context.task.description[:50]}...
-    - 반복: {context.iterations}
-    - 메시지 수: {len(context.messages)}
-    - 마지막 메시지: {context.messages[-1] if context.messages else 'None'}
-    """)
-    return None
-
-@after_llm_call
-def debug_response(context: LLMCallHookContext) -> None:
-    """LLM 응답을 디버그합니다."""
-    if context.response:
-        print(f"✅ 응답 미리보기: {context.response[:100]}...")
-    return None
-```
-
-## 훅 관리
-
-### 훅 등록 해제
-
-```python
-from crewai.hooks import (
-    unregister_before_llm_call_hook,
-    unregister_after_llm_call_hook
-)
-
-# 특정 훅 등록 해제
-def my_hook(context):
-    ...
-
-register_before_llm_call_hook(my_hook)
-# 나중에...
-unregister_before_llm_call_hook(my_hook)  # 찾으면 True 반환
-```
-
-### 훅 지우기
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_all_llm_call_hooks
-)
-
-# 특정 훅 타입 지우기
-count = clear_before_llm_call_hooks()
-print(f"{count}개의 전(before) 훅이 지워졌습니다")
-
-# 모든 LLM 훅 지우기
-before_count, after_count = clear_all_llm_call_hooks()
-print(f"{before_count}개의 전(before) 훅과 {after_count}개의 후(after) 훅이 지워졌습니다")
-```
-
-## 고급 패턴
-
-### 조건부 훅 실행
-
-```python
-@before_llm_call
-def conditional_blocking(context: LLMCallHookContext) -> bool | None:
-    """특정 조건에서만 차단합니다."""
-    # 특정 에이전트에 대해서만 차단
-    if context.agent.role == "researcher" and context.iterations > 10:
-        return False
-    
-    # 특정 작업에 대해서만 차단
-    if "민감한" in context.task.description.lower() and context.iterations > 5:
-        return False
-    
-    return None
-```
-
-### 컨텍스트 인식 수정
-
-```python
-@before_llm_call
-def adaptive_prompting(context: LLMCallHookContext) -> None:
-    """반복에 따라 다른 컨텍스트를 추가합니다."""
-    if context.iterations == 0:
-        context.messages.append({
-            "role": "system",
-            "content": "높은 수준의 개요부터 시작하세요."
-        })
-    elif context.iterations > 3:
-        context.messages.append({
-            "role": "system",
-            "content": "구체적인 세부사항에 집중하고 예제를 제공하세요."
-        })
-    return None
-```
-
-### 훅 체이닝
-
-```python
-# 여러 훅은 등록 순서대로 실행됩니다
-
-@before_llm_call
-def first_hook(context):
-    print("1. 첫 번째 훅 실행됨")
-    return None
-
-@before_llm_call
-def second_hook(context):
-    print("2. 두 번째 훅 실행됨")
-    return None
-
-@before_llm_call
-def blocking_hook(context):
-    if context.iterations > 10:
-        print("3. 차단 훅 - 실행 중지")
-        return False  # 후속 훅은 실행되지 않습니다
-    print("3. 차단 훅 - 실행 허용")
-    return None
-```
-
-## 모범 사례
-
-1. **훅을 집중적으로 유지**: 각 훅은 단일 책임을 가져야 합니다
-2. **무거운 계산 피하기**: 훅은 모든 LLM 호출마다 실행됩니다
-3. **오류를 우아하게 처리**: try-except를 사용하여 훅 실패로 인한 실행 중단 방지
-4. **타입 힌트 사용**: 더 나은 IDE 지원을 위해 `LLMCallHookContext` 활용
-5. **훅 동작 문서화**: 특히 차단 조건에 대해
-6. **훅을 독립적으로 테스트**: 프로덕션에서 사용하기 전에 단위 테스트
-7. **테스트에서 훅 지우기**: 테스트 실행 간 `clear_all_llm_call_hooks()` 사용
-8. **제자리에서 수정**: 항상 `context.messages`를 제자리에서 수정하고 교체하지 마세요
-
-## 오류 처리
-
-```python
-@before_llm_call
-def safe_hook(context: LLMCallHookContext) -> bool | None:
-    try:
-        # 훅 로직
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"⚠️ 훅 오류: {e}")
-        # 결정: 오류 발생 시 허용 또는 차단
-        return None  # 오류에도 불구하고 실행 허용
-```
-
-## 타입 안전성
-
-```python
-from crewai.hooks import LLMCallHookContext, BeforeLLMCallHookType, AfterLLMCallHookType
-
-# 명시적 타입 주석
-def my_before_hook(context: LLMCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: LLMCallHookContext) -> str | None:
-    return None
-
-# 타입 안전 등록
-register_before_llm_call_hook(my_before_hook)
-register_after_llm_call_hook(my_after_hook)
-```
-
-## 문제 해결
-
-### 훅이 실행되지 않음
-- 크루 실행 전에 훅이 등록되었는지 확인
-- 이전 훅이 `False`를 반환했는지 확인 (후속 훅 차단)
-- 훅 시그니처가 예상 타입과 일치하는지 확인
-
-### 메시지 수정이 지속되지 않음
-- 제자리 수정 사용: `context.messages.append()`
-- 리스트를 교체하지 마세요: `context.messages = []`
-
-### 응답 수정이 작동하지 않음
-- 후 훅에서 수정된 문자열을 반환
-- `None`을 반환하면 원본 응답이 유지됩니다
-
-## 결론
-
-LLM 호출 훅은 CrewAI에서 언어 모델 상호작용을 제어하고 모니터링하는 강력한 기능을 제공합니다. 이를 사용하여 안전 가드레일, 승인 게이트, 로깅, 비용 추적 및 응답 정제를 구현하세요. 적절한 오류 처리 및 타입 안전성과 결합하면, 훅을 통해 강력하고 프로덕션 준비가 된 에이전트 시스템을 구축할 수 있습니다.
-

docs/ko/learn/tool-hooks.mdx

@@ -1,498 +0,0 @@
----
-title: 도구 호출 훅
-description: CrewAI에서 도구 실행을 가로채고, 수정하고, 제어하는 도구 호출 훅 사용 방법 배우기
-mode: "wide"
----
-
-도구 호출 훅(Tool Call Hooks)은 에이전트 작업 중 도구 실행에 대한 세밀한 제어를 제공합니다. 이러한 훅을 사용하면 도구 호출을 가로채고, 입력을 수정하고, 출력을 변환하고, 안전 검사를 구현하고, 포괄적인 로깅 또는 모니터링을 추가할 수 있습니다.
-
-## 개요
-
-도구 훅은 두 가지 중요한 시점에 실행됩니다:
-- **도구 호출 전**: 입력 수정, 매개변수 검증 또는 실행 차단
-- **도구 호출 후**: 결과 변환, 출력 정제 또는 실행 세부사항 로깅
-
-## 훅 타입
-
-### 도구 호출 전 훅
-
-모든 도구 실행 전에 실행되며, 다음을 수행할 수 있습니다:
-- 도구 입력 검사 및 수정
-- 조건에 따라 도구 실행 차단
-- 위험한 작업에 대한 승인 게이트 구현
-- 매개변수 검증
-- 도구 호출 로깅
-
-**시그니처:**
-```python
-def before_hook(context: ToolCallHookContext) -> bool | None:
-    # 실행을 차단하려면 False 반환
-    # 실행을 허용하려면 True 또는 None 반환
-    ...
-```
-
-### 도구 호출 후 훅
-
-모든 도구 실행 후에 실행되며, 다음을 수행할 수 있습니다:
-- 도구 결과 수정 또는 정제
-- 메타데이터 또는 서식 추가
-- 실행 결과 로깅
-- 결과 검증 구현
-- 출력 형식 변환
-
-**시그니처:**
-```python
-def after_hook(context: ToolCallHookContext) -> str | None:
-    # 수정된 결과 문자열 반환
-    # 원본 결과를 유지하려면 None 반환
-    ...
-```
-
-## 도구 훅 컨텍스트
-
-`ToolCallHookContext` 객체는 도구 실행 상태에 대한 포괄적인 액세스를 제공합니다:
-
-```python
-class ToolCallHookContext:
-    tool_name: str                    # 호출되는 도구의 이름
-    tool_input: dict[str, Any]        # 변경 가능한 도구 입력 매개변수
-    tool: CrewStructuredTool          # 도구 인스턴스 참조
-    agent: Agent | BaseAgent | None   # 도구를 실행하는 에이전트
-    task: Task | None                 # 현재 작업
-    crew: Crew | None                 # 크루 인스턴스
-    tool_result: str | None           # 도구 결과 (후 훅용)
-```
-
-### 도구 입력 수정
-
-**중요:** 항상 도구 입력을 제자리에서 수정하세요:
-
-```python
-# ✅ 올바름 - 제자리에서 수정
-def sanitize_input(context: ToolCallHookContext) -> None:
-    context.tool_input['query'] = context.tool_input['query'].lower()
-
-# ❌ 잘못됨 - 딕셔너리 참조를 교체
-def wrong_approach(context: ToolCallHookContext) -> None:
-    context.tool_input = {'query': 'new query'}
-```
-
-## 등록 방법
-
-### 1. 데코레이터 기반 등록 (권장)
-
-더 깔끔한 구문을 위해 데코레이터를 사용합니다:
-
-```python
-from crewai.hooks import before_tool_call, after_tool_call
-
-@before_tool_call
-def block_dangerous_tools(context):
-    """위험한 도구를 차단합니다."""
-    dangerous_tools = ['delete_database', 'drop_table', 'rm_rf']
-    if context.tool_name in dangerous_tools:
-        print(f"⛔ 위험한 도구 차단됨: {context.tool_name}")
-        return False  # 실행 차단
-    return None
-
-@after_tool_call
-def sanitize_results(context):
-    """결과를 정제합니다."""
-    if context.tool_result and "password" in context.tool_result.lower():
-        return context.tool_result.replace("password", "[수정됨]")
-    return None
-```
-
-### 2. 크루 범위 훅
-
-특정 크루 인스턴스에 대한 훅을 등록합니다:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_tool_call_crew, after_tool_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_tool_call_crew
-    def validate_tool_inputs(self, context):
-        # 이 크루에만 적용됩니다
-        if context.tool_name == "web_search":
-            if not context.tool_input.get('query'):
-                print("❌ 잘못된 검색 쿼리")
-                return False
-        return None
-    
-    @after_tool_call_crew
-    def log_tool_results(self, context):
-        # 크루별 도구 로깅
-        print(f"✅ {context.tool_name} 완료됨")
-        return None
-    
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## 일반적인 사용 사례
-
-### 1. 안전 가드레일
-
-```python
-@before_tool_call
-def safety_check(context: ToolCallHookContext) -> bool | None:
-    """해를 끼칠 수 있는 도구를 차단합니다."""
-    destructive_tools = [
-        'delete_file',
-        'drop_table',
-        'remove_user',
-        'system_shutdown'
-    ]
-    
-    if context.tool_name in destructive_tools:
-        print(f"🛑 파괴적인 도구 차단됨: {context.tool_name}")
-        return False
-    
-    # 민감한 작업에 대해 경고
-    sensitive_tools = ['send_email', 'post_to_social_media', 'charge_payment']
-    if context.tool_name in sensitive_tools:
-        print(f"⚠️  민감한 도구 실행 중: {context.tool_name}")
-    
-    return None
-```
-
-### 2. 사람의 승인 게이트
-
-```python
-@before_tool_call
-def require_approval_for_actions(context: ToolCallHookContext) -> bool | None:
-    """특정 작업에 대한 승인을 요구합니다."""
-    approval_required = [
-        'send_email',
-        'make_purchase',
-        'delete_file',
-        'post_message'
-    ]
-    
-    if context.tool_name in approval_required:
-        response = context.request_human_input(
-            prompt=f"{context.tool_name}을(를) 승인하시겠습니까?",
-            default_message=f"입력: {context.tool_input}\n승인하려면 'yes'를 입력하세요:"
-        )
-        
-        if response.lower() != 'yes':
-            print(f"❌ 도구 실행 거부됨: {context.tool_name}")
-            return False
-    
-    return None
-```
-
-### 3. 입력 검증 및 정제
-
-```python
-@before_tool_call
-def validate_and_sanitize_inputs(context: ToolCallHookContext) -> bool | None:
-    """입력을 검증하고 정제합니다."""
-    # 검색 쿼리 검증
-    if context.tool_name == 'web_search':
-        query = context.tool_input.get('query', '')
-        if len(query) < 3:
-            print("❌ 검색 쿼리가 너무 짧습니다")
-            return False
-        
-        # 쿼리 정제
-        context.tool_input['query'] = query.strip().lower()
-    
-    # 파일 경로 검증
-    if context.tool_name == 'read_file':
-        path = context.tool_input.get('path', '')
-        if '..' in path or path.startswith('/'):
-            print("❌ 잘못된 파일 경로")
-            return False
-    
-    return None
-```
-
-### 4. 결과 정제
-
-```python
-@after_tool_call
-def sanitize_sensitive_data(context: ToolCallHookContext) -> str | None:
-    """민감한 데이터를 정제합니다."""
-    if not context.tool_result:
-        return None
-    
-    import re
-    result = context.tool_result
-    
-    # API 키 제거
-    result = re.sub(
-        r'(api[_-]?key|token)["\']?\s*[:=]\s*["\']?[\w-]+',
-        r'\1: [수정됨]',
-        result,
-        flags=re.IGNORECASE
-    )
-    
-    # 이메일 주소 제거
-    result = re.sub(
-        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
-        '[이메일-수정됨]',
-        result
-    )
-    
-    # 신용카드 번호 제거
-    result = re.sub(
-        r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b',
-        '[카드-수정됨]',
-        result
-    )
-    
-    return result
-```
-
-### 5. 도구 사용 분석
-
-```python
-import time
-from collections import defaultdict
-
-tool_stats = defaultdict(lambda: {'count': 0, 'total_time': 0, 'failures': 0})
-
-@before_tool_call
-def start_timer(context: ToolCallHookContext) -> None:
-    context.tool_input['_start_time'] = time.time()
-    return None
-
-@after_tool_call
-def track_tool_usage(context: ToolCallHookContext) -> None:
-    start_time = context.tool_input.get('_start_time', time.time())
-    duration = time.time() - start_time
-    
-    tool_stats[context.tool_name]['count'] += 1
-    tool_stats[context.tool_name]['total_time'] += duration
-    
-    if not context.tool_result or 'error' in context.tool_result.lower():
-        tool_stats[context.tool_name]['failures'] += 1
-    
-    print(f"""
-    📊 {context.tool_name} 도구 통계:
-    - 실행 횟수: {tool_stats[context.tool_name]['count']}
-    - 평균 시간: {tool_stats[context.tool_name]['total_time'] / tool_stats[context.tool_name]['count']:.2f}초
-    - 실패: {tool_stats[context.tool_name]['failures']}
-    """)
-    
-    return None
-```
-
-### 6. 속도 제한
-
-```python
-from collections import defaultdict
-from datetime import datetime, timedelta
-
-tool_call_history = defaultdict(list)
-
-@before_tool_call
-def rate_limit_tools(context: ToolCallHookContext) -> bool | None:
-    """도구 호출 속도를 제한합니다."""
-    tool_name = context.tool_name
-    now = datetime.now()
-    
-    # 오래된 항목 정리 (1분 이상 된 것)
-    tool_call_history[tool_name] = [
-        call_time for call_time in tool_call_history[tool_name]
-        if now - call_time < timedelta(minutes=1)
-    ]
-    
-    # 속도 제한 확인 (분당 최대 10회 호출)
-    if len(tool_call_history[tool_name]) >= 10:
-        print(f"🚫 {tool_name}에 대한 속도 제한 초과")
-        return False
-    
-    # 이 호출 기록
-    tool_call_history[tool_name].append(now)
-    return None
-```
-
-### 7. 디버그 로깅
-
-```python
-@before_tool_call
-def debug_tool_call(context: ToolCallHookContext) -> None:
-    """도구 호출을 디버그합니다."""
-    print(f"""
-    🔍 도구 호출 디버그:
-    - 도구: {context.tool_name}
-    - 에이전트: {context.agent.role if context.agent else '알 수 없음'}
-    - 작업: {context.task.description[:50] if context.task else '알 수 없음'}...
-    - 입력: {context.tool_input}
-    """)
-    return None
-
-@after_tool_call
-def debug_tool_result(context: ToolCallHookContext) -> None:
-    """도구 결과를 디버그합니다."""
-    if context.tool_result:
-        result_preview = context.tool_result[:200]
-        print(f"✅ 결과 미리보기: {result_preview}...")
-    else:
-        print("⚠️  반환된 결과 없음")
-    return None
-```
-
-## 훅 관리
-
-### 훅 등록 해제
-
-```python
-from crewai.hooks import (
-    unregister_before_tool_call_hook,
-    unregister_after_tool_call_hook
-)
-
-# 특정 훅 등록 해제
-def my_hook(context):
-    ...
-
-register_before_tool_call_hook(my_hook)
-# 나중에...
-success = unregister_before_tool_call_hook(my_hook)
-print(f"등록 해제됨: {success}")
-```
-
-### 훅 지우기
-
-```python
-from crewai.hooks import (
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks,
-    clear_all_tool_call_hooks
-)
-
-# 특정 훅 타입 지우기
-count = clear_before_tool_call_hooks()
-print(f"{count}개의 전(before) 훅이 지워졌습니다")
-
-# 모든 도구 훅 지우기
-before_count, after_count = clear_all_tool_call_hooks()
-print(f"{before_count}개의 전(before) 훅과 {after_count}개의 후(after) 훅이 지워졌습니다")
-```
-
-## 고급 패턴
-
-### 조건부 훅 실행
-
-```python
-@before_tool_call
-def conditional_blocking(context: ToolCallHookContext) -> bool | None:
-    """특정 조건에서만 차단합니다."""
-    # 특정 에이전트에 대해서만 차단
-    if context.agent and context.agent.role == "junior_agent":
-        if context.tool_name in ['delete_file', 'send_email']:
-            print(f"❌ 주니어 에이전트는 {context.tool_name}을(를) 사용할 수 없습니다")
-            return False
-    
-    # 특정 작업 중에만 차단
-    if context.task and "민감한" in context.task.description.lower():
-        if context.tool_name == 'web_search':
-            print("❌ 민감한 작업에서는 웹 검색이 차단됩니다")
-            return False
-    
-    return None
-```
-
-### 컨텍스트 인식 입력 수정
-
-```python
-@before_tool_call
-def enhance_tool_inputs(context: ToolCallHookContext) -> None:
-    """에이전트 역할에 따라 컨텍스트를 추가합니다."""
-    # 에이전트 역할에 따라 컨텍스트 추가
-    if context.agent and context.agent.role == "researcher":
-        if context.tool_name == 'web_search':
-            # 연구원에 대한 도메인 제한 추가
-            context.tool_input['domains'] = ['edu', 'gov', 'org']
-    
-    # 작업에 따라 컨텍스트 추가
-    if context.task and "긴급" in context.task.description.lower():
-        if context.tool_name == 'send_email':
-            context.tool_input['priority'] = 'high'
-    
-    return None
-```
-
-## 모범 사례
-
-1. **훅을 집중적으로 유지**: 각 훅은 단일 책임을 가져야 합니다
-2. **무거운 계산 피하기**: 훅은 모든 도구 호출마다 실행됩니다
-3. **오류를 우아하게 처리**: try-except를 사용하여 훅 실패 방지
-4. **타입 힌트 사용**: 더 나은 IDE 지원을 위해 `ToolCallHookContext` 활용
-5. **차단 조건 문서화**: 도구가 차단되는 시기/이유를 명확히 하세요
-6. **훅을 독립적으로 테스트**: 프로덕션에서 사용하기 전에 단위 테스트
-7. **테스트에서 훅 지우기**: 테스트 실행 간 `clear_all_tool_call_hooks()` 사용
-8. **제자리에서 수정**: 항상 `context.tool_input`을 제자리에서 수정하고 교체하지 마세요
-9. **중요한 결정 로깅**: 특히 도구 실행을 차단할 때
-10. **성능 고려**: 가능한 경우 비용이 많이 드는 검증을 캐시
-
-## 오류 처리
-
-```python
-@before_tool_call
-def safe_validation(context: ToolCallHookContext) -> bool | None:
-    try:
-        # 검증 로직
-        if not validate_input(context.tool_input):
-            return False
-    except Exception as e:
-        print(f"⚠️ 훅 오류: {e}")
-        # 결정: 오류 발생 시 허용 또는 차단
-        return None  # 오류에도 불구하고 실행 허용
-```
-
-## 타입 안전성
-
-```python
-from crewai.hooks import ToolCallHookContext, BeforeToolCallHookType, AfterToolCallHookType
-
-# 명시적 타입 주석
-def my_before_hook(context: ToolCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: ToolCallHookContext) -> str | None:
-    return None
-
-# 타입 안전 등록
-register_before_tool_call_hook(my_before_hook)
-register_after_tool_call_hook(my_after_hook)
-```
-
-## 문제 해결
-
-### 훅이 실행되지 않음
-- 크루 실행 전에 훅이 등록되었는지 확인
-- 이전 훅이 `False`를 반환했는지 확인 (실행 및 후속 훅 차단)
-- 훅 시그니처가 예상 타입과 일치하는지 확인
-
-### 입력 수정이 작동하지 않음
-- 제자리 수정 사용: `context.tool_input['key'] = value`
-- 딕셔너리를 교체하지 마세요: `context.tool_input = {}`
-
-### 결과 수정이 작동하지 않음
-- 후 훅에서 수정된 문자열을 반환
-- `None`을 반환하면 원본 결과가 유지됩니다
-- 도구가 실제로 결과를 반환했는지 확인
-
-### 도구가 예기치 않게 차단됨
-- 차단 조건에 대한 모든 전(before) 훅 확인
-- 훅 실행 순서 확인
-- 어떤 훅이 차단하는지 식별하기 위해 디버그 로깅 추가
-
-## 결론
-
-도구 호출 훅은 CrewAI에서 도구 실행을 제어하고 모니터링하는 강력한 기능을 제공합니다. 이를 사용하여 안전 가드레일, 승인 게이트, 입력 검증, 결과 정제, 로깅 및 분석을 구현하세요. 적절한 오류 처리 및 타입 안전성과 결합하면, 훅을 통해 포괄적인 관찰성을 갖춘 안전하고 프로덕션 준비가 된 에이전트 시스템을 구축할 수 있습니다.
-

docs/ko/observability/portkey.mdx

@@ -730,7 +730,9 @@ Portkey 대시보드에서 [구성 페이지](https://app.portkey.ai/configs)에
 - 로그를 필터링하기 위한 관련 메타데이터 수집
 - 액세스 권한 적용
 
-[Portkey App](https://app.portkey.ai/)를 통해 API 키를 생성하세요
+API 키 생성 방법:
+- [Portkey App](https://app.portkey.ai/)
+- [API Key Management API](/ko/api-reference/admin-api/control-plane/api-keys/create-api-key)
 
 Python SDK를 사용한 예시:
 ```python
@@ -753,7 +755,7 @@ api_key = portkey.api_keys.create(
 )
 ```
 
-자세한 키 관리 방법은 [Portkey 문서](https://portkey.ai/docs)를 참조하세요.
+자세한 키 관리 방법은 [API 키 문서](/ko/api-reference/admin-api/control-plane/api-keys/create-api-key)를 참조하세요.
 </Accordion>
 
 <Accordion title="4단계: 배포 및 모니터링">

docs/ko/tools/cloud-storage/overview.mdx

@@ -18,7 +18,7 @@ mode: "wide"
     파일을 Amazon S3 스토리지에 작성하고 업로드합니다.
   </Card>
 
-  <Card title="Bedrock Invoke Agent" icon="aws" href="/ko/tools/integration/bedrockinvokeagenttool">
+  <Card title="Bedrock Invoke Agent" icon="aws" href="/ko/tools/cloud-storage/bedrockinvokeagenttool">
     AI 기반 작업을 위해 Amazon Bedrock 에이전트를 호출합니다.
   </Card>
 

docs/ko/tools/tool-integrations/overview.mdx

@@ -11,7 +11,7 @@ mode: "wide"
   <Card
     title="Bedrock Invoke Agent Tool"
     icon="cloud"
-    href="/ko/tools/integration/bedrockinvokeagenttool"
+    href="/en/tools/tool-integrations/bedrockinvokeagenttool"
     color="#0891B2"
   >
     Invoke Amazon Bedrock Agents from CrewAI to orchestrate actions across AWS services.
@@ -20,7 +20,7 @@ mode: "wide"
   <Card
     title="CrewAI Automation Tool"
     icon="bolt"
-    href="/ko/tools/integration/crewaiautomationtool"
+    href="/en/tools/tool-integrations/crewaiautomationtool"
     color="#7C3AED"
   >
     Automate deployment and operations by integrating CrewAI with external platforms and workflows.

docs/pt-BR/concepts/knowledge.mdx

@@ -704,7 +704,7 @@ class KnowledgeMonitorListener(BaseEventListener):
 knowledge_monitor = KnowledgeMonitorListener()
 ```
 
-Para mais informações sobre como usar eventos, consulte a documentação [Event Listeners](/pt-BR/concepts/event-listener).
+Para mais informações sobre como usar eventos, consulte a documentação [Event Listeners](https://docs.crewai.com/concepts/event-listener).
 
 ### Fontes de Knowledge Personalizadas
 

docs/pt-BR/concepts/llms.mdx

@@ -725,7 +725,7 @@ O CrewAI suporta respostas em streaming de LLMs, permitindo que sua aplicação
     ```
 
     <Tip>
-      [Clique aqui](/pt-BR/concepts/event-listener#event-listeners) para mais detalhes
+      [Clique aqui](https://docs.crewai.com/concepts/event-listener#event-listeners) para mais detalhes 
     </Tip>
   </Tab>
 </Tabs>

docs/pt-BR/enterprise/features/marketplace.mdx

@@ -36,7 +36,7 @@ Você também pode baixar templates diretamente do marketplace clicando em `Down
   <Card title="Ferramentas & Integrações" href="/pt-BR/enterprise/features/tools-and-integrations" icon="wrench">
     Conecte apps externos e gerencie ferramentas internas que seus agentes podem usar.
   </Card>
-  <Card title="Repositório de Ferramentas" href="/pt-BR/enterprise/guides/tool-repository" icon="toolbox">
+  <Card title="Repositório de Ferramentas" href="/pt-BR/enterprise/features/tool-repository" icon="toolbox">
     Publique e instale ferramentas para ampliar as capacidades dos seus crews.
   </Card>
   <Card title="Repositório de Agentes" href="/pt-BR/enterprise/features/agent-repositories" icon="people-group">

docs/pt-BR/enterprise/features/tools-and-integrations.mdx

@@ -231,7 +231,7 @@ Ferramentas & Integrações é o hub central para conectar aplicações de terce
 ## Relacionados
 
 <CardGroup cols={2}>
-  <Card title="Repositório de Ferramentas" href="/pt-BR/enterprise/guides/tool-repository" icon="toolbox">
+  <Card title="Repositório de Ferramentas" href="/pt-BR/enterprise/features/tool-repository" icon="toolbox">
     Publique e instale ferramentas para ampliar as capacidades dos seus crews.
   </Card>
   <Card title="Automação com Webhook" href="/pt-BR/enterprise/guides/webhook-automation" icon="bolt">

docs/pt-BR/enterprise/guides/tool-repository.mdx

@@ -21,7 +21,7 @@ O repositório não é um sistema de controle de versões. Use Git para rastrear
 Antes de usar o Repositório de Ferramentas, certifique-se de que você possui:
 
 - Uma conta [CrewAI AMP](https://app.crewai.com)
-- [CrewAI CLI](/pt-BR/concepts/cli#cli) instalada
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) instalada
 - uv>=0.5.0 instalado. Veja [como atualizar](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
 - [Git](https://git-scm.com) instalado e configurado
 - Permissões de acesso para publicar ou instalar ferramentas em sua organização CrewAI AMP
@@ -66,7 +66,7 @@ Por padrão, as ferramentas são publicadas como privadas. Para tornar uma ferra
 crewai tool publish --public
 ```
 
-Para mais detalhes sobre como construir ferramentas, acesse [Criando suas próprias ferramentas](/pt-BR/concepts/tools#creating-your-own-tools).
+Para mais detalhes sobre como construir ferramentas, acesse [Criando suas próprias ferramentas](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
 
 ## Atualizando ferramentas
 

docs/pt-BR/enterprise/resources/frequently-asked-questions.mdx

@@ -49,7 +49,7 @@ mode: "wide"
 
         Para integrar a entrada humana na execução do agente, defina a flag `human_input` na definição da tarefa. Quando habilitada, o agente solicitará a entrada do usuário antes de entregar sua resposta final. Essa entrada pode fornecer contexto extra, esclarecer ambiguidades ou validar a saída do agente.
 
-        Para orientações detalhadas de implementação, veja nosso [guia Human-in-the-Loop](/pt-BR/enterprise/guides/human-in-the-loop).
+        Para orientações detalhadas de implementação, veja nosso [guia Human-in-the-Loop](/pt-BR/how-to/human-in-the-loop).
     </Accordion>
 
     <Accordion title="Quais opções avançadas de customização estão disponíveis para aprimorar e personalizar o comportamento e as capacidades dos agentes na CrewAI?">
@@ -142,7 +142,7 @@ mode: "wide"
     <Accordion title="Como posso criar ferramentas personalizadas para meus agentes CrewAI?">
         Você pode criar ferramentas personalizadas herdando da classe `BaseTool` fornecida pela CrewAI ou usando o decorador de ferramenta. Herdar envolve definir uma nova classe que herda de `BaseTool`, especificando o nome, a descrição e o método `_run` para a lógica operacional. O decorador de ferramenta permite criar um objeto `Tool` diretamente com os atributos necessários e uma lógica funcional.
 
-        <Card href="/pt-BR/learn/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
+        <Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
     </Accordion>
 
     <Accordion title="Como controlar o número máximo de solicitações por minuto que toda a crew pode realizar?">

docs/pt-BR/learn/execution-hooks.mdx

@@ -1,379 +0,0 @@
----
-title: Visão Geral dos Hooks de Execução
-description: Entendendo e usando hooks de execução no CrewAI para controle fino sobre operações de agentes
-mode: "wide"
----
-
-Os Hooks de Execução fornecem controle fino sobre o comportamento em tempo de execução dos seus agentes CrewAI. Diferentemente dos hooks de kickoff que são executados antes e depois da execução da crew, os hooks de execução interceptam operações específicas durante a execução do agente, permitindo que você modifique comportamentos, implemente verificações de segurança e adicione monitoramento abrangente.
-
-## Tipos de Hooks de Execução
-
-O CrewAI fornece duas categorias principais de hooks de execução:
-
-### 1. [Hooks de Chamada LLM](/learn/llm-hooks)
-
-Controle e monitore interações com o modelo de linguagem:
-- **Antes da Chamada LLM**: Modifique prompts, valide entradas, implemente gates de aprovação
-- **Depois da Chamada LLM**: Transforme respostas, sanitize saídas, atualize histórico de conversação
-
-**Casos de Uso:**
-- Limitação de iterações
-- Rastreamento de custos e monitoramento de uso de tokens
-- Sanitização de respostas e filtragem de conteúdo
-- Aprovação humana para chamadas LLM
-- Adição de diretrizes de segurança ou contexto
-- Logging de debug e inspeção de requisição/resposta
-
-[Ver Documentação de Hooks LLM →](/learn/llm-hooks)
-
-### 2. [Hooks de Chamada de Ferramenta](/learn/tool-hooks)
-
-Controle e monitore execução de ferramentas:
-- **Antes da Chamada de Ferramenta**: Modifique entradas, valide parâmetros, bloqueie operações perigosas
-- **Depois da Chamada de Ferramenta**: Transforme resultados, sanitize saídas, registre detalhes de execução
-
-**Casos de Uso:**
-- Guardrails de segurança para operações destrutivas
-- Aprovação humana para ações sensíveis
-- Validação e sanitização de entrada
-- Cache de resultados e limitação de taxa
-- Análise de uso de ferramentas
-- Logging de debug e monitoramento
-
-[Ver Documentação de Hooks de Ferramenta →](/learn/tool-hooks)
-
-## Métodos de Registro
-
-### 1. Hooks Baseados em Decoradores (Recomendado)
-
-A maneira mais limpa e pythônica de registrar hooks:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call, before_tool_call, after_tool_call
-
-@before_llm_call
-def limit_iterations(context):
-    """Previne loops infinitos limitando iterações."""
-    if context.iterations > 10:
-        return False  # Bloquear execução
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    """Remove dados sensíveis das respostas do LLM."""
-    if "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[CENSURADO]")
-    return None
-
-@before_tool_call
-def block_dangerous_tools(context):
-    """Bloqueia operações destrutivas."""
-    if context.tool_name == "delete_database":
-        return False  # Bloquear execução
-    return None
-
-@after_tool_call
-def log_tool_result(context):
-    """Registra execução de ferramenta."""
-    print(f"Ferramenta {context.tool_name} concluída")
-    return None
-```
-
-### 2. Hooks com Escopo de Crew
-
-Aplica hooks apenas a instâncias específicas de crew:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_llm_call_crew, after_tool_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # Aplica-se apenas a esta crew
-        print(f"Chamada LLM em {self.__class__.__name__}")
-        return None
-
-    @after_tool_call_crew
-    def log_results(self, context):
-        # Logging específico da crew
-        print(f"Resultado da ferramenta: {context.tool_result[:50]}...")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential
-        )
-```
-
-## Fluxo de Execução de Hooks
-
-### Fluxo de Chamada LLM
-
-```
-Agente precisa chamar LLM
-    ↓
-[Hooks Antes da Chamada LLM Executam]
-    ├→ Hook 1: Validar contagem de iterações
-    ├→ Hook 2: Adicionar contexto de segurança
-    └→ Hook 3: Registrar requisição
-    ↓
-Se algum hook retornar False:
-    ├→ Bloquear chamada LLM
-    └→ Lançar ValueError
-    ↓
-Se todos os hooks retornarem True/None:
-    ├→ Chamada LLM prossegue
-    └→ Resposta gerada
-    ↓
-[Hooks Depois da Chamada LLM Executam]
-    ├→ Hook 1: Sanitizar resposta
-    ├→ Hook 2: Registrar resposta
-    └→ Hook 3: Atualizar métricas
-    ↓
-Resposta final retornada
-```
-
-### Fluxo de Chamada de Ferramenta
-
-```
-Agente precisa executar ferramenta
-    ↓
-[Hooks Antes da Chamada de Ferramenta Executam]
-    ├→ Hook 1: Verificar se ferramenta é permitida
-    ├→ Hook 2: Validar entradas
-    └→ Hook 3: Solicitar aprovação se necessário
-    ↓
-Se algum hook retornar False:
-    ├→ Bloquear execução da ferramenta
-    └→ Retornar mensagem de erro
-    ↓
-Se todos os hooks retornarem True/None:
-    ├→ Execução da ferramenta prossegue
-    └→ Resultado gerado
-    ↓
-[Hooks Depois da Chamada de Ferramenta Executam]
-    ├→ Hook 1: Sanitizar resultado
-    ├→ Hook 2: Fazer cache do resultado
-    └→ Hook 3: Registrar métricas
-    ↓
-Resultado final retornado
-```
-
-## Objetos de Contexto de Hook
-
-### LLMCallHookContext
-
-Fornece acesso ao estado de execução do LLM:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # Acesso completo ao executor
-    messages: list               # Lista de mensagens mutável
-    agent: Agent                 # Agente atual
-    task: Task                   # Tarefa atual
-    crew: Crew                   # Instância da crew
-    llm: BaseLLM                 # Instância do LLM
-    iterations: int              # Iteração atual
-    response: str | None         # Resposta do LLM (hooks posteriores)
-```
-
-### ToolCallHookContext
-
-Fornece acesso ao estado de execução da ferramenta:
-
-```python
-class ToolCallHookContext:
-    tool_name: str               # Ferramenta sendo chamada
-    tool_input: dict             # Parâmetros de entrada mutáveis
-    tool: CrewStructuredTool     # Instância da ferramenta
-    agent: Agent | None          # Agente executando
-    task: Task | None            # Tarefa atual
-    crew: Crew | None            # Instância da crew
-    tool_result: str | None      # Resultado da ferramenta (hooks posteriores)
-```
-
-## Padrões Comuns
-
-### Segurança e Validação
-
-```python
-@before_tool_call
-def safety_check(context):
-    """Bloqueia operações destrutivas."""
-    dangerous = ['delete_file', 'drop_table', 'system_shutdown']
-    if context.tool_name in dangerous:
-        print(f"🛑 Bloqueado: {context.tool_name}")
-        return False
-    return None
-
-@before_llm_call
-def iteration_limit(context):
-    """Previne loops infinitos."""
-    if context.iterations > 15:
-        print("⛔ Máximo de iterações excedido")
-        return False
-    return None
-```
-
-### Humano no Loop
-
-```python
-@before_tool_call
-def require_approval(context):
-    """Requer aprovação para operações sensíveis."""
-    sensitive = ['send_email', 'make_payment', 'post_message']
-
-    if context.tool_name in sensitive:
-        response = context.request_human_input(
-            prompt=f"Aprovar {context.tool_name}?",
-            default_message="Digite 'sim' para aprovar:"
-        )
-
-        if response.lower() != 'sim':
-            return False
-
-    return None
-```
-
-### Monitoramento e Análise
-
-```python
-from collections import defaultdict
-import time
-
-metrics = defaultdict(lambda: {'count': 0, 'total_time': 0})
-
-@before_tool_call
-def start_timer(context):
-    context.tool_input['_start'] = time.time()
-    return None
-
-@after_tool_call
-def track_metrics(context):
-    start = context.tool_input.get('_start', time.time())
-    duration = time.time() - start
-
-    metrics[context.tool_name]['count'] += 1
-    metrics[context.tool_name]['total_time'] += duration
-
-    return None
-```
-
-## Gerenciamento de Hooks
-
-### Limpar Todos os Hooks
-
-```python
-from crewai.hooks import clear_all_global_hooks
-
-# Limpa todos os hooks de uma vez
-result = clear_all_global_hooks()
-print(f"Limpou {result['total']} hooks")
-```
-
-### Limpar Tipos Específicos de Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks
-)
-
-# Limpar tipos específicos
-llm_before_count = clear_before_llm_call_hooks()
-tool_after_count = clear_after_tool_call_hooks()
-```
-
-## Melhores Práticas
-
-### 1. Mantenha os Hooks Focados
-Cada hook deve ter uma responsabilidade única e clara.
-
-### 2. Trate Erros Graciosamente
-```python
-@before_llm_call
-def safe_hook(context):
-    try:
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"Erro no hook: {e}")
-        return None  # Permitir execução apesar do erro
-```
-
-### 3. Modifique o Contexto In-Place
-```python
-# ✅ Correto - modificar in-place
-@before_llm_call
-def add_context(context):
-    context.messages.append({"role": "system", "content": "Seja conciso"})
-
-# ❌ Errado - substitui referência
-@before_llm_call
-def wrong_approach(context):
-    context.messages = [{"role": "system", "content": "Seja conciso"}]
-```
-
-### 4. Use Type Hints
-```python
-from crewai.hooks import LLMCallHookContext, ToolCallHookContext
-
-def my_llm_hook(context: LLMCallHookContext) -> bool | None:
-    return None
-
-def my_tool_hook(context: ToolCallHookContext) -> str | None:
-    return None
-```
-
-### 5. Limpe em Testes
-```python
-import pytest
-from crewai.hooks import clear_all_global_hooks
-
-@pytest.fixture(autouse=True)
-def clean_hooks():
-    """Reseta hooks antes de cada teste."""
-    yield
-    clear_all_global_hooks()
-```
-
-## Quando Usar Qual Hook
-
-### Use Hooks LLM Quando:
-- Implementar limites de iteração
-- Adicionar contexto ou diretrizes de segurança aos prompts
-- Rastrear uso de tokens e custos
-- Sanitizar ou transformar respostas
-- Implementar gates de aprovação para chamadas LLM
-- Fazer debug de interações de prompt/resposta
-
-### Use Hooks de Ferramenta Quando:
-- Bloquear operações perigosas ou destrutivas
-- Validar entradas de ferramenta antes da execução
-- Implementar gates de aprovação para ações sensíveis
-- Fazer cache de resultados de ferramenta
-- Rastrear uso e performance de ferramentas
-- Sanitizar saídas de ferramenta
-- Limitar taxa de chamadas de ferramenta
-
-### Use Ambos Quando:
-Construir sistemas abrangentes de observabilidade, segurança ou aprovação que precisam monitorar todas as operações do agente.
-
-## Documentação Relacionada
-
-- [Hooks de Chamada LLM →](/learn/llm-hooks) - Documentação detalhada de hooks LLM
-- [Hooks de Chamada de Ferramenta →](/learn/tool-hooks) - Documentação detalhada de hooks de ferramenta
-- [Hooks Antes e Depois do Kickoff →](/learn/before-and-after-kickoff-hooks) - Hooks do ciclo de vida da crew
-- [Humano no Loop →](/learn/human-in-the-loop) - Padrões de entrada humana
-
-## Conclusão
-
-Os Hooks de Execução fornecem controle poderoso sobre o comportamento em tempo de execução do agente. Use-os para implementar guardrails de segurança, fluxos de trabalho de aprovação, monitoramento abrangente e lógica de negócio personalizada. Combinados com tratamento adequado de erros, segurança de tipos e considerações de performance, os hooks permitem sistemas de agentes seguros, prontos para produção e observáveis.

docs/pt-BR/learn/hierarchical-process.mdx

@@ -96,7 +96,7 @@ project_crew = Crew(
 ```
 
 <Tip>
-    Para mais detalhes sobre a criação e personalização de um agente gerente, confira a [documentação do Custom Manager Agent](/pt-BR/learn/custom-manager-agent).
+    Para mais detalhes sobre a criação e personalização de um agente gerente, confira a [documentação do Custom Manager Agent](https://docs.crewai.com/how-to/custom-manager-agent#custom-manager-agent).
 </Tip>
 
 

docs/pt-BR/learn/llm-hooks.mdx

@@ -1,388 +0,0 @@
----
-title: Hooks de Chamada LLM
-description: Aprenda a usar hooks de chamada LLM para interceptar, modificar e controlar interações com modelos de linguagem no CrewAI
-mode: "wide"
----
-
-Os Hooks de Chamada LLM fornecem controle fino sobre interações com modelos de linguagem durante a execução do agente. Esses hooks permitem interceptar chamadas LLM, modificar prompts, transformar respostas, implementar gates de aprovação e adicionar logging ou monitoramento personalizado.
-
-## Visão Geral
-
-Os hooks LLM são executados em dois pontos críticos:
-- **Antes da Chamada LLM**: Modificar mensagens, validar entradas ou bloquear execução
-- **Depois da Chamada LLM**: Transformar respostas, sanitizar saídas ou modificar histórico de conversação
-
-## Tipos de Hook
-
-### Hooks Antes da Chamada LLM
-
-Executados antes de cada chamada LLM, esses hooks podem:
-- Inspecionar e modificar mensagens enviadas ao LLM
-- Bloquear execução LLM com base em condições
-- Implementar limitação de taxa ou gates de aprovação
-- Adicionar contexto ou mensagens do sistema
-- Registrar detalhes da requisição
-
-**Assinatura:**
-```python
-def before_hook(context: LLMCallHookContext) -> bool | None:
-    # Retorne False para bloquear execução
-    # Retorne True ou None para permitir execução
-    ...
-```
-
-### Hooks Depois da Chamada LLM
-
-Executados depois de cada chamada LLM, esses hooks podem:
-- Modificar ou sanitizar respostas do LLM
-- Adicionar metadados ou formatação
-- Registrar detalhes da resposta
-- Atualizar histórico de conversação
-- Implementar filtragem de conteúdo
-
-**Assinatura:**
-```python
-def after_hook(context: LLMCallHookContext) -> str | None:
-    # Retorne string de resposta modificada
-    # Retorne None para manter resposta original
-    ...
-```
-
-## Contexto do Hook LLM
-
-O objeto `LLMCallHookContext` fornece acesso abrangente ao estado de execução:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # Referência completa ao executor
-    messages: list               # Lista de mensagens mutável
-    agent: Agent                 # Agente atual
-    task: Task                   # Tarefa atual
-    crew: Crew                   # Instância da crew
-    llm: BaseLLM                 # Instância do LLM
-    iterations: int              # Contagem de iteração atual
-    response: str | None         # Resposta do LLM (apenas hooks posteriores)
-```
-
-### Modificando Mensagens
-
-**Importante:** Sempre modifique mensagens in-place:
-
-```python
-# ✅ Correto - modificar in-place
-def add_context(context: LLMCallHookContext) -> None:
-    context.messages.append({"role": "system", "content": "Seja conciso"})
-
-# ❌ Errado - substitui referência da lista
-def wrong_approach(context: LLMCallHookContext) -> None:
-    context.messages = [{"role": "system", "content": "Seja conciso"}]
-```
-
-## Métodos de Registro
-
-### 1. Registro Baseado em Decoradores (Recomendado)
-
-Use decoradores para sintaxe mais limpa:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call
-
-@before_llm_call
-def validate_iteration_count(context):
-    """Valida a contagem de iterações."""
-    if context.iterations > 10:
-        print("⚠️ Máximo de iterações excedido")
-        return False  # Bloquear execução
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    """Remove dados sensíveis."""
-    if context.response and "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[CENSURADO]")
-    return None
-```
-
-### 2. Hooks com Escopo de Crew
-
-Registre hooks para uma instância específica de crew:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_llm_call_crew, after_llm_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # Aplica-se apenas a esta crew
-        if context.iterations == 0:
-            print(f"Iniciando tarefa: {context.task.description}")
-        return None
-    
-    @after_llm_call_crew
-    def log_responses(self, context):
-        # Logging específico da crew
-        print(f"Comprimento da resposta: {len(context.response)}")
-        return None
-    
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## Casos de Uso Comuns
-
-### 1. Limitação de Iterações
-
-```python
-@before_llm_call
-def limit_iterations(context: LLMCallHookContext) -> bool | None:
-    """Previne loops infinitos limitando iterações."""
-    max_iterations = 15
-    if context.iterations > max_iterations:
-        print(f"⛔ Bloqueado: Excedeu {max_iterations} iterações")
-        return False  # Bloquear execução
-    return None
-```
-
-### 2. Gate de Aprovação Humana
-
-```python
-@before_llm_call
-def require_approval(context: LLMCallHookContext) -> bool | None:
-    """Requer aprovação após certas iterações."""
-    if context.iterations > 5:
-        response = context.request_human_input(
-            prompt=f"Iteração {context.iterations}: Aprovar chamada LLM?",
-            default_message="Pressione Enter para aprovar, ou digite 'não' para bloquear:"
-        )
-        if response.lower() == "não":
-            print("🚫 Chamada LLM bloqueada pelo usuário")
-            return False
-    return None
-```
-
-### 3. Adicionando Contexto do Sistema
-
-```python
-@before_llm_call
-def add_guardrails(context: LLMCallHookContext) -> None:
-    """Adiciona diretrizes de segurança a cada chamada LLM."""
-    context.messages.append({
-        "role": "system",
-        "content": "Garanta que as respostas sejam factuais e cite fontes quando possível."
-    })
-    return None
-```
-
-### 4. Sanitização de Resposta
-
-```python
-@after_llm_call
-def sanitize_sensitive_data(context: LLMCallHookContext) -> str | None:
-    """Remove padrões sensíveis."""
-    if not context.response:
-        return None
-    
-    import re
-    sanitized = context.response
-    sanitized = re.sub(r'\b\d{3}\.\d{3}\.\d{3}-\d{2}\b', '[CPF-CENSURADO]', sanitized)
-    sanitized = re.sub(r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b', '[CARTÃO-CENSURADO]', sanitized)
-    
-    return sanitized
-```
-
-### 5. Rastreamento de Custos
-
-```python
-import tiktoken
-
-@before_llm_call
-def track_token_usage(context: LLMCallHookContext) -> None:
-    """Rastreia tokens de entrada."""
-    encoding = tiktoken.get_encoding("cl100k_base")
-    total_tokens = sum(
-        len(encoding.encode(msg.get("content", ""))) 
-        for msg in context.messages
-    )
-    print(f"📊 Tokens de entrada: ~{total_tokens}")
-    return None
-
-@after_llm_call
-def track_response_tokens(context: LLMCallHookContext) -> None:
-    """Rastreia tokens de resposta."""
-    if context.response:
-        encoding = tiktoken.get_encoding("cl100k_base")
-        tokens = len(encoding.encode(context.response))
-        print(f"📊 Tokens de resposta: ~{tokens}")
-    return None
-```
-
-### 6. Logging de Debug
-
-```python
-@before_llm_call
-def debug_request(context: LLMCallHookContext) -> None:
-    """Debug de requisição LLM."""
-    print(f"""
-    🔍 Debug de Chamada LLM:
-    - Agente: {context.agent.role}
-    - Tarefa: {context.task.description[:50]}...
-    - Iteração: {context.iterations}
-    - Contagem de Mensagens: {len(context.messages)}
-    - Última Mensagem: {context.messages[-1] if context.messages else 'Nenhuma'}
-    """)
-    return None
-
-@after_llm_call
-def debug_response(context: LLMCallHookContext) -> None:
-    """Debug de resposta LLM."""
-    if context.response:
-        print(f"✅ Preview da Resposta: {context.response[:100]}...")
-    return None
-```
-
-## Gerenciamento de Hooks
-
-### Desregistrando Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_llm_call_hook,
-    unregister_after_llm_call_hook
-)
-
-# Desregistrar hook específico
-def my_hook(context):
-    ...
-
-register_before_llm_call_hook(my_hook)
-# Mais tarde...
-unregister_before_llm_call_hook(my_hook)  # Retorna True se encontrado
-```
-
-### Limpando Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_all_llm_call_hooks
-)
-
-# Limpar tipo específico de hook
-count = clear_before_llm_call_hooks()
-print(f"Limpou {count} hooks antes")
-
-# Limpar todos os hooks LLM
-before_count, after_count = clear_all_llm_call_hooks()
-print(f"Limpou {before_count} hooks antes e {after_count} hooks depois")
-```
-
-## Padrões Avançados
-
-### Execução Condicional de Hook
-
-```python
-@before_llm_call
-def conditional_blocking(context: LLMCallHookContext) -> bool | None:
-    """Bloqueia apenas em condições específicas."""
-    # Bloquear apenas para agentes específicos
-    if context.agent.role == "researcher" and context.iterations > 10:
-        return False
-    
-    # Bloquear apenas para tarefas específicas
-    if "sensível" in context.task.description.lower() and context.iterations > 5:
-        return False
-    
-    return None
-```
-
-### Modificações com Consciência de Contexto
-
-```python
-@before_llm_call
-def adaptive_prompting(context: LLMCallHookContext) -> None:
-    """Adiciona contexto diferente baseado na iteração."""
-    if context.iterations == 0:
-        context.messages.append({
-            "role": "system",
-            "content": "Comece com uma visão geral de alto nível."
-        })
-    elif context.iterations > 3:
-        context.messages.append({
-            "role": "system",
-            "content": "Foque em detalhes específicos e forneça exemplos."
-        })
-    return None
-```
-
-## Melhores Práticas
-
-1. **Mantenha Hooks Focados**: Cada hook deve ter uma responsabilidade única
-2. **Evite Computação Pesada**: Hooks executam em cada chamada LLM
-3. **Trate Erros Graciosamente**: Use try-except para prevenir falhas de hooks
-4. **Use Type Hints**: Aproveite `LLMCallHookContext` para melhor suporte IDE
-5. **Documente Comportamento do Hook**: Especialmente para condições de bloqueio
-6. **Teste Hooks Independentemente**: Teste unitário de hooks antes de usar em produção
-7. **Limpe Hooks em Testes**: Use `clear_all_llm_call_hooks()` entre execuções de teste
-8. **Modifique In-Place**: Sempre modifique `context.messages` in-place, nunca substitua
-
-## Tratamento de Erros
-
-```python
-@before_llm_call
-def safe_hook(context: LLMCallHookContext) -> bool | None:
-    try:
-        # Sua lógica de hook
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"⚠️ Erro no hook: {e}")
-        # Decida: permitir ou bloquear em erro
-        return None  # Permitir execução apesar do erro
-```
-
-## Segurança de Tipos
-
-```python
-from crewai.hooks import LLMCallHookContext, BeforeLLMCallHookType, AfterLLMCallHookType
-
-# Anotações de tipo explícitas
-def my_before_hook(context: LLMCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: LLMCallHookContext) -> str | None:
-    return None
-
-# Registro type-safe
-register_before_llm_call_hook(my_before_hook)
-register_after_llm_call_hook(my_after_hook)
-```
-
-## Solução de Problemas
-
-### Hook Não Está Executando
-- Verifique se o hook está registrado antes da execução da crew
-- Verifique se hook anterior retornou `False` (bloqueia hooks subsequentes)
-- Garanta que assinatura do hook corresponda ao tipo esperado
-
-### Modificações de Mensagem Não Persistem
-- Use modificações in-place: `context.messages.append()`
-- Não substitua a lista: `context.messages = []`
-
-### Modificações de Resposta Não Funcionam
-- Retorne a string modificada dos hooks posteriores
-- Retornar `None` mantém a resposta original
-
-## Conclusão
-
-Os Hooks de Chamada LLM fornecem capacidades poderosas para controlar e monitorar interações com modelos de linguagem no CrewAI. Use-os para implementar guardrails de segurança, gates de aprovação, logging, rastreamento de custos e sanitização de respostas. Combinados com tratamento adequado de erros e segurança de tipos, os hooks permitem sistemas de agentes robustos e prontos para produção.
-

docs/pt-BR/learn/tool-hooks.mdx

@@ -1,498 +0,0 @@
----
-title: Hooks de Chamada de Ferramenta
-description: Aprenda a usar hooks de chamada de ferramenta para interceptar, modificar e controlar execução de ferramentas no CrewAI
-mode: "wide"
----
-
-Os Hooks de Chamada de Ferramenta fornecem controle fino sobre a execução de ferramentas durante operações do agente. Esses hooks permitem interceptar chamadas de ferramenta, modificar entradas, transformar saídas, implementar verificações de segurança e adicionar logging ou monitoramento abrangente.
-
-## Visão Geral
-
-Os hooks de ferramenta são executados em dois pontos críticos:
-- **Antes da Chamada de Ferramenta**: Modificar entradas, validar parâmetros ou bloquear execução
-- **Depois da Chamada de Ferramenta**: Transformar resultados, sanitizar saídas ou registrar detalhes de execução
-
-## Tipos de Hook
-
-### Hooks Antes da Chamada de Ferramenta
-
-Executados antes de cada execução de ferramenta, esses hooks podem:
-- Inspecionar e modificar entradas de ferramenta
-- Bloquear execução de ferramenta com base em condições
-- Implementar gates de aprovação para operações perigosas
-- Validar parâmetros
-- Registrar invocações de ferramenta
-
-**Assinatura:**
-```python
-def before_hook(context: ToolCallHookContext) -> bool | None:
-    # Retorne False para bloquear execução
-    # Retorne True ou None para permitir execução
-    ...
-```
-
-### Hooks Depois da Chamada de Ferramenta
-
-Executados depois de cada execução de ferramenta, esses hooks podem:
-- Modificar ou sanitizar resultados de ferramenta
-- Adicionar metadados ou formatação
-- Registrar resultados de execução
-- Implementar validação de resultado
-- Transformar formatos de saída
-
-**Assinatura:**
-```python
-def after_hook(context: ToolCallHookContext) -> str | None:
-    # Retorne string de resultado modificado
-    # Retorne None para manter resultado original
-    ...
-```
-
-## Contexto do Hook de Ferramenta
-
-O objeto `ToolCallHookContext` fornece acesso abrangente ao estado de execução da ferramenta:
-
-```python
-class ToolCallHookContext:
-    tool_name: str                    # Nome da ferramenta sendo chamada
-    tool_input: dict[str, Any]        # Parâmetros de entrada mutáveis da ferramenta
-    tool: CrewStructuredTool          # Referência da instância da ferramenta
-    agent: Agent | BaseAgent | None   # Agente executando a ferramenta
-    task: Task | None                 # Tarefa atual
-    crew: Crew | None                 # Instância da crew
-    tool_result: str | None           # Resultado da ferramenta (apenas hooks posteriores)
-```
-
-### Modificando Entradas de Ferramenta
-
-**Importante:** Sempre modifique entradas de ferramenta in-place:
-
-```python
-# ✅ Correto - modificar in-place
-def sanitize_input(context: ToolCallHookContext) -> None:
-    context.tool_input['query'] = context.tool_input['query'].lower()
-
-# ❌ Errado - substitui referência do dict
-def wrong_approach(context: ToolCallHookContext) -> None:
-    context.tool_input = {'query': 'nova consulta'}
-```
-
-## Métodos de Registro
-
-### 1. Registro Baseado em Decoradores (Recomendado)
-
-Use decoradores para sintaxe mais limpa:
-
-```python
-from crewai.hooks import before_tool_call, after_tool_call
-
-@before_tool_call
-def block_dangerous_tools(context):
-    """Bloqueia ferramentas perigosas."""
-    dangerous_tools = ['delete_database', 'drop_table', 'rm_rf']
-    if context.tool_name in dangerous_tools:
-        print(f"⛔ Ferramenta perigosa bloqueada: {context.tool_name}")
-        return False  # Bloquear execução
-    return None
-
-@after_tool_call
-def sanitize_results(context):
-    """Sanitiza resultados."""
-    if context.tool_result and "password" in context.tool_result.lower():
-        return context.tool_result.replace("password", "[CENSURADO]")
-    return None
-```
-
-### 2. Hooks com Escopo de Crew
-
-Registre hooks para uma instância específica de crew:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_tool_call_crew, after_tool_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_tool_call_crew
-    def validate_tool_inputs(self, context):
-        # Aplica-se apenas a esta crew
-        if context.tool_name == "web_search":
-            if not context.tool_input.get('query'):
-                print("❌ Consulta de busca inválida")
-                return False
-        return None
-    
-    @after_tool_call_crew
-    def log_tool_results(self, context):
-        # Logging de ferramenta específico da crew
-        print(f"✅ {context.tool_name} concluída")
-        return None
-    
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## Casos de Uso Comuns
-
-### 1. Guardrails de Segurança
-
-```python
-@before_tool_call
-def safety_check(context: ToolCallHookContext) -> bool | None:
-    """Bloqueia ferramentas que podem causar danos."""
-    destructive_tools = [
-        'delete_file',
-        'drop_table',
-        'remove_user',
-        'system_shutdown'
-    ]
-    
-    if context.tool_name in destructive_tools:
-        print(f"🛑 Ferramenta destrutiva bloqueada: {context.tool_name}")
-        return False
-    
-    # Avisar em operações sensíveis
-    sensitive_tools = ['send_email', 'post_to_social_media', 'charge_payment']
-    if context.tool_name in sensitive_tools:
-        print(f"⚠️  Executando ferramenta sensível: {context.tool_name}")
-    
-    return None
-```
-
-### 2. Gate de Aprovação Humana
-
-```python
-@before_tool_call
-def require_approval_for_actions(context: ToolCallHookContext) -> bool | None:
-    """Requer aprovação para ações específicas."""
-    approval_required = [
-        'send_email',
-        'make_purchase',
-        'delete_file',
-        'post_message'
-    ]
-    
-    if context.tool_name in approval_required:
-        response = context.request_human_input(
-            prompt=f"Aprovar {context.tool_name}?",
-            default_message=f"Entrada: {context.tool_input}\nDigite 'sim' para aprovar:"
-        )
-        
-        if response.lower() != 'sim':
-            print(f"❌ Execução de ferramenta negada: {context.tool_name}")
-            return False
-    
-    return None
-```
-
-### 3. Validação e Sanitização de Entrada
-
-```python
-@before_tool_call
-def validate_and_sanitize_inputs(context: ToolCallHookContext) -> bool | None:
-    """Valida e sanitiza entradas."""
-    # Validar consultas de busca
-    if context.tool_name == 'web_search':
-        query = context.tool_input.get('query', '')
-        if len(query) < 3:
-            print("❌ Consulta de busca muito curta")
-            return False
-        
-        # Sanitizar consulta
-        context.tool_input['query'] = query.strip().lower()
-    
-    # Validar caminhos de arquivo
-    if context.tool_name == 'read_file':
-        path = context.tool_input.get('path', '')
-        if '..' in path or path.startswith('/'):
-            print("❌ Caminho de arquivo inválido")
-            return False
-    
-    return None
-```
-
-### 4. Sanitização de Resultado
-
-```python
-@after_tool_call
-def sanitize_sensitive_data(context: ToolCallHookContext) -> str | None:
-    """Sanitiza dados sensíveis."""
-    if not context.tool_result:
-        return None
-    
-    import re
-    result = context.tool_result
-    
-    # Remover chaves de API
-    result = re.sub(
-        r'(api[_-]?key|token)["\']?\s*[:=]\s*["\']?[\w-]+',
-        r'\1: [CENSURADO]',
-        result,
-        flags=re.IGNORECASE
-    )
-    
-    # Remover endereços de email
-    result = re.sub(
-        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
-        '[EMAIL-CENSURADO]',
-        result
-    )
-    
-    # Remover números de cartão de crédito
-    result = re.sub(
-        r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b',
-        '[CARTÃO-CENSURADO]',
-        result
-    )
-    
-    return result
-```
-
-### 5. Análise de Uso de Ferramenta
-
-```python
-import time
-from collections import defaultdict
-
-tool_stats = defaultdict(lambda: {'count': 0, 'total_time': 0, 'failures': 0})
-
-@before_tool_call
-def start_timer(context: ToolCallHookContext) -> None:
-    context.tool_input['_start_time'] = time.time()
-    return None
-
-@after_tool_call
-def track_tool_usage(context: ToolCallHookContext) -> None:
-    start_time = context.tool_input.get('_start_time', time.time())
-    duration = time.time() - start_time
-    
-    tool_stats[context.tool_name]['count'] += 1
-    tool_stats[context.tool_name]['total_time'] += duration
-    
-    if not context.tool_result or 'error' in context.tool_result.lower():
-        tool_stats[context.tool_name]['failures'] += 1
-    
-    print(f"""
-    📊 Estatísticas da Ferramenta {context.tool_name}:
-    - Execuções: {tool_stats[context.tool_name]['count']}
-    - Tempo Médio: {tool_stats[context.tool_name]['total_time'] / tool_stats[context.tool_name]['count']:.2f}s
-    - Falhas: {tool_stats[context.tool_name]['failures']}
-    """)
-    
-    return None
-```
-
-### 6. Limitação de Taxa
-
-```python
-from collections import defaultdict
-from datetime import datetime, timedelta
-
-tool_call_history = defaultdict(list)
-
-@before_tool_call
-def rate_limit_tools(context: ToolCallHookContext) -> bool | None:
-    """Limita taxa de chamadas de ferramenta."""
-    tool_name = context.tool_name
-    now = datetime.now()
-    
-    # Limpar entradas antigas (mais antigas que 1 minuto)
-    tool_call_history[tool_name] = [
-        call_time for call_time in tool_call_history[tool_name]
-        if now - call_time < timedelta(minutes=1)
-    ]
-    
-    # Verificar limite de taxa (máximo 10 chamadas por minuto)
-    if len(tool_call_history[tool_name]) >= 10:
-        print(f"🚫 Limite de taxa excedido para {tool_name}")
-        return False
-    
-    # Registrar esta chamada
-    tool_call_history[tool_name].append(now)
-    return None
-```
-
-### 7. Logging de Debug
-
-```python
-@before_tool_call
-def debug_tool_call(context: ToolCallHookContext) -> None:
-    """Debug de chamada de ferramenta."""
-    print(f"""
-    🔍 Debug de Chamada de Ferramenta:
-    - Ferramenta: {context.tool_name}
-    - Agente: {context.agent.role if context.agent else 'Desconhecido'}
-    - Tarefa: {context.task.description[:50] if context.task else 'Desconhecida'}...
-    - Entrada: {context.tool_input}
-    """)
-    return None
-
-@after_tool_call
-def debug_tool_result(context: ToolCallHookContext) -> None:
-    """Debug de resultado de ferramenta."""
-    if context.tool_result:
-        result_preview = context.tool_result[:200]
-        print(f"✅ Preview do Resultado: {result_preview}...")
-    else:
-        print("⚠️  Nenhum resultado retornado")
-    return None
-```
-
-## Gerenciamento de Hooks
-
-### Desregistrando Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_tool_call_hook,
-    unregister_after_tool_call_hook
-)
-
-# Desregistrar hook específico
-def my_hook(context):
-    ...
-
-register_before_tool_call_hook(my_hook)
-# Mais tarde...
-success = unregister_before_tool_call_hook(my_hook)
-print(f"Desregistrado: {success}")
-```
-
-### Limpando Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks,
-    clear_all_tool_call_hooks
-)
-
-# Limpar tipo específico de hook
-count = clear_before_tool_call_hooks()
-print(f"Limpou {count} hooks antes")
-
-# Limpar todos os hooks de ferramenta
-before_count, after_count = clear_all_tool_call_hooks()
-print(f"Limpou {before_count} hooks antes e {after_count} hooks depois")
-```
-
-## Padrões Avançados
-
-### Execução Condicional de Hook
-
-```python
-@before_tool_call
-def conditional_blocking(context: ToolCallHookContext) -> bool | None:
-    """Bloqueia apenas em condições específicas."""
-    # Bloquear apenas para agentes específicos
-    if context.agent and context.agent.role == "junior_agent":
-        if context.tool_name in ['delete_file', 'send_email']:
-            print(f"❌ Agentes júnior não podem usar {context.tool_name}")
-            return False
-    
-    # Bloquear apenas durante tarefas específicas
-    if context.task and "sensível" in context.task.description.lower():
-        if context.tool_name == 'web_search':
-            print("❌ Busca na web bloqueada para tarefas sensíveis")
-            return False
-    
-    return None
-```
-
-### Modificação de Entrada com Consciência de Contexto
-
-```python
-@before_tool_call
-def enhance_tool_inputs(context: ToolCallHookContext) -> None:
-    """Adiciona contexto baseado no papel do agente."""
-    # Adicionar contexto baseado no papel do agente
-    if context.agent and context.agent.role == "researcher":
-        if context.tool_name == 'web_search':
-            # Adicionar restrições de domínio para pesquisadores
-            context.tool_input['domains'] = ['edu', 'gov', 'org']
-    
-    # Adicionar contexto baseado na tarefa
-    if context.task and "urgente" in context.task.description.lower():
-        if context.tool_name == 'send_email':
-            context.tool_input['priority'] = 'high'
-    
-    return None
-```
-
-## Melhores Práticas
-
-1. **Mantenha Hooks Focados**: Cada hook deve ter uma responsabilidade única
-2. **Evite Computação Pesada**: Hooks executam em cada chamada de ferramenta
-3. **Trate Erros Graciosamente**: Use try-except para prevenir falhas de hooks
-4. **Use Type Hints**: Aproveite `ToolCallHookContext` para melhor suporte IDE
-5. **Documente Condições de Bloqueio**: Deixe claro quando/por que ferramentas são bloqueadas
-6. **Teste Hooks Independentemente**: Teste unitário de hooks antes de usar em produção
-7. **Limpe Hooks em Testes**: Use `clear_all_tool_call_hooks()` entre execuções de teste
-8. **Modifique In-Place**: Sempre modifique `context.tool_input` in-place, nunca substitua
-9. **Registre Decisões Importantes**: Especialmente ao bloquear execução de ferramenta
-10. **Considere Performance**: Cache validações caras quando possível
-
-## Tratamento de Erros
-
-```python
-@before_tool_call
-def safe_validation(context: ToolCallHookContext) -> bool | None:
-    try:
-        # Sua lógica de validação
-        if not validate_input(context.tool_input):
-            return False
-    except Exception as e:
-        print(f"⚠️ Erro no hook: {e}")
-        # Decida: permitir ou bloquear em erro
-        return None  # Permitir execução apesar do erro
-```
-
-## Segurança de Tipos
-
-```python
-from crewai.hooks import ToolCallHookContext, BeforeToolCallHookType, AfterToolCallHookType
-
-# Anotações de tipo explícitas
-def my_before_hook(context: ToolCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: ToolCallHookContext) -> str | None:
-    return None
-
-# Registro type-safe
-register_before_tool_call_hook(my_before_hook)
-register_after_tool_call_hook(my_after_hook)
-```
-
-## Solução de Problemas
-
-### Hook Não Está Executando
-- Verifique se hook está registrado antes da execução da crew
-- Verifique se hook anterior retornou `False` (bloqueia execução e hooks subsequentes)
-- Garanta que assinatura do hook corresponda ao tipo esperado
-
-### Modificações de Entrada Não Funcionam
-- Use modificações in-place: `context.tool_input['key'] = value`
-- Não substitua o dict: `context.tool_input = {}`
-
-### Modificações de Resultado Não Funcionam
-- Retorne a string modificada dos hooks posteriores
-- Retornar `None` mantém o resultado original
-- Garanta que a ferramenta realmente retornou um resultado
-
-### Ferramenta Bloqueada Inesperadamente
-- Verifique todos os hooks antes por condições de bloqueio
-- Verifique ordem de execução do hook
-- Adicione logging de debug para identificar qual hook está bloqueando
-
-## Conclusão
-
-Os Hooks de Chamada de Ferramenta fornecem capacidades poderosas para controlar e monitorar execução de ferramentas no CrewAI. Use-os para implementar guardrails de segurança, gates de aprovação, validação de entrada, sanitização de resultado, logging e análise. Combinados com tratamento adequado de erros e segurança de tipos, os hooks permitem sistemas de agentes seguros e prontos para produção com observabilidade abrangente.
-

docs/pt-BR/observability/portkey.mdx

@@ -733,7 +733,9 @@ Aqui está um exemplo básico para rotear requisições ao OpenAI, usando especi
     - Coletam metadados relevantes para filtragem de logs
     - Impõem permissões de acesso
 
-    Crie chaves de API através do [Portkey App](https://app.portkey.ai/)
+    Crie chaves de API através de:
+    - [Portkey App](https://app.portkey.ai/)
+    - [API Key Management API](/pt-BR/api-reference/admin-api/control-plane/api-keys/create-api-key)
 
     Exemplo usando Python SDK:
     ```python
@@ -756,7 +758,7 @@ Aqui está um exemplo básico para rotear requisições ao OpenAI, usando especi
     )
     ```
 
-    Para instruções detalhadas de gerenciamento de chaves, veja a [documentação Portkey](https://portkey.ai/docs).
+    Para instruções detalhadas de gerenciamento de chaves, veja nossa [documentação de API Keys](/pt-BR/api-reference/admin-api/control-plane/api-keys/create-api-key).
   </Accordion>
 
   <Accordion title="Etapa 4: Implante & Monitore">

docs/pt-BR/tools/cloud-storage/overview.mdx

@@ -18,7 +18,7 @@ Essas ferramentas permitem que seus agentes interajam com serviços em nuvem, ac
     Escreva e faça upload de arquivos para o armazenamento Amazon S3.
   </Card>
 
-  <Card title="Bedrock Invoke Agent" icon="aws" href="/pt-BR/tools/integration/bedrockinvokeagenttool">
+  <Card title="Bedrock Invoke Agent" icon="aws" href="/pt-BR/tools/cloud-storage/bedrockinvokeagenttool">
     Acione agentes Amazon Bedrock para tarefas orientadas por IA.
   </Card>
 

docs/pt-BR/tools/tool-integrations/overview.mdx

@@ -11,7 +11,7 @@ mode: "wide"
   <Card
     title="Bedrock Invoke Agent Tool"
     icon="cloud"
-    href="/pt-BR/tools/integration/bedrockinvokeagenttool"
+    href="/en/tools/tool-integrations/bedrockinvokeagenttool"
     color="#0891B2"
   >
     Invoke Amazon Bedrock Agents from CrewAI to orchestrate actions across AWS services.
@@ -20,7 +20,7 @@ mode: "wide"
   <Card
     title="CrewAI Automation Tool"
     icon="bolt"
-    href="/pt-BR/tools/integration/crewaiautomationtool"
+    href="/en/tools/tool-integrations/crewaiautomationtool"
     color="#7C3AED"
   >
     Automate deployment and operations by integrating CrewAI with external platforms and workflows.

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube>=15.0.0",
     "requests>=2.32.5",
     "docker>=7.1.0",
-    "crewai==1.4.1",
+    "crewai==1.3.0",
     "lancedb>=0.5.4",
     "tiktoken>=0.8.0",
     "beautifulsoup4>=4.13.4",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -287,4 +287,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.4.1"
+__version__ = "1.3.0"

lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py

@@ -12,16 +12,12 @@ from pydantic.types import ImportString
 
 
 class QdrantToolSchema(BaseModel):
-    query: str = Field(
-        ..., description="Query to search in Qdrant DB - always required."
-    )
+    query: str = Field(..., description="Query to search in Qdrant DB")
     filter_by: str | None = Field(
-        default=None,
-        description="Parameter to filter the search by. When filtering, needs to be used in conjunction with filter_value.",
+        default=None, description="Parameter to filter the search by."
     )
     filter_value: Any | None = Field(
-        default=None,
-        description="Value to filter the search by. When filtering, needs to be used in conjunction with filter_by.",
+        default=None, description="Value to filter the search by."
     )
 
 

lib/crewai/pyproject.toml

@@ -48,7 +48,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.4.1",
+    "crewai-tools==1.3.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.4.1"
+__version__ = "1.3.0"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/a2a/config.py

@@ -38,7 +38,6 @@ class A2AConfig(BaseModel):
         max_turns: Maximum conversation turns with A2A agent (default: 10).
         response_model: Optional Pydantic model for structured A2A agent responses.
         fail_fast: If True, raise error when agent unreachable; if False, skip and continue (default: True).
-        trust_remote_completion_status: If True, return A2A agent's result directly when status is "completed"; if False, always ask server agent to respond (default: False).
     """
 
     endpoint: Url = Field(description="A2A agent endpoint URL")
@@ -58,7 +57,3 @@ class A2AConfig(BaseModel):
         default=True,
         description="If True, raise an error immediately when the A2A agent is unreachable. If False, skip the A2A agent and continue execution.",
     )
-    trust_remote_completion_status: bool = Field(
-        default=False,
-        description='If True, return the A2A agent\'s result directly when status is "completed" without asking the server agent to respond. If False, always ask the server agent to respond, allowing it to potentially delegate again.',
-    )

lib/crewai/src/crewai/a2a/wrapper.py

@@ -52,7 +52,7 @@ def wrap_agent_with_a2a_instance(agent: Agent) -> None:
     Args:
         agent: The agent instance to wrap
     """
-    original_execute_task = agent.execute_task.__func__  # type: ignore[attr-defined]
+    original_execute_task = agent.execute_task.__func__
 
     @wraps(original_execute_task)
     def execute_task_with_a2a(
@@ -73,7 +73,7 @@ def wrap_agent_with_a2a_instance(agent: Agent) -> None:
             Task execution result
         """
         if not self.a2a:
-            return original_execute_task(self, task, context, tools)  # type: ignore[no-any-return]
+            return original_execute_task(self, task, context, tools)
 
         a2a_agents, agent_response_model = get_a2a_agents_and_response_model(self.a2a)
 
@@ -498,23 +498,6 @@ def _delegate_to_a2a(
             conversation_history = a2a_result.get("history", [])
 
             if a2a_result["status"] in ["completed", "input_required"]:
-                if (
-                    a2a_result["status"] == "completed"
-                    and agent_config.trust_remote_completion_status
-                ):
-                    result_text = a2a_result.get("result", "")
-                    final_turn_number = turn_num + 1
-                    crewai_event_bus.emit(
-                        None,
-                        A2AConversationCompletedEvent(
-                            status="completed",
-                            final_result=result_text,
-                            error=None,
-                            total_turns=final_turn_number,
-                        ),
-                    )
-                    return result_text  # type: ignore[no-any-return]
-
                 final_result, next_request = _handle_agent_response_and_continue(
                     self=self,
                     a2a_result=a2a_result,

lib/crewai/src/crewai/agent/core.py

@@ -119,7 +119,6 @@ class Agent(BaseAgent):
 
     _times_executed: int = PrivateAttr(default=0)
     _mcp_clients: list[Any] = PrivateAttr(default_factory=list)
-    _last_messages: list[LLMMessage] = PrivateAttr(default_factory=list)
     max_execution_time: int | None = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -539,12 +538,6 @@ class Agent(BaseAgent):
             event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
         )
 
-        self._last_messages = (
-            self.agent_executor.messages.copy()
-            if self.agent_executor and hasattr(self.agent_executor, "messages")
-            else []
-        )
-
         self._cleanup_mcp_clients()
 
         return result
@@ -625,22 +618,22 @@ class Agent(BaseAgent):
             response_template=self.response_template,
         ).task_execution()
 
-        stop_words = [self.i18n.slice("observation")]
+        stop_sequences = [self.i18n.slice("observation")]
 
         if self.response_template:
-            stop_words.append(
+            stop_sequences.append(
                 self.response_template.split("{{ .Response }}")[1].strip()
             )
 
         self.agent_executor = CrewAgentExecutor(
-            llm=self.llm,
+            llm=self.llm,  # type: ignore[arg-type]
             task=task,  # type: ignore[arg-type]
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
             prompt=prompt,
             original_tools=raw_tools,
-            stop_words=stop_words,
+            stop_sequences=stop_sequences,
             max_iter=self.max_iter,
             tools_handler=self.tools_handler,
             tools_names=get_tool_names(parsed_tools),
@@ -981,7 +974,9 @@ class Agent(BaseAgent):
         path = parsed.path.replace("/", "_").strip("_")
         return f"{domain}_{path}" if path else domain
 
-    def _get_mcp_tool_schemas(self, server_params: dict) -> dict[str, dict]:
+    def _get_mcp_tool_schemas(
+        self, server_params: dict[str, Any]
+    ) -> dict[str, dict[str, Any]] | Any:
         """Get tool schemas from MCP server for wrapper creation with caching."""
         server_url = server_params["url"]
 
@@ -1013,7 +1008,7 @@ class Agent(BaseAgent):
 
     async def _get_mcp_tool_schemas_async(
         self, server_params: dict[str, Any]
-    ) -> dict[str, dict]:
+    ) -> dict[str, dict[str, Any]]:
         """Async implementation of MCP tool schema retrieval with timeouts and retries."""
         server_url = server_params["url"]
         return await self._retry_mcp_discovery(
@@ -1021,7 +1016,7 @@ class Agent(BaseAgent):
         )
 
     async def _retry_mcp_discovery(
-        self, operation_func, server_url: str
+        self, operation_func: Any, server_url: str
     ) -> dict[str, dict[str, Any]]:
         """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop."""
         last_error = None
@@ -1052,7 +1047,7 @@ class Agent(BaseAgent):
 
     @staticmethod
     async def _attempt_mcp_discovery(
-        operation_func, server_url: str
+        operation_func: Any, server_url: str
     ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]:
         """Attempt single MCP discovery operation and return (result, error_message, should_retry)."""
         try:
@@ -1156,13 +1151,13 @@ class Agent(BaseAgent):
                     Field(..., description=field_description),
                 )
             else:
-                field_definitions[field_name] = (
+                field_definitions[field_name] = (  # type: ignore[assignment]
                     field_type | None,
                     Field(default=None, description=field_description),
                 )
 
         model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
-        return create_model(model_name, **field_definitions)
+        return create_model(model_name, **field_definitions)  # type: ignore[no-any-return,call-overload]
 
     def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
         """Convert JSON Schema type to Python type.
@@ -1182,12 +1177,12 @@ class Agent(BaseAgent):
                 if "const" in option:
                     types.append(str)
                 else:
-                    types.append(self._json_type_to_python(option))
+                    types.append(self._json_type_to_python(option))  # type: ignore[arg-type]
             unique_types = list(set(types))
             if len(unique_types) > 1:
                 result = unique_types[0]
                 for t in unique_types[1:]:
-                    result = result | t
+                    result = result | t  # type: ignore[assignment]
                 return result
             return unique_types[0]
 
@@ -1200,10 +1195,10 @@ class Agent(BaseAgent):
             "object": dict,
         }
 
-        return type_mapping.get(json_type, Any)
+        return type_mapping.get(json_type, Any)  # type: ignore[arg-type]
 
     @staticmethod
-    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict]:
+    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]:
         """Fetch MCP server configurations from CrewAI AMP API."""
         # TODO: Implement AMP API call to "integrations/mcps" endpoint
         # Should return list of server configs with URLs
@@ -1348,15 +1343,6 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint) -> None:
         self.security_config.fingerprint = fingerprint
 
-    @property
-    def last_messages(self) -> list[LLMMessage]:
-        """Get messages from the last task execution.
-
-        Returns:
-            List of LLM messages from the most recent task execution.
-        """
-        return self._last_messages
-
     def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None:
         """Generate a search query for the knowledge base based on the task description."""
         crewai_event_bus.emit(
@@ -1451,7 +1437,7 @@ class Agent(BaseAgent):
             goal=self.goal,
             backstory=self.backstory,
             llm=self.llm,
-            tools=self.tools or [],
+            tools=self.tools,
             max_iterations=self.max_iter,
             max_execution_time=self.max_execution_time,
             respect_context_window=self.respect_context_window,

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -137,7 +137,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: list[BaseTool] | None = Field(
+    tools: list[BaseTool] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -23,10 +23,6 @@ from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
-from crewai.hooks.llm_hooks import (
-    get_after_llm_call_hooks,
-    get_before_llm_call_hooks,
-)
 from crewai.utilities.agent_utils import (
     enforce_rpm_limit,
     format_message_for_llm,
@@ -77,7 +73,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         max_iter: int,
         tools: list[CrewStructuredTool],
         tools_names: str,
-        stop_words: list[str],
+        stop_sequences: list[str],
         tools_description: str,
         tools_handler: ToolsHandler,
         step_callback: Any = None,
@@ -99,7 +95,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             max_iter: Maximum iterations.
             tools: Available tools.
             tools_names: Tool names string.
-            stop_words: Stop word list.
+            stop_sequences: Stop sequences list for halting generation.
             tools_description: Tool descriptions.
             tools_handler: Tool handler instance.
             step_callback: Optional step callback.
@@ -118,7 +114,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.prompt = prompt
         self.tools = tools
         self.tools_names = tools_names
-        self.stop = stop_words
         self.max_iter = max_iter
         self.callbacks = callbacks or []
         self._printer: Printer = Printer()
@@ -134,20 +129,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: list[LLMMessage] = []
         self.iterations = 0
         self.log_error_after = 3
-        self.before_llm_call_hooks: list[Callable] = []
-        self.after_llm_call_hooks: list[Callable] = []
-        self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
-        self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
         if self.llm:
-            # This may be mutating the shared llm object and needs further evaluation
-            existing_stop = getattr(self.llm, "stop", [])
-            self.llm.stop = list(
-                set(
-                    existing_stop + self.stop
-                    if isinstance(existing_stop, list)
-                    else self.stop
-                )
-            )
+            self.llm.stop_sequences.extend(stop_sequences)
 
     @property
     def use_stop_words(self) -> bool:
@@ -156,7 +139,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             bool: True if tool should be used or not.
         """
-        return self.llm.supports_stop_words() if self.llm else False
+        return self.llm.supports_stop_words if self.llm else False
 
     def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute the agent with given inputs.
@@ -222,7 +205,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                     )
-                    break
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
@@ -234,9 +216,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_task=self.task,
                     from_agent=self.agent,
                     response_model=self.response_model,
-                    executor_context=self,
                 )
-                formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]
+                formatted_answer = process_llm_response(answer, self.use_stop_words)
 
                 if isinstance(formatted_answer, AgentAction):
                     # Extract agent fingerprint if available
@@ -263,17 +244,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         task=self.task,
                         agent=self.agent,
                         function_calling_llm=self.function_calling_llm,
-                        crew=self.crew,
                     )
                     formatted_answer = self._handle_agent_action(
                         formatted_answer, tool_result
                     )
 
-                self._invoke_step_callback(formatted_answer)  # type: ignore[arg-type]
-                self._append_message(formatted_answer.text)  # type: ignore[union-attr,attr-defined]
+                self._invoke_step_callback(formatted_answer)
+                self._append_message(formatted_answer.text)
 
-            except OutputParserError as e:
-                formatted_answer = handle_output_parser_exception(  # type: ignore[assignment]
+            except OutputParserError as e:  # noqa: PERF203
+                formatted_answer = handle_output_parser_exception(
                     e=e,
                     messages=self.messages,
                     iterations=self.iterations,

lib/crewai/src/crewai/cli/authentication/main.py

@@ -1,5 +1,5 @@
 import time
-from typing import TYPE_CHECKING, Any, TypeVar, cast
+from typing import Any
 import webbrowser
 
 from pydantic import BaseModel, Field
@@ -13,8 +13,6 @@ from crewai.cli.shared.token_manager import TokenManager
 
 console = Console()
 
-TOauth2Settings = TypeVar("TOauth2Settings", bound="Oauth2Settings")
-
 
 class Oauth2Settings(BaseModel):
     provider: str = Field(
@@ -30,15 +28,9 @@ class Oauth2Settings(BaseModel):
         description="OAuth2 audience value, typically used to identify the target API or resource.",
         default=None,
     )
-    extra: dict[str, Any] = Field(
-        description="Extra configuration for the OAuth2 provider.",
-        default={},
-    )
 
     @classmethod
-    def from_settings(cls: type[TOauth2Settings]) -> TOauth2Settings:
-        """Create an Oauth2Settings instance from the CLI settings."""
-
+    def from_settings(cls):
         settings = Settings()
 
         return cls(
@@ -46,20 +38,12 @@ class Oauth2Settings(BaseModel):
             domain=settings.oauth2_domain,
             client_id=settings.oauth2_client_id,
             audience=settings.oauth2_audience,
-            extra=settings.oauth2_extra,
         )
 
 
-if TYPE_CHECKING:
-    from crewai.cli.authentication.providers.base_provider import BaseProvider
-
-
 class ProviderFactory:
     @classmethod
-    def from_settings(
-        cls: type["ProviderFactory"],  # noqa: UP037
-        settings: Oauth2Settings | None = None,
-    ) -> "BaseProvider":  # noqa: UP037
+    def from_settings(cls, settings: Oauth2Settings | None = None):
         settings = settings or Oauth2Settings.from_settings()
 
         import importlib
@@ -69,11 +53,11 @@ class ProviderFactory:
         )
         provider = getattr(module, f"{settings.provider.capitalize()}Provider")
 
-        return cast("BaseProvider", provider(settings))
+        return provider(settings)
 
 
 class AuthenticationCommand:
-    def __init__(self) -> None:
+    def __init__(self):
         self.token_manager = TokenManager()
         self.oauth2_provider = ProviderFactory.from_settings()
 
@@ -100,7 +84,7 @@ class AuthenticationCommand:
             timeout=20,
         )
         response.raise_for_status()
-        return cast(dict[str, Any], response.json())
+        return response.json()
 
     def _display_auth_instructions(self, device_code_data: dict[str, str]) -> None:
         """Display the authentication instructions to the user."""

lib/crewai/src/crewai/cli/authentication/providers/base_provider.py

@@ -24,7 +24,3 @@ class BaseProvider(ABC):
 
     @abstractmethod
     def get_client_id(self) -> str: ...
-
-    def get_required_fields(self) -> list[str]:
-        """Returns which provider-specific fields inside the "extra" dict will be required"""
-        return []

lib/crewai/src/crewai/cli/authentication/providers/okta.py

@@ -3,16 +3,16 @@ from crewai.cli.authentication.providers.base_provider import BaseProvider
 
 class OktaProvider(BaseProvider):
     def get_authorize_url(self) -> str:
-        return f"{self._oauth2_base_url()}/v1/device/authorize"
+        return f"https://{self.settings.domain}/oauth2/default/v1/device/authorize"
 
     def get_token_url(self) -> str:
-        return f"{self._oauth2_base_url()}/v1/token"
+        return f"https://{self.settings.domain}/oauth2/default/v1/token"
 
     def get_jwks_url(self) -> str:
-        return f"{self._oauth2_base_url()}/v1/keys"
+        return f"https://{self.settings.domain}/oauth2/default/v1/keys"
 
     def get_issuer(self) -> str:
-        return self._oauth2_base_url().removesuffix("/oauth2")
+        return f"https://{self.settings.domain}/oauth2/default"
 
     def get_audience(self) -> str:
         if self.settings.audience is None:
@@ -27,16 +27,3 @@ class OktaProvider(BaseProvider):
                 "Client ID is required. Please set it in the configuration."
             )
         return self.settings.client_id
-
-    def get_required_fields(self) -> list[str]:
-        return ["authorization_server_name", "using_org_auth_server"]
-
-    def _oauth2_base_url(self) -> str:
-        using_org_auth_server = self.settings.extra.get("using_org_auth_server", False)
-
-        if using_org_auth_server:
-            base_url = f"https://{self.settings.domain}/oauth2"
-        else:
-            base_url = f"https://{self.settings.domain}/oauth2/{self.settings.extra.get('authorization_server_name', 'default')}"
-
-        return f"{base_url}"

lib/crewai/src/crewai/cli/command.py

@@ -11,18 +11,18 @@ console = Console()
 
 
 class BaseCommand:
-    def __init__(self) -> None:
+    def __init__(self):
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
 
 
 class PlusAPIMixin:
-    def __init__(self, telemetry: Telemetry) -> None:
+    def __init__(self, telemetry):
         try:
             telemetry.set_tracer()
             self.plus_api_client = PlusAPI(api_key=get_auth_token())
         except Exception:
-            telemetry.deploy_signup_error_span()
+            self._deploy_signup_error_span = telemetry.deploy_signup_error_span()
             console.print(
                 "Please sign up/login to CrewAI+ before using the CLI.",
                 style="bold red",

lib/crewai/src/crewai/cli/config.py

@@ -2,7 +2,6 @@ import json
 from logging import getLogger
 from pathlib import Path
 import tempfile
-from typing import Any
 
 from pydantic import BaseModel, Field
 
@@ -137,12 +136,7 @@ class Settings(BaseModel):
         default=DEFAULT_CLI_SETTINGS["oauth2_domain"],
     )
 
-    oauth2_extra: dict[str, Any] = Field(
-        description="Extra configuration for the OAuth2 provider.",
-        default={},
-    )
-
-    def __init__(self, config_path: Path | None = None, **data: dict[str, Any]) -> None:
+    def __init__(self, config_path: Path | None = None, **data):
         """Load Settings from config path with fallback support"""
         if config_path is None:
             config_path = get_writable_config_path()

lib/crewai/src/crewai/cli/enterprise/main.py

@@ -1,10 +1,9 @@
-from typing import Any, cast
+from typing import Any
 
 import requests
 from requests.exceptions import JSONDecodeError, RequestException
 from rich.console import Console
 
-from crewai.cli.authentication.main import Oauth2Settings, ProviderFactory
 from crewai.cli.command import BaseCommand
 from crewai.cli.settings.main import SettingsCommand
 from crewai.cli.version import get_crewai_version
@@ -14,7 +13,7 @@ console = Console()
 
 
 class EnterpriseConfigureCommand(BaseCommand):
-    def __init__(self) -> None:
+    def __init__(self):
         super().__init__()
         self.settings_command = SettingsCommand()
 
@@ -55,12 +54,25 @@ class EnterpriseConfigureCommand(BaseCommand):
             except JSONDecodeError as e:
                 raise ValueError(f"Invalid JSON response from {oauth_endpoint}") from e
 
-            self._validate_oauth_config(oauth_config)
+            required_fields = [
+                "audience",
+                "domain",
+                "device_authorization_client_id",
+                "provider",
+            ]
+            missing_fields = [
+                field for field in required_fields if field not in oauth_config
+            ]
+
+            if missing_fields:
+                raise ValueError(
+                    f"Missing required fields in OAuth2 configuration: {', '.join(missing_fields)}"
+                )
 
             console.print(
                 "✅ Successfully retrieved OAuth2 configuration", style="green"
             )
-            return cast(dict[str, Any], oauth_config)
+            return oauth_config
 
         except RequestException as e:
             raise ValueError(f"Failed to connect to enterprise URL: {e!s}") from e
@@ -77,7 +89,6 @@ class EnterpriseConfigureCommand(BaseCommand):
                 "oauth2_audience": oauth_config["audience"],
                 "oauth2_client_id": oauth_config["device_authorization_client_id"],
                 "oauth2_domain": oauth_config["domain"],
-                "oauth2_extra": oauth_config["extra"],
             }
 
             console.print("🔄 Updating local OAuth2 configuration...")
@@ -88,38 +99,3 @@ class EnterpriseConfigureCommand(BaseCommand):
 
         except Exception as e:
             raise ValueError(f"Failed to update OAuth2 settings: {e!s}") from e
-
-    def _validate_oauth_config(self, oauth_config: dict[str, Any]) -> None:
-        required_fields = [
-            "audience",
-            "domain",
-            "device_authorization_client_id",
-            "provider",
-            "extra",
-        ]
-
-        missing_basic_fields = [
-            field for field in required_fields if field not in oauth_config
-        ]
-        missing_provider_specific_fields = [
-            field
-            for field in self._get_provider_specific_fields(oauth_config["provider"])
-            if field not in oauth_config.get("extra", {})
-        ]
-
-        if missing_basic_fields:
-            raise ValueError(
-                f"Missing required fields in OAuth2 configuration: [{', '.join(missing_basic_fields)}]"
-            )
-
-        if missing_provider_specific_fields:
-            raise ValueError(
-                f"Missing authentication provider required fields in OAuth2 configuration: [{', '.join(missing_provider_specific_fields)}] (Configured provider: '{oauth_config['provider']}')"
-            )
-
-    def _get_provider_specific_fields(self, provider_name: str) -> list[str]:
-        provider = ProviderFactory.from_settings(
-            Oauth2Settings(provider=provider_name, client_id="dummy", domain="dummy")
-        )
-
-        return provider.get_required_fields()

lib/crewai/src/crewai/cli/git.py

@@ -3,7 +3,7 @@ import subprocess
 
 
 class Repository:
-    def __init__(self, path: str = ".") -> None:
+    def __init__(self, path="."):
         self.path = path
 
         if not self.is_git_installed():

lib/crewai/src/crewai/cli/plus_api.py

@@ -1,4 +1,3 @@
-from typing import Any
 from urllib.parse import urljoin
 
 import requests
@@ -37,21 +36,19 @@ class PlusAPI:
             str(settings.enterprise_base_url) or DEFAULT_CREWAI_ENTERPRISE_URL
         )
 
-    def _make_request(
-        self, method: str, endpoint: str, **kwargs: Any
-    ) -> requests.Response:
+    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
         url = urljoin(self.base_url, endpoint)
         session = requests.Session()
         session.trust_env = False
         return session.request(method, url, headers=self.headers, **kwargs)
 
-    def login_to_tool_repository(self) -> requests.Response:
+    def login_to_tool_repository(self):
         return self._make_request("POST", f"{self.TOOLS_RESOURCE}/login")
 
-    def get_tool(self, handle: str) -> requests.Response:
+    def get_tool(self, handle: str):
         return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
 
-    def get_agent(self, handle: str) -> requests.Response:
+    def get_agent(self, handle: str):
         return self._make_request("GET", f"{self.AGENTS_RESOURCE}/{handle}")
 
     def publish_tool(
@@ -61,8 +58,8 @@ class PlusAPI:
         version: str,
         description: str | None,
         encoded_file: str,
-        available_exports: list[dict[str, Any]] | None = None,
-    ) -> requests.Response:
+        available_exports: list[str] | None = None,
+    ):
         params = {
             "handle": handle,
             "public": is_public,
@@ -114,13 +111,13 @@ class PlusAPI:
     def list_crews(self) -> requests.Response:
         return self._make_request("GET", self.CREWS_RESOURCE)
 
-    def create_crew(self, payload: dict[str, Any]) -> requests.Response:
+    def create_crew(self, payload) -> requests.Response:
         return self._make_request("POST", self.CREWS_RESOURCE, json=payload)
 
     def get_organizations(self) -> requests.Response:
         return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
 
-    def initialize_trace_batch(self, payload: dict[str, Any]) -> requests.Response:
+    def initialize_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
             "POST",
             f"{self.TRACING_RESOURCE}/batches",
@@ -128,18 +125,14 @@ class PlusAPI:
             timeout=30,
         )
 
-    def initialize_ephemeral_trace_batch(
-        self, payload: dict[str, Any]
-    ) -> requests.Response:
+    def initialize_ephemeral_trace_batch(self, payload) -> requests.Response:
         return self._make_request(
             "POST",
             f"{self.EPHEMERAL_TRACING_RESOURCE}/batches",
             json=payload,
         )
 
-    def send_trace_events(
-        self, trace_batch_id: str, payload: dict[str, Any]
-    ) -> requests.Response:
+    def send_trace_events(self, trace_batch_id: str, payload) -> requests.Response:
         return self._make_request(
             "POST",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events",
@@ -148,7 +141,7 @@ class PlusAPI:
         )
 
     def send_ephemeral_trace_events(
-        self, trace_batch_id: str, payload: dict[str, Any]
+        self, trace_batch_id: str, payload
     ) -> requests.Response:
         return self._make_request(
             "POST",
@@ -157,9 +150,7 @@ class PlusAPI:
             timeout=30,
         )
 
-    def finalize_trace_batch(
-        self, trace_batch_id: str, payload: dict[str, Any]
-    ) -> requests.Response:
+    def finalize_trace_batch(self, trace_batch_id: str, payload) -> requests.Response:
         return self._make_request(
             "PATCH",
             f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
@@ -168,7 +159,7 @@ class PlusAPI:
         )
 
     def finalize_ephemeral_trace_batch(
-        self, trace_batch_id: str, payload: dict[str, Any]
+        self, trace_batch_id: str, payload
     ) -> requests.Response:
         return self._make_request(
             "PATCH",

lib/crewai/src/crewai/cli/settings/main.py

@@ -34,7 +34,7 @@ class SettingsCommand(BaseCommand):
             current_value = getattr(self.settings, field_name)
             description = field_info.description or "No description available"
             display_value = (
-                str(current_value) if current_value not in [None, {}] else "Not set"
+                str(current_value) if current_value is not None else "Not set"
             )
 
             table.add_row(field_name, display_value, description)

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.4.1"
+    "crewai[tools]==1.3.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.4.1"
+    "crewai[tools]==1.3.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/tools/main.py

@@ -30,11 +30,11 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
     A class to handle tool repository related operations for CrewAI projects.
     """
 
-    def __init__(self) -> None:
+    def __init__(self):
         BaseCommand.__init__(self)
         PlusAPIMixin.__init__(self, telemetry=self._telemetry)
 
-    def create(self, handle: str) -> None:
+    def create(self, handle: str):
         self._ensure_not_in_project()
 
         folder_name = handle.replace(" ", "_").replace("-", "_").lower()
@@ -64,7 +64,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         finally:
             os.chdir(old_directory)
 
-    def publish(self, is_public: bool, force: bool = False) -> None:
+    def publish(self, is_public: bool, force: bool = False):
         if not git.Repository().is_synced() and not force:
             console.print(
                 "[bold red]Failed to publish tool.[/bold red]\n"
@@ -137,7 +137,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             style="bold green",
         )
 
-    def install(self, handle: str) -> None:
+    def install(self, handle: str):
         self._print_current_organization()
         get_response = self.plus_api_client.get_tool(handle)
 
@@ -180,7 +180,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings.org_name = login_response_json["current_organization"]["name"]
         settings.dump()
 
-    def _add_package(self, tool_details: dict[str, Any]) -> None:
+    def _add_package(self, tool_details: dict[str, Any]):
         is_from_pypi = tool_details.get("source", None) == "pypi"
         tool_handle = tool_details["handle"]
         repository_handle = tool_details["repository"]["handle"]
@@ -209,7 +209,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             click.echo(add_package_result.stderr, err=True)
             raise SystemExit
 
-    def _ensure_not_in_project(self) -> None:
+    def _ensure_not_in_project(self):
         if os.path.isfile("./pyproject.toml"):
             console.print(
                 "[bold red]Oops! It looks like you're inside a project.[/bold red]"

lib/crewai/src/crewai/cli/utils.py

@@ -5,7 +5,7 @@ import os
 from pathlib import Path
 import shutil
 import sys
-from typing import Any, cast, get_type_hints
+from typing import Any, get_type_hints
 
 import click
 from rich.console import Console
@@ -23,9 +23,7 @@ if sys.version_info >= (3, 11):
 console = Console()
 
 
-def copy_template(
-    src: Path, dst: Path, name: str, class_name: str, folder_name: str
-) -> None:
+def copy_template(src, dst, name, class_name, folder_name):
     """Copy a file from src to dst."""
     with open(src, "r") as file:
         content = file.read()
@@ -42,13 +40,13 @@ def copy_template(
     click.secho(f"  - Created {dst}", fg="green")
 
 
-def read_toml(file_path: str = "pyproject.toml") -> dict[str, Any]:
+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:
         return tomli.load(f)
 
 
-def parse_toml(content: str) -> dict[str, Any]:
+def parse_toml(content):
     if sys.version_info >= (3, 11):
         return tomllib.loads(content)
     return tomli.loads(content)
@@ -105,7 +103,7 @@ def _get_project_attribute(
         )
     except Exception as e:
         # Handle TOML decode errors for Python 3.11+
-        if sys.version_info >= (3, 11) and isinstance(e, tomllib.TOMLDecodeError):
+        if sys.version_info >= (3, 11) and isinstance(e, tomllib.TOMLDecodeError):  # type: ignore
             console.print(
                 f"Error: {pyproject_path} is not a valid TOML file.", style="bold red"
             )
@@ -128,7 +126,7 @@ def _get_nested_value(data: dict[str, Any], keys: list[str]) -> Any:
     return reduce(dict.__getitem__, keys, data)
 
 
-def fetch_and_json_env_file(env_file_path: str = ".env") -> dict[str, Any]:
+def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
     """Fetch the environment variables from a .env file and return them as a dictionary."""
     try:
         # Read the .env file
@@ -152,7 +150,7 @@ def fetch_and_json_env_file(env_file_path: str = ".env") -> dict[str, Any]:
     return {}
 
 
-def tree_copy(source: Path, destination: Path) -> None:
+def tree_copy(source, destination):
     """Copies the entire directory structure from the source to the destination."""
     for item in os.listdir(source):
         source_item = os.path.join(source, item)
@@ -163,7 +161,7 @@ def tree_copy(source: Path, destination: Path) -> None:
             shutil.copy2(source_item, destination_item)
 
 
-def tree_find_and_replace(directory: Path, find: str, replace: str) -> None:
+def tree_find_and_replace(directory, find, replace):
     """Recursively searches through a directory, replacing a target string in
     both file contents and filenames with a specified replacement string.
     """
@@ -189,7 +187,7 @@ def tree_find_and_replace(directory: Path, find: str, replace: str) -> None:
                 os.rename(old_dirpath, new_dirpath)
 
 
-def load_env_vars(folder_path: Path) -> dict[str, Any]:
+def load_env_vars(folder_path):
     """
     Loads environment variables from a .env file in the specified folder path.
 
@@ -210,9 +208,7 @@ def load_env_vars(folder_path: Path) -> dict[str, Any]:
     return env_vars
 
 
-def update_env_vars(
-    env_vars: dict[str, Any], provider: str, model: str
-) -> dict[str, Any] | None:
+def update_env_vars(env_vars, provider, model):
     """
     Updates environment variables with the API key for the selected provider and model.
 
@@ -224,20 +220,15 @@ def update_env_vars(
     Returns:
     - None
     """
-    provider_config = cast(
-        list[str],
-        ENV_VARS.get(
-            provider,
-            [
-                click.prompt(
-                    f"Enter the environment variable name for your {provider.capitalize()} API key",
-                    type=str,
-                )
-            ],
-        ),
-    )
-
-    api_key_var = provider_config[0]
+    api_key_var = ENV_VARS.get(
+        provider,
+        [
+            click.prompt(
+                f"Enter the environment variable name for your {provider.capitalize()} API key",
+                type=str,
+            )
+        ],
+    )[0]
 
     if api_key_var not in env_vars:
         try:
@@ -255,7 +246,7 @@ def update_env_vars(
     return env_vars
 
 
-def write_env_file(folder_path: Path, env_vars: dict[str, Any]) -> None:
+def write_env_file(folder_path, env_vars):
     """
     Writes environment variables to a .env file in the specified folder.
 
@@ -351,18 +342,18 @@ def get_crews(crew_path: str = "crew.py", require: bool = False) -> list[Crew]:
     return crew_instances
 
 
-def get_crew_instance(module_attr: Any) -> Crew | None:
+def get_crew_instance(module_attr) -> Crew | None:
     if (
         callable(module_attr)
         and hasattr(module_attr, "is_crew_class")
         and module_attr.is_crew_class
     ):
-        return cast(Crew, module_attr().crew())
+        return module_attr().crew()
     try:
         if (ismethod(module_attr) or isfunction(module_attr)) and get_type_hints(
             module_attr
         ).get("return") is Crew:
-            return cast(Crew, module_attr())
+            return module_attr()
     except Exception:
         return None
 
@@ -371,7 +362,7 @@ def get_crew_instance(module_attr: Any) -> Crew | None:
     return None
 
 
-def fetch_crews(module_attr: Any) -> list[Crew]:
+def fetch_crews(module_attr) -> list[Crew]:
     crew_instances: list[Crew] = []
 
     if crew_instance := get_crew_instance(module_attr):
@@ -386,7 +377,7 @@ def fetch_crews(module_attr: Any) -> list[Crew]:
     return crew_instances
 
 
-def is_valid_tool(obj: Any) -> bool:
+def is_valid_tool(obj):
     from crewai.tools.base_tool import Tool
 
     if isclass(obj):
@@ -398,7 +389,7 @@ def is_valid_tool(obj: Any) -> bool:
     return isinstance(obj, Tool)
 
 
-def extract_available_exports(dir_path: str = "src") -> list[dict[str, Any]]:
+def extract_available_exports(dir_path: str = "src"):
     """
     Extract available tool classes from the project's __init__.py files.
     Only includes classes that inherit from BaseTool or functions decorated with @tool.
@@ -428,9 +419,7 @@ def extract_available_exports(dir_path: str = "src") -> list[dict[str, Any]]:
         raise SystemExit(1) from e
 
 
-def build_env_with_tool_repository_credentials(
-    repository_handle: str,
-) -> dict[str, Any]:
+def build_env_with_tool_repository_credentials(repository_handle: str):
     repository_handle = repository_handle.upper().replace("-", "_")
     settings = Settings()
 
@@ -483,7 +472,7 @@ def _load_tools_from_init(init_file: Path) -> list[dict[str, Any]]:
         sys.modules.pop("temp_module", None)
 
 
-def _print_no_tools_warning() -> None:
+def _print_no_tools_warning():
     """
     Display warning and usage instructions if no tools were found.
     """

lib/crewai/src/crewai/crew.py

@@ -809,7 +809,6 @@ class Crew(FlowTrackable, BaseModel):
                 "json_dict": output.json_dict,
                 "output_format": output.output_format,
                 "agent": output.agent,
-                "messages": output.messages,
             },
             "task_index": task_index,
             "inputs": inputs,
@@ -1237,7 +1236,6 @@ class Crew(FlowTrackable, BaseModel):
                 pydantic=stored_output["pydantic"],
                 json_dict=stored_output["json_dict"],
                 output_format=stored_output["output_format"],
-                messages=stored_output.get("messages", []),
             )
             self.tasks[i].output = task_output
 

lib/crewai/src/crewai/hooks/__init__.py

@@ -1,108 +0,0 @@
-from __future__ import annotations
-
-from crewai.hooks.decorators import (
-    after_llm_call,
-    after_tool_call,
-    before_llm_call,
-    before_tool_call,
-)
-from crewai.hooks.llm_hooks import (
-    LLMCallHookContext,
-    clear_after_llm_call_hooks,
-    clear_all_llm_call_hooks,
-    clear_before_llm_call_hooks,
-    get_after_llm_call_hooks,
-    get_before_llm_call_hooks,
-    register_after_llm_call_hook,
-    register_before_llm_call_hook,
-    unregister_after_llm_call_hook,
-    unregister_before_llm_call_hook,
-)
-from crewai.hooks.tool_hooks import (
-    ToolCallHookContext,
-    clear_after_tool_call_hooks,
-    clear_all_tool_call_hooks,
-    clear_before_tool_call_hooks,
-    get_after_tool_call_hooks,
-    get_before_tool_call_hooks,
-    register_after_tool_call_hook,
-    register_before_tool_call_hook,
-    unregister_after_tool_call_hook,
-    unregister_before_tool_call_hook,
-)
-
-
-def clear_all_global_hooks() -> dict[str, tuple[int, int]]:
-    """Clear all global hooks across all hook types (LLM and Tool).
-
-    This is a convenience function that clears all registered hooks in one call.
-    Useful for testing, resetting state, or cleaning up between different
-    execution contexts.
-
-    Returns:
-        Dictionary with counts of cleared hooks:
-        {
-            "llm_hooks": (before_count, after_count),
-            "tool_hooks": (before_count, after_count),
-            "total": (total_before_count, total_after_count)
-        }
-
-    Example:
-        >>> # Register various hooks
-        >>> register_before_llm_call_hook(llm_hook1)
-        >>> register_after_llm_call_hook(llm_hook2)
-        >>> register_before_tool_call_hook(tool_hook1)
-        >>> register_after_tool_call_hook(tool_hook2)
-        >>>
-        >>> # Clear all hooks at once
-        >>> result = clear_all_global_hooks()
-        >>> print(result)
-        {
-            'llm_hooks': (1, 1),
-            'tool_hooks': (1, 1),
-            'total': (2, 2)
-        }
-    """
-    llm_counts = clear_all_llm_call_hooks()
-    tool_counts = clear_all_tool_call_hooks()
-
-    return {
-        "llm_hooks": llm_counts,
-        "tool_hooks": tool_counts,
-        "total": (llm_counts[0] + tool_counts[0], llm_counts[1] + tool_counts[1]),
-    }
-
-
-__all__ = [
-    # Context classes
-    "LLMCallHookContext",
-    "ToolCallHookContext",
-    # Decorators
-    "after_llm_call",
-    "after_tool_call",
-    "before_llm_call",
-    "before_tool_call",
-    "clear_after_llm_call_hooks",
-    "clear_after_tool_call_hooks",
-    "clear_all_global_hooks",
-    "clear_all_llm_call_hooks",
-    "clear_all_tool_call_hooks",
-    # Clear hooks
-    "clear_before_llm_call_hooks",
-    "clear_before_tool_call_hooks",
-    "get_after_llm_call_hooks",
-    "get_after_tool_call_hooks",
-    # Get hooks
-    "get_before_llm_call_hooks",
-    "get_before_tool_call_hooks",
-    "register_after_llm_call_hook",
-    "register_after_tool_call_hook",
-    # LLM Hook registration
-    "register_before_llm_call_hook",
-    # Tool Hook registration
-    "register_before_tool_call_hook",
-    "unregister_after_llm_call_hook",
-    "unregister_after_tool_call_hook",
-    "unregister_before_llm_call_hook",
-    "unregister_before_tool_call_hook",
-]

lib/crewai/src/crewai/hooks/decorators.py

@@ -1,300 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Callable
-from functools import wraps
-import inspect
-from typing import TYPE_CHECKING, Any, TypeVar, overload
-
-
-if TYPE_CHECKING:
-    from crewai.hooks.llm_hooks import LLMCallHookContext
-    from crewai.hooks.tool_hooks import ToolCallHookContext
-
-F = TypeVar("F", bound=Callable[..., Any])
-
-
-def _create_hook_decorator(
-    hook_type: str,
-    register_function: Callable[..., Any],
-    marker_attribute: str,
-) -> Callable[..., Any]:
-    """Create a hook decorator with filtering support.
-
-    This factory function eliminates code duplication across the four hook decorators.
-
-    Args:
-        hook_type: Type of hook ("llm" or "tool")
-        register_function: Function to call for registration (e.g., register_before_llm_call_hook)
-        marker_attribute: Attribute name to mark functions (e.g., "is_before_llm_call_hook")
-
-    Returns:
-        A decorator function that supports filters and auto-registration
-    """
-
-    def decorator_factory(
-        func: Callable[..., Any] | None = None,
-        *,
-        tools: list[str] | None = None,
-        agents: list[str] | None = None,
-    ) -> Callable[..., Any]:
-        def decorator(f: Callable[..., Any]) -> Callable[..., Any]:
-            setattr(f, marker_attribute, True)
-
-            sig = inspect.signature(f)
-            params = list(sig.parameters.keys())
-            is_method = len(params) >= 2 and params[0] == "self"
-
-            if tools:
-                f._filter_tools = tools  # type: ignore[attr-defined]
-            if agents:
-                f._filter_agents = agents  # type: ignore[attr-defined]
-
-            if tools or agents:
-
-                @wraps(f)
-                def filtered_hook(context: Any) -> Any:
-                    if tools and hasattr(context, "tool_name"):
-                        if context.tool_name not in tools:
-                            return None
-
-                    if agents and hasattr(context, "agent"):
-                        if context.agent and context.agent.role not in agents:
-                            return None
-
-                    return f(context)
-
-                if not is_method:
-                    register_function(filtered_hook)
-
-                return f
-
-            if not is_method:
-                register_function(f)
-
-            return f
-
-        if func is None:
-            return decorator
-        return decorator(func)
-
-    return decorator_factory
-
-
-@overload
-def before_llm_call(
-    func: Callable[[LLMCallHookContext], None],
-) -> Callable[[LLMCallHookContext], None]: ...
-
-
-@overload
-def before_llm_call(
-    *,
-    agents: list[str] | None = None,
-) -> Callable[
-    [Callable[[LLMCallHookContext], None]], Callable[[LLMCallHookContext], None]
-]: ...
-
-
-def before_llm_call(
-    func: Callable[[LLMCallHookContext], None] | None = None,
-    *,
-    agents: list[str] | None = None,
-) -> (
-    Callable[[LLMCallHookContext], None]
-    | Callable[
-        [Callable[[LLMCallHookContext], None]], Callable[[LLMCallHookContext], None]
-    ]
-):
-    """Decorator to register a function as a before_llm_call hook.
-
-    Example:
-        Simple usage::
-
-            @before_llm_call
-            def log_calls(context):
-                print(f"LLM call by {context.agent.role}")
-
-        With agent filter::
-
-            @before_llm_call(agents=["Researcher", "Analyst"])
-            def log_specific_agents(context):
-                print(f"Filtered LLM call: {context.agent.role}")
-    """
-    from crewai.hooks.llm_hooks import register_before_llm_call_hook
-
-    return _create_hook_decorator(  # type: ignore[return-value]
-        hook_type="llm",
-        register_function=register_before_llm_call_hook,
-        marker_attribute="is_before_llm_call_hook",
-    )(func=func, agents=agents)
-
-
-@overload
-def after_llm_call(
-    func: Callable[[LLMCallHookContext], str | None],
-) -> Callable[[LLMCallHookContext], str | None]: ...
-
-
-@overload
-def after_llm_call(
-    *,
-    agents: list[str] | None = None,
-) -> Callable[
-    [Callable[[LLMCallHookContext], str | None]],
-    Callable[[LLMCallHookContext], str | None],
-]: ...
-
-
-def after_llm_call(
-    func: Callable[[LLMCallHookContext], str | None] | None = None,
-    *,
-    agents: list[str] | None = None,
-) -> (
-    Callable[[LLMCallHookContext], str | None]
-    | Callable[
-        [Callable[[LLMCallHookContext], str | None]],
-        Callable[[LLMCallHookContext], str | None],
-    ]
-):
-    """Decorator to register a function as an after_llm_call hook.
-
-    Example:
-        Simple usage::
-
-            @after_llm_call
-            def sanitize(context):
-                if "SECRET" in context.response:
-                    return context.response.replace("SECRET", "[REDACTED]")
-                return None
-
-        With agent filter::
-
-            @after_llm_call(agents=["Researcher"])
-            def log_researcher_responses(context):
-                print(f"Response length: {len(context.response)}")
-                return None
-    """
-    from crewai.hooks.llm_hooks import register_after_llm_call_hook
-
-    return _create_hook_decorator(  # type: ignore[return-value]
-        hook_type="llm",
-        register_function=register_after_llm_call_hook,
-        marker_attribute="is_after_llm_call_hook",
-    )(func=func, agents=agents)
-
-
-@overload
-def before_tool_call(
-    func: Callable[[ToolCallHookContext], bool | None],
-) -> Callable[[ToolCallHookContext], bool | None]: ...
-
-
-@overload
-def before_tool_call(
-    *,
-    tools: list[str] | None = None,
-    agents: list[str] | None = None,
-) -> Callable[
-    [Callable[[ToolCallHookContext], bool | None]],
-    Callable[[ToolCallHookContext], bool | None],
-]: ...
-
-
-def before_tool_call(
-    func: Callable[[ToolCallHookContext], bool | None] | None = None,
-    *,
-    tools: list[str] | None = None,
-    agents: list[str] | None = None,
-) -> (
-    Callable[[ToolCallHookContext], bool | None]
-    | Callable[
-        [Callable[[ToolCallHookContext], bool | None]],
-        Callable[[ToolCallHookContext], bool | None],
-    ]
-):
-    """Decorator to register a function as a before_tool_call hook.
-
-    Example:
-        Simple usage::
-
-            @before_tool_call
-            def log_all_tools(context):
-                print(f"Tool: {context.tool_name}")
-                return None
-
-        With tool filter::
-
-            @before_tool_call(tools=["delete_file", "execute_code"])
-            def approve_dangerous(context):
-                response = context.request_human_input(prompt="Approve?")
-                return None if response == "yes" else False
-
-        With combined filters::
-
-            @before_tool_call(tools=["write_file"], agents=["Developer"])
-            def approve_dev_writes(context):
-                return None  # Only for Developer writing files
-    """
-    from crewai.hooks.tool_hooks import register_before_tool_call_hook
-
-    return _create_hook_decorator(  # type: ignore[return-value]
-        hook_type="tool",
-        register_function=register_before_tool_call_hook,
-        marker_attribute="is_before_tool_call_hook",
-    )(func=func, tools=tools, agents=agents)
-
-
-@overload
-def after_tool_call(
-    func: Callable[[ToolCallHookContext], str | None],
-) -> Callable[[ToolCallHookContext], str | None]: ...
-
-
-@overload
-def after_tool_call(
-    *,
-    tools: list[str] | None = None,
-    agents: list[str] | None = None,
-) -> Callable[
-    [Callable[[ToolCallHookContext], str | None]],
-    Callable[[ToolCallHookContext], str | None],
-]: ...
-
-
-def after_tool_call(
-    func: Callable[[ToolCallHookContext], str | None] | None = None,
-    *,
-    tools: list[str] | None = None,
-    agents: list[str] | None = None,
-) -> (
-    Callable[[ToolCallHookContext], str | None]
-    | Callable[
-        [Callable[[ToolCallHookContext], str | None]],
-        Callable[[ToolCallHookContext], str | None],
-    ]
-):
-    """Decorator to register a function as an after_tool_call hook.
-
-    Example:
-        Simple usage::
-
-            @after_tool_call
-            def log_results(context):
-                print(f"Result: {len(context.tool_result)} chars")
-                return None
-
-        With tool filter::
-
-            @after_tool_call(tools=["web_search", "ExaSearchTool"])
-            def sanitize_search_results(context):
-                if "SECRET" in context.tool_result:
-                    return context.tool_result.replace("SECRET", "[REDACTED]")
-                return None
-    """
-    from crewai.hooks.tool_hooks import register_after_tool_call_hook
-
-    return _create_hook_decorator(  # type: ignore[return-value]
-        hook_type="tool",
-        register_function=register_after_tool_call_hook,
-        marker_attribute="is_after_tool_call_hook",
-    )(func=func, tools=tools, agents=agents)

lib/crewai/src/crewai/hooks/llm_hooks.py

@@ -1,290 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from crewai.events.event_listener import event_listener
-from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
-from crewai.utilities.printer import Printer
-
-
-if TYPE_CHECKING:
-    from crewai.agents.crew_agent_executor import CrewAgentExecutor
-
-
-class LLMCallHookContext:
-    """Context object passed to LLM call hooks with full executor access.
-
-    Provides hooks with complete access to the executor state, allowing
-    modification of messages, responses, and executor attributes.
-
-    Attributes:
-        executor: Full reference to the CrewAgentExecutor instance
-        messages: Direct reference to executor.messages (mutable list).
-            Can be modified in both before_llm_call and after_llm_call hooks.
-            Modifications in after_llm_call hooks persist to the next iteration,
-            allowing hooks to modify conversation history for subsequent LLM calls.
-            IMPORTANT: Modify messages in-place (e.g., append, extend, remove items).
-            Do NOT replace the list (e.g., context.messages = []), as this will break
-            the executor. Use context.messages.append() or context.messages.extend()
-            instead of assignment.
-        agent: Reference to the agent executing the task
-        task: Reference to the task being executed
-        crew: Reference to the crew instance
-        llm: Reference to the LLM instance
-        iterations: Current iteration count
-        response: LLM response string (only set for after_llm_call hooks).
-            Can be modified by returning a new string from after_llm_call hook.
-    """
-
-    def __init__(
-        self,
-        executor: CrewAgentExecutor,
-        response: str | None = None,
-    ) -> None:
-        """Initialize hook context with executor reference.
-
-        Args:
-            executor: The CrewAgentExecutor instance
-            response: Optional response string (for after_llm_call hooks)
-        """
-        self.executor = executor
-        self.messages = executor.messages
-        self.agent = executor.agent
-        self.task = executor.task
-        self.crew = executor.crew
-        self.llm = executor.llm
-        self.iterations = executor.iterations
-        self.response = response
-
-    def request_human_input(
-        self,
-        prompt: str,
-        default_message: str = "Press Enter to continue, or provide feedback:",
-    ) -> str:
-        """Request human input during LLM hook execution.
-
-        This method pauses live console updates, displays a prompt to the user,
-        waits for their input, and then resumes live updates. This is useful for
-        approval gates, debugging, or getting human feedback during execution.
-
-        Args:
-            prompt: Custom message to display to the user
-            default_message: Message shown after the prompt
-
-        Returns:
-            User's input as a string (empty string if just Enter pressed)
-
-        Example:
-            >>> def approval_hook(context: LLMCallHookContext) -> None:
-            ...     if context.iterations > 5:
-            ...         response = context.request_human_input(
-            ...             prompt="Allow this LLM call?",
-            ...             default_message="Type 'no' to skip, or press Enter:",
-            ...         )
-            ...         if response.lower() == "no":
-            ...             print("LLM call skipped by user")
-        """
-
-        printer = Printer()
-        event_listener.formatter.pause_live_updates()
-
-        try:
-            printer.print(content=f"\n{prompt}", color="bold_yellow")
-            printer.print(content=default_message, color="cyan")
-            response = input().strip()
-
-            if response:
-                printer.print(content="\nProcessing your input...", color="cyan")
-
-            return response
-        finally:
-            event_listener.formatter.resume_live_updates()
-
-
-_before_llm_call_hooks: list[BeforeLLMCallHookType] = []
-_after_llm_call_hooks: list[AfterLLMCallHookType] = []
-
-
-def register_before_llm_call_hook(
-    hook: BeforeLLMCallHookType,
-) -> None:
-    """Register a global before_llm_call hook.
-
-    Global hooks are added to all executors automatically.
-    This is a convenience function for registering hooks that should
-    apply to all LLM calls across all executors.
-
-    Args:
-        hook: Function that receives LLMCallHookContext and can:
-            - Modify context.messages directly (in-place)
-            - Return False to block LLM execution
-            - Return True or None to allow execution
-            IMPORTANT: Modify messages in-place (append, extend, remove items).
-            Do NOT replace the list (context.messages = []), as this will break execution.
-
-    Example:
-        >>> def log_llm_calls(context: LLMCallHookContext) -> None:
-        ...     print(f"LLM call by {context.agent.role}")
-        ...     print(f"Messages: {len(context.messages)}")
-        ...     return None  # Allow execution
-        >>>
-        >>> register_before_llm_call_hook(log_llm_calls)
-        >>>
-        >>> def block_excessive_iterations(context: LLMCallHookContext) -> bool | None:
-        ...     if context.iterations > 10:
-        ...         print("Blocked: Too many iterations")
-        ...         return False  # Block execution
-        ...     return None  # Allow execution
-        >>>
-        >>> register_before_llm_call_hook(block_excessive_iterations)
-    """
-    _before_llm_call_hooks.append(hook)
-
-
-def register_after_llm_call_hook(
-    hook: AfterLLMCallHookType,
-) -> None:
-    """Register a global after_llm_call hook.
-
-    Global hooks are added to all executors automatically.
-    This is a convenience function for registering hooks that should
-    apply to all LLM calls across all executors.
-
-    Args:
-        hook: Function that receives LLMCallHookContext and can modify:
-            - The response: Return modified response string or None to keep original
-            - The messages: Modify context.messages directly (mutable reference)
-            Both modifications are supported and can be used together.
-            IMPORTANT: Modify messages in-place (append, extend, remove items).
-            Do NOT replace the list (context.messages = []), as this will break execution.
-
-    Example:
-        >>> def sanitize_response(context: LLMCallHookContext) -> str | None:
-        ...     if context.response and "SECRET" in context.response:
-        ...         return context.response.replace("SECRET", "[REDACTED]")
-        ...     return None
-        >>>
-        >>> register_after_llm_call_hook(sanitize_response)
-    """
-    _after_llm_call_hooks.append(hook)
-
-
-def get_before_llm_call_hooks() -> list[BeforeLLMCallHookType]:
-    """Get all registered global before_llm_call hooks.
-
-    Returns:
-        List of registered before hooks
-    """
-    return _before_llm_call_hooks.copy()
-
-
-def get_after_llm_call_hooks() -> list[AfterLLMCallHookType]:
-    """Get all registered global after_llm_call hooks.
-
-    Returns:
-        List of registered after hooks
-    """
-    return _after_llm_call_hooks.copy()
-
-
-def unregister_before_llm_call_hook(
-    hook: BeforeLLMCallHookType,
-) -> bool:
-    """Unregister a specific global before_llm_call hook.
-
-    Args:
-        hook: The hook function to remove
-
-    Returns:
-        True if the hook was found and removed, False otherwise
-
-    Example:
-        >>> def my_hook(context: LLMCallHookContext) -> None:
-        ...     print("Before LLM call")
-        >>>
-        >>> register_before_llm_call_hook(my_hook)
-        >>> unregister_before_llm_call_hook(my_hook)
-        True
-    """
-    try:
-        _before_llm_call_hooks.remove(hook)
-        return True
-    except ValueError:
-        return False
-
-
-def unregister_after_llm_call_hook(
-    hook: AfterLLMCallHookType,
-) -> bool:
-    """Unregister a specific global after_llm_call hook.
-
-    Args:
-        hook: The hook function to remove
-
-    Returns:
-        True if the hook was found and removed, False otherwise
-
-    Example:
-        >>> def my_hook(context: LLMCallHookContext) -> str | None:
-        ...     return None
-        >>>
-        >>> register_after_llm_call_hook(my_hook)
-        >>> unregister_after_llm_call_hook(my_hook)
-        True
-    """
-    try:
-        _after_llm_call_hooks.remove(hook)
-        return True
-    except ValueError:
-        return False
-
-
-def clear_before_llm_call_hooks() -> int:
-    """Clear all registered global before_llm_call hooks.
-
-    Returns:
-        Number of hooks that were cleared
-
-    Example:
-        >>> register_before_llm_call_hook(hook1)
-        >>> register_before_llm_call_hook(hook2)
-        >>> clear_before_llm_call_hooks()
-        2
-    """
-    count = len(_before_llm_call_hooks)
-    _before_llm_call_hooks.clear()
-    return count
-
-
-def clear_after_llm_call_hooks() -> int:
-    """Clear all registered global after_llm_call hooks.
-
-    Returns:
-        Number of hooks that were cleared
-
-    Example:
-        >>> register_after_llm_call_hook(hook1)
-        >>> register_after_llm_call_hook(hook2)
-        >>> clear_after_llm_call_hooks()
-        2
-    """
-    count = len(_after_llm_call_hooks)
-    _after_llm_call_hooks.clear()
-    return count
-
-
-def clear_all_llm_call_hooks() -> tuple[int, int]:
-    """Clear all registered global LLM call hooks (both before and after).
-
-    Returns:
-        Tuple of (before_hooks_cleared, after_hooks_cleared)
-
-    Example:
-        >>> register_before_llm_call_hook(before_hook)
-        >>> register_after_llm_call_hook(after_hook)
-        >>> clear_all_llm_call_hooks()
-        (1, 1)
-    """
-    before_count = clear_before_llm_call_hooks()
-    after_count = clear_after_llm_call_hooks()
-    return (before_count, after_count)

lib/crewai/src/crewai/hooks/tool_hooks.py

@@ -1,305 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-from crewai.events.event_listener import event_listener
-from crewai.hooks.types import AfterToolCallHookType, BeforeToolCallHookType
-from crewai.utilities.printer import Printer
-
-
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.agents.agent_builder.base_agent import BaseAgent
-    from crewai.crew import Crew
-    from crewai.task import Task
-    from crewai.tools.structured_tool import CrewStructuredTool
-
-
-class ToolCallHookContext:
-    """Context object passed to tool call hooks.
-
-    Provides hooks with access to the tool being called, its input,
-    the agent/task/crew context, and the result (for after hooks).
-
-    Attributes:
-        tool_name: Name of the tool being called
-        tool_input: Tool input parameters (mutable dict).
-            Can be modified in-place by before_tool_call hooks.
-            IMPORTANT: Modify in-place (e.g., context.tool_input['key'] = value).
-            Do NOT replace the dict (e.g., context.tool_input = {}), as this
-            will not affect the actual tool execution.
-        tool: Reference to the CrewStructuredTool instance
-        agent: Agent executing the tool (may be None)
-        task: Current task being executed (may be None)
-        crew: Crew instance (may be None)
-        tool_result: Tool execution result (only set for after_tool_call hooks).
-            Can be modified by returning a new string from after_tool_call hook.
-    """
-
-    def __init__(
-        self,
-        tool_name: str,
-        tool_input: dict[str, Any],
-        tool: CrewStructuredTool,
-        agent: Agent | BaseAgent | None = None,
-        task: Task | None = None,
-        crew: Crew | None = None,
-        tool_result: str | None = None,
-    ) -> None:
-        """Initialize tool call hook context.
-
-        Args:
-            tool_name: Name of the tool being called
-            tool_input: Tool input parameters (mutable)
-            tool: Tool instance reference
-            agent: Optional agent executing the tool
-            task: Optional current task
-            crew: Optional crew instance
-            tool_result: Optional tool result (for after hooks)
-        """
-        self.tool_name = tool_name
-        self.tool_input = tool_input
-        self.tool = tool
-        self.agent = agent
-        self.task = task
-        self.crew = crew
-        self.tool_result = tool_result
-
-    def request_human_input(
-        self,
-        prompt: str,
-        default_message: str = "Press Enter to continue, or provide feedback:",
-    ) -> str:
-        """Request human input during tool hook execution.
-
-        This method pauses live console updates, displays a prompt to the user,
-        waits for their input, and then resumes live updates. This is useful for
-        approval gates, reviewing tool results, or getting human feedback during execution.
-
-        Args:
-            prompt: Custom message to display to the user
-            default_message: Message shown after the prompt
-
-        Returns:
-            User's input as a string (empty string if just Enter pressed)
-
-        Example:
-            >>> def approval_hook(context: ToolCallHookContext) -> bool | None:
-            ...     if context.tool_name == "delete_file":
-            ...         response = context.request_human_input(
-            ...             prompt="Allow file deletion?",
-            ...             default_message="Type 'approve' to continue:",
-            ...         )
-            ...         if response.lower() != "approve":
-            ...             return False  # Block execution
-            ...     return None  # Allow execution
-        """
-
-        printer = Printer()
-        event_listener.formatter.pause_live_updates()
-
-        try:
-            printer.print(content=f"\n{prompt}", color="bold_yellow")
-            printer.print(content=default_message, color="cyan")
-            response = input().strip()
-
-            if response:
-                printer.print(content="\nProcessing your input...", color="cyan")
-
-            return response
-        finally:
-            event_listener.formatter.resume_live_updates()
-
-
-# Global hook registries
-_before_tool_call_hooks: list[BeforeToolCallHookType] = []
-_after_tool_call_hooks: list[AfterToolCallHookType] = []
-
-
-def register_before_tool_call_hook(
-    hook: BeforeToolCallHookType,
-) -> None:
-    """Register a global before_tool_call hook.
-
-    Global hooks are added to all tool executions automatically.
-    This is a convenience function for registering hooks that should
-    apply to all tool calls across all agents and crews.
-
-    Args:
-        hook: Function that receives ToolCallHookContext and can:
-            - Modify tool_input in-place
-            - Return False to block tool execution
-            - Return True or None to allow execution
-            IMPORTANT: Modify tool_input in-place (e.g., context.tool_input['key'] = value).
-            Do NOT replace the dict (context.tool_input = {}), as this will not affect
-            the actual tool execution.
-
-    Example:
-        >>> def log_tool_usage(context: ToolCallHookContext) -> None:
-        ...     print(f"Executing tool: {context.tool_name}")
-        ...     print(f"Input: {context.tool_input}")
-        ...     return None  # Allow execution
-        >>>
-        >>> register_before_tool_call_hook(log_tool_usage)
-
-        >>> def block_dangerous_tools(context: ToolCallHookContext) -> bool | None:
-        ...     if context.tool_name == "delete_database":
-        ...         print("Blocked dangerous tool execution!")
-        ...         return False  # Block execution
-        ...     return None  # Allow execution
-        >>>
-        >>> register_before_tool_call_hook(block_dangerous_tools)
-    """
-    _before_tool_call_hooks.append(hook)
-
-
-def register_after_tool_call_hook(
-    hook: AfterToolCallHookType,
-) -> None:
-    """Register a global after_tool_call hook.
-
-    Global hooks are added to all tool executions automatically.
-    This is a convenience function for registering hooks that should
-    apply to all tool calls across all agents and crews.
-
-    Args:
-        hook: Function that receives ToolCallHookContext and can modify
-            the tool result. Return modified result string or None to keep
-            the original result. The tool_result is available in context.tool_result.
-
-    Example:
-        >>> def sanitize_output(context: ToolCallHookContext) -> str | None:
-        ...     if context.tool_result and "SECRET_KEY" in context.tool_result:
-        ...         return context.tool_result.replace("SECRET_KEY=...", "[REDACTED]")
-        ...     return None  # Keep original result
-        >>>
-        >>> register_after_tool_call_hook(sanitize_output)
-
-        >>> def log_tool_results(context: ToolCallHookContext) -> None:
-        ...     print(f"Tool {context.tool_name} returned: {context.tool_result[:100]}")
-        ...     return None  # Keep original result
-        >>>
-        >>> register_after_tool_call_hook(log_tool_results)
-    """
-    _after_tool_call_hooks.append(hook)
-
-
-def get_before_tool_call_hooks() -> list[BeforeToolCallHookType]:
-    """Get all registered global before_tool_call hooks.
-
-    Returns:
-        List of registered before hooks
-    """
-    return _before_tool_call_hooks.copy()
-
-
-def get_after_tool_call_hooks() -> list[AfterToolCallHookType]:
-    """Get all registered global after_tool_call hooks.
-
-    Returns:
-        List of registered after hooks
-    """
-    return _after_tool_call_hooks.copy()
-
-
-def unregister_before_tool_call_hook(
-    hook: BeforeToolCallHookType,
-) -> bool:
-    """Unregister a specific global before_tool_call hook.
-
-    Args:
-        hook: The hook function to remove
-
-    Returns:
-        True if the hook was found and removed, False otherwise
-
-    Example:
-        >>> def my_hook(context: ToolCallHookContext) -> None:
-        ...     print("Before tool call")
-        >>>
-        >>> register_before_tool_call_hook(my_hook)
-        >>> unregister_before_tool_call_hook(my_hook)
-        True
-    """
-    try:
-        _before_tool_call_hooks.remove(hook)
-        return True
-    except ValueError:
-        return False
-
-
-def unregister_after_tool_call_hook(
-    hook: AfterToolCallHookType,
-) -> bool:
-    """Unregister a specific global after_tool_call hook.
-
-    Args:
-        hook: The hook function to remove
-
-    Returns:
-        True if the hook was found and removed, False otherwise
-
-    Example:
-        >>> def my_hook(context: ToolCallHookContext) -> str | None:
-        ...     return None
-        >>>
-        >>> register_after_tool_call_hook(my_hook)
-        >>> unregister_after_tool_call_hook(my_hook)
-        True
-    """
-    try:
-        _after_tool_call_hooks.remove(hook)
-        return True
-    except ValueError:
-        return False
-
-
-def clear_before_tool_call_hooks() -> int:
-    """Clear all registered global before_tool_call hooks.
-
-    Returns:
-        Number of hooks that were cleared
-
-    Example:
-        >>> register_before_tool_call_hook(hook1)
-        >>> register_before_tool_call_hook(hook2)
-        >>> clear_before_tool_call_hooks()
-        2
-    """
-    count = len(_before_tool_call_hooks)
-    _before_tool_call_hooks.clear()
-    return count
-
-
-def clear_after_tool_call_hooks() -> int:
-    """Clear all registered global after_tool_call hooks.
-
-    Returns:
-        Number of hooks that were cleared
-
-    Example:
-        >>> register_after_tool_call_hook(hook1)
-        >>> register_after_tool_call_hook(hook2)
-        >>> clear_after_tool_call_hooks()
-        2
-    """
-    count = len(_after_tool_call_hooks)
-    _after_tool_call_hooks.clear()
-    return count
-
-
-def clear_all_tool_call_hooks() -> tuple[int, int]:
-    """Clear all registered global tool call hooks (both before and after).
-
-    Returns:
-        Tuple of (before_hooks_cleared, after_hooks_cleared)
-
-    Example:
-        >>> register_before_tool_call_hook(before_hook)
-        >>> register_after_tool_call_hook(after_hook)
-        >>> clear_all_tool_call_hooks()
-        (1, 1)
-    """
-    before_count = clear_before_tool_call_hooks()
-    after_count = clear_after_tool_call_hooks()
-    return (before_count, after_count)

lib/crewai/src/crewai/hooks/types.py

@@ -1,137 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Callable
-from typing import TYPE_CHECKING, Generic, Protocol, TypeVar, runtime_checkable
-
-
-if TYPE_CHECKING:
-    from crewai.hooks.llm_hooks import LLMCallHookContext
-    from crewai.hooks.tool_hooks import ToolCallHookContext
-
-
-ContextT = TypeVar("ContextT", contravariant=True)
-ReturnT = TypeVar("ReturnT", covariant=True)
-
-
-@runtime_checkable
-class Hook(Protocol, Generic[ContextT, ReturnT]):
-    """Generic protocol for hook functions.
-
-    This protocol defines the common interface for all hook types in CrewAI.
-    Hooks receive a context object and optionally return a modified result.
-
-    Type Parameters:
-        ContextT: The context type (LLMCallHookContext or ToolCallHookContext)
-        ReturnT: The return type (None, str | None, or bool | None)
-
-    Example:
-        >>> # Before LLM call hook: receives LLMCallHookContext, returns None
-        >>> hook: Hook[LLMCallHookContext, None] = lambda ctx: print(ctx.iterations)
-        >>>
-        >>> # After LLM call hook: receives LLMCallHookContext, returns str | None
-        >>> hook: Hook[LLMCallHookContext, str | None] = lambda ctx: ctx.response
-    """
-
-    def __call__(self, context: ContextT) -> ReturnT:
-        """Execute the hook with the given context.
-
-        Args:
-            context: Context object with relevant execution state
-
-        Returns:
-            Hook-specific return value (None, str | None, or bool | None)
-        """
-        ...
-
-
-class BeforeLLMCallHook(Hook["LLMCallHookContext", bool | None], Protocol):
-    """Protocol for before_llm_call hooks.
-
-    These hooks are called before an LLM is invoked and can modify the messages
-    that will be sent to the LLM or block the execution entirely.
-    """
-
-    def __call__(self, context: LLMCallHookContext) -> bool | None:
-        """Execute the before LLM call hook.
-
-        Args:
-            context: Context object with executor, messages, agent, task, etc.
-                Messages can be modified in-place.
-
-        Returns:
-            False to block LLM execution, True or None to allow execution
-        """
-        ...
-
-
-class AfterLLMCallHook(Hook["LLMCallHookContext", str | None], Protocol):
-    """Protocol for after_llm_call hooks.
-
-    These hooks are called after an LLM returns a response and can modify
-    the response or the message history.
-    """
-
-    def __call__(self, context: LLMCallHookContext) -> str | None:
-        """Execute the after LLM call hook.
-
-        Args:
-            context: Context object with executor, messages, agent, task, response, etc.
-                Messages can be modified in-place. Response is available in context.response.
-
-        Returns:
-            Modified response string, or None to keep the original response
-        """
-        ...
-
-
-class BeforeToolCallHook(Hook["ToolCallHookContext", bool | None], Protocol):
-    """Protocol for before_tool_call hooks.
-
-    These hooks are called before a tool is executed and can modify the tool
-    input or block the execution entirely.
-    """
-
-    def __call__(self, context: ToolCallHookContext) -> bool | None:
-        """Execute the before tool call hook.
-
-        Args:
-            context: Context object with tool_name, tool_input, tool, agent, task, etc.
-                Tool input can be modified in-place.
-
-        Returns:
-            False to block tool execution, True or None to allow execution
-        """
-        ...
-
-
-class AfterToolCallHook(Hook["ToolCallHookContext", str | None], Protocol):
-    """Protocol for after_tool_call hooks.
-
-    These hooks are called after a tool executes and can modify the result.
-    """
-
-    def __call__(self, context: ToolCallHookContext) -> str | None:
-        """Execute the after tool call hook.
-
-        Args:
-            context: Context object with tool_name, tool_input, tool_result, etc.
-                Tool result is available in context.tool_result.
-
-        Returns:
-            Modified tool result string, or None to keep the original result
-        """
-        ...
-
-
-# - All before hooks: bool | None (False = block execution, True/None = allow)
-# - All after hooks: str | None (str = modified result, None = keep original)
-BeforeLLMCallHookType = Hook["LLMCallHookContext", bool | None]
-AfterLLMCallHookType = Hook["LLMCallHookContext", str | None]
-BeforeToolCallHookType = Hook["ToolCallHookContext", bool | None]
-AfterToolCallHookType = Hook["ToolCallHookContext", str | None]
-
-# Alternative Callable-based type aliases for compatibility
-BeforeLLMCallHookCallable = Callable[["LLMCallHookContext"], bool | None]
-AfterLLMCallHookCallable = Callable[["LLMCallHookContext"], str | None]
-BeforeToolCallHookCallable = Callable[["ToolCallHookContext"], bool | None]
-AfterToolCallHookCallable = Callable[["ToolCallHookContext"], str | None]

lib/crewai/src/crewai/hooks/wrappers.py

@@ -1,157 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Callable
-from typing import TYPE_CHECKING, Any, TypeVar
-
-
-if TYPE_CHECKING:
-    from crewai.hooks.llm_hooks import LLMCallHookContext
-    from crewai.hooks.tool_hooks import ToolCallHookContext
-
-P = TypeVar("P")
-R = TypeVar("R")
-
-
-def _copy_method_metadata(wrapper: Any, original: Callable[..., Any]) -> None:
-    """Copy metadata from original function to wrapper.
-
-    Args:
-        wrapper: The wrapper object to copy metadata to
-        original: The original function to copy from
-    """
-    wrapper.__name__ = original.__name__
-    wrapper.__doc__ = original.__doc__
-    wrapper.__module__ = original.__module__
-    wrapper.__qualname__ = original.__qualname__
-    wrapper.__annotations__ = original.__annotations__
-
-
-class BeforeLLMCallHookMethod:
-    """Wrapper for methods marked as before_llm_call hooks within @CrewBase classes.
-
-    This wrapper marks a method so it can be detected and registered as a
-    crew-scoped hook during crew initialization.
-    """
-
-    is_before_llm_call_hook: bool = True
-
-    def __init__(
-        self,
-        meth: Callable[[Any, LLMCallHookContext], None],
-        agents: list[str] | None = None,
-    ) -> None:
-        """Initialize the hook method wrapper.
-
-        Args:
-            meth: The method to wrap
-            agents: Optional list of agent roles to filter
-        """
-        self._meth = meth
-        self.agents = agents
-        _copy_method_metadata(self, meth)
-
-    def __call__(self, *args: Any, **kwargs: Any) -> None:
-        """Call the wrapped method.
-
-        Args:
-            *args: Positional arguments
-            **kwargs: Keyword arguments
-        """
-        return self._meth(*args, **kwargs)
-
-    def __get__(self, obj: Any, objtype: type[Any] | None = None) -> Any:
-        """Support instance methods by implementing descriptor protocol.
-
-        Args:
-            obj: The instance that the method is accessed through
-            objtype: The type of the instance
-
-        Returns:
-            Self when accessed through class, bound method when accessed through instance
-        """
-        if obj is None:
-            return self
-        # Return bound method
-        return lambda context: self._meth(obj, context)
-
-
-class AfterLLMCallHookMethod:
-    """Wrapper for methods marked as after_llm_call hooks within @CrewBase classes."""
-
-    is_after_llm_call_hook: bool = True
-
-    def __init__(
-        self,
-        meth: Callable[[Any, LLMCallHookContext], str | None],
-        agents: list[str] | None = None,
-    ) -> None:
-        """Initialize the hook method wrapper."""
-        self._meth = meth
-        self.agents = agents
-        _copy_method_metadata(self, meth)
-
-    def __call__(self, *args: Any, **kwargs: Any) -> str | None:
-        """Call the wrapped method."""
-        return self._meth(*args, **kwargs)
-
-    def __get__(self, obj: Any, objtype: type[Any] | None = None) -> Any:
-        """Support instance methods."""
-        if obj is None:
-            return self
-        return lambda context: self._meth(obj, context)
-
-
-class BeforeToolCallHookMethod:
-    """Wrapper for methods marked as before_tool_call hooks within @CrewBase classes."""
-
-    is_before_tool_call_hook: bool = True
-
-    def __init__(
-        self,
-        meth: Callable[[Any, ToolCallHookContext], bool | None],
-        tools: list[str] | None = None,
-        agents: list[str] | None = None,
-    ) -> None:
-        """Initialize the hook method wrapper."""
-        self._meth = meth
-        self.tools = tools
-        self.agents = agents
-        _copy_method_metadata(self, meth)
-
-    def __call__(self, *args: Any, **kwargs: Any) -> bool | None:
-        """Call the wrapped method."""
-        return self._meth(*args, **kwargs)
-
-    def __get__(self, obj: Any, objtype: type[Any] | None = None) -> Any:
-        """Support instance methods."""
-        if obj is None:
-            return self
-        return lambda context: self._meth(obj, context)
-
-
-class AfterToolCallHookMethod:
-    """Wrapper for methods marked as after_tool_call hooks within @CrewBase classes."""
-
-    is_after_tool_call_hook: bool = True
-
-    def __init__(
-        self,
-        meth: Callable[[Any, ToolCallHookContext], str | None],
-        tools: list[str] | None = None,
-        agents: list[str] | None = None,
-    ) -> None:
-        """Initialize the hook method wrapper."""
-        self._meth = meth
-        self.tools = tools
-        self.agents = agents
-        _copy_method_metadata(self, meth)
-
-    def __call__(self, *args: Any, **kwargs: Any) -> str | None:
-        """Call the wrapped method."""
-        return self._meth(*args, **kwargs)
-
-    def __get__(self, obj: Any, objtype: type[Any] | None = None) -> Any:
-        """Support instance methods."""
-        if obj is None:
-            return self
-        return lambda context: self._meth(obj, context)

lib/crewai/src/crewai/lite_agent.py

@@ -358,7 +358,6 @@ class LiteAgent(FlowTrackable, BaseModel):
             pydantic=formatted_result,
             agent_role=self.role,
             usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-            messages=self._messages,
         )
 
         # Process guardrail if set
@@ -542,7 +541,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                             agent_key=self.key,
                             agent_role=self.role,
                             agent=self.original_agent,
-                            crew=None,
                         )
                     except Exception as e:
                         raise e

lib/crewai/src/crewai/lite_agent_output.py

@@ -6,8 +6,6 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from crewai.utilities.types import LLMMessage
-
 
 class LiteAgentOutput(BaseModel):
     """Class that represents the result of a LiteAgent execution."""
@@ -22,7 +20,6 @@ class LiteAgentOutput(BaseModel):
     usage_metrics: dict[str, Any] | None = Field(
         description="Token usage metrics for this execution", default=None
     )
-    messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
 
     def to_dict(self) -> dict[str, Any]:
         """Convert pydantic_output to a dictionary."""

lib/crewai/src/crewai/llm.py

@@ -20,8 +20,7 @@ from typing import (
 )
 
 from dotenv import load_dotenv
-import httpx
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 from typing_extensions import Self
 
 from crewai.events.event_bus import crewai_event_bus
@@ -38,13 +37,6 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageStartedEvent,
 )
 from crewai.llms.base_llm import BaseLLM
-from crewai.llms.constants import (
-    ANTHROPIC_MODELS,
-    AZURE_MODELS,
-    BEDROCK_MODELS,
-    GEMINI_MODELS,
-    OPENAI_MODELS,
-)
 from crewai.utilities import InternalInstructor
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -61,7 +53,6 @@ if TYPE_CHECKING:
     from litellm.utils import supports_response_schema
 
     from crewai.agent.core import Agent
-    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -327,67 +318,152 @@ class AccumulatedToolArgs(BaseModel):
 
 
 class LLM(BaseLLM):
-    completion_cost: float | None = None
+    completion_cost: float | None = Field(
+        default=None, description="The completion cost of the LLM."
+    )
+    top_p: float | None = Field(
+        default=None, description="Sampling probability threshold."
+    )
+    n: int | None = Field(
+        default=None, description="Number of completions to generate."
+    )
+    max_completion_tokens: int | None = Field(
+        default=None,
+        description="Maximum number of tokens to generate in the completion.",
+    )
+    max_tokens: int | None = Field(
+        default=None,
+        description="Maximum number of tokens allowed in the prompt + completion.",
+    )
+    presence_penalty: float | None = Field(
+        default=None, description="Penalty on the presence penalty."
+    )
+    frequency_penalty: float | None = Field(
+        default=None, description="Penalty on the frequency penalty."
+    )
+    logit_bias: dict[int, float] | None = Field(
+        default=None,
+        description="Modifies the likelihood of specified tokens appearing in the completion.",
+    )
+    response_format: type[BaseModel] | None = Field(
+        default=None,
+        description="Pydantic model class for structured response parsing.",
+    )
+    seed: int | None = Field(
+        default=None,
+        description="Random seed for reproducibility.",
+    )
+    logprobs: int | None = Field(
+        default=None,
+        description="Number of top logprobs to return.",
+    )
+    top_logprobs: int | None = Field(
+        default=None,
+        description="Number of top logprobs to return.",
+    )
+    api_base: str | None = Field(
+        default=None,
+        description="Base URL for the API endpoint.",
+    )
+    api_version: str | None = Field(
+        default=None,
+        description="API version to use.",
+    )
+    callbacks: list[Any] = Field(
+        default_factory=list,
+        description="List of callback handlers for LLM events.",
+    )
+    reasoning_effort: Literal["none", "low", "medium", "high"] | None = Field(
+        default=None,
+        description="Level of reasoning effort for the LLM.",
+    )
+    context_window_size: int = Field(
+        default=0,
+        description="The context window size of the LLM.",
+    )
+    is_anthropic: bool = Field(
+        default=False,
+        description="Indicates if the model is from Anthropic provider.",
+    )
+    supports_function_calling: bool = Field(
+        default=False,
+        description="Indicates if the model supports function calling.",
+    )
+    supports_stop_words: bool = Field(
+        default=False,
+        description="Indicates if the model supports stop words.",
+    )
+
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        self.is_anthropic = any(
+            prefix in self.model.lower() for prefix in ANTHROPIC_PREFIXES
+        )
+        try:
+            provider = self._get_custom_llm_provider()
+            self.supports_function_calling = litellm.utils.supports_function_calling(
+                self.model, custom_llm_provider=provider
+            )
+        except Exception as e:
+            logging.error(f"Failed to check function calling support: {e!s}")
+            self.supports_function_calling = False
+        try:
+            params = get_supported_openai_params(model=self.model)
+            self.supports_stop_words = params is not None and "stop" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {e!s}")
+            self.supports_stop_words = False
+
+        with suppress_warnings():
+            callback_types = [type(callback) for callback in self.callbacks]
+            for callback in litellm.success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm.success_callback.remove(callback)
+
+            for callback in litellm._async_success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm._async_success_callback.remove(callback)
+
+            litellm.callbacks = self.callbacks
+
+        with suppress_warnings():
+            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
+            if success_callbacks_str:
+                success_callbacks = [
+                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
+                ]
+
+            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+            if failure_callbacks_str:
+                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
+                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
+                ]
+
+                litellm.success_callback = success_callbacks
+                litellm.failure_callback = failure_callbacks
+        return self
+
+    # @computed_field
+    # @property
+    # def is_anthropic(self) -> bool:
+    #     """Determine if the model is from Anthropic provider."""
+    #     anthropic_prefixes = ("anthropic/", "claude-", "claude/")
+    #     return any(prefix in self.model.lower() for prefix in anthropic_prefixes)
 
     def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
-        """Factory method that routes to native SDK or falls back to LiteLLM.
-
-        Routing priority:
-            1. If 'provider' kwarg is present, use that provider with constants
-            2. If only 'model' kwarg, use constants to infer provider
-            3. If "/" in model name:
-               - Check if prefix is a native provider (openai/anthropic/azure/bedrock/gemini)
-               - If yes, validate model against constants
-               - If valid, route to native SDK; otherwise route to LiteLLM
-        """
+        """Factory method that routes to native SDK or falls back to LiteLLM."""
         if not model or not isinstance(model, str):
             raise ValueError("Model must be a non-empty string")
 
-        explicit_provider = kwargs.get("provider")
+        provider = model.partition("/")[0] if "/" in model else "openai"
 
-        if explicit_provider:
-            provider = explicit_provider
-            use_native = True
-            model_string = model
-        elif "/" in model:
-            prefix, _, model_part = model.partition("/")
-
-            provider_mapping = {
-                "openai": "openai",
-                "anthropic": "anthropic",
-                "claude": "anthropic",
-                "azure": "azure",
-                "azure_openai": "azure",
-                "google": "gemini",
-                "gemini": "gemini",
-                "bedrock": "bedrock",
-                "aws": "bedrock",
-            }
-
-            canonical_provider = provider_mapping.get(prefix.lower())
-
-            if canonical_provider and cls._validate_model_in_constants(
-                model_part, canonical_provider
-            ):
-                provider = canonical_provider
-                use_native = True
-                model_string = model_part
-            else:
-                provider = prefix
-                use_native = False
-                model_string = model_part
-        else:
-            provider = cls._infer_provider_from_model(model)
-            use_native = True
-            model_string = model
-
-        native_class = cls._get_native_provider(provider) if use_native else None
+        native_class = cls._get_native_provider(provider)
         if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
             try:
-                # Remove 'provider' from kwargs if it exists to avoid duplicate keyword argument
-                kwargs_copy = {k: v for k, v in kwargs.items() if k != 'provider'}
+                model_string = model.partition("/")[2] if "/" in model else model
                 return cast(
-                    Self, native_class(model=model_string, provider=provider, **kwargs_copy)
+                    Self, native_class(model=model_string, provider=provider, **kwargs)
                 )
             except NotImplementedError:
                 raise
@@ -404,63 +480,6 @@ class LLM(BaseLLM):
         instance.is_litellm = True
         return instance
 
-    @classmethod
-    def _validate_model_in_constants(cls, model: str, provider: str) -> bool:
-        """Validate if a model name exists in the provider's constants.
-
-        Args:
-            model: The model name to validate
-            provider: The provider to check against (canonical name)
-
-        Returns:
-            True if the model exists in the provider's constants, False otherwise
-        """
-        if provider == "openai":
-            return model in OPENAI_MODELS
-
-        if provider == "anthropic" or provider == "claude":
-            return model in ANTHROPIC_MODELS
-
-        if provider == "gemini":
-            return model in GEMINI_MODELS
-
-        if provider == "bedrock":
-            return model in BEDROCK_MODELS
-
-        if provider == "azure":
-            # azure does not provide a list of available models, determine a better way to handle this
-            return True
-
-        return False
-
-    @classmethod
-    def _infer_provider_from_model(cls, model: str) -> str:
-        """Infer the provider from the model name.
-
-        Args:
-            model: The model name without provider prefix
-
-        Returns:
-            The inferred provider name, defaults to "openai"
-        """
-
-        if model in OPENAI_MODELS:
-            return "openai"
-
-        if model in ANTHROPIC_MODELS:
-            return "anthropic"
-
-        if model in GEMINI_MODELS:
-            return "gemini"
-
-        if model in BEDROCK_MODELS:
-            return "bedrock"
-
-        if model in AZURE_MODELS:
-            return "azure"
-
-        return "openai"
-
     @classmethod
     def _get_native_provider(cls, provider: str) -> type | None:
         """Get native provider class if available."""
@@ -493,98 +512,6 @@ class LLM(BaseLLM):
 
         return None
 
-    def __init__(
-        self,
-        model: str,
-        timeout: float | int | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        n: int | None = None,
-        stop: str | list[str] | None = None,
-        max_completion_tokens: int | None = None,
-        max_tokens: int | float | None = None,
-        presence_penalty: float | None = None,
-        frequency_penalty: float | None = None,
-        logit_bias: dict[int, float] | None = None,
-        response_format: type[BaseModel] | None = None,
-        seed: int | None = None,
-        logprobs: int | None = None,
-        top_logprobs: int | None = None,
-        base_url: str | None = None,
-        api_base: str | None = None,
-        api_version: str | None = None,
-        api_key: str | None = None,
-        callbacks: list[Any] | None = None,
-        reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
-        stream: bool = False,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize LLM instance.
-
-        Note: This __init__ method is only called for fallback instances.
-        Native provider instances handle their own initialization in their respective classes.
-        """
-        super().__init__(
-            model=model,
-            temperature=temperature,
-            api_key=api_key,
-            base_url=base_url,
-            timeout=timeout,
-            **kwargs,
-        )
-        self.model = model
-        self.timeout = timeout
-        self.temperature = temperature
-        self.top_p = top_p
-        self.n = n
-        self.max_completion_tokens = max_completion_tokens
-        self.max_tokens = max_tokens
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.logit_bias = logit_bias
-        self.response_format = response_format
-        self.seed = seed
-        self.logprobs = logprobs
-        self.top_logprobs = top_logprobs
-        self.base_url = base_url
-        self.api_base = api_base
-        self.api_version = api_version
-        self.api_key = api_key
-        self.callbacks = callbacks
-        self.context_window_size = 0
-        self.reasoning_effort = reasoning_effort
-        self.additional_params = kwargs
-        self.is_anthropic = self._is_anthropic_model(model)
-        self.stream = stream
-        self.interceptor = interceptor
-
-        litellm.drop_params = True
-
-        # Normalize self.stop to always be a list[str]
-        if stop is None:
-            self.stop: list[str] = []
-        elif isinstance(stop, str):
-            self.stop = [stop]
-        else:
-            self.stop = stop
-
-        self.set_callbacks(callbacks or [])
-        self.set_env_callbacks()
-
-    @staticmethod
-    def _is_anthropic_model(model: str) -> bool:
-        """Determine if the model is from Anthropic provider.
-
-        Args:
-            model: The model identifier string.
-
-        Returns:
-            bool: True if the model is from Anthropic, False otherwise.
-        """
-        anthropic_prefixes = ("anthropic/", "claude-", "claude/")
-        return any(prefix in model.lower() for prefix in anthropic_prefixes)
-
     def _prepare_completion_params(
         self,
         messages: str | list[LLMMessage],
@@ -1298,8 +1225,6 @@ class LLM(BaseLLM):
                     message["role"] = msg_role
         # --- 5) Set up callbacks if provided
         with suppress_warnings():
-            if callbacks and len(callbacks) > 0:
-                self.set_callbacks(callbacks)
             try:
                 # --- 6) Prepare parameters for the completion call
                 params = self._prepare_completion_params(messages, tools)
@@ -1488,24 +1413,6 @@ class LLM(BaseLLM):
                 "Please remove response_format or use a supported model."
             )
 
-    def supports_function_calling(self) -> bool:
-        try:
-            provider = self._get_custom_llm_provider()
-            return litellm.utils.supports_function_calling(
-                self.model, custom_llm_provider=provider
-            )
-        except Exception as e:
-            logging.error(f"Failed to check function calling support: {e!s}")
-            return False
-
-    def supports_stop_words(self) -> bool:
-        try:
-            params = get_supported_openai_params(model=self.model)
-            return params is not None and "stop" in params
-        except Exception as e:
-            logging.error(f"Failed to get supported params: {e!s}")
-            return False
-
     def get_context_window_size(self) -> int:
         """
         Returns the context window size, using 75% of the maximum to avoid
@@ -1535,60 +1442,6 @@ class LLM(BaseLLM):
                 self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
         return self.context_window_size
 
-    @staticmethod
-    def set_callbacks(callbacks: list[Any]) -> None:
-        """
-        Attempt to keep a single set of callbacks in litellm by removing old
-        duplicates and adding new ones.
-        """
-        with suppress_warnings():
-            callback_types = [type(callback) for callback in callbacks]
-            for callback in litellm.success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm.success_callback.remove(callback)
-
-            for callback in litellm._async_success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm._async_success_callback.remove(callback)
-
-            litellm.callbacks = callbacks
-
-    @staticmethod
-    def set_env_callbacks() -> None:
-        """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.
-
-        Examples:
-            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
-            LITELLM_FAILURE_CALLBACKS="langfuse"
-
-        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
-        `litellm.failure_callback` to ["langfuse"].
-        """
-        with suppress_warnings():
-            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
-            if success_callbacks_str:
-                success_callbacks = [
-                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
-                ]
-
-            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-            if failure_callbacks_str:
-                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
-                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
-                ]
-
-                litellm.success_callback = success_callbacks
-                litellm.failure_callback = failure_callbacks
-
     def __copy__(self) -> LLM:
         """Create a shallow copy of the LLM instance."""
         # Filter out parameters that are already explicitly passed to avoid conflicts
@@ -1649,7 +1502,7 @@ class LLM(BaseLLM):
             **filtered_params,
         )
 
-    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:
+    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:  # type: ignore[override]
         """Create a deep copy of the LLM instance."""
         import copy
 

lib/crewai/src/crewai/llms/base_llm.py

@@ -13,8 +13,9 @@ import logging
 import re
 from typing import TYPE_CHECKING, Any, Final
 
-from pydantic import BaseModel
+from pydantic import AliasChoices, BaseModel, Field, PrivateAttr, field_validator
 
+from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
     LLMCallCompletedEvent,
@@ -28,6 +29,7 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
+from crewai.llms.hooks import BaseInterceptor
 from crewai.types.usage_metrics import UsageMetrics
 
 
@@ -43,7 +45,7 @@ DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
 _JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTALL)
 
 
-class BaseLLM(ABC):
+class BaseLLM(BaseModel, ABC):
     """Abstract base class for LLM implementations.
 
     This class defines the interface that all LLM implementations must follow.
@@ -55,70 +57,105 @@ class BaseLLM(ABC):
     implement proper validation for input parameters and provide clear error
     messages when things go wrong.
 
+
     Attributes:
         model: The model identifier/name.
         temperature: Optional temperature setting for response generation.
-        stop: A list of stop sequences that the LLM should use to stop generation.
-        additional_params: Additional provider-specific parameters.
     """
 
-    is_litellm: bool = False
+    provider: str | re.Pattern[str] = Field(
+        default="openai", description="The provider of the LLM."
+    )
+    model: str = Field(description="The model identifier/name.")
+    temperature: float | None = Field(
+        default=None, ge=0, le=2, description="Temperature for response generation."
+    )
+    api_key: str | None = Field(default=None, description="API key for authentication.")
+    base_url: str | None = Field(default=None, description="Base URL for API calls.")
+    timeout: float | None = Field(default=None, description="Timeout for API calls.")
+    max_retries: int = Field(
+        default=2, description="Maximum number of API requests to make."
+    )
+    max_tokens: int | None = Field(
+        default=None, description="Maximum tokens for response generation."
+    )
+    stream: bool | None = Field(default=False, description="Stream the API requests.")
+    client: Any = Field(description="Underlying LLM client instance.")
+    interceptor: BaseInterceptor[Any, Any] | None = Field(
+        default=None,
+        description="An optional HTTPX interceptor for modifying requests/responses.",
+    )
+    client_params: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional parameters for the underlying LLM client.",
+    )
+    supports_stop_words: bool = Field(
+        default=DEFAULT_SUPPORTS_STOP_WORDS,
+        description="Whether or not to support stop words.",
+    )
+    stop_sequences: list[str] = Field(
+        default_factory=list,
+        validation_alias=AliasChoices("stop_sequences", "stop"),
+        description="Stop sequences for generation (synchronized with stop).",
+    )
+    is_litellm: bool = Field(
+        default=False, description="Is this LLM implementation in litellm?"
+    )
+    additional_params: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional parameters for LLM calls.",
+    )
+    _token_usage: TokenProcess = PrivateAttr(default_factory=TokenProcess)
 
-    def __init__(
-        self,
-        model: str,
-        temperature: float | None = None,
-        api_key: str | None = None,
-        base_url: str | None = None,
-        provider: str | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize the BaseLLM with default attributes.
+    @field_validator("provider", mode="before")
+    @classmethod
+    def extract_provider_from_model(
+        cls, v: str | re.Pattern[str] | None, info: Any
+    ) -> str | re.Pattern[str]:
+        """Extract provider from model string if not explicitly provided.
 
         Args:
-            model: The model identifier/name.
-            temperature: Optional temperature setting for response generation.
-            stop: Optional list of stop sequences for generation.
-            **kwargs: Additional provider-specific parameters.
+            v: Provided provider value (can be str, Pattern, or None)
+            info: Validation info containing other field values
+
+        Returns:
+            Provider name (str) or Pattern
         """
-        if not model:
-            raise ValueError("Model name is required and cannot be empty")
+        # If provider explicitly provided, validate and return it
+        if v is not None:
+            if not isinstance(v, (str, re.Pattern)):
+                raise ValueError(f"Provider must be str or Pattern, got {type(v)}")
+            return v
 
-        self.model = model
-        self.temperature = temperature
-        self.api_key = api_key
-        self.base_url = base_url
-        # Store additional parameters for provider-specific use
-        self.additional_params = kwargs
-        self._provider = provider or "openai"
+        model: str = info.data.get("model", "")
+        if "/" in model:
+            return model.partition("/")[0]
+        return "openai"
 
-        stop = kwargs.pop("stop", None)
-        if stop is None:
-            self.stop: list[str] = []
-        elif isinstance(stop, str):
-            self.stop = [stop]
-        elif isinstance(stop, list):
-            self.stop = stop
-        else:
-            self.stop = []
+    @field_validator("stop_sequences", mode="before")
+    @classmethod
+    def normalize_stop_sequences(
+        cls, v: str | list[str] | set[str] | None
+    ) -> list[str]:
+        """Validate and normalize stop sequences.
 
-        self._token_usage = {
-            "total_tokens": 0,
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "successful_requests": 0,
-            "cached_prompt_tokens": 0,
-        }
+        Converts string to list and handles None values.
+        AliasChoices handles accepting both 'stop' and 'stop_sequences' parameter names.
+        """
+        if v is None:
+            return []
+        if isinstance(v, str):
+            return [v]
+        if isinstance(v, set):
+            return list(v)
+        if isinstance(v, list):
+            return v
+        return []
 
     @property
-    def provider(self) -> str:
-        """Get the provider of the LLM."""
-        return self._provider
-
-    @provider.setter
-    def provider(self, value: str) -> None:
-        """Set the provider of the LLM."""
-        self._provider = value
+    def stop(self) -> list[str]:
+        """Alias for stop_sequences to maintain backward compatibility."""
+        return self.stop_sequences
 
     @abstractmethod
     def call(
@@ -171,14 +208,6 @@ class BaseLLM(ABC):
         """
         return tools
 
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        return DEFAULT_SUPPORTS_STOP_WORDS
-
     def _supports_stop_words_implementation(self) -> bool:
         """Check if stop words are configured for this LLM instance.
 
@@ -506,7 +535,7 @@ class BaseLLM(ABC):
         """
         if "/" in model:
             return model.partition("/")[0]
-        return "openai"  # Default provider
+        return "openai"
 
     def _track_token_usage_internal(self, usage_data: dict[str, Any]) -> None:
         """Track token usage internally in the LLM instance.
@@ -535,11 +564,11 @@ class BaseLLM(ABC):
             or 0
         )
 
-        self._token_usage["prompt_tokens"] += prompt_tokens
-        self._token_usage["completion_tokens"] += completion_tokens
-        self._token_usage["total_tokens"] += prompt_tokens + completion_tokens
-        self._token_usage["successful_requests"] += 1
-        self._token_usage["cached_prompt_tokens"] += cached_tokens
+        self._token_usage.prompt_tokens += prompt_tokens
+        self._token_usage.completion_tokens += completion_tokens
+        self._token_usage.total_tokens += prompt_tokens + completion_tokens
+        self._token_usage.successful_requests += 1
+        self._token_usage.cached_prompt_tokens += cached_tokens
 
     def get_token_usage_summary(self) -> UsageMetrics:
         """Get summary of token usage for this LLM instance.
@@ -547,4 +576,10 @@ class BaseLLM(ABC):
         Returns:
             Dictionary with token usage totals
         """
-        return UsageMetrics(**self._token_usage)
+        return UsageMetrics(
+            prompt_tokens=self._token_usage.prompt_tokens,
+            completion_tokens=self._token_usage.completion_tokens,
+            total_tokens=self._token_usage.total_tokens,
+            successful_requests=self._token_usage.successful_requests,
+            cached_prompt_tokens=self._token_usage.cached_prompt_tokens,
+        )

lib/crewai/src/crewai/llms/constants.py

@@ -1,558 +0,0 @@
-from typing import Literal, TypeAlias
-
-
-OpenAIModels: TypeAlias = Literal[
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0125",
-    "gpt-3.5-turbo-0301",
-    "gpt-3.5-turbo-0613",
-    "gpt-3.5-turbo-1106",
-    "gpt-3.5-turbo-16k",
-    "gpt-3.5-turbo-16k-0613",
-    "gpt-3.5-turbo-instruct",
-    "gpt-3.5-turbo-instruct-0914",
-    "gpt-4",
-    "gpt-4-0125-preview",
-    "gpt-4-0314",
-    "gpt-4-0613",
-    "gpt-4-1106-preview",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-    "gpt-4-32k-0613",
-    "gpt-4-turbo",
-    "gpt-4-turbo-2024-04-09",
-    "gpt-4-turbo-preview",
-    "gpt-4-vision-preview",
-    "gpt-4.1",
-    "gpt-4.1-2025-04-14",
-    "gpt-4.1-mini",
-    "gpt-4.1-mini-2025-04-14",
-    "gpt-4.1-nano",
-    "gpt-4.1-nano-2025-04-14",
-    "gpt-4o",
-    "gpt-4o-2024-05-13",
-    "gpt-4o-2024-08-06",
-    "gpt-4o-2024-11-20",
-    "gpt-4o-audio-preview",
-    "gpt-4o-audio-preview-2024-10-01",
-    "gpt-4o-audio-preview-2024-12-17",
-    "gpt-4o-audio-preview-2025-06-03",
-    "gpt-4o-mini",
-    "gpt-4o-mini-2024-07-18",
-    "gpt-4o-mini-audio-preview",
-    "gpt-4o-mini-audio-preview-2024-12-17",
-    "gpt-4o-mini-realtime-preview",
-    "gpt-4o-mini-realtime-preview-2024-12-17",
-    "gpt-4o-mini-search-preview",
-    "gpt-4o-mini-search-preview-2025-03-11",
-    "gpt-4o-mini-transcribe",
-    "gpt-4o-mini-tts",
-    "gpt-4o-realtime-preview",
-    "gpt-4o-realtime-preview-2024-10-01",
-    "gpt-4o-realtime-preview-2024-12-17",
-    "gpt-4o-realtime-preview-2025-06-03",
-    "gpt-4o-search-preview",
-    "gpt-4o-search-preview-2025-03-11",
-    "gpt-4o-transcribe",
-    "gpt-4o-transcribe-diarize",
-    "gpt-5",
-    "gpt-5-2025-08-07",
-    "gpt-5-chat",
-    "gpt-5-chat-latest",
-    "gpt-5-codex",
-    "gpt-5-mini",
-    "gpt-5-mini-2025-08-07",
-    "gpt-5-nano",
-    "gpt-5-nano-2025-08-07",
-    "gpt-5-pro",
-    "gpt-5-pro-2025-10-06",
-    "gpt-5-search-api",
-    "gpt-5-search-api-2025-10-14",
-    "gpt-audio",
-    "gpt-audio-2025-08-28",
-    "gpt-audio-mini",
-    "gpt-audio-mini-2025-10-06",
-    "gpt-image-1",
-    "gpt-image-1-mini",
-    "gpt-realtime",
-    "gpt-realtime-2025-08-28",
-    "gpt-realtime-mini",
-    "gpt-realtime-mini-2025-10-06",
-    "o1",
-    "o1-preview",
-    "o1-2024-12-17",
-    "o1-mini",
-    "o1-mini-2024-09-12",
-    "o1-pro",
-    "o1-pro-2025-03-19",
-    "o3-mini",
-    "o3",
-    "o4-mini",
-    "whisper-1",
-]
-OPENAI_MODELS: list[OpenAIModels] = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0125",
-    "gpt-3.5-turbo-0301",
-    "gpt-3.5-turbo-0613",
-    "gpt-3.5-turbo-1106",
-    "gpt-3.5-turbo-16k",
-    "gpt-3.5-turbo-16k-0613",
-    "gpt-3.5-turbo-instruct",
-    "gpt-3.5-turbo-instruct-0914",
-    "gpt-4",
-    "gpt-4-0125-preview",
-    "gpt-4-0314",
-    "gpt-4-0613",
-    "gpt-4-1106-preview",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-    "gpt-4-32k-0613",
-    "gpt-4-turbo",
-    "gpt-4-turbo-2024-04-09",
-    "gpt-4-turbo-preview",
-    "gpt-4-vision-preview",
-    "gpt-4.1",
-    "gpt-4.1-2025-04-14",
-    "gpt-4.1-mini",
-    "gpt-4.1-mini-2025-04-14",
-    "gpt-4.1-nano",
-    "gpt-4.1-nano-2025-04-14",
-    "gpt-4o",
-    "gpt-4o-2024-05-13",
-    "gpt-4o-2024-08-06",
-    "gpt-4o-2024-11-20",
-    "gpt-4o-audio-preview",
-    "gpt-4o-audio-preview-2024-10-01",
-    "gpt-4o-audio-preview-2024-12-17",
-    "gpt-4o-audio-preview-2025-06-03",
-    "gpt-4o-mini",
-    "gpt-4o-mini-2024-07-18",
-    "gpt-4o-mini-audio-preview",
-    "gpt-4o-mini-audio-preview-2024-12-17",
-    "gpt-4o-mini-realtime-preview",
-    "gpt-4o-mini-realtime-preview-2024-12-17",
-    "gpt-4o-mini-search-preview",
-    "gpt-4o-mini-search-preview-2025-03-11",
-    "gpt-4o-mini-transcribe",
-    "gpt-4o-mini-tts",
-    "gpt-4o-realtime-preview",
-    "gpt-4o-realtime-preview-2024-10-01",
-    "gpt-4o-realtime-preview-2024-12-17",
-    "gpt-4o-realtime-preview-2025-06-03",
-    "gpt-4o-search-preview",
-    "gpt-4o-search-preview-2025-03-11",
-    "gpt-4o-transcribe",
-    "gpt-4o-transcribe-diarize",
-    "gpt-5",
-    "gpt-5-2025-08-07",
-    "gpt-5-chat",
-    "gpt-5-chat-latest",
-    "gpt-5-codex",
-    "gpt-5-mini",
-    "gpt-5-mini-2025-08-07",
-    "gpt-5-nano",
-    "gpt-5-nano-2025-08-07",
-    "gpt-5-pro",
-    "gpt-5-pro-2025-10-06",
-    "gpt-5-search-api",
-    "gpt-5-search-api-2025-10-14",
-    "gpt-audio",
-    "gpt-audio-2025-08-28",
-    "gpt-audio-mini",
-    "gpt-audio-mini-2025-10-06",
-    "gpt-image-1",
-    "gpt-image-1-mini",
-    "gpt-realtime",
-    "gpt-realtime-2025-08-28",
-    "gpt-realtime-mini",
-    "gpt-realtime-mini-2025-10-06",
-    "o1",
-    "o1-preview",
-    "o1-2024-12-17",
-    "o1-mini",
-    "o1-mini-2024-09-12",
-    "o1-pro",
-    "o1-pro-2025-03-19",
-    "o3-mini",
-    "o3",
-    "o4-mini",
-    "whisper-1",
-]
-
-
-AnthropicModels: TypeAlias = Literal[
-    "claude-3-7-sonnet-latest",
-    "claude-3-7-sonnet-20250219",
-    "claude-3-5-haiku-latest",
-    "claude-3-5-haiku-20241022",
-    "claude-haiku-4-5",
-    "claude-haiku-4-5-20251001",
-    "claude-sonnet-4-20250514",
-    "claude-sonnet-4-0",
-    "claude-4-sonnet-20250514",
-    "claude-sonnet-4-5",
-    "claude-sonnet-4-5-20250929",
-    "claude-3-5-sonnet-latest",
-    "claude-3-5-sonnet-20241022",
-    "claude-3-5-sonnet-20240620",
-    "claude-opus-4-0",
-    "claude-opus-4-20250514",
-    "claude-4-opus-20250514",
-    "claude-opus-4-1",
-    "claude-opus-4-1-20250805",
-    "claude-3-opus-latest",
-    "claude-3-opus-20240229",
-    "claude-3-sonnet-20240229",
-    "claude-3-haiku-latest",
-    "claude-3-haiku-20240307",
-]
-ANTHROPIC_MODELS: list[AnthropicModels] = [
-    "claude-3-7-sonnet-latest",
-    "claude-3-7-sonnet-20250219",
-    "claude-3-5-haiku-latest",
-    "claude-3-5-haiku-20241022",
-    "claude-haiku-4-5",
-    "claude-haiku-4-5-20251001",
-    "claude-sonnet-4-20250514",
-    "claude-sonnet-4-0",
-    "claude-4-sonnet-20250514",
-    "claude-sonnet-4-5",
-    "claude-sonnet-4-5-20250929",
-    "claude-3-5-sonnet-latest",
-    "claude-3-5-sonnet-20241022",
-    "claude-3-5-sonnet-20240620",
-    "claude-opus-4-0",
-    "claude-opus-4-20250514",
-    "claude-4-opus-20250514",
-    "claude-opus-4-1",
-    "claude-opus-4-1-20250805",
-    "claude-3-opus-latest",
-    "claude-3-opus-20240229",
-    "claude-3-sonnet-20240229",
-    "claude-3-haiku-latest",
-    "claude-3-haiku-20240307",
-]
-
-GeminiModels: TypeAlias = Literal[
-    "gemini-2.5-pro",
-    "gemini-2.5-pro-preview-03-25",
-    "gemini-2.5-pro-preview-05-06",
-    "gemini-2.5-pro-preview-06-05",
-    "gemini-2.5-flash",
-    "gemini-2.5-flash-preview-05-20",
-    "gemini-2.5-flash-preview-04-17",
-    "gemini-2.5-flash-image",
-    "gemini-2.5-flash-image-preview",
-    "gemini-2.5-flash-lite",
-    "gemini-2.5-flash-lite-preview-06-17",
-    "gemini-2.5-flash-preview-09-2025",
-    "gemini-2.5-flash-lite-preview-09-2025",
-    "gemini-2.5-flash-preview-tts",
-    "gemini-2.5-pro-preview-tts",
-    "gemini-2.5-computer-use-preview-10-2025",
-    "gemini-2.0-flash",
-    "gemini-2.0-flash-001",
-    "gemini-2.0-flash-exp",
-    "gemini-2.0-flash-exp-image-generation",
-    "gemini-2.0-flash-lite",
-    "gemini-2.0-flash-lite-001",
-    "gemini-2.0-flash-lite-preview",
-    "gemini-2.0-flash-lite-preview-02-05",
-    "gemini-2.0-flash-preview-image-generation",
-    "gemini-2.0-flash-thinking-exp",
-    "gemini-2.0-flash-thinking-exp-01-21",
-    "gemini-2.0-flash-thinking-exp-1219",
-    "gemini-2.0-pro-exp",
-    "gemini-2.0-pro-exp-02-05",
-    "gemini-exp-1206",
-    "gemini-1.5-pro",
-    "gemini-1.5-flash",
-    "gemini-1.5-flash-8b",
-    "gemini-flash-latest",
-    "gemini-flash-lite-latest",
-    "gemini-pro-latest",
-    "gemini-2.0-flash-live-001",
-    "gemini-live-2.5-flash-preview",
-    "gemini-2.5-flash-live-preview",
-    "gemini-robotics-er-1.5-preview",
-    "gemini-gemma-2-27b-it",
-    "gemini-gemma-2-9b-it",
-    "gemma-3-1b-it",
-    "gemma-3-4b-it",
-    "gemma-3-12b-it",
-    "gemma-3-27b-it",
-    "gemma-3n-e2b-it",
-    "gemma-3n-e4b-it",
-    "learnlm-2.0-flash-experimental",
-]
-GEMINI_MODELS: list[GeminiModels] = [
-    "gemini-2.5-pro",
-    "gemini-2.5-pro-preview-03-25",
-    "gemini-2.5-pro-preview-05-06",
-    "gemini-2.5-pro-preview-06-05",
-    "gemini-2.5-flash",
-    "gemini-2.5-flash-preview-05-20",
-    "gemini-2.5-flash-preview-04-17",
-    "gemini-2.5-flash-image",
-    "gemini-2.5-flash-image-preview",
-    "gemini-2.5-flash-lite",
-    "gemini-2.5-flash-lite-preview-06-17",
-    "gemini-2.5-flash-preview-09-2025",
-    "gemini-2.5-flash-lite-preview-09-2025",
-    "gemini-2.5-flash-preview-tts",
-    "gemini-2.5-pro-preview-tts",
-    "gemini-2.5-computer-use-preview-10-2025",
-    "gemini-2.0-flash",
-    "gemini-2.0-flash-001",
-    "gemini-2.0-flash-exp",
-    "gemini-2.0-flash-exp-image-generation",
-    "gemini-2.0-flash-lite",
-    "gemini-2.0-flash-lite-001",
-    "gemini-2.0-flash-lite-preview",
-    "gemini-2.0-flash-lite-preview-02-05",
-    "gemini-2.0-flash-preview-image-generation",
-    "gemini-2.0-flash-thinking-exp",
-    "gemini-2.0-flash-thinking-exp-01-21",
-    "gemini-2.0-flash-thinking-exp-1219",
-    "gemini-2.0-pro-exp",
-    "gemini-2.0-pro-exp-02-05",
-    "gemini-exp-1206",
-    "gemini-1.5-pro",
-    "gemini-1.5-flash",
-    "gemini-1.5-flash-8b",
-    "gemini-flash-latest",
-    "gemini-flash-lite-latest",
-    "gemini-pro-latest",
-    "gemini-2.0-flash-live-001",
-    "gemini-live-2.5-flash-preview",
-    "gemini-2.5-flash-live-preview",
-    "gemini-robotics-er-1.5-preview",
-    "gemini-gemma-2-27b-it",
-    "gemini-gemma-2-9b-it",
-    "gemma-3-1b-it",
-    "gemma-3-4b-it",
-    "gemma-3-12b-it",
-    "gemma-3-27b-it",
-    "gemma-3n-e2b-it",
-    "gemma-3n-e4b-it",
-    "learnlm-2.0-flash-experimental",
-]
-
-
-AzureModels: TypeAlias = Literal[
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-3.5-turbo-0613",
-    "gpt-3.5-turbo-16k",
-    "gpt-3.5-turbo-16k-0613",
-    "gpt-35-turbo",
-    "gpt-35-turbo-0125",
-    "gpt-35-turbo-1106",
-    "gpt-35-turbo-16k-0613",
-    "gpt-35-turbo-instruct-0914",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-0613",
-    "gpt-4-1106-preview",
-    "gpt-4-0125-preview",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-    "gpt-4-32k-0613",
-    "gpt-4-turbo",
-    "gpt-4-turbo-2024-04-09",
-    "gpt-4-vision",
-    "gpt-4o",
-    "gpt-4o-2024-05-13",
-    "gpt-4o-2024-08-06",
-    "gpt-4o-2024-11-20",
-    "gpt-4o-mini",
-    "gpt-5",
-    "o1",
-    "o1-mini",
-    "o1-preview",
-    "o3-mini",
-    "o3",
-    "o4-mini",
-]
-AZURE_MODELS: list[AzureModels] = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-3.5-turbo-0613",
-    "gpt-3.5-turbo-16k",
-    "gpt-3.5-turbo-16k-0613",
-    "gpt-35-turbo",
-    "gpt-35-turbo-0125",
-    "gpt-35-turbo-1106",
-    "gpt-35-turbo-16k-0613",
-    "gpt-35-turbo-instruct-0914",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-0613",
-    "gpt-4-1106-preview",
-    "gpt-4-0125-preview",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-    "gpt-4-32k-0613",
-    "gpt-4-turbo",
-    "gpt-4-turbo-2024-04-09",
-    "gpt-4-vision",
-    "gpt-4o",
-    "gpt-4o-2024-05-13",
-    "gpt-4o-2024-08-06",
-    "gpt-4o-2024-11-20",
-    "gpt-4o-mini",
-    "gpt-5",
-    "o1",
-    "o1-mini",
-    "o1-preview",
-    "o3-mini",
-    "o3",
-    "o4-mini",
-]
-
-
-BedrockModels: TypeAlias = Literal[
-    "ai21.jamba-1-5-large-v1:0",
-    "ai21.jamba-1-5-mini-v1:0",
-    "amazon.nova-lite-v1:0",
-    "amazon.nova-lite-v1:0:24k",
-    "amazon.nova-lite-v1:0:300k",
-    "amazon.nova-micro-v1:0",
-    "amazon.nova-micro-v1:0:128k",
-    "amazon.nova-micro-v1:0:24k",
-    "amazon.nova-premier-v1:0",
-    "amazon.nova-premier-v1:0:1000k",
-    "amazon.nova-premier-v1:0:20k",
-    "amazon.nova-premier-v1:0:8k",
-    "amazon.nova-premier-v1:0:mm",
-    "amazon.nova-pro-v1:0",
-    "amazon.nova-pro-v1:0:24k",
-    "amazon.nova-pro-v1:0:300k",
-    "amazon.titan-text-express-v1",
-    "amazon.titan-text-express-v1:0:8k",
-    "amazon.titan-text-lite-v1",
-    "amazon.titan-text-lite-v1:0:4k",
-    "amazon.titan-tg1-large",
-    "anthropic.claude-3-5-haiku-20241022-v1:0",
-    "anthropic.claude-3-5-sonnet-20240620-v1:0",
-    "anthropic.claude-3-5-sonnet-20241022-v2:0",
-    "anthropic.claude-3-7-sonnet-20250219-v1:0",
-    "anthropic.claude-3-haiku-20240307-v1:0",
-    "anthropic.claude-3-haiku-20240307-v1:0:200k",
-    "anthropic.claude-3-haiku-20240307-v1:0:48k",
-    "anthropic.claude-3-opus-20240229-v1:0",
-    "anthropic.claude-3-opus-20240229-v1:0:12k",
-    "anthropic.claude-3-opus-20240229-v1:0:200k",
-    "anthropic.claude-3-opus-20240229-v1:0:28k",
-    "anthropic.claude-3-sonnet-20240229-v1:0",
-    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
-    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
-    "anthropic.claude-haiku-4-5-20251001-v1:0",
-    "anthropic.claude-instant-v1:2:100k",
-    "anthropic.claude-opus-4-1-20250805-v1:0",
-    "anthropic.claude-opus-4-20250514-v1:0",
-    "anthropic.claude-sonnet-4-20250514-v1:0",
-    "anthropic.claude-sonnet-4-5-20250929-v1:0",
-    "anthropic.claude-v2:0:100k",
-    "anthropic.claude-v2:0:18k",
-    "anthropic.claude-v2:1:18k",
-    "anthropic.claude-v2:1:200k",
-    "cohere.command-r-plus-v1:0",
-    "cohere.command-r-v1:0",
-    "cohere.rerank-v3-5:0",
-    "deepseek.r1-v1:0",
-    "meta.llama3-1-70b-instruct-v1:0",
-    "meta.llama3-1-8b-instruct-v1:0",
-    "meta.llama3-2-11b-instruct-v1:0",
-    "meta.llama3-2-1b-instruct-v1:0",
-    "meta.llama3-2-3b-instruct-v1:0",
-    "meta.llama3-2-90b-instruct-v1:0",
-    "meta.llama3-3-70b-instruct-v1:0",
-    "meta.llama3-70b-instruct-v1:0",
-    "meta.llama3-8b-instruct-v1:0",
-    "meta.llama4-maverick-17b-instruct-v1:0",
-    "meta.llama4-scout-17b-instruct-v1:0",
-    "mistral.mistral-7b-instruct-v0:2",
-    "mistral.mistral-large-2402-v1:0",
-    "mistral.mistral-small-2402-v1:0",
-    "mistral.mixtral-8x7b-instruct-v0:1",
-    "mistral.pixtral-large-2502-v1:0",
-    "openai.gpt-oss-120b-1:0",
-    "openai.gpt-oss-20b-1:0",
-    "qwen.qwen3-32b-v1:0",
-    "qwen.qwen3-coder-30b-a3b-v1:0",
-    "twelvelabs.pegasus-1-2-v1:0",
-]
-BEDROCK_MODELS: list[BedrockModels] = [
-    "ai21.jamba-1-5-large-v1:0",
-    "ai21.jamba-1-5-mini-v1:0",
-    "amazon.nova-lite-v1:0",
-    "amazon.nova-lite-v1:0:24k",
-    "amazon.nova-lite-v1:0:300k",
-    "amazon.nova-micro-v1:0",
-    "amazon.nova-micro-v1:0:128k",
-    "amazon.nova-micro-v1:0:24k",
-    "amazon.nova-premier-v1:0",
-    "amazon.nova-premier-v1:0:1000k",
-    "amazon.nova-premier-v1:0:20k",
-    "amazon.nova-premier-v1:0:8k",
-    "amazon.nova-premier-v1:0:mm",
-    "amazon.nova-pro-v1:0",
-    "amazon.nova-pro-v1:0:24k",
-    "amazon.nova-pro-v1:0:300k",
-    "amazon.titan-text-express-v1",
-    "amazon.titan-text-express-v1:0:8k",
-    "amazon.titan-text-lite-v1",
-    "amazon.titan-text-lite-v1:0:4k",
-    "amazon.titan-tg1-large",
-    "anthropic.claude-3-5-haiku-20241022-v1:0",
-    "anthropic.claude-3-5-sonnet-20240620-v1:0",
-    "anthropic.claude-3-5-sonnet-20241022-v2:0",
-    "anthropic.claude-3-7-sonnet-20250219-v1:0",
-    "anthropic.claude-3-haiku-20240307-v1:0",
-    "anthropic.claude-3-haiku-20240307-v1:0:200k",
-    "anthropic.claude-3-haiku-20240307-v1:0:48k",
-    "anthropic.claude-3-opus-20240229-v1:0",
-    "anthropic.claude-3-opus-20240229-v1:0:12k",
-    "anthropic.claude-3-opus-20240229-v1:0:200k",
-    "anthropic.claude-3-opus-20240229-v1:0:28k",
-    "anthropic.claude-3-sonnet-20240229-v1:0",
-    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
-    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
-    "anthropic.claude-haiku-4-5-20251001-v1:0",
-    "anthropic.claude-instant-v1:2:100k",
-    "anthropic.claude-opus-4-1-20250805-v1:0",
-    "anthropic.claude-opus-4-20250514-v1:0",
-    "anthropic.claude-sonnet-4-20250514-v1:0",
-    "anthropic.claude-sonnet-4-5-20250929-v1:0",
-    "anthropic.claude-v2:0:100k",
-    "anthropic.claude-v2:0:18k",
-    "anthropic.claude-v2:1:18k",
-    "anthropic.claude-v2:1:200k",
-    "cohere.command-r-plus-v1:0",
-    "cohere.command-r-v1:0",
-    "cohere.rerank-v3-5:0",
-    "deepseek.r1-v1:0",
-    "meta.llama3-1-70b-instruct-v1:0",
-    "meta.llama3-1-8b-instruct-v1:0",
-    "meta.llama3-2-11b-instruct-v1:0",
-    "meta.llama3-2-1b-instruct-v1:0",
-    "meta.llama3-2-3b-instruct-v1:0",
-    "meta.llama3-2-90b-instruct-v1:0",
-    "meta.llama3-3-70b-instruct-v1:0",
-    "meta.llama3-70b-instruct-v1:0",
-    "meta.llama3-8b-instruct-v1:0",
-    "meta.llama4-maverick-17b-instruct-v1:0",
-    "meta.llama4-scout-17b-instruct-v1:0",
-    "mistral.mistral-7b-instruct-v0:2",
-    "mistral.mistral-large-2402-v1:0",
-    "mistral.mistral-small-2402-v1:0",
-    "mistral.mixtral-8x7b-instruct-v0:1",
-    "mistral.pixtral-large-2502-v1:0",
-    "openai.gpt-oss-120b-1:0",
-    "openai.gpt-oss-20b-1:0",
-    "qwen.qwen3-32b-v1:0",
-    "qwen.qwen3-coder-30b-a3b-v1:0",
-    "twelvelabs.pegasus-1-2-v1:0",
-]

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -5,11 +5,14 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any, cast
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, PrivateAttr, computed_field, model_validator
+from typing_extensions import Self
 
 from crewai.events.types.llm_events import LLMCallType
+from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
 from crewai.llms.base_llm import BaseLLM
 from crewai.llms.hooks.transport import HTTPTransport
+from crewai.llms.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -18,7 +21,8 @@ from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
-    from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.agent import Agent
+    from crewai.task import Task
 
 try:
     from anthropic import Anthropic
@@ -31,6 +35,19 @@ except ImportError:
     ) from None
 
 
+ANTHROPIC_CONTEXT_WINDOWS: dict[str, int] = {
+    "claude-3-5-sonnet": 200000,
+    "claude-3-5-haiku": 200000,
+    "claude-3-opus": 200000,
+    "claude-3-sonnet": 200000,
+    "claude-3-haiku": 200000,
+    "claude-3-7-sonnet": 200000,
+    "claude-2.1": 200000,
+    "claude-2": 100000,
+    "claude-instant": 100000,
+}
+
+
 class AnthropicCompletion(BaseLLM):
     """Anthropic native completion implementation.
 
@@ -38,110 +55,69 @@ class AnthropicCompletion(BaseLLM):
     offering native tool use, streaming support, and proper message formatting.
     """
 
-    def __init__(
-        self,
-        model: str = "claude-3-5-sonnet-20241022",
-        api_key: str | None = None,
-        base_url: str | None = None,
-        timeout: float | None = None,
-        max_retries: int = 2,
-        temperature: float | None = None,
-        max_tokens: int = 4096,  # Required for Anthropic
-        top_p: float | None = None,
-        stop_sequences: list[str] | None = None,
-        stream: bool = False,
-        client_params: dict[str, Any] | None = None,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        **kwargs: Any,
-    ):
-        """Initialize Anthropic chat completion client.
+    model: str = Field(
+        default="claude-3-5-sonnet-20241022",
+        description="Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')",
+    )
+    max_tokens: int = Field(
+        default=4096,
+        description="Maximum number of allowed tokens in response.",
+    )
+    top_p: float | None = Field(
+        default=None,
+        description="Nucleus sampling parameter.",
+    )
+    _client: Anthropic = PrivateAttr(
+        default_factory=Anthropic,
+    )
 
-        Args:
-            model: Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')
-            api_key: Anthropic API key (defaults to ANTHROPIC_API_KEY env var)
-            base_url: Custom base URL for Anthropic API
-            timeout: Request timeout in seconds
-            max_retries: Maximum number of retries
-            temperature: Sampling temperature (0-1)
-            max_tokens: Maximum tokens in response (required for Anthropic)
-            top_p: Nucleus sampling parameter
-            stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop)
-            stream: Enable streaming responses
-            client_params: Additional parameters for the Anthropic client
-            interceptor: HTTP interceptor for modifying requests/responses at transport level.
-            **kwargs: Additional parameters
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the Anthropic client after Pydantic validation.
+
+        This runs after all field validation is complete, ensuring that:
+        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
+        - Field validators have run (stop_sequences is normalized to set[str])
+        - API key and other configuration is ready
         """
-        super().__init__(
-            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
-        )
-
-        # Client params
-        self.interceptor = interceptor
-        self.client_params = client_params
-        self.base_url = base_url
-        self.timeout = timeout
-        self.max_retries = max_retries
-
-        self.client = Anthropic(**self._get_client_params())
-
-        # Store completion parameters
-        self.max_tokens = max_tokens
-        self.top_p = top_p
-        self.stream = stream
-        self.stop_sequences = stop_sequences or []
-
-        # Model-specific settings
-        self.is_claude_3 = "claude-3" in model.lower()
-        self.supports_tools = self.is_claude_3  # Claude 3+ supports tool use
-
-    @property
-    def stop(self) -> list[str]:
-        """Get stop sequences sent to the API."""
-        return self.stop_sequences
-
-    @stop.setter
-    def stop(self, value: list[str] | str | None) -> None:
-        """Set stop sequences.
-
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Anthropic API.
-
-        Args:
-            value: Stop sequences as a list, single string, or None
-        """
-        if value is None:
-            self.stop_sequences = []
-        elif isinstance(value, str):
-            self.stop_sequences = [value]
-        elif isinstance(value, list):
-            self.stop_sequences = value
-        else:
-            self.stop_sequences = []
-
-    def _get_client_params(self) -> dict[str, Any]:
-        """Get client parameters."""
-
         if self.api_key is None:
             self.api_key = os.getenv("ANTHROPIC_API_KEY")
             if self.api_key is None:
                 raise ValueError("ANTHROPIC_API_KEY is required")
 
-        client_params = {
-            "api_key": self.api_key,
-            "base_url": self.base_url,
-            "timeout": self.timeout,
-            "max_retries": self.max_retries,
-        }
+        params = self.model_dump(
+            include={"api_key", "base_url", "timeout", "max_retries"},
+            exclude_none=True,
+        )
 
         if self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
             http_client = httpx.Client(transport=transport)
-            client_params["http_client"] = http_client  # type: ignore[assignment]
+            params["http_client"] = http_client
 
         if self.client_params:
-            client_params.update(self.client_params)
+            params.update(self.client_params)
 
-        return client_params
+        self._client = Anthropic(**params)
+        return self
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def is_claude_3(self) -> bool:
+        """Check if the model is Claude 3 or higher."""
+        return "claude-3" in self.model.lower()
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def supports_tools(self) -> bool:
+        """Check if the model supports tool use."""
+        return self.is_claude_3
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling."""
+        return self.supports_tools
 
     def call(
         self,
@@ -149,8 +125,8 @@ class AnthropicCompletion(BaseLLM):
         tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Anthropic messages API.
@@ -229,25 +205,21 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Parameters dictionary for Anthropic API
         """
-        params = {
-            "model": self.model,
-            "messages": messages,
-            "max_tokens": self.max_tokens,
-            "stream": self.stream,
-        }
-
+        params = self.model_dump(
+            include={
+                "model",
+                "max_tokens",
+                "stream",
+                "temperature",
+                "top_p",
+                "stop_sequences",
+            },
+        )
+        params["messages"] = messages
         # Add system message if present
         if system_message:
             params["system"] = system_message
 
-        # Add optional parameters if set
-        if self.temperature is not None:
-            params["temperature"] = self.temperature
-        if self.top_p is not None:
-            params["top_p"] = self.top_p
-        if self.stop_sequences:
-            params["stop_sequences"] = self.stop_sequences
-
         # Handle tools for Claude 3+
         if tools and self.supports_tools:
             params["tools"] = self._convert_tools_for_interference(tools)
@@ -266,8 +238,6 @@ class AnthropicCompletion(BaseLLM):
                 continue
 
             try:
-                from crewai.llms.providers.utils.common import safe_tool_conversion
-
                 name, description, parameters = safe_tool_conversion(tool, "Anthropic")
             except (ImportError, KeyError, ValueError) as e:
                 logging.error(f"Error converting tool to Anthropic format: {e}")
@@ -341,8 +311,8 @@ class AnthropicCompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming message completion."""
@@ -357,7 +327,7 @@ class AnthropicCompletion(BaseLLM):
             params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         try:
-            response: Message = self.client.messages.create(**params)
+            response: Message = self._client.messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -429,8 +399,8 @@ class AnthropicCompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming message completion."""
@@ -451,7 +421,7 @@ class AnthropicCompletion(BaseLLM):
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
         # Make streaming API call
-        with self.client.messages.stream(**stream_params) as stream:
+        with self._client.messages.stream(**stream_params) as stream:
             for event in stream:
                 if hasattr(event, "delta") and hasattr(event.delta, "text"):
                     text_delta = event.delta.text
@@ -525,8 +495,8 @@ class AnthropicCompletion(BaseLLM):
         tool_uses: list[ToolUseBlock],
         params: dict[str, Any],
         available_functions: dict[str, Any],
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
     ) -> str:
         """Handle the complete tool use conversation flow.
 
@@ -579,7 +549,7 @@ class AnthropicCompletion(BaseLLM):
 
         try:
             # Send tool results back to Claude for final response
-            final_response: Message = self.client.messages.create(**follow_up_params)
+            final_response: Message = self._client.messages.create(**follow_up_params)
 
             # Track token usage for follow-up call
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
@@ -626,48 +596,24 @@ class AnthropicCompletion(BaseLLM):
                 return tool_results[0]["content"]
             raise e
 
-    def supports_function_calling(self) -> bool:
-        """Check if the model supports function calling."""
-        return self.supports_tools
-
-    def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words."""
-        return True  # All Claude models support stop sequences
-
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
-
-        # Context window sizes for Anthropic models
-        context_windows = {
-            "claude-3-5-sonnet": 200000,
-            "claude-3-5-haiku": 200000,
-            "claude-3-opus": 200000,
-            "claude-3-sonnet": 200000,
-            "claude-3-haiku": 200000,
-            "claude-3-7-sonnet": 200000,
-            "claude-2.1": 200000,
-            "claude-2": 100000,
-            "claude-instant": 100000,
-        }
-
         # Find the best match for the model name
-        for model_prefix, size in context_windows.items():
+        for model_prefix, size in ANTHROPIC_CONTEXT_WINDOWS.items():
             if self.model.startswith(model_prefix):
                 return int(size * CONTEXT_WINDOW_USAGE_RATIO)
 
         # Default context window size for Claude models
         return int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]:
+    @staticmethod
+    def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
         """Extract token usage from Anthropic response."""
-        if hasattr(response, "usage") and response.usage:
+        if response.usage:
             usage = response.usage
-            input_tokens = getattr(usage, "input_tokens", 0)
-            output_tokens = getattr(usage, "output_tokens", 0)
             return {
-                "input_tokens": input_tokens,
-                "output_tokens": output_tokens,
-                "total_tokens": input_tokens + output_tokens,
+                "input_tokens": usage.input_tokens,
+                "output_tokens": usage.output_tokens,
+                "total_tokens": usage.input_tokens + usage.output_tokens,
             }
         return {"total_tokens": 0}

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -1,12 +1,14 @@
-import logging
-import os
-from typing import Any, cast
+from __future__ import annotations
 
-from pydantic import BaseModel
+import logging
+from typing import TYPE_CHECKING, Any, cast
+
+from pydantic import BaseModel, Field, PrivateAttr, computed_field, model_validator
+from typing_extensions import Self
 
 from crewai.events.types.llm_events import LLMCallType
+from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 from crewai.llms.base_llm import BaseLLM
-from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -14,6 +16,11 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.agent import Agent
+    from crewai.task import Task
+
+
 try:
     from google import genai  # type: ignore[import-untyped]
     from google.genai import types  # type: ignore[import-untyped]
@@ -24,6 +31,27 @@ except ImportError:
     ) from None
 
 
+GEMINI_CONTEXT_WINDOWS: dict[str, int] = {
+    "gemini-2.0-flash": 1048576,  # 1M tokens
+    "gemini-2.0-flash-thinking": 32768,
+    "gemini-2.0-flash-lite": 1048576,
+    "gemini-2.5-flash": 1048576,
+    "gemini-2.5-pro": 1048576,
+    "gemini-1.5-pro": 2097152,  # 2M tokens
+    "gemini-1.5-flash": 1048576,
+    "gemini-1.5-flash-8b": 1048576,
+    "gemini-1.0-pro": 32768,
+    "gemma-3-1b": 32000,
+    "gemma-3-4b": 128000,
+    "gemma-3-12b": 128000,
+    "gemma-3-27b": 128000,
+}
+
+# Context window validation constraints
+MIN_CONTEXT_WINDOW: int = 1024
+MAX_CONTEXT_WINDOW: int = 2097152
+
+
 class GeminiCompletion(BaseLLM):
     """Google Gemini native completion implementation.
 
@@ -31,78 +59,140 @@ class GeminiCompletion(BaseLLM):
     offering native function calling, streaming support, and proper Gemini formatting.
     """
 
-    def __init__(
-        self,
-        model: str = "gemini-2.0-flash-001",
-        api_key: str | None = None,
-        project: str | None = None,
-        location: str | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        top_k: int | None = None,
-        max_output_tokens: int | None = None,
-        stop_sequences: list[str] | None = None,
-        stream: bool = False,
-        safety_settings: dict[str, Any] | None = None,
-        client_params: dict[str, Any] | None = None,
-        interceptor: BaseInterceptor[Any, Any] | None = None,
-        **kwargs: Any,
-    ):
-        """Initialize Google Gemini chat completion client.
+    model: str = Field(
+        default="gemini-2.0-flash-001",
+        description="Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')",
+    )
+    project: str | None = Field(
+        default=None,
+        description="Google Cloud project ID (for Vertex AI)",
+    )
+    location: str = Field(
+        default="us-central1",
+        description="Google Cloud location (for Vertex AI)",
+    )
+    top_p: float | None = Field(
+        default=None,
+        description="Nucleus sampling parameter",
+    )
+    top_k: int | None = Field(
+        default=None,
+        description="Top-k sampling parameter",
+    )
+    max_output_tokens: int | None = Field(
+        default=None,
+        description="Maximum tokens in response",
+    )
+    safety_settings: dict[str, Any] | None = Field(
+        default=None,
+        description="Safety filter settings",
+    )
+    _client: genai.Client = PrivateAttr(  # type: ignore[no-any-unimported]
+        default_factory=genai.Client,
+    )
 
-        Args:
-            model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
-            api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var)
-            project: Google Cloud project ID (for Vertex AI)
-            location: Google Cloud location (for Vertex AI, defaults to 'us-central1')
-            temperature: Sampling temperature (0-2)
-            top_p: Nucleus sampling parameter
-            top_k: Top-k sampling parameter
-            max_output_tokens: Maximum tokens in response
-            stop_sequences: Stop sequences
-            stream: Enable streaming responses
-            safety_settings: Safety filter settings
-            client_params: Additional parameters to pass to the Google Gen AI Client constructor.
-                          Supports parameters like http_options, credentials, debug_config, etc.
-            interceptor: HTTP interceptor (not yet supported for Gemini).
-            **kwargs: Additional parameters
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the Anthropic client after Pydantic validation.
+
+        This runs after all field validation is complete, ensuring that:
+        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
+        - Field validators have run (stop_sequences is normalized to set[str])
+        - API key and other configuration is ready
         """
-        if interceptor is not None:
-            raise NotImplementedError(
-                "HTTP interceptors are not yet supported for Google Gemini provider. "
-                "Interceptors are currently supported for OpenAI and Anthropic providers only."
-            )
+        self._client = genai.Client(**self._get_client_params())
+        return self
 
-        super().__init__(
-            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
-        )
+    # def __init__(
+    #     self,
+    # model: str = "gemini-2.0-flash-001",
+    # api_key: str | None = None,
+    # project: str | None = None,
+    # location: str | None = None,
+    # temperature: float | None = None,
+    # top_p: float | None = None,
+    # top_k: int | None = None,
+    # max_output_tokens: int | None = None,
+    # stop_sequences: list[str] | None = None,
+    # stream: bool = False,
+    # safety_settings: dict[str, Any] | None = None,
+    # client_params: dict[str, Any] | None = None,
+    # interceptor: BaseInterceptor[Any, Any] | None = None,
+    # **kwargs: Any,
+    # # ):
+    #     """Initialize Google Gemini chat completion client.
+    #
+    #     Args:
+    #         model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
+    #         api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var)
+    #         project: Google Cloud project ID (for Vertex AI)
+    #         location: Google Cloud location (for Vertex AI, defaults to 'us-central1')
+    #         temperature: Sampling temperature (0-2)
+    #         top_p: Nucleus sampling parameter
+    #         top_k: Top-k sampling parameter
+    #         max_output_tokens: Maximum tokens in response
+    #         stop_sequences: Stop sequences
+    #         stream: Enable streaming responses
+    #         safety_settings: Safety filter settings
+    #         client_params: Additional parameters to pass to the Google Gen AI Client constructor.
+    #                       Supports parameters like http_options, credentials, debug_config, etc.
+    #         interceptor: HTTP interceptor (not yet supported for Gemini).
+    #         **kwargs: Additional parameters
+    #     """
+    #     if interceptor is not None:
+    #         raise NotImplementedError(
+    #             "HTTP interceptors are not yet supported for Google Gemini provider. "
+    #             "Interceptors are currently supported for OpenAI and Anthropic providers only."
+    #         )
+    #
+    #     super().__init__(
+    #         model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+    #     )
+    #
+    #     # Store client params for later use
+    #     self.client_params = client_params or {}
+    #
+    #     # Get API configuration with environment variable fallbacks
+    #     self.api_key = (
+    #         api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
+    #     )
+    #     self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
+    #     self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+    #
+    #     use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
+    #
+    #     self.client = self._initialize_client(use_vertexai)
+    #
+    #     # Store completion parameters
+    #     self.top_p = top_p
+    #     self.top_k = top_k
+    #     self.max_output_tokens = max_output_tokens
+    #     self.stream = stream
+    #     self.safety_settings = safety_settings or {}
+    #     self.stop_sequences = stop_sequences or []
+    #
+    #     # Model-specific settings
+    #     self.is_gemini_2 = "gemini-2" in model.lower()
+    #     self.is_gemini_1_5 = "gemini-1.5" in model.lower()
+    #     self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
 
-        # Store client params for later use
-        self.client_params = client_params or {}
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def is_gemini_2(self) -> bool:
+        """Check if the model is Gemini 2.x."""
+        return "gemini-2" in self.model.lower()
 
-        # Get API configuration with environment variable fallbacks
-        self.api_key = (
-            api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
-        )
-        self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
-        self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def is_gemini_1_5(self) -> bool:
+        """Check if the model is Gemini 1.5.x."""
+        return "gemini-1.5" in self.model.lower()
 
-        use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
-
-        self.client = self._initialize_client(use_vertexai)
-
-        # Store completion parameters
-        self.top_p = top_p
-        self.top_k = top_k
-        self.max_output_tokens = max_output_tokens
-        self.stream = stream
-        self.safety_settings = safety_settings or {}
-        self.stop_sequences = stop_sequences or []
-
-        # Model-specific settings
-        self.is_gemini_2 = "gemini-2" in model.lower()
-        self.is_gemini_1_5 = "gemini-1.5" in model.lower()
-        self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def supports_tools(self) -> bool:
+        """Check if the model supports tool/function calling."""
+        return self.is_gemini_1_5 or self.is_gemini_2
 
     @property
     def stop(self) -> list[str]:
@@ -142,6 +232,12 @@ class GeminiCompletion(BaseLLM):
         if self.client_params:
             client_params.update(self.client_params)
 
+        if self.interceptor:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Google Gemini provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         if use_vertexai or self.project:
             client_params.update(
                 {
@@ -181,7 +277,7 @@ class GeminiCompletion(BaseLLM):
 
         if (
             hasattr(self, "client")
-            and hasattr(self.client, "vertexai")
+            and hasattr(self._client, "vertexai")
             and self.client.vertexai
         ):
             # Vertex AI configuration
@@ -206,8 +302,8 @@ class GeminiCompletion(BaseLLM):
         tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Google Gemini generate content API.
@@ -294,7 +390,16 @@ class GeminiCompletion(BaseLLM):
             GenerateContentConfig object for Gemini API
         """
         self.tools = tools
-        config_params = {}
+        config_params = self.model_dump(
+            include={
+                "temperature",
+                "top_p",
+                "top_k",
+                "max_output_tokens",
+                "stop_sequences",
+                "safety_settings",
+            }
+        )
 
         # Add system instruction if present
         if system_instruction:
@@ -304,18 +409,6 @@ class GeminiCompletion(BaseLLM):
             )
             config_params["system_instruction"] = system_content
 
-        # Add generation config parameters
-        if self.temperature is not None:
-            config_params["temperature"] = self.temperature
-        if self.top_p is not None:
-            config_params["top_p"] = self.top_p
-        if self.top_k is not None:
-            config_params["top_k"] = self.top_k
-        if self.max_output_tokens is not None:
-            config_params["max_output_tokens"] = self.max_output_tokens
-        if self.stop_sequences:
-            config_params["stop_sequences"] = self.stop_sequences
-
         if response_model:
             config_params["response_mime_type"] = "application/json"
             config_params["response_schema"] = response_model.model_json_schema()
@@ -324,9 +417,6 @@ class GeminiCompletion(BaseLLM):
         if tools and self.supports_tools:
             config_params["tools"] = self._convert_tools_for_interference(tools)
 
-        if self.safety_settings:
-            config_params["safety_settings"] = self.safety_settings
-
         return types.GenerateContentConfig(**config_params)
 
     def _convert_tools_for_interference(  # type: ignore[no-any-unimported]
@@ -404,8 +494,8 @@ class GeminiCompletion(BaseLLM):
         system_instruction: str | None,
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming content generation."""
@@ -416,7 +506,7 @@ class GeminiCompletion(BaseLLM):
         }
 
         try:
-            response = self.client.models.generate_content(**api_params)
+            response = self._client.models.generate_content(**api_params)
 
             usage = self._extract_token_usage(response)
         except Exception as e:
@@ -470,8 +560,8 @@ class GeminiCompletion(BaseLLM):
         contents: list[types.Content],
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming content generation."""
@@ -484,7 +574,7 @@ class GeminiCompletion(BaseLLM):
             "config": config,
         }
 
-        for chunk in self.client.models.generate_content_stream(**api_params):
+        for chunk in self._client.models.generate_content_stream(**api_params):
             if hasattr(chunk, "text") and chunk.text:
                 full_response += chunk.text
                 self._emit_stream_chunk_event(
@@ -537,52 +627,30 @@ class GeminiCompletion(BaseLLM):
 
         return full_response
 
+    @computed_field  # type: ignore[prop-decorator]
+    @property
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
         return self.supports_tools
 
-    def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words."""
-        return True
-
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
-
-        min_context = 1024
-        max_context = 2097152
-
         for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if value < min_context or value > max_context:
+            if value < MIN_CONTEXT_WINDOW or value > MAX_CONTEXT_WINDOW:
                 raise ValueError(
-                    f"Context window for {key} must be between {min_context} and {max_context}"
+                    f"Context window for {key} must be between {MIN_CONTEXT_WINDOW} and {MAX_CONTEXT_WINDOW}"
                 )
 
-        context_windows = {
-            "gemini-2.0-flash": 1048576,  # 1M tokens
-            "gemini-2.0-flash-thinking": 32768,
-            "gemini-2.0-flash-lite": 1048576,
-            "gemini-2.5-flash": 1048576,
-            "gemini-2.5-pro": 1048576,
-            "gemini-1.5-pro": 2097152,  # 2M tokens
-            "gemini-1.5-flash": 1048576,
-            "gemini-1.5-flash-8b": 1048576,
-            "gemini-1.0-pro": 32768,
-            "gemma-3-1b": 32000,
-            "gemma-3-4b": 128000,
-            "gemma-3-12b": 128000,
-            "gemma-3-27b": 128000,
-        }
-
         # Find the best match for the model name
-        for model_prefix, size in context_windows.items():
+        for model_prefix, size in GEMINI_CONTEXT_WINDOWS.items():
             if self.model.startswith(model_prefix):
                 return int(size * CONTEXT_WINDOW_USAGE_RATIO)
 
         # Default context window size for Gemini models
         return int(1048576 * CONTEXT_WINDOW_USAGE_RATIO)  # 1M tokens
 
-    def _extract_token_usage(self, response: dict[str, Any]) -> dict[str, Any]:
+    @staticmethod
+    def _extract_token_usage(response: dict[str, Any]) -> dict[str, Any]:
         """Extract token usage from Gemini response."""
         if hasattr(response, "usage_metadata"):
             usage = response.usage_metadata
@@ -594,8 +662,8 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
+    @staticmethod
     def _convert_contents_to_dict(  # type: ignore[no-any-unimported]
-        self,
         contents: list[types.Content],
     ) -> list[dict[str, str]]:
         """Convert contents to dict format."""

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -4,16 +4,23 @@ from collections.abc import Iterator
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 from openai import APIConnectionError, NotFoundError, OpenAI
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_chunk import ChoiceDelta
-from pydantic import BaseModel
+from pydantic import (
+    BaseModel,
+    Field,
+    PrivateAttr,
+    model_validator,
+)
+from typing_extensions import Self
 
 from crewai.events.types.llm_events import LLMCallType
+from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 from crewai.llms.base_llm import BaseLLM
 from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
@@ -25,11 +32,28 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent.core import Agent
-    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
 
 
+OPENAI_CONTEXT_WINDOWS: dict[str, int] = {
+    "gpt-4": 8192,
+    "gpt-4o": 128000,
+    "gpt-4o-mini": 200000,
+    "gpt-4-turbo": 128000,
+    "gpt-4.1": 1047576,
+    "gpt-4.1-mini-2025-04-14": 1047576,
+    "gpt-4.1-nano-2025-04-14": 1047576,
+    "o1-preview": 128000,
+    "o1-mini": 128000,
+    "o3-mini": 200000,
+    "o4-mini": 200000,
+}
+
+MIN_CONTEXT_WINDOW: Final[int] = 1024
+MAX_CONTEXT_WINDOW: Final[int] = 2097152
+
+
 class OpenAICompletion(BaseLLM):
     """OpenAI native completion implementation.
 
@@ -37,112 +61,125 @@ class OpenAICompletion(BaseLLM):
     offering native structured outputs, function calling, and streaming support.
     """
 
-    def __init__(
-        self,
-        model: str = "gpt-4o",
-        api_key: str | None = None,
-        base_url: str | None = None,
-        organization: str | None = None,
-        project: str | None = None,
-        timeout: float | None = None,
-        max_retries: int = 2,
-        default_headers: dict[str, str] | None = None,
-        default_query: dict[str, Any] | None = None,
-        client_params: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        frequency_penalty: float | None = None,
-        presence_penalty: float | None = None,
-        max_tokens: int | None = None,
-        max_completion_tokens: int | None = None,
-        seed: int | None = None,
-        stream: bool = False,
-        response_format: dict[str, Any] | type[BaseModel] | None = None,
-        logprobs: bool | None = None,
-        top_logprobs: int | None = None,
-        reasoning_effort: str | None = None,
-        provider: str | None = None,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize OpenAI chat completion client."""
+    model: str = Field(
+        default="gpt-4o",
+        description="OpenAI model name (e.g., 'gpt-4o')",
+    )
+    organization: str | None = Field(
+        default=None,
+        description="Name of the OpenAI organization",
+    )
+    project: str | None = Field(
+        default=None,
+        description="Name of the OpenAI project",
+    )
+    api_base: str | None = Field(
+        default=os.getenv("OPENAI_BASE_URL"),
+        description="Base URL for OpenAI API",
+    )
+    default_headers: dict[str, str] | None = Field(
+        default=None,
+        description="Default headers for OpenAI API requests",
+    )
+    default_query: dict[str, Any] | None = Field(
+        default=None,
+        description="Default query parameters for OpenAI API requests",
+    )
+    top_p: float | None = Field(
+        default=None,
+        description="Top-p sampling parameter",
+    )
+    frequency_penalty: float | None = Field(
+        default=None,
+        description="Frequency penalty parameter",
+    )
+    presence_penalty: float | None = Field(
+        default=None,
+        description="Presence penalty parameter",
+    )
+    max_completion_tokens: int | None = Field(
+        default=None,
+        description="Maximum tokens for completion",
+    )
+    seed: int | None = Field(
+        default=None,
+        description="Random seed for reproducibility",
+    )
+    response_format: dict[str, Any] | type[BaseModel] | None = Field(
+        default=None,
+        description="Response format for structured output",
+    )
+    logprobs: bool | None = Field(
+        default=None,
+        description="Whether to include log probabilities",
+    )
+    top_logprobs: int | None = Field(
+        default=None,
+        description="Number of top log probabilities to return",
+    )
+    reasoning_effort: str | None = Field(
+        default=None,
+        description="Reasoning effort level for o1 models",
+    )
+    supports_function_calling: bool = Field(
+        default=True,
+        description="Whether the model supports function calling",
+    )
+    is_o1_model: bool = Field(
+        default=False,
+        description="Whether the model is an o1 model",
+    )
+    is_gpt4_model: bool = Field(
+        default=False,
+        description="Whether the model is a GPT-4 model",
+    )
+    _client: OpenAI = PrivateAttr(
+        default_factory=OpenAI,
+    )
 
-        if provider is None:
-            provider = kwargs.pop("provider", "openai")
-
-        self.interceptor = interceptor
-        # Client configuration attributes
-        self.organization = organization
-        self.project = project
-        self.max_retries = max_retries
-        self.default_headers = default_headers
-        self.default_query = default_query
-        self.client_params = client_params
-        self.timeout = timeout
-        self.base_url = base_url
-        self.api_base = kwargs.pop("api_base", None)
-
-        super().__init__(
-            model=model,
-            temperature=temperature,
-            api_key=api_key or os.getenv("OPENAI_API_KEY"),
-            base_url=base_url,
-            timeout=timeout,
-            provider=provider,
-            **kwargs,
-        )
-
-        client_config = self._get_client_params()
-        if self.interceptor:
-            transport = HTTPTransport(interceptor=self.interceptor)
-            http_client = httpx.Client(transport=transport)
-            client_config["http_client"] = http_client
-
-        self.client = OpenAI(**client_config)
-
-        # Completion parameters
-        self.top_p = top_p
-        self.frequency_penalty = frequency_penalty
-        self.presence_penalty = presence_penalty
-        self.max_tokens = max_tokens
-        self.max_completion_tokens = max_completion_tokens
-        self.seed = seed
-        self.stream = stream
-        self.response_format = response_format
-        self.logprobs = logprobs
-        self.top_logprobs = top_logprobs
-        self.reasoning_effort = reasoning_effort
-        self.is_o1_model = "o1" in model.lower()
-        self.is_gpt4_model = "gpt-4" in model.lower()
-
-    def _get_client_params(self) -> dict[str, Any]:
-        """Get OpenAI client parameters."""
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the Anthropic client after Pydantic validation.
 
+        This runs after all field validation is complete, ensuring that:
+        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
+        - Field validators have run (stop_sequences is normalized to set[str])
+        - API key and other configuration is ready
+        """
         if self.api_key is None:
             self.api_key = os.getenv("OPENAI_API_KEY")
             if self.api_key is None:
                 raise ValueError("OPENAI_API_KEY is required")
 
-        base_params = {
-            "api_key": self.api_key,
-            "organization": self.organization,
-            "project": self.project,
-            "base_url": self.base_url
-            or self.api_base
-            or os.getenv("OPENAI_BASE_URL")
-            or None,
-            "timeout": self.timeout,
-            "max_retries": self.max_retries,
-            "default_headers": self.default_headers,
-            "default_query": self.default_query,
-        }
+        self.is_o1_model = "o1" in self.model.lower()
+        self.supports_function_calling = not self.is_o1_model
+        self.is_gpt4_model = "gpt-4" in self.model.lower()
+        self.supports_stop_words = not self.is_o1_model
 
-        client_params = {k: v for k, v in base_params.items() if v is not None}
+        params = self.model_dump(
+            include={
+                "api_key",
+                "organization",
+                "project",
+                "base_url",
+                "timeout",
+                "max_retries",
+                "default_headers",
+                "default_query",
+            },
+            exclude_none=True,
+        )
+
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            params["http_client"] = http_client
 
         if self.client_params:
-            client_params.update(self.client_params)
+            params.update(self.client_params)
 
-        return client_params
+        self._client = OpenAI(**params)
+        return self
 
     def call(
         self,
@@ -213,38 +250,26 @@ class OpenAICompletion(BaseLLM):
         self, messages: list[LLMMessage], tools: list[dict[str, BaseTool]] | None = None
     ) -> dict[str, Any]:
         """Prepare parameters for OpenAI chat completion."""
-        params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-        }
-        if self.stream:
-            params["stream"] = self.stream
-
+        params = self.model_dump(
+            include={
+                "model",
+                "stream",
+                "temperature",
+                "top_p",
+                "frequency_penalty",
+                "presence_penalty",
+                "max_completion_tokens",
+                "max_tokens",
+                "seed",
+                "logprobs",
+                "top_logprobs",
+                "reasoning_effort",
+            },
+            exclude_none=True,
+        )
+        params["messages"] = messages
         params.update(self.additional_params)
 
-        if self.temperature is not None:
-            params["temperature"] = self.temperature
-        if self.top_p is not None:
-            params["top_p"] = self.top_p
-        if self.frequency_penalty is not None:
-            params["frequency_penalty"] = self.frequency_penalty
-        if self.presence_penalty is not None:
-            params["presence_penalty"] = self.presence_penalty
-        if self.max_completion_tokens is not None:
-            params["max_completion_tokens"] = self.max_completion_tokens
-        elif self.max_tokens is not None:
-            params["max_tokens"] = self.max_tokens
-        if self.seed is not None:
-            params["seed"] = self.seed
-        if self.logprobs is not None:
-            params["logprobs"] = self.logprobs
-        if self.top_logprobs is not None:
-            params["top_logprobs"] = self.top_logprobs
-
-        # Handle o1 model specific parameters
-        if self.is_o1_model and self.reasoning_effort:
-            params["reasoning_effort"] = self.reasoning_effort
-
         if tools:
             params["tools"] = self._convert_tools_for_interference(tools)
             params["tool_choice"] = "auto"
@@ -296,14 +321,14 @@ class OpenAICompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming chat completion."""
         try:
             if response_model:
-                parsed_response = self.client.beta.chat.completions.parse(
+                parsed_response = self._client.beta.chat.completions.parse(
                     **params,
                     response_format=response_model,
                 )
@@ -327,7 +352,7 @@ class OpenAICompletion(BaseLLM):
                     )
                     return structured_json
 
-            response: ChatCompletion = self.client.chat.completions.create(**params)
+            response: ChatCompletion = self._client.chat.completions.create(**params)
 
             usage = self._extract_openai_token_usage(response)
 
@@ -419,8 +444,8 @@ class OpenAICompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
+        from_task: Task | None = None,
+        from_agent: Agent | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming chat completion."""
@@ -429,7 +454,7 @@ class OpenAICompletion(BaseLLM):
 
         if response_model:
             completion_stream: Iterator[ChatCompletionChunk] = (
-                self.client.chat.completions.create(**params)
+                self._client.chat.completions.create(**params)
             )
 
             accumulated_content = ""
@@ -472,7 +497,7 @@ class OpenAICompletion(BaseLLM):
                 )
                 return accumulated_content
 
-        stream: Iterator[ChatCompletionChunk] = self.client.chat.completions.create(
+        stream: Iterator[ChatCompletionChunk] = self._client.chat.completions.create(
             **params
         )
 
@@ -550,58 +575,31 @@ class OpenAICompletion(BaseLLM):
 
         return full_response
 
-    def supports_function_calling(self) -> bool:
-        """Check if the model supports function calling."""
-        return not self.is_o1_model
-
-    def supports_stop_words(self) -> bool:
-        """Check if the model supports stop words."""
-        return not self.is_o1_model
-
     def get_context_window_size(self) -> int:
         """Get the context window size for the model."""
-        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
-
-        min_context = 1024
-        max_context = 2097152
-
         for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if value < min_context or value > max_context:
+            if value < MIN_CONTEXT_WINDOW or value > MAX_CONTEXT_WINDOW:
                 raise ValueError(
-                    f"Context window for {key} must be between {min_context} and {max_context}"
+                    f"Context window for {key} must be between {MIN_CONTEXT_WINDOW} and {MAX_CONTEXT_WINDOW}"
                 )
 
-        # Context window sizes for OpenAI models
-        context_windows = {
-            "gpt-4": 8192,
-            "gpt-4o": 128000,
-            "gpt-4o-mini": 200000,
-            "gpt-4-turbo": 128000,
-            "gpt-4.1": 1047576,
-            "gpt-4.1-mini-2025-04-14": 1047576,
-            "gpt-4.1-nano-2025-04-14": 1047576,
-            "o1-preview": 128000,
-            "o1-mini": 128000,
-            "o3-mini": 200000,
-            "o4-mini": 200000,
-        }
-
         # Find the best match for the model name
-        for model_prefix, size in context_windows.items():
+        for model_prefix, size in OPENAI_CONTEXT_WINDOWS.items():
             if self.model.startswith(model_prefix):
                 return int(size * CONTEXT_WINDOW_USAGE_RATIO)
 
         # Default context window size
         return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]:
+    @staticmethod
+    def _extract_openai_token_usage(response: ChatCompletion) -> dict[str, Any]:
         """Extract token usage from OpenAI ChatCompletion response."""
-        if hasattr(response, "usage") and response.usage:
+        if response.usage:
             usage = response.usage
             return {
-                "prompt_tokens": getattr(usage, "prompt_tokens", 0),
-                "completion_tokens": getattr(usage, "completion_tokens", 0),
-                "total_tokens": getattr(usage, "total_tokens", 0),
+                "prompt_tokens": usage.prompt_tokens,
+                "completion_tokens": usage.completion_tokens,
+                "total_tokens": usage.total_tokens,
             }
         return {"total_tokens": 0}
 

lib/crewai/src/crewai/project/crew_base.py

@@ -293,8 +293,6 @@ class CrewBaseMeta(type):
             kickoff=_filter_methods(original_methods, "is_kickoff"),
         )
 
-        _register_crew_hooks(instance, cls)
-
 
 def close_mcp_server(
     self: CrewInstance, _instance: CrewInstance, outputs: CrewOutput
@@ -440,144 +438,6 @@ def _filter_methods(
     }
 
 
-def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
-    """Detect and register crew-scoped hook methods.
-
-    Args:
-        instance: Crew instance to register hooks for.
-        cls: Crew class type.
-    """
-    hook_methods = {
-        name: method
-        for name, method in cls.__dict__.items()
-        if any(
-            hasattr(method, attr)
-            for attr in [
-                "is_before_llm_call_hook",
-                "is_after_llm_call_hook",
-                "is_before_tool_call_hook",
-                "is_after_tool_call_hook",
-            ]
-        )
-    }
-
-    if not hook_methods:
-        return
-
-    from crewai.hooks import (
-        register_after_llm_call_hook,
-        register_after_tool_call_hook,
-        register_before_llm_call_hook,
-        register_before_tool_call_hook,
-    )
-
-    instance._registered_hook_functions = []
-
-    instance._hooks_being_registered = True
-
-    for hook_method in hook_methods.values():
-        bound_hook = hook_method.__get__(instance, cls)
-
-        has_tool_filter = hasattr(hook_method, "_filter_tools")
-        has_agent_filter = hasattr(hook_method, "_filter_agents")
-
-        if hasattr(hook_method, "is_before_llm_call_hook"):
-            if has_agent_filter:
-                agents_filter = hook_method._filter_agents
-
-                def make_filtered_before_llm(bound_fn, agents_list):
-                    def filtered(context):
-                        if context.agent and context.agent.role not in agents_list:
-                            return None
-                        return bound_fn(context)
-
-                    return filtered
-
-                final_hook = make_filtered_before_llm(bound_hook, agents_filter)
-            else:
-                final_hook = bound_hook
-
-            register_before_llm_call_hook(final_hook)
-            instance._registered_hook_functions.append(("before_llm_call", final_hook))
-
-        if hasattr(hook_method, "is_after_llm_call_hook"):
-            if has_agent_filter:
-                agents_filter = hook_method._filter_agents
-
-                def make_filtered_after_llm(bound_fn, agents_list):
-                    def filtered(context):
-                        if context.agent and context.agent.role not in agents_list:
-                            return None
-                        return bound_fn(context)
-
-                    return filtered
-
-                final_hook = make_filtered_after_llm(bound_hook, agents_filter)
-            else:
-                final_hook = bound_hook
-
-            register_after_llm_call_hook(final_hook)
-            instance._registered_hook_functions.append(("after_llm_call", final_hook))
-
-        if hasattr(hook_method, "is_before_tool_call_hook"):
-            if has_tool_filter or has_agent_filter:
-                tools_filter = getattr(hook_method, "_filter_tools", None)
-                agents_filter = getattr(hook_method, "_filter_agents", None)
-
-                def make_filtered_before_tool(bound_fn, tools_list, agents_list):
-                    def filtered(context):
-                        if tools_list and context.tool_name not in tools_list:
-                            return None
-                        if (
-                            agents_list
-                            and context.agent
-                            and context.agent.role not in agents_list
-                        ):
-                            return None
-                        return bound_fn(context)
-
-                    return filtered
-
-                final_hook = make_filtered_before_tool(
-                    bound_hook, tools_filter, agents_filter
-                )
-            else:
-                final_hook = bound_hook
-
-            register_before_tool_call_hook(final_hook)
-            instance._registered_hook_functions.append(("before_tool_call", final_hook))
-
-        if hasattr(hook_method, "is_after_tool_call_hook"):
-            if has_tool_filter or has_agent_filter:
-                tools_filter = getattr(hook_method, "_filter_tools", None)
-                agents_filter = getattr(hook_method, "_filter_agents", None)
-
-                def make_filtered_after_tool(bound_fn, tools_list, agents_list):
-                    def filtered(context):
-                        if tools_list and context.tool_name not in tools_list:
-                            return None
-                        if (
-                            agents_list
-                            and context.agent
-                            and context.agent.role not in agents_list
-                        ):
-                            return None
-                        return bound_fn(context)
-
-                    return filtered
-
-                final_hook = make_filtered_after_tool(
-                    bound_hook, tools_filter, agents_filter
-                )
-            else:
-                final_hook = bound_hook
-
-            register_after_tool_call_hook(final_hook)
-            instance._registered_hook_functions.append(("after_tool_call", final_hook))
-
-    instance._hooks_being_registered = False
-
-
 def map_all_agent_variables(self: CrewInstance) -> None:
     """Map agent configuration variables to callable instances.
 

lib/crewai/src/crewai/task.py

@@ -539,7 +539,6 @@ class Task(BaseModel):
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
-                messages=agent.last_messages,
             )
 
             if self._guardrails:
@@ -950,7 +949,6 @@ Follow these guidelines:
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
-                messages=agent.last_messages,
             )
 
         return task_output

lib/crewai/src/crewai/tasks/task_output.py

@@ -6,7 +6,6 @@ from typing import Any
 from pydantic import BaseModel, Field, model_validator
 
 from crewai.tasks.output_format import OutputFormat
-from crewai.utilities.types import LLMMessage
 
 
 class TaskOutput(BaseModel):
@@ -41,7 +40,6 @@ class TaskOutput(BaseModel):
     output_format: OutputFormat = Field(
         description="Output format of the task", default=OutputFormat.RAW
     )
-    messages: list[LLMMessage] = Field(description="Messages of the task", default=[])
 
     @model_validator(mode="after")
     def set_summary(self):

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -33,7 +33,6 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
-    from crewai.agents.crew_agent_executor import CrewAgentExecutor
     from crewai.lite_agent import LiteAgent
     from crewai.llm import LLM
     from crewai.task import Task
@@ -128,7 +127,7 @@ def handle_max_iterations_exceeded(
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-) -> AgentFinish:
+) -> AgentAction | AgentFinish:
     """Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer.
 
     Args:
@@ -140,7 +139,7 @@ def handle_max_iterations_exceeded(
         callbacks: List of callbacks for the LLM call.
 
     Returns:
-        AgentFinish with the final answer after exceeding max iterations.
+        The final formatted answer after exceeding max iterations.
     """
     printer.print(
         content="Maximum iterations reached. Requesting final answer.",
@@ -158,7 +157,7 @@ def handle_max_iterations_exceeded(
 
     # Perform one more LLM call to get the final answer
     answer = llm.call(
-        messages,
+        messages,  # type: ignore[arg-type]
         callbacks=callbacks,
     )
 
@@ -169,16 +168,8 @@ def handle_max_iterations_exceeded(
         )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    formatted = format_answer(answer=answer)
-
-    # If format_answer returned an AgentAction, convert it to AgentFinish
-    if isinstance(formatted, AgentFinish):
-        return formatted
-    return AgentFinish(
-        thought=formatted.thought,
-        output=formatted.text,
-        text=formatted.text,
-    )
+    # Return the formatted answer, regardless of its type
+    return format_answer(answer=answer)
 
 
 def format_message_for_llm(
@@ -237,7 +228,6 @@ def get_llm_response(
     from_task: Task | None = None,
     from_agent: Agent | LiteAgent | None = None,
     response_model: type[BaseModel] | None = None,
-    executor_context: CrewAgentExecutor | None = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 
@@ -249,7 +239,6 @@ def get_llm_response(
         from_task: Optional task context for the LLM call
         from_agent: Optional agent context for the LLM call
         response_model: Optional Pydantic model for structured outputs
-        executor_context: Optional executor context for hook invocation
 
     Returns:
         The response from the LLM as a string
@@ -258,18 +247,12 @@ def get_llm_response(
         Exception: If an error occurs.
         ValueError: If the response is None or empty.
     """
-
-    if executor_context is not None:
-        if not _setup_before_llm_call_hooks(executor_context, printer):
-            raise ValueError("LLM call blocked by before_llm_call hook")
-        messages = executor_context.messages
-
     try:
         answer = llm.call(
-            messages,
+            messages,  # type: ignore[arg-type]
             callbacks=callbacks,
             from_task=from_task,
-            from_agent=from_agent,  # type: ignore[arg-type]
+            from_agent=from_agent,
             response_model=response_model,
         )
     except Exception as e:
@@ -281,7 +264,7 @@ def get_llm_response(
         )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    return _setup_after_llm_call_hooks(executor_context, answer, printer)
+    return answer
 
 
 def process_llm_response(
@@ -311,8 +294,8 @@ def handle_agent_action_core(
     formatted_answer: AgentAction,
     tool_result: ToolResult,
     messages: list[LLMMessage] | None = None,
-    step_callback: Callable | None = None,  # type: ignore[type-arg]
-    show_logs: Callable | None = None,  # type: ignore[type-arg]
+    step_callback: Callable | None = None,
+    show_logs: Callable | None = None,
 ) -> AgentAction | AgentFinish:
     """Core logic for handling agent actions and tool results.
 
@@ -498,7 +481,7 @@ def summarize_messages(
             ),
         ]
         summary = llm.call(
-            messages,
+            messages,  # type: ignore[arg-type]
             callbacks=callbacks,
         )
         summarized_contents.append({"content": str(summary)})
@@ -670,103 +653,3 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
             else:
                 attributes[key] = value
     return attributes
-
-
-def _setup_before_llm_call_hooks(
-    executor_context: CrewAgentExecutor | None, printer: Printer
-) -> bool:
-    """Setup and invoke before_llm_call hooks for the executor context.
-
-    Args:
-        executor_context: The executor context to setup the hooks for.
-        printer: Printer instance for error logging.
-
-    Returns:
-        True if LLM execution should proceed, False if blocked by a hook.
-    """
-    if executor_context and executor_context.before_llm_call_hooks:
-        from crewai.hooks.llm_hooks import LLMCallHookContext
-
-        original_messages = executor_context.messages
-
-        hook_context = LLMCallHookContext(executor_context)
-        try:
-            for hook in executor_context.before_llm_call_hooks:
-                result = hook(hook_context)
-                if result is False:
-                    printer.print(
-                        content="LLM call blocked by before_llm_call hook",
-                        color="yellow",
-                    )
-                    return False
-        except Exception as e:
-            printer.print(
-                content=f"Error in before_llm_call hook: {e}",
-                color="yellow",
-            )
-
-        if not isinstance(executor_context.messages, list):
-            printer.print(
-                content=(
-                    "Warning: before_llm_call hook replaced messages with non-list. "
-                    "Restoring original messages list. Hooks should modify messages in-place, "
-                    "not replace the list (e.g., use context.messages.append() not context.messages = [])."
-                ),
-                color="yellow",
-            )
-            if isinstance(original_messages, list):
-                executor_context.messages = original_messages
-            else:
-                executor_context.messages = []
-
-    return True
-
-
-def _setup_after_llm_call_hooks(
-    executor_context: CrewAgentExecutor | None,
-    answer: str,
-    printer: Printer,
-) -> str:
-    """Setup and invoke after_llm_call hooks for the executor context.
-
-    Args:
-        executor_context: The executor context to setup the hooks for.
-        answer: The LLM response string.
-        printer: Printer instance for error logging.
-
-    Returns:
-        The potentially modified response string.
-    """
-    if executor_context and executor_context.after_llm_call_hooks:
-        from crewai.hooks.llm_hooks import LLMCallHookContext
-
-        original_messages = executor_context.messages
-
-        hook_context = LLMCallHookContext(executor_context, response=answer)
-        try:
-            for hook in executor_context.after_llm_call_hooks:
-                modified_response = hook(hook_context)
-                if modified_response is not None and isinstance(modified_response, str):
-                    answer = modified_response
-
-        except Exception as e:
-            printer.print(
-                content=f"Error in after_llm_call hook: {e}",
-                color="yellow",
-            )
-
-        if not isinstance(executor_context.messages, list):
-            printer.print(
-                content=(
-                    "Warning: after_llm_call hook replaced messages with non-list. "
-                    "Restoring original messages list. Hooks should modify messages in-place, "
-                    "not replace the list (e.g., use context.messages.append() not context.messages = [])."
-                ),
-                color="yellow",
-            )
-            if isinstance(original_messages, list):
-                executor_context.messages = original_messages
-            else:
-                executor_context.messages = []
-
-    return answer

lib/crewai/src/crewai/utilities/evaluators/task_evaluator.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, cast
+from typing import TYPE_CHECKING, cast
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, Field
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.task_events import TaskEvaluationEvent
@@ -25,90 +25,16 @@ class Entity(BaseModel):
 
 
 class TaskEvaluation(BaseModel):
-    model_config = ConfigDict(extra="ignore")
-
     suggestions: list[str] = Field(
-        default_factory=list,
         description="Suggestions to improve future similar tasks."
     )
-    quality: float | None = Field(
-        default=None,
+    quality: float = Field(
         description="A score from 0 to 10 evaluating on completion, quality, and overall performance, all taking into account the task description, expected output, and the result of the task."
     )
     entities: list[Entity] = Field(
-        default_factory=list,
         description="Entities extracted from the task output."
     )
 
-    @model_validator(mode="before")
-    @classmethod
-    def map_score_to_quality(cls, data: Any) -> Any:
-        """Map 'score' field to 'quality' if quality is missing."""
-        if isinstance(data, dict):
-            if "quality" not in data and "score" in data:
-                data["quality"] = data["score"]
-        return data
-
-    @field_validator("suggestions", mode="before")
-    @classmethod
-    def normalize_suggestions(cls, v: Any) -> list[str]:
-        """Normalize suggestions from various formats to list[str].
-
-        Handles:
-        - None → []
-        - str → [str]
-        - dict with "point" → [point]
-        - dict without "point" → [str(dict)]
-        - list → flatten using same rules per item
-        """
-        if v is None:
-            return []
-
-        if isinstance(v, str):
-            return [v]
-
-        if isinstance(v, dict):
-            if "point" in v:
-                return [str(v["point"])]
-            return [str(v)]
-
-        if isinstance(v, list):
-            result = []
-            for item in v:
-                if isinstance(item, str):
-                    result.append(item)
-                elif isinstance(item, dict):
-                    if "point" in item:
-                        result.append(str(item["point"]))
-                    else:
-                        result.append(str(item))
-                else:
-                    result.append(str(item))
-            return result
-
-        return [str(v)]
-
-    @field_validator("quality", mode="before")
-    @classmethod
-    def coerce_quality(cls, v: Any) -> float | None:
-        """Coerce quality to float, accepting int and numeric strings.
-
-        Returns None if value is None, empty string, or cannot be parsed.
-        """
-        if v is None or v == "":
-            return None
-
-        if isinstance(v, (int, float)):
-            return float(v)
-
-        if isinstance(v, str):
-            try:
-                return float(v)
-            except ValueError:
-                return None
-
-        return None
-
 
 class TrainingTaskEvaluation(BaseModel):
     suggestions: list[str] = Field(

lib/crewai/src/crewai/utilities/tool_utils.py

@@ -4,23 +4,16 @@ from typing import TYPE_CHECKING
 
 from crewai.agents.parser import AgentAction
 from crewai.agents.tools_handler import ToolsHandler
-from crewai.hooks.tool_hooks import (
-    ToolCallHookContext,
-    get_after_tool_call_hooks,
-    get_before_tool_call_hooks,
-)
 from crewai.security.fingerprint import Fingerprint
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_types import ToolResult
 from crewai.tools.tool_usage import ToolUsage, ToolUsageError
 from crewai.utilities.i18n import I18N
-from crewai.utilities.logger import Logger
 
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
     from crewai.agents.agent_builder.base_agent import BaseAgent
-    from crewai.crew import Crew
     from crewai.llm import LLM
     from crewai.llms.base_llm import BaseLLM
     from crewai.task import Task
@@ -37,13 +30,9 @@ def execute_tool_and_check_finality(
     agent: Agent | BaseAgent | None = None,
     function_calling_llm: BaseLLM | LLM | None = None,
     fingerprint_context: dict[str, str] | None = None,
-    crew: Crew | None = None,
 ) -> ToolResult:
     """Execute a tool and check if the result should be treated as a final answer.
 
-    This function integrates tool hooks for before and after tool execution,
-    allowing programmatic interception and modification of tool calls.
-
     Args:
         agent_action: The action containing the tool to execute
         tools: List of available tools
@@ -55,12 +44,10 @@ def execute_tool_and_check_finality(
         agent: Optional agent instance for tool execution
         function_calling_llm: Optional LLM for function calling
         fingerprint_context: Optional context for fingerprinting
-        crew: Optional crew instance for hook context
 
     Returns:
         ToolResult containing the execution result and whether it should be treated as a final answer
     """
-    logger = Logger(verbose=crew.verbose if crew else False)
     tool_name_to_tool_map = {tool.name: tool for tool in tools}
 
     if agent_key and agent_role and agent:
@@ -96,62 +83,10 @@ def execute_tool_and_check_finality(
     ] or tool_calling.tool_name.casefold().replace("_", " ") in [
         name.casefold().strip() for name in tool_name_to_tool_map
     ]:
-        tool = tool_name_to_tool_map.get(tool_calling.tool_name)
-        if not tool:
-            tool_result = i18n.errors("wrong_tool_name").format(
-                tool=tool_calling.tool_name,
-                tools=", ".join([t.name.casefold() for t in tools]),
-            )
-            return ToolResult(result=tool_result, result_as_answer=False)
-
-        tool_input = tool_calling.arguments if tool_calling.arguments else {}
-        hook_context = ToolCallHookContext(
-            tool_name=tool_calling.tool_name,
-            tool_input=tool_input,
-            tool=tool,
-            agent=agent,
-            task=task,
-            crew=crew,
-        )
-
-        before_hooks = get_before_tool_call_hooks()
-        try:
-            for hook in before_hooks:
-                result = hook(hook_context)
-                if result is False:
-                    blocked_message = (
-                        f"Tool execution blocked by hook. "
-                        f"Tool: {tool_calling.tool_name}"
-                    )
-                    return ToolResult(blocked_message, False)
-        except Exception as e:
-            logger.log("error", f"Error in before_tool_call hook: {e}")
-
         tool_result = tool_usage.use(tool_calling, agent_action.text)
-
-        after_hook_context = ToolCallHookContext(
-            tool_name=tool_calling.tool_name,
-            tool_input=tool_input,
-            tool=tool,
-            agent=agent,
-            task=task,
-            crew=crew,
-            tool_result=tool_result,
-        )
-
-        # Execute after_tool_call hooks
-        after_hooks = get_after_tool_call_hooks()
-        modified_result = tool_result
-        try:
-            for hook in after_hooks:
-                hook_result = hook(after_hook_context)
-                if hook_result is not None:
-                    modified_result = hook_result
-                    after_hook_context.tool_result = modified_result
-        except Exception as e:
-            logger.log("error", f"Error in after_tool_call hook: {e}")
-
-        return ToolResult(modified_result, tool.result_as_answer)
+        tool = tool_name_to_tool_map.get(tool_calling.tool_name)
+        if tool:
+            return ToolResult(tool_result, tool.result_as_answer)
 
     # Handle invalid tool name
     tool_result = i18n.errors("wrong_tool_name").format(

lib/crewai/src/crewai/utilities/types.py

@@ -1,8 +1,6 @@
 """Types for CrewAI utilities."""
 
-from typing import Any, Literal
-
-from typing_extensions import TypedDict
+from typing import Any, Literal, TypedDict
 
 
 class LLMMessage(TypedDict):

lib/crewai/tests/agents/test_a2a_trust_completion_status.py

@@ -1,147 +0,0 @@
-"""Test trust_remote_completion_status flag in A2A wrapper."""
-
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai.a2a.config import A2AConfig
-
-try:
-    from a2a.types import Message, Role
-
-    A2A_SDK_INSTALLED = True
-except ImportError:
-    A2A_SDK_INSTALLED = False
-
-
-@pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
-def test_trust_remote_completion_status_true_returns_directly():
-    """When trust_remote_completion_status=True and A2A returns completed, return result directly."""
-    from crewai.a2a.wrapper import _delegate_to_a2a
-    from crewai.a2a.types import AgentResponseProtocol
-    from crewai import Agent, Task
-
-    a2a_config = A2AConfig(
-        endpoint="http://test-endpoint.com",
-        trust_remote_completion_status=True,
-    )
-
-    agent = Agent(
-        role="test manager",
-        goal="coordinate",
-        backstory="test",
-        a2a=a2a_config,
-    )
-
-    task = Task(description="test", expected_output="test", agent=agent)
-
-    class MockResponse:
-        is_a2a = True
-        message = "Please help"
-        a2a_ids = ["http://test-endpoint.com/"]
-
-    with (
-        patch("crewai.a2a.wrapper.execute_a2a_delegation") as mock_execute,
-        patch("crewai.a2a.wrapper._fetch_agent_cards_concurrently") as mock_fetch,
-    ):
-        mock_card = MagicMock()
-        mock_card.name = "Test"
-        mock_fetch.return_value = ({"http://test-endpoint.com/": mock_card}, {})
-
-        # A2A returns completed
-        mock_execute.return_value = {
-            "status": "completed",
-            "result": "Done by remote",
-            "history": [],
-        }
-
-        # This should return directly without checking LLM response
-        result = _delegate_to_a2a(
-            self=agent,
-            agent_response=MockResponse(),
-            task=task,
-            original_fn=lambda *args, **kwargs: "fallback",
-            context=None,
-            tools=None,
-            agent_cards={"http://test-endpoint.com/": mock_card},
-            original_task_description="test",
-        )
-
-        assert result == "Done by remote"
-        assert mock_execute.call_count == 1
-
-
-@pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
-def test_trust_remote_completion_status_false_continues_conversation():
-    """When trust_remote_completion_status=False and A2A returns completed, ask server agent."""
-    from crewai.a2a.wrapper import _delegate_to_a2a
-    from crewai import Agent, Task
-
-    a2a_config = A2AConfig(
-        endpoint="http://test-endpoint.com",
-        trust_remote_completion_status=False,
-    )
-
-    agent = Agent(
-        role="test manager",
-        goal="coordinate",
-        backstory="test",
-        a2a=a2a_config,
-    )
-
-    task = Task(description="test", expected_output="test", agent=agent)
-
-    class MockResponse:
-        is_a2a = True
-        message = "Please help"
-        a2a_ids = ["http://test-endpoint.com/"]
-
-    call_count = 0
-
-    def mock_original_fn(self, task, context, tools):
-        nonlocal call_count
-        call_count += 1
-        if call_count == 1:
-            # Server decides to finish
-            return '{"is_a2a": false, "message": "Server final answer", "a2a_ids": []}'
-        return "unexpected"
-
-    with (
-        patch("crewai.a2a.wrapper.execute_a2a_delegation") as mock_execute,
-        patch("crewai.a2a.wrapper._fetch_agent_cards_concurrently") as mock_fetch,
-    ):
-        mock_card = MagicMock()
-        mock_card.name = "Test"
-        mock_fetch.return_value = ({"http://test-endpoint.com/": mock_card}, {})
-
-        # A2A returns completed
-        mock_execute.return_value = {
-            "status": "completed",
-            "result": "Done by remote",
-            "history": [],
-        }
-
-        result = _delegate_to_a2a(
-            self=agent,
-            agent_response=MockResponse(),
-            task=task,
-            original_fn=mock_original_fn,
-            context=None,
-            tools=None,
-            agent_cards={"http://test-endpoint.com/": mock_card},
-            original_task_description="test",
-        )
-
-        # Should call original_fn to get server response
-        assert call_count >= 1
-        assert result == "Server final answer"
-
-
-@pytest.mark.skipif(not A2A_SDK_INSTALLED, reason="Requires a2a-sdk to be installed")
-def test_default_trust_remote_completion_status_is_false():
-    """Verify that default value of trust_remote_completion_status is False."""
-    a2a_config = A2AConfig(
-        endpoint="http://test-endpoint.com",
-    )
-
-    assert a2a_config.trust_remote_completion_status is False

lib/crewai/tests/agents/test_agent.py

@@ -508,47 +508,7 @@ def test_agent_custom_max_iterations():
     assert isinstance(result, str)
     assert len(result) > 0
     assert call_count > 0
-    # With max_iter=1, expect 2 calls:
-    # - Call 1: iteration 0
-    # - Call 2: iteration 1 (max reached, handle_max_iterations_exceeded called, then loop breaks)
-    assert call_count == 2
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.timeout(30)
-def test_agent_max_iterations_stops_loop():
-    """Test that agent execution terminates when max_iter is reached."""
-
-    @tool
-    def get_data(step: str) -> str:
-        """Get data for a step. Always returns data requiring more steps."""
-        return f"Data for {step}: incomplete, need to query more steps."
-
-    agent = Agent(
-        role="data collector",
-        goal="collect data using the get_data tool",
-        backstory="You must use the get_data tool extensively",
-        max_iter=2,
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Use get_data tool for step1, step2, step3, step4, step5, step6, step7, step8, step9, and step10. Do NOT stop until you've called it for ALL steps.",
-        expected_output="A summary of all data collected",
-    )
-
-    result = agent.execute_task(
-        task=task,
-        tools=[get_data],
-    )
-
-    assert result is not None
-    assert isinstance(result, str)
-
-    assert agent.agent_executor.iterations <= agent.max_iter + 2, (
-        f"Agent ran {agent.agent_executor.iterations} iterations "
-        f"but should stop around {agent.max_iter + 1}. "
-    )
+    assert call_count == 3
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

lib/crewai/tests/agents/test_lite_agent.py

@@ -238,27 +238,6 @@ def test_lite_agent_returns_usage_metrics():
     assert result.usage_metrics["total_tokens"] > 0
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_lite_agent_output_includes_messages():
-    """Test that LiteAgentOutput includes messages from agent execution."""
-    llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
-        role="Research Assistant",
-        goal="Find information about the population of Tokyo",
-        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
-        llm=llm,
-        tools=[WebSearchTool()],
-        verbose=True,
-    )
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-
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-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_lite_agent_returns_usage_metrics_async():

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-      - '149998392'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149998392'
-      x-ratelimit-reset-project-tokens:
-      - 0s
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_48c359c72cdc47aeb89c6d6eeffdce7d
-    status:
-      code: 200
-      message: OK
-version: 1

lib/crewai/tests/cli/authentication/providers/test_okta.py

@@ -37,36 +37,6 @@ class TestOktaProvider:
         provider = OktaProvider(settings)
         expected_url = "https://my-company.okta.com/oauth2/default/v1/device/authorize"
         assert provider.get_authorize_url() == expected_url
-    
-    def test_get_authorize_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777/v1/device/authorize"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/v1/device/authorize"
-        assert provider.get_authorize_url() == expected_url
 
     def test_get_token_url(self):
         expected_url = "https://test-domain.okta.com/oauth2/default/v1/token"
@@ -83,36 +53,6 @@ class TestOktaProvider:
         expected_url = "https://another-domain.okta.com/oauth2/default/v1/token"
         assert provider.get_token_url() == expected_url
 
-    def test_get_token_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777/v1/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_token_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/v1/token"
-        assert provider.get_token_url() == expected_url
-
     def test_get_jwks_url(self):
         expected_url = "https://test-domain.okta.com/oauth2/default/v1/keys"
         assert self.provider.get_jwks_url() == expected_url
@@ -128,36 +68,6 @@ class TestOktaProvider:
         expected_url = "https://dev.okta.com/oauth2/default/v1/keys"
         assert provider.get_jwks_url() == expected_url
 
-    def test_get_jwks_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777/v1/keys"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/v1/keys"
-        assert provider.get_jwks_url() == expected_url
-
     def test_get_issuer(self):
         expected_issuer = "https://test-domain.okta.com/oauth2/default"
         assert self.provider.get_issuer() == expected_issuer
@@ -173,36 +83,6 @@ class TestOktaProvider:
         expected_issuer = "https://prod.okta.com/oauth2/default"
         assert provider.get_issuer() == expected_issuer
 
-    def test_get_issuer_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_issuer = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_issuer = "https://test-domain.okta.com"
-        assert provider.get_issuer() == expected_issuer
-
     def test_get_audience(self):
         assert self.provider.get_audience() == "test-audience"
 
@@ -220,38 +100,3 @@ class TestOktaProvider:
 
     def test_get_client_id(self):
         assert self.provider.get_client_id() == "test-client-id"
-
-    def test_get_required_fields(self):
-        assert set(self.provider.get_required_fields()) == set(["authorization_server_name", "using_org_auth_server"]) 
-    
-    def test_oauth2_base_url(self):
-        assert self.provider._oauth2_base_url() == "https://test-domain.okta.com/oauth2/default"
-    
-    def test_oauth2_base_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-
-        provider = OktaProvider(settings)
-        assert provider._oauth2_base_url() == "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777"
-
-    def test_oauth2_base_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        assert provider._oauth2_base_url() == "https://test-domain.okta.com/oauth2"

lib/crewai/tests/cli/enterprise/test_main.py

@@ -37,8 +37,7 @@ class TestEnterpriseConfigureCommand(unittest.TestCase):
             'audience': 'test_audience',
             'domain': 'test.domain.com',
             'device_authorization_client_id': 'test_client_id',
-            'provider': 'workos',
-            'extra': {}
+            'provider': 'workos'
         }
         mock_requests_get.return_value = mock_response
 
@@ -61,12 +60,11 @@ class TestEnterpriseConfigureCommand(unittest.TestCase):
             ('oauth2_provider', 'workos'),
             ('oauth2_audience', 'test_audience'),
             ('oauth2_client_id', 'test_client_id'),
-            ('oauth2_domain', 'test.domain.com'),
-            ('oauth2_extra', {})
+            ('oauth2_domain', 'test.domain.com')
         ]
 
         actual_calls = self.mock_settings_command.set.call_args_list
-        self.assertEqual(len(actual_calls), 6)
+        self.assertEqual(len(actual_calls), 5)
 
         for i, (key, value) in enumerate(expected_calls):
             call_args = actual_calls[i][0]

lib/crewai/tests/hooks/__init__.py

@@ -1,2 +0,0 @@
-"""Tests for CrewAI hooks functionality."""
-

lib/crewai/tests/hooks/test_crew_scoped_hooks.py

@@ -1,619 +0,0 @@
-"""Tests for crew-scoped hooks within @CrewBase classes."""
-
-from __future__ import annotations
-
-from unittest.mock import Mock
-
-import pytest
-
-from crewai import Agent, Crew
-from crewai.hooks import (
-    LLMCallHookContext,
-    ToolCallHookContext,
-    before_llm_call,
-    before_tool_call,
-    get_before_llm_call_hooks,
-    get_before_tool_call_hooks,
-)
-from crewai.project import CrewBase, agent, crew
-
-
-@pytest.fixture(autouse=True)
-def clear_hooks():
-    """Clear global hooks before and after each test."""
-    from crewai.hooks import llm_hooks, tool_hooks
-
-    # Store original hooks
-    original_before_llm = llm_hooks._before_llm_call_hooks.copy()
-    original_before_tool = tool_hooks._before_tool_call_hooks.copy()
-
-    # Clear hooks
-    llm_hooks._before_llm_call_hooks.clear()
-    tool_hooks._before_tool_call_hooks.clear()
-
-    yield
-
-    # Restore original hooks
-    llm_hooks._before_llm_call_hooks.clear()
-    tool_hooks._before_tool_call_hooks.clear()
-    llm_hooks._before_llm_call_hooks.extend(original_before_llm)
-    tool_hooks._before_tool_call_hooks.extend(original_before_tool)
-
-
-class TestCrewScopedHooks:
-    """Test hooks defined as methods within @CrewBase classes."""
-
-    def test_crew_scoped_hook_is_registered_on_instance_creation(self):
-        """Test that crew-scoped hooks are registered when crew instance is created."""
-
-        @CrewBase
-        class TestCrew:
-            @before_llm_call
-            def my_hook(self, context):
-                pass
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Check hooks before instance creation
-        hooks_before = get_before_llm_call_hooks()
-        initial_count = len(hooks_before)
-
-        # Create instance - should register the hook
-        crew_instance = TestCrew()
-
-        # Check hooks after instance creation
-        hooks_after = get_before_llm_call_hooks()
-
-        # Should have one more hook registered
-        assert len(hooks_after) == initial_count + 1
-
-    def test_crew_scoped_hook_has_access_to_self(self):
-        """Test that crew-scoped hooks can access self and instance variables."""
-        execution_log = []
-
-        @CrewBase
-        class TestCrew:
-            def __init__(self):
-                self.crew_name = "TestCrew"
-                self.call_count = 0
-
-            @before_llm_call
-            def my_hook(self, context):
-                # Can access self
-                self.call_count += 1
-                execution_log.append(f"{self.crew_name}:{self.call_count}")
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-
-        # Get the registered hook
-        hooks = get_before_llm_call_hooks()
-        crew_hook = hooks[-1]  # Last registered hook
-
-        # Create mock context
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Test")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context = LLMCallHookContext(executor=mock_executor)
-
-        # Execute hook multiple times
-        crew_hook(context)
-        crew_hook(context)
-
-        # Verify hook accessed self and modified instance state
-        assert len(execution_log) == 2
-        assert execution_log[0] == "TestCrew:1"
-        assert execution_log[1] == "TestCrew:2"
-        assert crew_instance.call_count == 2
-
-    def test_multiple_crews_have_isolated_hooks(self):
-        """Test that different crew instances have isolated hooks."""
-        crew1_executions = []
-        crew2_executions = []
-
-        @CrewBase
-        class Crew1:
-            @before_llm_call
-            def crew1_hook(self, context):
-                crew1_executions.append("crew1")
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        @CrewBase
-        class Crew2:
-            @before_llm_call
-            def crew2_hook(self, context):
-                crew2_executions.append("crew2")
-
-            @agent
-            def analyst(self):
-                return Agent(role="Analyst", goal="Analyze", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create both instances
-        instance1 = Crew1()
-        instance2 = Crew2()
-
-        # Both hooks should be registered
-        hooks = get_before_llm_call_hooks()
-        assert len(hooks) >= 2
-
-        # Create mock context
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Test")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context = LLMCallHookContext(executor=mock_executor)
-
-        # Execute all hooks
-        for hook in hooks:
-            hook(context)
-
-        # Both hooks should have executed
-        assert "crew1" in crew1_executions
-        assert "crew2" in crew2_executions
-
-    def test_crew_scoped_hook_with_filters(self):
-        """Test that filtered crew-scoped hooks work correctly."""
-        execution_log = []
-
-        @CrewBase
-        class TestCrew:
-            @before_tool_call(tools=["delete_file"])
-            def filtered_hook(self, context):
-                execution_log.append(f"filtered:{context.tool_name}")
-                return None
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-
-        # Get registered hooks
-        hooks = get_before_tool_call_hooks()
-        crew_hook = hooks[-1]  # Last registered
-
-        # Test with matching tool
-        mock_tool = Mock()
-        context1 = ToolCallHookContext(
-            tool_name="delete_file", tool_input={}, tool=mock_tool
-        )
-        crew_hook(context1)
-
-        assert len(execution_log) == 1
-        assert execution_log[0] == "filtered:delete_file"
-
-        # Test with non-matching tool
-        context2 = ToolCallHookContext(
-            tool_name="read_file", tool_input={}, tool=mock_tool
-        )
-        crew_hook(context2)
-
-        # Should still be 1 (filtered hook didn't run)
-        assert len(execution_log) == 1
-
-    def test_crew_scoped_hook_no_double_registration(self):
-        """Test that crew-scoped hooks are not registered twice."""
-
-        @CrewBase
-        class TestCrew:
-            @before_llm_call
-            def my_hook(self, context):
-                pass
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Get initial hook count
-        initial_hooks = len(get_before_llm_call_hooks())
-
-        # Create first instance
-        instance1 = TestCrew()
-
-        # Should add 1 hook
-        hooks_after_first = get_before_llm_call_hooks()
-        assert len(hooks_after_first) == initial_hooks + 1
-
-        # Create second instance
-        instance2 = TestCrew()
-
-        # Should add another hook (one per instance)
-        hooks_after_second = get_before_llm_call_hooks()
-        assert len(hooks_after_second) == initial_hooks + 2
-
-    def test_crew_scoped_hook_method_signature(self):
-        """Test that crew-scoped hooks have correct signature (self + context)."""
-
-        @CrewBase
-        class TestCrew:
-            def __init__(self):
-                self.test_value = "test"
-
-            @before_llm_call
-            def my_hook(self, context):
-                # Should be able to access both self and context
-                return f"{self.test_value}:{context.iterations}"
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-
-        # Verify the hook method has is_before_llm_call_hook marker
-        assert hasattr(crew_instance.my_hook, "__func__")
-        hook_func = crew_instance.my_hook.__func__
-        assert hasattr(hook_func, "is_before_llm_call_hook")
-        assert hook_func.is_before_llm_call_hook is True
-
-    def test_crew_scoped_with_agent_filter(self):
-        """Test crew-scoped hooks with agent filters."""
-        execution_log = []
-
-        @CrewBase
-        class TestCrew:
-            @before_llm_call(agents=["Researcher"])
-            def filtered_hook(self, context):
-                execution_log.append(context.agent.role)
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-
-        # Get hooks
-        hooks = get_before_llm_call_hooks()
-        crew_hook = hooks[-1]
-
-        # Test with matching agent
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Researcher")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context1 = LLMCallHookContext(executor=mock_executor)
-        crew_hook(context1)
-
-        assert len(execution_log) == 1
-        assert execution_log[0] == "Researcher"
-
-        # Test with non-matching agent
-        mock_executor.agent.role = "Analyst"
-        context2 = LLMCallHookContext(executor=mock_executor)
-        crew_hook(context2)
-
-        # Should still be 1 (filtered out)
-        assert len(execution_log) == 1
-
-
-class TestCrewScopedHookAttributes:
-    """Test that crew-scoped hooks have correct attributes set."""
-
-    def test_hook_marker_attribute_is_set(self):
-        """Test that decorator sets marker attribute on method."""
-
-        @CrewBase
-        class TestCrew:
-            @before_llm_call
-            def my_hook(self, context):
-                pass
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Check the unbound method has the marker
-        assert hasattr(TestCrew.__dict__["my_hook"], "is_before_llm_call_hook")
-        assert TestCrew.__dict__["my_hook"].is_before_llm_call_hook is True
-
-    def test_filter_attributes_are_preserved(self):
-        """Test that filter attributes are preserved on methods."""
-
-        @CrewBase
-        class TestCrew:
-            @before_tool_call(tools=["delete_file"], agents=["Dev"])
-            def filtered_hook(self, context):
-                return None
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Check filter attributes are set
-        hook_method = TestCrew.__dict__["filtered_hook"]
-        assert hasattr(hook_method, "is_before_tool_call_hook")
-        assert hasattr(hook_method, "_filter_tools")
-        assert hasattr(hook_method, "_filter_agents")
-        assert hook_method._filter_tools == ["delete_file"]
-        assert hook_method._filter_agents == ["Dev"]
-
-    def test_registered_hooks_tracked_on_instance(self):
-        """Test that registered hooks are tracked on the crew instance."""
-
-        @CrewBase
-        class TestCrew:
-            @before_llm_call
-            def llm_hook(self, context):
-                pass
-
-            @before_tool_call
-            def tool_hook(self, context):
-                return None
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-
-        # Check that hooks are tracked
-        assert hasattr(crew_instance, "_registered_hook_functions")
-        assert isinstance(crew_instance._registered_hook_functions, list)
-        assert len(crew_instance._registered_hook_functions) == 2
-
-        # Check hook types
-        hook_types = [ht for ht, _ in crew_instance._registered_hook_functions]
-        assert "before_llm_call" in hook_types
-        assert "before_tool_call" in hook_types
-
-
-class TestCrewScopedHookExecution:
-    """Test execution behavior of crew-scoped hooks."""
-
-    def test_crew_hook_executes_with_bound_self(self):
-        """Test that crew-scoped hook executes with self properly bound."""
-        execution_log = []
-
-        @CrewBase
-        class TestCrew:
-            def __init__(self):
-                self.instance_id = id(self)
-
-            @before_llm_call
-            def my_hook(self, context):
-                # Should have access to self
-                execution_log.append(self.instance_id)
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-        expected_id = crew_instance.instance_id
-
-        # Get and execute hook
-        hooks = get_before_llm_call_hooks()
-        crew_hook = hooks[-1]
-
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Test")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context = LLMCallHookContext(executor=mock_executor)
-
-        # Execute hook
-        crew_hook(context)
-
-        # Verify it had access to self
-        assert len(execution_log) == 1
-        assert execution_log[0] == expected_id
-
-    def test_crew_hook_can_modify_instance_state(self):
-        """Test that crew-scoped hooks can modify instance variables."""
-
-        @CrewBase
-        class TestCrew:
-            def __init__(self):
-                self.counter = 0
-
-            @before_tool_call
-            def increment_counter(self, context):
-                self.counter += 1
-                return None
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create instance
-        crew_instance = TestCrew()
-        assert crew_instance.counter == 0
-
-        # Get and execute hook
-        hooks = get_before_tool_call_hooks()
-        crew_hook = hooks[-1]
-
-        mock_tool = Mock()
-        context = ToolCallHookContext(tool_name="test", tool_input={}, tool=mock_tool)
-
-        # Execute hook 3 times
-        crew_hook(context)
-        crew_hook(context)
-        crew_hook(context)
-
-        # Verify counter was incremented
-        assert crew_instance.counter == 3
-
-    def test_multiple_instances_maintain_separate_state(self):
-        """Test that multiple instances of the same crew maintain separate state."""
-
-        @CrewBase
-        class TestCrew:
-            def __init__(self):
-                self.call_count = 0
-
-            @before_llm_call
-            def count_calls(self, context):
-                self.call_count += 1
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Create two instances
-        instance1 = TestCrew()
-        instance2 = TestCrew()
-
-        # Get all hooks (should include hooks from both instances)
-        all_hooks = get_before_llm_call_hooks()
-
-        # Find hooks for each instance (last 2 registered)
-        hook1 = all_hooks[-2]
-        hook2 = all_hooks[-1]
-
-        # Create mock context
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Test")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context = LLMCallHookContext(executor=mock_executor)
-
-        # Execute first hook twice
-        hook1(context)
-        hook1(context)
-
-        # Execute second hook once
-        hook2(context)
-
-        # Each instance should have independent state
-        # Note: We can't easily verify which hook belongs to which instance
-        # in this test without more introspection, but the fact that it doesn't
-        # crash and hooks can maintain state proves isolation works
-
-
-class TestSignatureDetection:
-    """Test that signature detection correctly identifies methods vs functions."""
-
-    def test_method_signature_detected(self):
-        """Test that methods with 'self' parameter are detected."""
-        import inspect
-
-        @CrewBase
-        class TestCrew:
-            @before_llm_call
-            def method_hook(self, context):
-                pass
-
-            @agent
-            def researcher(self):
-                return Agent(role="Researcher", goal="Research", backstory="Expert")
-
-            @crew
-            def crew(self):
-                return Crew(agents=self.agents, tasks=[], verbose=False)
-
-        # Check that method has self parameter
-        method = TestCrew.__dict__["method_hook"]
-        sig = inspect.signature(method)
-        params = list(sig.parameters.keys())
-        assert params[0] == "self"
-        assert len(params) == 2  # self + context
-
-    def test_standalone_function_signature_detected(self):
-        """Test that standalone functions without 'self' are detected."""
-        import inspect
-
-        @before_llm_call
-        def standalone_hook(context):
-            pass
-
-        # Should have only context parameter (no self)
-        sig = inspect.signature(standalone_hook)
-        params = list(sig.parameters.keys())
-        assert "self" not in params
-        assert len(params) == 1  # Just context
-
-        # Should be registered
-        hooks = get_before_llm_call_hooks()
-        assert len(hooks) >= 1

lib/crewai/tests/hooks/test_decorators.py

@@ -1,335 +0,0 @@
-"""Tests for decorator-based hook registration."""
-
-from __future__ import annotations
-
-from unittest.mock import Mock
-
-import pytest
-
-from crewai.hooks import (
-    after_llm_call,
-    after_tool_call,
-    before_llm_call,
-    before_tool_call,
-    get_after_llm_call_hooks,
-    get_after_tool_call_hooks,
-    get_before_llm_call_hooks,
-    get_before_tool_call_hooks,
-)
-from crewai.hooks.llm_hooks import LLMCallHookContext
-from crewai.hooks.tool_hooks import ToolCallHookContext
-
-
-@pytest.fixture(autouse=True)
-def clear_hooks():
-    """Clear global hooks before and after each test."""
-    from crewai.hooks import llm_hooks, tool_hooks
-
-    # Store original hooks
-    original_before_llm = llm_hooks._before_llm_call_hooks.copy()
-    original_after_llm = llm_hooks._after_llm_call_hooks.copy()
-    original_before_tool = tool_hooks._before_tool_call_hooks.copy()
-    original_after_tool = tool_hooks._after_tool_call_hooks.copy()
-
-    # Clear hooks
-    llm_hooks._before_llm_call_hooks.clear()
-    llm_hooks._after_llm_call_hooks.clear()
-    tool_hooks._before_tool_call_hooks.clear()
-    tool_hooks._after_tool_call_hooks.clear()
-
-    yield
-
-    # Restore original hooks
-    llm_hooks._before_llm_call_hooks.clear()
-    llm_hooks._after_llm_call_hooks.clear()
-    tool_hooks._before_tool_call_hooks.clear()
-    tool_hooks._after_tool_call_hooks.clear()
-    llm_hooks._before_llm_call_hooks.extend(original_before_llm)
-    llm_hooks._after_llm_call_hooks.extend(original_after_llm)
-    tool_hooks._before_tool_call_hooks.extend(original_before_tool)
-    tool_hooks._after_tool_call_hooks.extend(original_after_tool)
-
-
-class TestLLMHookDecorators:
-    """Test LLM hook decorators."""
-
-    def test_before_llm_call_decorator_registers_hook(self):
-        """Test that @before_llm_call decorator registers the hook."""
-
-        @before_llm_call
-        def test_hook(context):
-            pass
-
-        hooks = get_before_llm_call_hooks()
-        assert len(hooks) == 1
-
-    def test_after_llm_call_decorator_registers_hook(self):
-        """Test that @after_llm_call decorator registers the hook."""
-
-        @after_llm_call
-        def test_hook(context):
-            return None
-
-        hooks = get_after_llm_call_hooks()
-        assert len(hooks) == 1
-
-    def test_decorated_hook_executes_correctly(self):
-        """Test that decorated hook executes and modifies behavior."""
-        execution_log = []
-
-        @before_llm_call
-        def test_hook(context):
-            execution_log.append("executed")
-
-        # Create mock context
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Test")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context = LLMCallHookContext(executor=mock_executor)
-
-        # Execute the hook
-        hooks = get_before_llm_call_hooks()
-        hooks[0](context)
-
-        assert len(execution_log) == 1
-        assert execution_log[0] == "executed"
-
-    def test_before_llm_call_with_agent_filter(self):
-        """Test that agent filter works correctly."""
-        execution_log = []
-
-        @before_llm_call(agents=["Researcher"])
-        def filtered_hook(context):
-            execution_log.append(context.agent.role)
-
-        hooks = get_before_llm_call_hooks()
-        assert len(hooks) == 1
-
-        # Test with matching agent
-        mock_executor = Mock()
-        mock_executor.messages = []
-        mock_executor.agent = Mock(role="Researcher")
-        mock_executor.task = Mock()
-        mock_executor.crew = Mock()
-        mock_executor.llm = Mock()
-        mock_executor.iterations = 0
-
-        context = LLMCallHookContext(executor=mock_executor)
-        hooks[0](context)
-
-        assert len(execution_log) == 1
-        assert execution_log[0] == "Researcher"
-
-        # Test with non-matching agent
-        mock_executor.agent.role = "Analyst"
-        context2 = LLMCallHookContext(executor=mock_executor)
-        hooks[0](context2)
-
-        # Should still be 1 (hook didn't execute)
-        assert len(execution_log) == 1
-
-
-class TestToolHookDecorators:
-    """Test tool hook decorators."""
-
-    def test_before_tool_call_decorator_registers_hook(self):
-        """Test that @before_tool_call decorator registers the hook."""
-
-        @before_tool_call
-        def test_hook(context):
-            return None
-
-        hooks = get_before_tool_call_hooks()
-        assert len(hooks) == 1
-
-    def test_after_tool_call_decorator_registers_hook(self):
-        """Test that @after_tool_call decorator registers the hook."""
-
-        @after_tool_call
-        def test_hook(context):
-            return None
-
-        hooks = get_after_tool_call_hooks()
-        assert len(hooks) == 1
-
-    def test_before_tool_call_with_tool_filter(self):
-        """Test that tool filter works correctly."""
-        execution_log = []
-
-        @before_tool_call(tools=["delete_file", "execute_code"])
-        def filtered_hook(context):
-            execution_log.append(context.tool_name)
-            return None
-
-        hooks = get_before_tool_call_hooks()
-        assert len(hooks) == 1
-
-        # Test with matching tool
-        mock_tool = Mock()
-        context = ToolCallHookContext(
-            tool_name="delete_file",
-            tool_input={},
-            tool=mock_tool,
-        )
-        hooks[0](context)
-
-        assert len(execution_log) == 1
-        assert execution_log[0] == "delete_file"
-
-        # Test with non-matching tool
-        context2 = ToolCallHookContext(
-            tool_name="read_file",
-            tool_input={},
-            tool=mock_tool,
-        )
-        hooks[0](context2)
-
-        # Should still be 1 (hook didn't execute for read_file)
-        assert len(execution_log) == 1
-
-    def test_before_tool_call_with_combined_filters(self):
-        """Test that combined tool and agent filters work."""
-        execution_log = []
-
-        @before_tool_call(tools=["write_file"], agents=["Developer"])
-        def filtered_hook(context):
-            execution_log.append(f"{context.tool_name}-{context.agent.role}")
-            return None
-
-        hooks = get_before_tool_call_hooks()
-        mock_tool = Mock()
-        mock_agent = Mock(role="Developer")
-
-        # Test with both matching
-        context = ToolCallHookContext(
-            tool_name="write_file",
-            tool_input={},
-            tool=mock_tool,
-            agent=mock_agent,
-        )
-        hooks[0](context)
-
-        assert len(execution_log) == 1
-        assert execution_log[0] == "write_file-Developer"
-
-        # Test with tool matching but agent not
-        mock_agent.role = "Researcher"
-        context2 = ToolCallHookContext(
-            tool_name="write_file",
-            tool_input={},
-            tool=mock_tool,
-            agent=mock_agent,
-        )
-        hooks[0](context2)
-
-        # Should still be 1 (hook didn't execute)
-        assert len(execution_log) == 1
-
-    def test_after_tool_call_with_filter(self):
-        """Test that after_tool_call decorator with filter works."""
-
-        @after_tool_call(tools=["web_search"])
-        def filtered_hook(context):
-            if context.tool_result:
-                return context.tool_result.upper()
-            return None
-
-        hooks = get_after_tool_call_hooks()
-        mock_tool = Mock()
-
-        # Test with matching tool
-        context = ToolCallHookContext(
-            tool_name="web_search",
-            tool_input={},
-            tool=mock_tool,
-            tool_result="result",
-        )
-        result = hooks[0](context)
-
-        assert result == "RESULT"
-
-        # Test with non-matching tool
-        context2 = ToolCallHookContext(
-            tool_name="other_tool",
-            tool_input={},
-            tool=mock_tool,
-            tool_result="result",
-        )
-        result2 = hooks[0](context2)
-
-        assert result2 is None  # Hook didn't run, returns None
-
-
-class TestDecoratorAttributes:
-    """Test that decorators set proper attributes on functions."""
-
-    def test_before_llm_call_sets_attribute(self):
-        """Test that decorator sets is_before_llm_call_hook attribute."""
-
-        @before_llm_call
-        def test_hook(context):
-            pass
-
-        assert hasattr(test_hook, "is_before_llm_call_hook")
-        assert test_hook.is_before_llm_call_hook is True
-
-    def test_before_tool_call_sets_attributes_with_filters(self):
-        """Test that decorator with filters sets filter attributes."""
-
-        @before_tool_call(tools=["delete_file"], agents=["Dev"])
-        def test_hook(context):
-            return None
-
-        assert hasattr(test_hook, "is_before_tool_call_hook")
-        assert test_hook.is_before_tool_call_hook is True
-        assert hasattr(test_hook, "_filter_tools")
-        assert test_hook._filter_tools == ["delete_file"]
-        assert hasattr(test_hook, "_filter_agents")
-        assert test_hook._filter_agents == ["Dev"]
-
-
-class TestMultipleDecorators:
-    """Test using multiple decorators together."""
-
-    def test_multiple_decorators_all_register(self):
-        """Test that multiple decorated functions all register."""
-
-        @before_llm_call
-        def hook1(context):
-            pass
-
-        @before_llm_call
-        def hook2(context):
-            pass
-
-        @after_llm_call
-        def hook3(context):
-            return None
-
-        before_hooks = get_before_llm_call_hooks()
-        after_hooks = get_after_llm_call_hooks()
-
-        assert len(before_hooks) == 2
-        assert len(after_hooks) == 1
-
-    def test_decorator_and_manual_registration_work_together(self):
-        """Test that decorators and manual registration can be mixed."""
-        from crewai.hooks import register_before_tool_call_hook
-
-        @before_tool_call
-        def decorated_hook(context):
-            return None
-
-        def manual_hook(context):
-            return None
-
-        register_before_tool_call_hook(manual_hook)
-
-        hooks = get_before_tool_call_hooks()
-
-        assert len(hooks) == 2