Fix stop sequences synchronization for Anthropic, Bedrock, and Gemini providers

- Add stop property getter/setter to AnthropicCompletion to sync stop with stop_sequences
- Add stop property getter/setter to BedrockCompletion to sync stop with stop_sequences
- Add stop property getter/setter to GeminiCompletion to sync stop with stop_sequences
- Add comprehensive tests for all three providers to verify synchronization works correctly

This fixes issue #3836 where CrewAgentExecutor sets llm.stop but the provider-specific
stop_sequences attribute (which is sent to the API) was not synchronized, causing the
model to generate entire conversations instead of stopping at the expected boundaries.

The fix ensures that when CrewAgentExecutor sets llm.stop, the value is properly
synchronized with the provider-specific stop_sequences attribute that gets sent to
the API, preventing massive token usage and cost overruns.

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

.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"

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/mcp/overview.mdx

@@ -11,13 +11,9 @@ The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP)
 
 CrewAI offers **two approaches** for MCP integration:
 
-### 🚀 **Simple DSL Integration** (Recommended)
+### Simple DSL Integration** (Recommended)
 
-Use the `mcps` field directly on agents for seamless MCP tool integration. The DSL supports both **string references** (for quick setup) and **structured configurations** (for full control).
-
-#### String-Based References (Quick Setup)
-
-Perfect for remote HTTPS servers and CrewAI AMP marketplace:
+Use the `mcps` field directly on agents for seamless MCP tool integration:
 
 ```python
 from crewai import Agent
@@ -36,46 +32,6 @@ agent = Agent(
 # MCP tools are now automatically available to your agent!
 ```
 
-#### Structured Configurations (Full Control)
-
-For complete control over connection settings, tool filtering, and all transport types:
-
-```python
-from crewai import Agent
-from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
-from crewai.mcp.filters import create_static_tool_filter
-
-agent = Agent(
-    role="Advanced Research Analyst",
-    goal="Research with full control over MCP connections",
-    backstory="Expert researcher with advanced tool access",
-    mcps=[
-        # Stdio transport for local servers
-        MCPServerStdio(
-            command="npx",
-            args=["-y", "@modelcontextprotocol/server-filesystem"],
-            env={"API_KEY": "your_key"},
-            tool_filter=create_static_tool_filter(
-                allowed_tool_names=["read_file", "list_directory"]
-            ),
-            cache_tools_list=True,
-        ),
-        # HTTP/Streamable HTTP transport for remote servers
-        MCPServerHTTP(
-            url="https://api.example.com/mcp",
-            headers={"Authorization": "Bearer your_token"},
-            streamable=True,
-            cache_tools_list=True,
-        ),
-        # SSE transport for real-time streaming
-        MCPServerSSE(
-            url="https://stream.example.com/mcp/sse",
-            headers={"Authorization": "Bearer your_token"},
-        ),
-    ]
-)
-```
-
 ### 🔧 **Advanced: MCPServerAdapter** (For Complex Scenarios)
 
 For advanced use cases requiring manual connection management, the `crewai-tools` library provides the `MCPServerAdapter` class.
@@ -112,14 +68,12 @@ uv pip install 'crewai-tools[mcp]'
 
 ## Quick Start: Simple DSL Integration
 
-The easiest way to integrate MCP servers is using the `mcps` field on your agents. You can use either string references or structured configurations.
-
-### Quick Start with String References
+The easiest way to integrate MCP servers is using the `mcps` field on your agents:
 
 ```python
 from crewai import Agent, Task, Crew
 
-# Create agent with MCP tools using string references
+# Create agent with MCP tools
 research_agent = Agent(
     role="Research Analyst",
     goal="Find and analyze information using advanced search tools",
@@ -142,53 +96,13 @@ crew = Crew(agents=[research_agent], tasks=[research_task])
 result = crew.kickoff()
 ```
 
-### Quick Start with Structured Configurations
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
-
-# Create agent with structured MCP configurations
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and analyze information using advanced search tools",
-    backstory="Expert researcher with access to multiple data sources",
-    mcps=[
-        # Local stdio server
-        MCPServerStdio(
-            command="python",
-            args=["local_server.py"],
-            env={"API_KEY": "your_key"},
-        ),
-        # Remote HTTP server
-        MCPServerHTTP(
-            url="https://api.research.com/mcp",
-            headers={"Authorization": "Bearer your_token"},
-        ),
-    ]
-)
-
-# Create task
-research_task = Task(
-    description="Research the latest developments in AI agent frameworks",
-    expected_output="Comprehensive research report with citations",
-    agent=research_agent
-)
-
-# Create and run crew
-crew = Crew(agents=[research_agent], tasks=[research_task])
-result = crew.kickoff()
-```
-
 That's it! The MCP tools are automatically discovered and available to your agent.
 
 ## MCP Reference Formats
 
-The `mcps` field supports both **string references** (for quick setup) and **structured configurations** (for full control). You can mix both formats in the same list.
+The `mcps` field supports various reference formats for maximum flexibility:
 
-### String-Based References
-
-#### External MCP Servers
+### External MCP Servers
 
 ```python
 mcps=[
@@ -203,7 +117,7 @@ mcps=[
 ]
 ```
 
-#### CrewAI AMP Marketplace
+### CrewAI AMP Marketplace
 
 ```python
 mcps=[
@@ -219,166 +133,17 @@ mcps=[
 ]
 ```
 
-### Structured Configurations
-
-#### Stdio Transport (Local Servers)
-
-Perfect for local MCP servers that run as processes:
-
-```python
-from crewai.mcp import MCPServerStdio
-from crewai.mcp.filters import create_static_tool_filter
-
-mcps=[
-    MCPServerStdio(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-filesystem"],
-        env={"API_KEY": "your_key"},
-        tool_filter=create_static_tool_filter(
-            allowed_tool_names=["read_file", "write_file"]
-        ),
-        cache_tools_list=True,
-    ),
-    # Python-based server
-    MCPServerStdio(
-        command="python",
-        args=["path/to/server.py"],
-        env={"UV_PYTHON": "3.12", "API_KEY": "your_key"},
-    ),
-]
-```
-
-#### HTTP/Streamable HTTP Transport (Remote Servers)
-
-For remote MCP servers over HTTP/HTTPS:
-
-```python
-from crewai.mcp import MCPServerHTTP
-
-mcps=[
-    # Streamable HTTP (default)
-    MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer your_token"},
-        streamable=True,
-        cache_tools_list=True,
-    ),
-    # Standard HTTP
-    MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer your_token"},
-        streamable=False,
-    ),
-]
-```
-
-#### SSE Transport (Real-Time Streaming)
-
-For remote servers using Server-Sent Events:
-
-```python
-from crewai.mcp import MCPServerSSE
-
-mcps=[
-    MCPServerSSE(
-        url="https://stream.example.com/mcp/sse",
-        headers={"Authorization": "Bearer your_token"},
-        cache_tools_list=True,
-    ),
-]
-```
-
 ### Mixed References
 
-You can combine string references and structured configurations:
-
 ```python
-from crewai.mcp import MCPServerStdio, MCPServerHTTP
-
 mcps=[
-    # String references
     "https://external-api.com/mcp",              # External server
+    "https://weather.service.com/mcp#forecast",  # Specific external tool
     "crewai-amp:financial-insights",             # AMP service
-
-    # Structured configurations
-    MCPServerStdio(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-filesystem"],
-    ),
-    MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer token"},
-    ),
+    "crewai-amp:data-analysis#sentiment_tool"    # Specific AMP tool
 ]
 ```
 
-### Tool Filtering
-
-Structured configurations support advanced tool filtering:
-
-```python
-from crewai.mcp import MCPServerStdio
-from crewai.mcp.filters import create_static_tool_filter, create_dynamic_tool_filter, ToolFilterContext
-
-# Static filtering (allow/block lists)
-static_filter = create_static_tool_filter(
-    allowed_tool_names=["read_file", "write_file"],
-    blocked_tool_names=["delete_file"],
-)
-
-# Dynamic filtering (context-aware)
-def dynamic_filter(context: ToolFilterContext, tool: dict) -> bool:
-    # Block dangerous tools for certain agent roles
-    if context.agent.role == "Code Reviewer":
-        if "delete" in tool.get("name", "").lower():
-            return False
-    return True
-
-mcps=[
-    MCPServerStdio(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-filesystem"],
-        tool_filter=static_filter,  # or dynamic_filter
-    ),
-]
-```
-
-## Configuration Parameters
-
-Each transport type supports specific configuration options:
-
-### MCPServerStdio Parameters
-
-- **`command`** (required): Command to execute (e.g., `"python"`, `"node"`, `"npx"`, `"uvx"`)
-- **`args`** (optional): List of command arguments (e.g., `["server.py"]` or `["-y", "@mcp/server"]`)
-- **`env`** (optional): Dictionary of environment variables to pass to the process
-- **`tool_filter`** (optional): Tool filter function for filtering available tools
-- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
-
-### MCPServerHTTP Parameters
-
-- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp"`)
-- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
-- **`streamable`** (optional): Whether to use streamable HTTP transport (default: `True`)
-- **`tool_filter`** (optional): Tool filter function for filtering available tools
-- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
-
-### MCPServerSSE Parameters
-
-- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp/sse"`)
-- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
-- **`tool_filter`** (optional): Tool filter function for filtering available tools
-- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
-
-### Common Parameters
-
-All transport types support:
-- **`tool_filter`**: Filter function to control which tools are available. Can be:
-  - `None` (default): All tools are available
-  - Static filter: Created with `create_static_tool_filter()` for allow/block lists
-  - Dynamic filter: Created with `create_dynamic_tool_filter()` for context-aware filtering
-- **`cache_tools_list`**: When `True`, caches the tool list after first discovery to improve performance on subsequent connections
-
 ## Key Features
 
 - 🔄 **Automatic Tool Discovery**: Tools are automatically discovered and integrated
@@ -387,47 +152,26 @@ All transport types support:
 - 🛡️ **Error Resilience**: Graceful handling of unavailable servers
 - ⏱️ **Timeout Protection**: Built-in timeouts prevent hanging connections
 - 📊 **Transparent Integration**: Works seamlessly with existing CrewAI features
-- 🔧 **Full Transport Support**: Stdio, HTTP/Streamable HTTP, and SSE transports
-- 🎯 **Advanced Filtering**: Static and dynamic tool filtering capabilities
-- 🔐 **Flexible Authentication**: Support for headers, environment variables, and query parameters
 
 ## Error Handling
 
-The MCP DSL integration is designed to be resilient and handles failures gracefully:
+The MCP DSL integration is designed to be resilient:
 
 ```python
-from crewai import Agent
-from crewai.mcp import MCPServerStdio, MCPServerHTTP
-
 agent = Agent(
     role="Resilient Agent",
     goal="Continue working despite server issues",
     backstory="Agent that handles failures gracefully",
     mcps=[
-        # String references
         "https://reliable-server.com/mcp",        # Will work
         "https://unreachable-server.com/mcp",     # Will be skipped gracefully
-        "crewai-amp:working-service",             # Will work
-
-        # Structured configs
-        MCPServerStdio(
-            command="python",
-            args=["reliable_server.py"],          # Will work
-        ),
-        MCPServerHTTP(
-            url="https://slow-server.com/mcp",     # Will timeout gracefully
-        ),
+        "https://slow-server.com/mcp",            # Will timeout gracefully
+        "crewai-amp:working-service"              # Will work
     ]
 )
 # Agent will use tools from working servers and log warnings for failing ones
 ```
 
-All connection errors are handled gracefully:
-- **Connection failures**: Logged as warnings, agent continues with available tools
-- **Timeout errors**: Connections timeout after 30 seconds (configurable)
-- **Authentication errors**: Logged clearly for debugging
-- **Invalid configurations**: Validation errors are raised at agent creation time
-
 ## Advanced: MCPServerAdapter
 
 For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server.

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/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/agent/core.py

@@ -40,16 +40,6 @@ from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
 from crewai.lite_agent import LiteAgent
 from crewai.llms.base_llm import BaseLLM
-from crewai.mcp import (
-    MCPClient,
-    MCPServerConfig,
-    MCPServerHTTP,
-    MCPServerSSE,
-    MCPServerStdio,
-)
-from crewai.mcp.transports.http import HTTPTransport
-from crewai.mcp.transports.sse import SSETransport
-from crewai.mcp.transports.stdio import StdioTransport
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.fingerprint import Fingerprint
@@ -118,8 +108,6 @@ 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",
@@ -538,15 +526,6 @@ class Agent(BaseAgent):
             self,
             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
 
     def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> Any:
@@ -670,70 +649,30 @@ class Agent(BaseAgent):
             self._logger.log("error", f"Error getting platform tools: {e!s}")
             return []
 
-    def get_mcp_tools(self, mcps: list[str | MCPServerConfig]) -> list[BaseTool]:
-        """Convert MCP server references/configs to CrewAI tools.
-
-        Supports both string references (backwards compatible) and structured
-        configuration objects (MCPServerStdio, MCPServerHTTP, MCPServerSSE).
-
-        Args:
-            mcps: List of MCP server references (strings) or configurations.
-
-        Returns:
-            List of BaseTool instances from MCP servers.
-        """
+    def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]:
+        """Convert MCP server references to CrewAI tools."""
         all_tools = []
-        clients = []
 
-        for mcp_config in mcps:
-            if isinstance(mcp_config, str):
-                tools = self._get_mcp_tools_from_string(mcp_config)
-            else:
-                tools, client = self._get_native_mcp_tools(mcp_config)
-                if client:
-                    clients.append(client)
+        for mcp_ref in mcps:
+            try:
+                if mcp_ref.startswith("crewai-amp:"):
+                    tools = self._get_amp_mcp_tools(mcp_ref)
+                elif mcp_ref.startswith("https://"):
+                    tools = self._get_external_mcp_tools(mcp_ref)
+                else:
+                    continue
 
-            all_tools.extend(tools)
+                all_tools.extend(tools)
+                self._logger.log(
+                    "info", f"Successfully loaded {len(tools)} tools from {mcp_ref}"
+                )
+
+            except Exception as e:
+                self._logger.log("warning", f"Skipping MCP {mcp_ref} due to error: {e}")
+                continue
 
-        # Store clients for cleanup
-        self._mcp_clients.extend(clients)
         return all_tools
 
-    def _cleanup_mcp_clients(self) -> None:
-        """Cleanup MCP client connections after task execution."""
-        if not self._mcp_clients:
-            return
-
-        async def _disconnect_all() -> None:
-            for client in self._mcp_clients:
-                if client and hasattr(client, "connected") and client.connected:
-                    await client.disconnect()
-
-        try:
-            asyncio.run(_disconnect_all())
-        except Exception as e:
-            self._logger.log("error", f"Error during MCP client cleanup: {e}")
-        finally:
-            self._mcp_clients.clear()
-
-    def _get_mcp_tools_from_string(self, mcp_ref: str) -> list[BaseTool]:
-        """Get tools from legacy string-based MCP references.
-
-        This method maintains backwards compatibility with string-based
-        MCP references (https://... and crewai-amp:...).
-
-        Args:
-            mcp_ref: String reference to MCP server.
-
-        Returns:
-            List of BaseTool instances.
-        """
-        if mcp_ref.startswith("crewai-amp:"):
-            return self._get_amp_mcp_tools(mcp_ref)
-        if mcp_ref.startswith("https://"):
-            return self._get_external_mcp_tools(mcp_ref)
-        return []
-
     def _get_external_mcp_tools(self, mcp_ref: str) -> list[BaseTool]:
         """Get tools from external HTTPS MCP server with graceful error handling."""
         from crewai.tools.mcp_tool_wrapper import MCPToolWrapper
@@ -792,164 +731,6 @@ class Agent(BaseAgent):
             )
             return []
 
-    def _get_native_mcp_tools(
-        self, mcp_config: MCPServerConfig
-    ) -> tuple[list[BaseTool], Any | None]:
-        """Get tools from MCP server using structured configuration.
-
-        This method creates an MCP client based on the configuration type,
-        connects to the server, discovers tools, applies filtering, and
-        returns wrapped tools along with the client instance for cleanup.
-
-        Args:
-            mcp_config: MCP server configuration (MCPServerStdio, MCPServerHTTP, or MCPServerSSE).
-
-        Returns:
-            Tuple of (list of BaseTool instances, MCPClient instance for cleanup).
-        """
-        from crewai.tools.base_tool import BaseTool
-        from crewai.tools.mcp_native_tool import MCPNativeTool
-
-        if isinstance(mcp_config, MCPServerStdio):
-            transport = StdioTransport(
-                command=mcp_config.command,
-                args=mcp_config.args,
-                env=mcp_config.env,
-            )
-            server_name = f"{mcp_config.command}_{'_'.join(mcp_config.args)}"
-        elif isinstance(mcp_config, MCPServerHTTP):
-            transport = HTTPTransport(
-                url=mcp_config.url,
-                headers=mcp_config.headers,
-                streamable=mcp_config.streamable,
-            )
-            server_name = self._extract_server_name(mcp_config.url)
-        elif isinstance(mcp_config, MCPServerSSE):
-            transport = SSETransport(
-                url=mcp_config.url,
-                headers=mcp_config.headers,
-            )
-            server_name = self._extract_server_name(mcp_config.url)
-        else:
-            raise ValueError(f"Unsupported MCP server config type: {type(mcp_config)}")
-
-        client = MCPClient(
-            transport=transport,
-            cache_tools_list=mcp_config.cache_tools_list,
-        )
-
-        async def _setup_client_and_list_tools() -> list[dict[str, Any]]:
-            """Async helper to connect and list tools in same event loop."""
-
-            try:
-                if not client.connected:
-                    await client.connect()
-
-                tools_list = await client.list_tools()
-
-                try:
-                    await client.disconnect()
-                    # Small delay to allow background tasks to finish cleanup
-                    # This helps prevent "cancel scope in different task" errors
-                    # when asyncio.run() closes the event loop
-                    await asyncio.sleep(0.1)
-                except Exception as e:
-                    self._logger.log("error", f"Error during disconnect: {e}")
-
-                return tools_list
-            except Exception as e:
-                if client.connected:
-                    await client.disconnect()
-                    await asyncio.sleep(0.1)
-                raise RuntimeError(
-                    f"Error during setup client and list tools: {e}"
-                ) from e
-
-        try:
-            try:
-                asyncio.get_running_loop()
-                import concurrent.futures
-
-                with concurrent.futures.ThreadPoolExecutor() as executor:
-                    future = executor.submit(
-                        asyncio.run, _setup_client_and_list_tools()
-                    )
-                    tools_list = future.result()
-            except RuntimeError:
-                try:
-                    tools_list = asyncio.run(_setup_client_and_list_tools())
-                except RuntimeError as e:
-                    error_msg = str(e).lower()
-                    if "cancel scope" in error_msg or "task" in error_msg:
-                        raise ConnectionError(
-                            "MCP connection failed due to event loop cleanup issues. "
-                            "This may be due to authentication errors or server unavailability."
-                        ) from e
-                except asyncio.CancelledError as e:
-                    raise ConnectionError(
-                        "MCP connection was cancelled. This may indicate an authentication "
-                        "error or server unavailability."
-                    ) from e
-
-            if mcp_config.tool_filter:
-                filtered_tools = []
-                for tool in tools_list:
-                    if callable(mcp_config.tool_filter):
-                        try:
-                            from crewai.mcp.filters import ToolFilterContext
-
-                            context = ToolFilterContext(
-                                agent=self,
-                                server_name=server_name,
-                                run_context=None,
-                            )
-                            if mcp_config.tool_filter(context, tool):
-                                filtered_tools.append(tool)
-                        except (TypeError, AttributeError):
-                            if mcp_config.tool_filter(tool):
-                                filtered_tools.append(tool)
-                    else:
-                        # Not callable - include tool
-                        filtered_tools.append(tool)
-                tools_list = filtered_tools
-
-            tools = []
-            for tool_def in tools_list:
-                tool_name = tool_def.get("name", "")
-                if not tool_name:
-                    continue
-
-                # Convert inputSchema to Pydantic model if present
-                args_schema = None
-                if tool_def.get("inputSchema"):
-                    args_schema = self._json_schema_to_pydantic(
-                        tool_name, tool_def["inputSchema"]
-                    )
-
-                tool_schema = {
-                    "description": tool_def.get("description", ""),
-                    "args_schema": args_schema,
-                }
-
-                try:
-                    native_tool = MCPNativeTool(
-                        mcp_client=client,
-                        tool_name=tool_name,
-                        tool_schema=tool_schema,
-                        server_name=server_name,
-                    )
-                    tools.append(native_tool)
-                except Exception as e:
-                    self._logger.log("error", f"Failed to create native MCP tool: {e}")
-                    continue
-
-            return cast(list[BaseTool], tools), client
-        except Exception as e:
-            if client.connected:
-                asyncio.run(client.disconnect())
-
-            raise RuntimeError(f"Failed to get native MCP tools: {e}") from e
-
     def _get_amp_mcp_tools(self, amp_ref: str) -> list[BaseTool]:
         """Get tools from CrewAI AMP MCP marketplace."""
         # Parse: "crewai-amp:mcp-name" or "crewai-amp:mcp-name#tool_name"
@@ -1348,15 +1129,6 @@ 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(

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

@@ -25,7 +25,6 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.mcp.config import MCPServerConfig
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
@@ -195,7 +194,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default=None,
         description="List of applications or application/action combinations that the agent can access through CrewAI Platform. Can contain app names (e.g., 'gmail') or specific actions (e.g., 'gmail/send_email')",
     )
-    mcps: list[str | MCPServerConfig] | None = Field(
+    mcps: list[str] | None = Field(
         default=None,
         description="List of MCP server references. Supports 'https://server.com/path' for external servers and 'crewai-amp:mcp-name' for AMP marketplace. Use '#tool_name' suffix for specific tools.",
     )
@@ -254,36 +253,20 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
 
     @field_validator("mcps")
     @classmethod
-    def validate_mcps(
-        cls, mcps: list[str | MCPServerConfig] | None
-    ) -> list[str | MCPServerConfig] | None:
-        """Validate MCP server references and configurations.
-
-        Supports both string references (for backwards compatibility) and
-        structured configuration objects (MCPServerStdio, MCPServerHTTP, MCPServerSSE).
-        """
+    def validate_mcps(cls, mcps: list[str] | None) -> list[str] | None:
         if not mcps:
             return mcps
 
         validated_mcps = []
         for mcp in mcps:
-            if isinstance(mcp, str):
-                if mcp.startswith(("https://", "crewai-amp:")):
-                    validated_mcps.append(mcp)
-                else:
-                    raise ValueError(
-                        f"Invalid MCP reference: {mcp}. "
-                        "String references must start with 'https://' or 'crewai-amp:'"
-                    )
-
-            elif isinstance(mcp, (MCPServerConfig)):
+            if mcp.startswith(("https://", "crewai-amp:")):
                 validated_mcps.append(mcp)
             else:
                 raise ValueError(
-                    f"Invalid MCP configuration: {type(mcp)}. "
-                    "Must be a string reference or MCPServerConfig instance."
+                    f"Invalid MCP reference: {mcp}. Must start with 'https://' or 'crewai-amp:'"
                 )
-        return validated_mcps
+
+        return list(set(validated_mcps))
 
     @model_validator(mode="after")
     def validate_and_set_attributes(self) -> Self:
@@ -360,7 +343,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         """Get platform tools for the specified list of applications and/or application/action combinations."""
 
     @abstractmethod
-    def get_mcp_tools(self, mcps: list[str | MCPServerConfig]) -> list[BaseTool]:
+    def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]:
         """Get MCP tools for the specified list of MCP server references."""
 
     def copy(self) -> Self:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"

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

@@ -214,7 +214,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                     )
-                    break
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
@@ -227,7 +226,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_agent=self.agent,
                     response_model=self.response_model,
                 )
-                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
@@ -259,11 +258,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         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/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/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/events/__init__.py

@@ -16,6 +16,7 @@ from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.depends import Depends
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.handler_graph import CircularDependencyError
+
 from crewai.events.types.crew_events import (
     CrewKickoffCompletedEvent,
     CrewKickoffFailedEvent,
@@ -60,14 +61,6 @@ from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
-from crewai.events.types.mcp_events import (
-    MCPConnectionCompletedEvent,
-    MCPConnectionFailedEvent,
-    MCPConnectionStartedEvent,
-    MCPToolExecutionCompletedEvent,
-    MCPToolExecutionFailedEvent,
-    MCPToolExecutionStartedEvent,
-)
 from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
     MemoryQueryFailedEvent,
@@ -160,12 +153,6 @@ __all__ = [
     "LiteAgentExecutionCompletedEvent",
     "LiteAgentExecutionErrorEvent",
     "LiteAgentExecutionStartedEvent",
-    "MCPConnectionCompletedEvent",
-    "MCPConnectionFailedEvent",
-    "MCPConnectionStartedEvent",
-    "MCPToolExecutionCompletedEvent",
-    "MCPToolExecutionFailedEvent",
-    "MCPToolExecutionStartedEvent",
     "MemoryQueryCompletedEvent",
     "MemoryQueryFailedEvent",
     "MemoryQueryStartedEvent",

lib/crewai/src/crewai/events/event_listener.py

@@ -65,14 +65,6 @@ from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
     AgentLogsStartedEvent,
 )
-from crewai.events.types.mcp_events import (
-    MCPConnectionCompletedEvent,
-    MCPConnectionFailedEvent,
-    MCPConnectionStartedEvent,
-    MCPToolExecutionCompletedEvent,
-    MCPToolExecutionFailedEvent,
-    MCPToolExecutionStartedEvent,
-)
 from crewai.events.types.reasoning_events import (
     AgentReasoningCompletedEvent,
     AgentReasoningFailedEvent,
@@ -623,67 +615,5 @@ class EventListener(BaseEventListener):
                 event.total_turns,
             )
 
-        # ----------- MCP EVENTS -----------
-
-        @crewai_event_bus.on(MCPConnectionStartedEvent)
-        def on_mcp_connection_started(source, event: MCPConnectionStartedEvent):
-            self.formatter.handle_mcp_connection_started(
-                event.server_name,
-                event.server_url,
-                event.transport_type,
-                event.is_reconnect,
-                event.connect_timeout,
-            )
-
-        @crewai_event_bus.on(MCPConnectionCompletedEvent)
-        def on_mcp_connection_completed(source, event: MCPConnectionCompletedEvent):
-            self.formatter.handle_mcp_connection_completed(
-                event.server_name,
-                event.server_url,
-                event.transport_type,
-                event.connection_duration_ms,
-                event.is_reconnect,
-            )
-
-        @crewai_event_bus.on(MCPConnectionFailedEvent)
-        def on_mcp_connection_failed(source, event: MCPConnectionFailedEvent):
-            self.formatter.handle_mcp_connection_failed(
-                event.server_name,
-                event.server_url,
-                event.transport_type,
-                event.error,
-                event.error_type,
-            )
-
-        @crewai_event_bus.on(MCPToolExecutionStartedEvent)
-        def on_mcp_tool_execution_started(source, event: MCPToolExecutionStartedEvent):
-            self.formatter.handle_mcp_tool_execution_started(
-                event.server_name,
-                event.tool_name,
-                event.tool_args,
-            )
-
-        @crewai_event_bus.on(MCPToolExecutionCompletedEvent)
-        def on_mcp_tool_execution_completed(
-            source, event: MCPToolExecutionCompletedEvent
-        ):
-            self.formatter.handle_mcp_tool_execution_completed(
-                event.server_name,
-                event.tool_name,
-                event.tool_args,
-                event.result,
-                event.execution_duration_ms,
-            )
-
-        @crewai_event_bus.on(MCPToolExecutionFailedEvent)
-        def on_mcp_tool_execution_failed(source, event: MCPToolExecutionFailedEvent):
-            self.formatter.handle_mcp_tool_execution_failed(
-                event.server_name,
-                event.tool_name,
-                event.tool_args,
-                event.error,
-                event.error_type,
-            )
-
 
 event_listener = EventListener()

lib/crewai/src/crewai/events/event_types.py

@@ -40,14 +40,6 @@ from crewai.events.types.llm_guardrail_events import (
     LLMGuardrailCompletedEvent,
     LLMGuardrailStartedEvent,
 )
-from crewai.events.types.mcp_events import (
-    MCPConnectionCompletedEvent,
-    MCPConnectionFailedEvent,
-    MCPConnectionStartedEvent,
-    MCPToolExecutionCompletedEvent,
-    MCPToolExecutionFailedEvent,
-    MCPToolExecutionStartedEvent,
-)
 from crewai.events.types.memory_events import (
     MemoryQueryCompletedEvent,
     MemoryQueryFailedEvent,
@@ -123,10 +115,4 @@ EventTypes = (
     | MemoryQueryFailedEvent
     | MemoryRetrievalStartedEvent
     | MemoryRetrievalCompletedEvent
-    | MCPConnectionStartedEvent
-    | MCPConnectionCompletedEvent
-    | MCPConnectionFailedEvent
-    | MCPToolExecutionStartedEvent
-    | MCPToolExecutionCompletedEvent
-    | MCPToolExecutionFailedEvent
 )

lib/crewai/src/crewai/events/types/mcp_events.py

@@ -1,85 +0,0 @@
-from datetime import datetime
-from typing import Any
-
-from crewai.events.base_events import BaseEvent
-
-
-class MCPEvent(BaseEvent):
-    """Base event for MCP operations."""
-
-    server_name: str
-    server_url: str | None = None
-    transport_type: str | None = None  # "stdio", "http", "sse"
-    agent_id: str | None = None
-    agent_role: str | None = None
-    from_agent: Any | None = None
-    from_task: Any | None = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        self._set_agent_params(data)
-        self._set_task_params(data)
-
-
-class MCPConnectionStartedEvent(MCPEvent):
-    """Event emitted when starting to connect to an MCP server."""
-
-    type: str = "mcp_connection_started"
-    connect_timeout: int | None = None
-    is_reconnect: bool = (
-        False  # True if this is a reconnection, False for first connection
-    )
-
-
-class MCPConnectionCompletedEvent(MCPEvent):
-    """Event emitted when successfully connected to an MCP server."""
-
-    type: str = "mcp_connection_completed"
-    started_at: datetime | None = None
-    completed_at: datetime | None = None
-    connection_duration_ms: float | None = None
-    is_reconnect: bool = (
-        False  # True if this was a reconnection, False for first connection
-    )
-
-
-class MCPConnectionFailedEvent(MCPEvent):
-    """Event emitted when connection to an MCP server fails."""
-
-    type: str = "mcp_connection_failed"
-    error: str
-    error_type: str | None = None  # "timeout", "authentication", "network", etc.
-    started_at: datetime | None = None
-    failed_at: datetime | None = None
-
-
-class MCPToolExecutionStartedEvent(MCPEvent):
-    """Event emitted when starting to execute an MCP tool."""
-
-    type: str = "mcp_tool_execution_started"
-    tool_name: str
-    tool_args: dict[str, Any] | None = None
-
-
-class MCPToolExecutionCompletedEvent(MCPEvent):
-    """Event emitted when MCP tool execution completes."""
-
-    type: str = "mcp_tool_execution_completed"
-    tool_name: str
-    tool_args: dict[str, Any] | None = None
-    result: Any | None = None
-    started_at: datetime | None = None
-    completed_at: datetime | None = None
-    execution_duration_ms: float | None = None
-
-
-class MCPToolExecutionFailedEvent(MCPEvent):
-    """Event emitted when MCP tool execution fails."""
-
-    type: str = "mcp_tool_execution_failed"
-    tool_name: str
-    tool_args: dict[str, Any] | None = None
-    error: str
-    error_type: str | None = None  # "timeout", "validation", "server_error", etc.
-    started_at: datetime | None = None
-    failed_at: datetime | None = None

lib/crewai/src/crewai/events/utils/console_formatter.py

@@ -2248,203 +2248,3 @@ class ConsoleFormatter:
 
         self.current_a2a_conversation_branch = None
         self.current_a2a_turn_count = 0
-
-    # ----------- MCP EVENTS -----------
-
-    def handle_mcp_connection_started(
-        self,
-        server_name: str,
-        server_url: str | None = None,
-        transport_type: str | None = None,
-        is_reconnect: bool = False,
-        connect_timeout: int | None = None,
-    ) -> None:
-        """Handle MCP connection started event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        reconnect_text = " (Reconnecting)" if is_reconnect else ""
-        content.append(f"MCP Connection Started{reconnect_text}\n\n", style="cyan bold")
-        content.append("Server: ", style="white")
-        content.append(f"{server_name}\n", style="cyan")
-
-        if server_url:
-            content.append("URL: ", style="white")
-            content.append(f"{server_url}\n", style="cyan dim")
-
-        if transport_type:
-            content.append("Transport: ", style="white")
-            content.append(f"{transport_type}\n", style="cyan")
-
-        if connect_timeout:
-            content.append("Timeout: ", style="white")
-            content.append(f"{connect_timeout}s\n", style="cyan")
-
-        panel = self.create_panel(content, "🔌 MCP Connection", "cyan")
-        self.print(panel)
-        self.print()
-
-    def handle_mcp_connection_completed(
-        self,
-        server_name: str,
-        server_url: str | None = None,
-        transport_type: str | None = None,
-        connection_duration_ms: float | None = None,
-        is_reconnect: bool = False,
-    ) -> None:
-        """Handle MCP connection completed event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        reconnect_text = " (Reconnected)" if is_reconnect else ""
-        content.append(
-            f"MCP Connection Completed{reconnect_text}\n\n", style="green bold"
-        )
-        content.append("Server: ", style="white")
-        content.append(f"{server_name}\n", style="green")
-
-        if server_url:
-            content.append("URL: ", style="white")
-            content.append(f"{server_url}\n", style="green dim")
-
-        if transport_type:
-            content.append("Transport: ", style="white")
-            content.append(f"{transport_type}\n", style="green")
-
-        if connection_duration_ms is not None:
-            content.append("Duration: ", style="white")
-            content.append(f"{connection_duration_ms:.2f}ms\n", style="green")
-
-        panel = self.create_panel(content, "✅ MCP Connected", "green")
-        self.print(panel)
-        self.print()
-
-    def handle_mcp_connection_failed(
-        self,
-        server_name: str,
-        server_url: str | None = None,
-        transport_type: str | None = None,
-        error: str = "",
-        error_type: str | None = None,
-    ) -> None:
-        """Handle MCP connection failed event."""
-        if not self.verbose:
-            return
-
-        content = Text()
-        content.append("MCP Connection Failed\n\n", style="red bold")
-        content.append("Server: ", style="white")
-        content.append(f"{server_name}\n", style="red")
-
-        if server_url:
-            content.append("URL: ", style="white")
-            content.append(f"{server_url}\n", style="red dim")
-
-        if transport_type:
-            content.append("Transport: ", style="white")
-            content.append(f"{transport_type}\n", style="red")
-
-        if error_type:
-            content.append("Error Type: ", style="white")
-            content.append(f"{error_type}\n", style="red")
-
-        if error:
-            content.append("\nError: ", style="white bold")
-            error_preview = error[:500] + "..." if len(error) > 500 else error
-            content.append(f"{error_preview}\n", style="red")
-
-        panel = self.create_panel(content, "❌ MCP Connection Failed", "red")
-        self.print(panel)
-        self.print()
-
-    def handle_mcp_tool_execution_started(
-        self,
-        server_name: str,
-        tool_name: str,
-        tool_args: dict[str, Any] | None = None,
-    ) -> None:
-        """Handle MCP tool execution started event."""
-        if not self.verbose:
-            return
-
-        content = self.create_status_content(
-            "MCP Tool Execution Started",
-            tool_name,
-            "yellow",
-            tool_args=tool_args or {},
-            Server=server_name,
-        )
-
-        panel = self.create_panel(content, "🔧 MCP Tool", "yellow")
-        self.print(panel)
-        self.print()
-
-    def handle_mcp_tool_execution_completed(
-        self,
-        server_name: str,
-        tool_name: str,
-        tool_args: dict[str, Any] | None = None,
-        result: Any | None = None,
-        execution_duration_ms: float | None = None,
-    ) -> None:
-        """Handle MCP tool execution completed event."""
-        if not self.verbose:
-            return
-
-        content = self.create_status_content(
-            "MCP Tool Execution Completed",
-            tool_name,
-            "green",
-            tool_args=tool_args or {},
-            Server=server_name,
-        )
-
-        if execution_duration_ms is not None:
-            content.append("Duration: ", style="white")
-            content.append(f"{execution_duration_ms:.2f}ms\n", style="green")
-
-        if result is not None:
-            result_str = str(result)
-            if len(result_str) > 500:
-                result_str = result_str[:497] + "..."
-            content.append("\nResult: ", style="white bold")
-            content.append(f"{result_str}\n", style="green")
-
-        panel = self.create_panel(content, "✅ MCP Tool Completed", "green")
-        self.print(panel)
-        self.print()
-
-    def handle_mcp_tool_execution_failed(
-        self,
-        server_name: str,
-        tool_name: str,
-        tool_args: dict[str, Any] | None = None,
-        error: str = "",
-        error_type: str | None = None,
-    ) -> None:
-        """Handle MCP tool execution failed event."""
-        if not self.verbose:
-            return
-
-        content = self.create_status_content(
-            "MCP Tool Execution Failed",
-            tool_name,
-            "red",
-            tool_args=tool_args or {},
-            Server=server_name,
-        )
-
-        if error_type:
-            content.append("Error Type: ", style="white")
-            content.append(f"{error_type}\n", style="red")
-
-        if error:
-            content.append("\nError: ", style="white bold")
-            error_preview = error[:500] + "..." if len(error) > 500 else error
-            content.append(f"{error_preview}\n", style="red")
-
-        panel = self.create_panel(content, "❌ MCP Tool Failed", "red")
-        self.print(panel)
-        self.print()

lib/crewai/src/crewai/flow/flow.py

@@ -15,6 +15,7 @@ import logging
 from typing import (
     Any,
     ClassVar,
+    Final,
     Generic,
     Literal,
     ParamSpec,
@@ -44,7 +45,7 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from crewai.flow.constants import AND_CONDITION, OR_CONDITION
+from crewai.flow.visualization import build_flow_structure, render_interactive
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -57,16 +58,18 @@ from crewai.flow.flow_wrappers import (
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey
 from crewai.flow.utils import (
-    _extract_all_methods,
-    _normalize_condition,
     get_possible_return_constants,
     is_flow_condition_dict,
+    is_flow_condition_list,
     is_flow_method,
     is_flow_method_callable,
     is_flow_method_name,
     is_simple_flow_condition,
+    _extract_all_methods,
+    _extract_all_methods_recursive,
+    _normalize_condition,
 )
-from crewai.flow.visualization import build_flow_structure, render_interactive
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
 from crewai.utilities.printer import Printer, PrinterColor
 
 
@@ -428,8 +431,6 @@ class FlowMeta(type):
                         possible_returns = get_possible_return_constants(attr_value)
                         if possible_returns:
                             router_paths[attr_name] = possible_returns
-                        else:
-                            router_paths[attr_name] = []
 
         cls._start_methods = start_methods  # type: ignore[attr-defined]
         cls._listeners = listeners  # type: ignore[attr-defined]
@@ -494,7 +495,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             or should_auto_collect_first_time_traces()
         ):
             trace_listener = TraceCollectionListener()
-            trace_listener.setup_listeners(crewai_event_bus)
+            trace_listener.setup_listeners(crewai_event_bus)  # type: ignore[no-untyped-call]
         # Apply any additional kwargs
         if kwargs:
             self._initialize_state(kwargs)
@@ -600,26 +601,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
 
     def _copy_state(self) -> T:
-        """Create a copy of the current state.
-
-        Returns:
-            A copy of the current state
-        """
-        if isinstance(self._state, BaseModel):
-            try:
-                return self._state.model_copy(deep=True)
-            except (TypeError, AttributeError):
-                try:
-                    state_dict = self._state.model_dump()
-                    model_class = type(self._state)
-                    return model_class(**state_dict)
-                except Exception:
-                    return self._state.model_copy(deep=False)
-        else:
-            try:
-                return copy.deepcopy(self._state)
-            except (TypeError, AttributeError):
-                return cast(T, self._state.copy())
+        return copy.deepcopy(self._state)
 
     @property
     def state(self) -> T:
@@ -944,8 +926,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 trace_listener = TraceCollectionListener()
                 if trace_listener.batch_manager.batch_owner_type == "flow":
                     if trace_listener.first_time_handler.is_first_time:
-                        trace_listener.first_time_handler.mark_events_collected()
-                        trace_listener.first_time_handler.handle_execution_completion()
+                        trace_listener.first_time_handler.mark_events_collected()  # type: ignore[no-untyped-call]
+                        trace_listener.first_time_handler.handle_execution_completion()  # type: ignore[no-untyped-call]
                     else:
                         trace_listener.batch_manager.finalize_batch()
 

lib/crewai/src/crewai/flow/types.py

@@ -21,7 +21,6 @@ P = ParamSpec("P")
 R = TypeVar("R", covariant=True)
 
 FlowMethodName = NewType("FlowMethodName", str)
-FlowRouteName = NewType("FlowRouteName", str)
 PendingListenerKey = NewType(
     "PendingListenerKey",
     Annotated[str, "nested flow conditions use 'listener_name:object_id'"],

lib/crewai/src/crewai/flow/utils.py

@@ -19,11 +19,11 @@ import ast
 from collections import defaultdict, deque
 import inspect
 import textwrap
-from typing import TYPE_CHECKING, Any
+from typing import Any, TYPE_CHECKING
 
 from typing_extensions import TypeIs
 
-from crewai.flow.constants import AND_CONDITION, OR_CONDITION
+from crewai.flow.constants import OR_CONDITION, AND_CONDITION
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -33,7 +33,6 @@ from crewai.flow.flow_wrappers import (
 from crewai.flow.types import FlowMethodCallable, FlowMethodName
 from crewai.utilities.printer import Printer
 
-
 if TYPE_CHECKING:
     from crewai.flow.flow import Flow
 
@@ -41,22 +40,6 @@ _printer = Printer()
 
 
 def get_possible_return_constants(function: Any) -> list[str] | None:
-    """Extract possible string return values from a function using AST parsing.
-
-    This function analyzes the source code of a router method to identify
-    all possible string values it might return. It handles:
-    - Direct string literals: return "value"
-    - Variable assignments: x = "value"; return x
-    - Dictionary lookups: d = {"k": "v"}; return d[key]
-    - Conditional returns: return "a" if cond else "b"
-    - State attributes: return self.state.attr (infers from class context)
-
-    Args:
-        function: The function to analyze.
-
-    Returns:
-        List of possible string return values, or None if analysis fails.
-    """
     try:
         source = inspect.getsource(function)
     except OSError:
@@ -99,7 +82,6 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
     return_values: set[str] = set()
     dict_definitions: dict[str, list[str]] = {}
     variable_values: dict[str, list[str]] = {}
-    state_attribute_values: dict[str, list[str]] = {}
 
     def extract_string_constants(node: ast.expr) -> list[str]:
         """Recursively extract all string constants from an AST node."""
@@ -109,17 +91,6 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
         elif isinstance(node, ast.IfExp):
             strings.extend(extract_string_constants(node.body))
             strings.extend(extract_string_constants(node.orelse))
-        elif isinstance(node, ast.Call):
-            if (
-                isinstance(node.func, ast.Attribute)
-                and node.func.attr == "get"
-                and len(node.args) >= 2
-            ):
-                default_arg = node.args[1]
-                if isinstance(default_arg, ast.Constant) and isinstance(
-                    default_arg.value, str
-                ):
-                    strings.append(default_arg.value)
         return strings
 
     class VariableAssignmentVisitor(ast.NodeVisitor):
@@ -153,22 +124,6 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
 
             self.generic_visit(node)
 
-    def get_attribute_chain(node: ast.expr) -> str | None:
-        """Extract the full attribute chain from an AST node.
-
-        Examples:
-            self.state.run_type -> "self.state.run_type"
-            x.y.z -> "x.y.z"
-            simple_var -> "simple_var"
-        """
-        if isinstance(node, ast.Name):
-            return node.id
-        if isinstance(node, ast.Attribute):
-            base = get_attribute_chain(node.value)
-            if base:
-                return f"{base}.{node.attr}"
-        return None
-
     class ReturnVisitor(ast.NodeVisitor):
         def visit_Return(self, node: ast.Return) -> None:
             if (
@@ -184,94 +139,21 @@ def get_possible_return_constants(function: Any) -> list[str] | None:
                         for v in dict_definitions[var_name_dict]:
                             return_values.add(v)
             elif node.value:
-                var_name_ret = get_attribute_chain(node.value)
+                var_name_ret: str | None = None
+                if isinstance(node.value, ast.Name):
+                    var_name_ret = node.value.id
+                elif isinstance(node.value, ast.Attribute):
+                    var_name_ret = f"{node.value.value.id if isinstance(node.value.value, ast.Name) else '_'}.{node.value.attr}"
 
                 if var_name_ret and var_name_ret in variable_values:
                     for v in variable_values[var_name_ret]:
                         return_values.add(v)
-                elif var_name_ret and var_name_ret in state_attribute_values:
-                    for v in state_attribute_values[var_name_ret]:
-                        return_values.add(v)
 
             self.generic_visit(node)
 
         def visit_If(self, node: ast.If) -> None:
             self.generic_visit(node)
 
-    # Try to get the class context to infer state attribute values
-    try:
-        if hasattr(function, "__self__"):
-            # Method is bound, get the class
-            class_obj = function.__self__.__class__
-        elif hasattr(function, "__qualname__") and "." in function.__qualname__:
-            # Method is unbound but we can try to get class from module
-            class_name = function.__qualname__.rsplit(".", 1)[0]
-            if hasattr(function, "__globals__"):
-                class_obj = function.__globals__.get(class_name)
-            else:
-                class_obj = None
-        else:
-            class_obj = None
-
-        if class_obj is not None:
-            try:
-                class_source = inspect.getsource(class_obj)
-                class_source = textwrap.dedent(class_source)
-                class_ast = ast.parse(class_source)
-
-                # Look for comparisons and assignments involving state attributes
-                class StateAttributeVisitor(ast.NodeVisitor):
-                    def visit_Compare(self, node: ast.Compare) -> None:
-                        """Find comparisons like: self.state.attr == "value" """
-                        left_attr = get_attribute_chain(node.left)
-
-                        if left_attr:
-                            for comparator in node.comparators:
-                                if isinstance(comparator, ast.Constant) and isinstance(
-                                    comparator.value, str
-                                ):
-                                    if left_attr not in state_attribute_values:
-                                        state_attribute_values[left_attr] = []
-                                    if (
-                                        comparator.value
-                                        not in state_attribute_values[left_attr]
-                                    ):
-                                        state_attribute_values[left_attr].append(
-                                            comparator.value
-                                        )
-
-                        # Also check right side
-                        for comparator in node.comparators:
-                            right_attr = get_attribute_chain(comparator)
-                            if (
-                                right_attr
-                                and isinstance(node.left, ast.Constant)
-                                and isinstance(node.left.value, str)
-                            ):
-                                if right_attr not in state_attribute_values:
-                                    state_attribute_values[right_attr] = []
-                                if (
-                                    node.left.value
-                                    not in state_attribute_values[right_attr]
-                                ):
-                                    state_attribute_values[right_attr].append(
-                                        node.left.value
-                                    )
-
-                        self.generic_visit(node)
-
-                StateAttributeVisitor().visit(class_ast)
-            except Exception as e:
-                _printer.print(
-                    f"Could not analyze class context for {function.__name__}: {e}",
-                    color="yellow",
-                )
-    except Exception as e:
-        _printer.print(
-            f"Could not introspect class for {function.__name__}: {e}",
-            color="yellow",
-        )
-
     VariableAssignmentVisitor().visit(code_ast)
     ReturnVisitor().visit(code_ast)
 

lib/crewai/src/crewai/flow/visualization/assets/interactive_flow.html.j2

@@ -6,7 +6,6 @@
     <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
     <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
     <link rel="stylesheet" href="'{{ css_path }}'" />
-    <script src="https://unpkg.com/lucide@latest"></script>
     <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/prism.min.js"></script>
     <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/components/prism-python.min.js"></script>
     <script src="'{{ js_path }}'"></script>
@@ -24,129 +23,93 @@
             <div class="drawer-title" id="drawer-node-name">Node Details</div>
             <div style="display: flex; align-items: center;">
                 <button class="drawer-open-ide" id="drawer-open-ide" style="display: none;">
-                    <i data-lucide="file-code" style="width: 16px; height: 16px;"></i>
+                    <svg viewBox="0 0 16 16" fill="none" stroke="currentColor" stroke-width="2">
+                        <path d="M4 2 L12 2 L12 14 L4 14 Z" stroke-linecap="round" stroke-linejoin="round"/>
+                        <path d="M6 5 L10 5 M6 8 L10 8 M6 11 L10 11" stroke-linecap="round"/>
+                    </svg>
                     Open in IDE
                 </button>
-                <button class="drawer-close" id="drawer-close">
-                    <i data-lucide="x" style="width: 20px; height: 20px;"></i>
-                </button>
+                <button class="drawer-close" id="drawer-close">×</button>
             </div>
         </div>
         <div class="drawer-content" id="drawer-content"></div>
     </div>
 
     <div id="info">
-        <div style="text-align: center;">
+        <div style="text-align: center; margin-bottom: 20px;">
             <img src="https://cdn.prod.website-files.com/68de1ee6d7c127849807d7a6/68de1ee6d7c127849807d7ef_Logo.svg"
                  alt="CrewAI Logo"
-                 style="width: 144px; height: auto;">
+                 style="width: 120px; height: auto;">
+        </div>
+        <h3>Flow Execution</h3>
+        <div class="stats">
+            <p><strong>Nodes:</strong> '{{ dag_nodes_count }}'</p>
+            <p><strong>Edges:</strong> '{{ dag_edges_count }}'</p>
+            <p><strong>Topological Paths:</strong> '{{ execution_paths }}'</p>
+        </div>
+        <div class="legend">
+            <div class="legend-title">Node Types</div>
+            <div class="legend-item">
+                <div class="legend-color" style="background: '{{ CREWAI_ORANGE }}';"></div>
+                <span>Start Methods</span>
+            </div>
+            <div class="legend-item">
+                <div class="legend-color" style="background: '{{ DARK_GRAY }}'; border: 3px solid '{{ CREWAI_ORANGE }}';"></div>
+                <span>Router Methods</span>
+            </div>
+            <div class="legend-item">
+                <div class="legend-color" style="background: '{{ DARK_GRAY }}';"></div>
+                <span>Listen Methods</span>
+            </div>
+        </div>
+        <div class="legend">
+            <div class="legend-title">Edge Types</div>
+            <div class="legend-item">
+                <svg width="24" height="12" style="margin-right: 12px;">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2" stroke-dasharray="5,5"/>
+                </svg>
+                <span>Router Paths</span>
+            </div>
+            <div class="legend-item">
+                <svg width="24" height="12" style="margin-right: 12px;" class="legend-or-line">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="var(--edge-or-color)" stroke-width="2"/>
+                </svg>
+                <span>OR Conditions</span>
+            </div>
+            <div class="legend-item">
+                <svg width="24" height="12" style="margin-right: 12px;">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2"/>
+                </svg>
+                <span>AND Conditions</span>
+            </div>
+        </div>
+        <div class="instructions">
+            <strong>Interactions:</strong><br>
+            • Drag to pan<br>
+            • Scroll to zoom<br><br>
+            <strong>IDE:</strong>
+            <select id="ide-selector" style="width: 100%; padding: 4px; margin-top: 4px; border-radius: 3px; border: 1px solid #e0e0e0; background: white; font-size: 12px; cursor: pointer; pointer-events: auto; position: relative; z-index: 10;">
+                <option value="auto">Auto-detect</option>
+                <option value="pycharm">PyCharm</option>
+                <option value="vscode">VS Code</option>
+                <option value="jetbrains">JetBrains (Toolbox)</option>
+            </select>
         </div>
     </div>
 
-
     <!-- Custom navigation controls -->
     <div class="nav-controls">
-        <div class="nav-button" id="theme-toggle" title="Toggle Dark Mode">
-            <i data-lucide="moon" style="width: 18px; height: 18px;"></i>
-        </div>
-        <div class="nav-button" id="zoom-in" title="Zoom In">
-            <i data-lucide="zoom-in" style="width: 18px; height: 18px;"></i>
-        </div>
-        <div class="nav-button" id="zoom-out" title="Zoom Out">
-            <i data-lucide="zoom-out" style="width: 18px; height: 18px;"></i>
-        </div>
-        <div class="nav-button" id="fit" title="Fit to Screen">
-            <i data-lucide="maximize-2" style="width: 18px; height: 18px;"></i>
-        </div>
-        <div class="nav-button" id="export-png" title="Export to PNG">
-            <i data-lucide="image" style="width: 18px; height: 18px;"></i>
-        </div>
-        <div class="nav-button" id="export-pdf" title="Export to PDF">
-            <i data-lucide="file-text" style="width: 18px; height: 18px;"></i>
-        </div>
-        <!-- <div class="nav-button" id="export-json" title="Export to JSON">
-            <i data-lucide="braces" style="width: 18px; height: 18px;"></i>
-        </div> -->
+        <div class="nav-button" id="theme-toggle" title="Toggle Dark Mode">🌙</div>
+        <div class="nav-button" id="zoom-in" title="Zoom In">+</div>
+        <div class="nav-button" id="zoom-out" title="Zoom Out">−</div>
+        <div class="nav-button" id="fit" title="Fit to Screen">⊡</div>
+        <div class="nav-button" id="export-png" title="Export to PNG">🖼</div>
+        <div class="nav-button" id="export-pdf" title="Export to PDF">📄</div>
+        <div class="nav-button" id="export-json" title="Export to JSON">{}</div>
     </div>
 
     <div id="network-container">
         <div id="network"></div>
     </div>
-
-    <!-- Info panel at bottom -->
-    <div id="legend-panel">
-        <!-- Stats Section -->
-        <div class="legend-section">
-            <div class="legend-stats-row">
-                <div class="legend-stat-item">
-                    <span class="stat-value">'{{ dag_nodes_count }}'</span>
-                    <span class="stat-label">Nodes</span>
-                </div>
-                <div class="legend-stat-item">
-                    <span class="stat-value">'{{ dag_edges_count }}'</span>
-                    <span class="stat-label">Edges</span>
-                </div>
-                <div class="legend-stat-item">
-                    <span class="stat-value">'{{ execution_paths }}'</span>
-                    <span class="stat-label">Paths</span>
-                </div>
-            </div>
-        </div>
-
-        <!-- Node Types Section -->
-        <div class="legend-section">
-            <div class="legend-group">
-                <div class="legend-item-compact">
-                    <div class="legend-color-small" style="background: var(--node-bg-start);"></div>
-                    <span>Start</span>
-                </div>
-                <div class="legend-item-compact">
-                    <div class="legend-color-small" style="background: var(--node-bg-router); border: 2px solid var(--node-border-start);"></div>
-                    <span>Router</span>
-                </div>
-                <div class="legend-item-compact">
-                    <div class="legend-color-small" style="background: var(--node-bg-listen); border: 2px solid var(--node-border-listen);"></div>
-                    <span>Listen</span>
-                </div>
-            </div>
-        </div>
-
-        <!-- Edge Types Section -->
-        <div class="legend-section">
-            <div class="legend-group">
-                <div class="legend-item-compact">
-                    <svg>
-                        <line x1="0" y1="7" x2="29" y2="7" stroke="var(--edge-router-color)" stroke-width="2" stroke-dasharray="4,4"/>
-                    </svg>
-                    <span>Router</span>
-                </div>
-                <div class="legend-item-compact">
-                    <svg class="legend-or-line">
-                        <line x1="0" y1="7" x2="29" y2="7" stroke="var(--edge-or-color)" stroke-width="2"/>
-                    </svg>
-                    <span>OR</span>
-                </div>
-                <div class="legend-item-compact">
-                    <svg>
-                        <line x1="0" y1="7" x2="29" y2="7" stroke="var(--edge-router-color)" stroke-width="2"/>
-                    </svg>
-                    <span>AND</span>
-                </div>
-            </div>
-        </div>
-
-        <!-- IDE Selector Section -->
-        <div class="legend-section">
-            <div class="legend-ide-column">
-                <label class="legend-ide-label">IDE</label>
-                <select id="ide-selector" class="legend-ide-select">
-                    <option value="auto">Auto-detect</option>
-                    <option value="pycharm">PyCharm</option>
-                    <option value="vscode">VS Code</option>
-                    <option value="jetbrains">JetBrains</option>
-                </select>
-            </div>
-        </div>
-    </div>
 </body>
 </html>

lib/crewai/src/crewai/flow/visualization/assets/style.css

@@ -13,14 +13,6 @@
     --edge-label-text: '{{ GRAY }}';
     --edge-label-bg: rgba(255, 255, 255, 0.8);
     --edge-or-color: #000000;
-    --edge-router-color: '{{ CREWAI_ORANGE }}';
-    --node-border-start: #C94238;
-    --node-border-listen: #3D3D3D;
-    --node-bg-start: #FF7066;
-    --node-bg-router: #FFFFFF;
-    --node-bg-listen: #FFFFFF;
-    --node-text-color: #FFFFFF;
-    --nav-button-hover: #f5f5f5;
 }
 
 [data-theme="dark"] {
@@ -38,14 +30,6 @@
     --edge-label-text: #c9d1d9;
     --edge-label-bg: rgba(22, 27, 34, 0.9);
     --edge-or-color: #ffffff;
-    --edge-router-color: '{{ CREWAI_ORANGE }}';
-    --node-border-start: #FF5A50;
-    --node-border-listen: #666666;
-    --node-bg-start: #B33830;
-    --node-bg-router: #3D3D3D;
-    --node-bg-listen: #3D3D3D;
-    --node-text-color: #FFFFFF;
-    --nav-button-hover: #30363d;
 }
 
 @keyframes dash {
@@ -88,10 +72,12 @@ body {
     position: absolute;
     top: 20px;
     left: 20px;
-    background: transparent;
+    background: var(--bg-secondary);
     padding: 20px;
     border-radius: 8px;
+    box-shadow: 0 4px 12px var(--shadow-strong);
     max-width: 320px;
+    border: 1px solid var(--border-color);
     z-index: 10000;
     pointer-events: auto;
     transition: background 0.3s ease, border-color 0.3s ease, box-shadow 0.3s ease;
@@ -139,16 +125,12 @@ h3 {
     margin-right: 12px;
     border-radius: 3px;
     box-sizing: border-box;
-    transition: background 0.3s ease, border-color 0.3s ease;
 }
 .legend-item span {
     color: var(--text-secondary);
     font-size: 13px;
     transition: color 0.3s ease;
 }
-.legend-item svg line {
-    transition: stroke 0.3s ease;
-}
 .instructions {
     margin-top: 15px;
     padding-top: 15px;
@@ -173,7 +155,7 @@ h3 {
     bottom: 20px;
     right: auto;
     display: grid;
-    grid-template-columns: repeat(3, 40px);
+    grid-template-columns: repeat(4, 40px);
     gap: 8px;
     z-index: 10002;
     pointer-events: auto;
@@ -183,187 +165,10 @@ h3 {
 .nav-controls.drawer-open {
 }
 
-#legend-panel {
-    position: fixed;
-    left: 164px;
-    bottom: 20px;
-    right: 20px;
-    height: 92px;
-    background: var(--bg-secondary);
-    backdrop-filter: blur(12px) saturate(180%);
-    -webkit-backdrop-filter: blur(12px) saturate(180%);
-    border: 1px solid var(--border-subtle);
-    border-radius: 6px;
-    box-shadow: 0 2px 8px var(--shadow-color);
-    display: grid;
-    grid-template-columns: repeat(4, 1fr);
-    align-items: center;
-    gap: 0;
-    padding: 0 24px;
-    box-sizing: border-box;
-    z-index: 10001;
-    pointer-events: auto;
-    transition: background 0.3s ease, border-color 0.3s ease, box-shadow 0.3s ease, right 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-}
-
-#legend-panel.drawer-open {
-    right: 405px;
-}
-
-.legend-section {
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    min-width: 0;
-    width: -webkit-fill-available;
-    width: -moz-available;
-    width: stretch;
-    position: relative;
-}
-
-.legend-section:not(:last-child)::after {
-    content: '';
-    position: absolute;
-    right: 0;
-    top: 50%;
-    transform: translateY(-50%);
-    width: 1px;
-    height: 48px;
-    background: var(--border-color);
-    transition: background 0.3s ease;
-}
-
-.legend-stats-row {
-    display: flex;
-    gap: 32px;
-    justify-content: center;
-    align-items: center;
-    min-width: 0;
-}
-
-.legend-stat-item {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    gap: 4px;
-}
-
-.stat-value {
-    font-size: 19px;
-    font-weight: 700;
-    color: var(--text-primary);
-    line-height: 1;
-    transition: color 0.3s ease;
-}
-
-.stat-label {
-    font-size: 8px;
-    font-weight: 500;
-    text-transform: uppercase;
-    color: var(--text-secondary);
-    letter-spacing: 0.5px;
-    transition: color 0.3s ease;
-}
-
-.legend-items-row {
-    display: flex;
-    gap: 16px;
-    align-items: center;
-    justify-content: center;
-    min-width: 0;
-}
-
-.legend-group {
-    display: flex;
-    gap: 16px;
-    align-items: center;
-}
-
-.legend-item-compact {
-    display: flex;
-    align-items: center;
-    gap: 6px;
-}
-
-.legend-item-compact span {
-    font-size: 12px;
-    font-weight: 500;
-    text-transform: uppercase;
-    color: var(--text-secondary);
-    letter-spacing: 0.5px;
-    white-space: nowrap;
-    font-family: inherit;
-    line-height: 1;
-    transition: color 0.3s ease;
-}
-
-.legend-color-small {
-    width: 17px;
-    height: 17px;
-    border-radius: 2px;
-    box-sizing: border-box;
-    flex-shrink: 0;
-    transition: background 0.3s ease, border-color 0.3s ease;
-}
-
-.legend-item-compact svg {
-    display: block;
-    flex-shrink: 0;
-    width: 29px;
-    height: 14px;
-}
-
-.legend-item-compact svg line {
-    transition: stroke 0.3s ease;
-}
-
-.legend-ide-column {
-    display: flex;
-    flex-direction: row;
-    gap: 8px;
-    align-items: center;
-    justify-content: center;
-    min-width: 0;
-    width: 100%;
-}
-
-.legend-ide-label {
-    font-size: 12px;
-    font-weight: 500;
-    text-transform: uppercase;
-    color: var(--text-secondary);
-    letter-spacing: 0.5px;
-    transition: color 0.3s ease;
-    white-space: nowrap;
-}
-
-.legend-ide-select {
-    width: 120px;
-    padding: 6px 10px;
-    border-radius: 4px;
-    border: 1px solid var(--border-subtle);
-    background: var(--bg-primary);
-    color: var(--text-primary);
-    font-size: 11px;
-    cursor: pointer;
-    transition: all 0.3s ease;
-}
-
-.legend-ide-select:hover {
-    border-color: var(--text-secondary);
-}
-
-.legend-ide-select:focus {
-    outline: none;
-    border-color: '{{ CREWAI_ORANGE }}';
-}
-
 .nav-button {
     width: 40px;
     height: 40px;
     background: var(--bg-secondary);
-    backdrop-filter: blur(12px) saturate(180%);
-    -webkit-backdrop-filter: blur(12px) saturate(180%);
     border: 1px solid var(--border-subtle);
     border-radius: 6px;
     display: flex;
@@ -376,12 +181,12 @@ h3 {
     user-select: none;
     pointer-events: auto;
     position: relative;
-    z-index: 10002;
+    z-index: 10001;
     transition: background 0.3s ease, border-color 0.3s ease, color 0.3s ease, box-shadow 0.3s ease;
 }
 
 .nav-button:hover {
-    background: var(--nav-button-hover);
+    background: var(--border-subtle);
 }
 
 #drawer {
@@ -393,10 +198,9 @@ h3 {
     background: var(--bg-drawer);
     box-shadow: -4px 0 12px var(--shadow-strong);
     transition: right 0.3s cubic-bezier(0.4, 0, 0.2, 1), background 0.3s ease, box-shadow 0.3s ease;
-    z-index: 10003;
-    overflow: hidden;
-    transform: translateZ(0);
-    isolation: isolate;
+    z-index: 2000;
+    overflow-y: auto;
+    padding: 24px;
 }
 
 #drawer.open {
@@ -443,22 +247,17 @@ h3 {
     justify-content: space-between;
     align-items: center;
     margin-bottom: 20px;
-    padding: 24px 24px 16px 24px;
+    padding-bottom: 16px;
     border-bottom: 2px solid '{{ CREWAI_ORANGE }}';
     position: relative;
     z-index: 2001;
 }
 
 .drawer-title {
-    font-size: 15px;
+    font-size: 20px;
     font-weight: 700;
     color: var(--text-primary);
     transition: color 0.3s ease;
-    overflow: hidden;
-    text-overflow: ellipsis;
-    white-space: nowrap;
-    flex: 1;
-    min-width: 0;
 }
 
 .drawer-close {
@@ -470,19 +269,12 @@ h3 {
     padding: 4px 8px;
     line-height: 1;
     transition: color 0.3s ease;
-    display: flex;
-    align-items: center;
-    justify-content: center;
 }
 
 .drawer-close:hover {
     color: '{{ CREWAI_ORANGE }}';
 }
 
-.drawer-close i {
-    display: block;
-}
-
 .drawer-open-ide {
     background: '{{ CREWAI_ORANGE }}';
     border: none;
@@ -500,9 +292,6 @@ h3 {
     position: relative;
     z-index: 9999;
     pointer-events: auto;
-    white-space: nowrap;
-    flex-shrink: 0;
-    min-width: fit-content;
 }
 
 .drawer-open-ide:hover {
@@ -516,19 +305,14 @@ h3 {
     box-shadow: 0 1px 4px rgba(255, 90, 80, 0.2);
 }
 
-.drawer-open-ide svg,
-.drawer-open-ide i {
+.drawer-open-ide svg {
     width: 14px;
     height: 14px;
-    display: block;
 }
 
 .drawer-content {
     color: '{{ DARK_GRAY }}';
     line-height: 1.6;
-    padding: 0 24px 24px 24px;
-    overflow-y: auto;
-    height: calc(100vh - 95px);
 }
 
 .drawer-section {
@@ -544,10 +328,6 @@ h3 {
     position: relative;
 }
 
-.drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1)) {
-    grid-template-columns: 1fr 2fr;
-}
-
 .drawer-metadata-grid::before {
     content: '';
     position: absolute;
@@ -639,35 +419,20 @@ h3 {
     grid-column: 2;
     display: flex;
     flex-direction: column;
-    justify-content: flex-start;
-    align-items: flex-start;
+    justify-content: center;
 }
 
 .drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1))::after {
-    right: 66.666%;
-}
-
-.drawer-metadata-grid:has(.drawer-section:nth-child(3):nth-last-child(1))::before {
-    left: 33.333%;
-}
-
-.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(1) .drawer-section-title {
-    align-self: flex-start;
-}
-
-.drawer-metadata-grid .drawer-section:nth-child(3):nth-last-child(1) > *:not(.drawer-section-title) {
-    width: 100%;
-    align-self: stretch;
+    right: 50%;
 }
 
 .drawer-section-title {
     font-size: 12px;
     text-transform: uppercase;
-    color: var(--text-secondary);
+    color: '{{ GRAY }}';
     letter-spacing: 0.5px;
     margin-bottom: 8px;
     font-weight: 600;
-    transition: color 0.3s ease;
 }
 
 .drawer-badge {
@@ -700,44 +465,9 @@ h3 {
     padding: 3px 0;
 }
 
-.drawer-metadata-grid .drawer-section .drawer-list {
-    display: flex;
-    flex-direction: column;
-    gap: 6px;
-}
-
-.drawer-metadata-grid .drawer-section .drawer-list li {
-    border-bottom: none;
-    padding: 0;
-}
-
 .drawer-metadata-grid .drawer-section:nth-child(3) .drawer-list li {
     border-bottom: none;
-    padding: 0;
-}
-
-.drawer-metadata-grid .drawer-section {
-    overflow: visible;
-}
-
-.drawer-metadata-grid .drawer-section .condition-group,
-.drawer-metadata-grid .drawer-section .trigger-group {
-    width: 100%;
-    box-sizing: border-box;
-}
-
-.drawer-metadata-grid .drawer-section .condition-children {
-    width: 100%;
-}
-
-.drawer-metadata-grid .drawer-section .trigger-group-items {
-    width: 100%;
-}
-
-.drawer-metadata-grid .drawer-section .drawer-code-link {
-    word-break: break-word;
-    overflow-wrap: break-word;
-    max-width: 100%;
+    padding: 3px 0;
 }
 
 .drawer-code {
@@ -761,7 +491,6 @@ h3 {
     cursor: pointer;
     transition: all 0.2s;
     display: inline-block;
-    margin: 3px 2px;
 }
 
 .drawer-code-link:hover {

lib/crewai/src/crewai/flow/visualization/builder.py

@@ -3,13 +3,12 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from collections.abc import Iterable
 import inspect
 from typing import TYPE_CHECKING, Any
 
 from crewai.flow.constants import AND_CONDITION, OR_CONDITION
 from crewai.flow.flow_wrappers import FlowCondition
-from crewai.flow.types import FlowMethodName, FlowRouteName
+from crewai.flow.types import FlowMethodName
 from crewai.flow.utils import (
     is_flow_condition_dict,
     is_simple_flow_condition,
@@ -198,6 +197,8 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
             node_metadata["type"] = "router"
             router_methods.append(method_name)
 
+            node_metadata["condition_type"] = "IF"
+
             if method_name in flow._router_paths:
                 node_metadata["router_paths"] = [
                     str(p) for p in flow._router_paths[method_name]
@@ -209,13 +210,9 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
             ]
 
         if hasattr(method, "__condition_type__") and method.__condition_type__:
-            node_metadata["trigger_condition_type"] = method.__condition_type__
             if "condition_type" not in node_metadata:
                 node_metadata["condition_type"] = method.__condition_type__
 
-        if node_metadata.get("is_router") and "condition_type" not in node_metadata:
-            node_metadata["condition_type"] = "IF"
-
         if (
             hasattr(method, "__trigger_condition__")
             and method.__trigger_condition__ is not None
@@ -301,9 +298,6 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
         nodes[method_name] = node_metadata
 
     for listener_name, condition_data in flow._listeners.items():
-        if listener_name in router_methods:
-            continue
-
         if is_simple_flow_condition(condition_data):
             cond_type, methods = condition_data
             edges.extend(
@@ -321,60 +315,6 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
                 _create_edges_from_condition(condition_data, str(listener_name), nodes)
             )
 
-    for method_name, node_metadata in nodes.items():  # type: ignore[assignment]
-        if node_metadata.get("is_router") and "trigger_methods" in node_metadata:
-            trigger_methods = node_metadata["trigger_methods"]
-            condition_type = node_metadata.get("trigger_condition_type", OR_CONDITION)
-
-            if "trigger_condition" in node_metadata:
-                edges.extend(
-                    _create_edges_from_condition(
-                        node_metadata["trigger_condition"],  # type: ignore[arg-type]
-                        method_name,
-                        nodes,
-                    )
-                )
-            else:
-                edges.extend(
-                    StructureEdge(
-                        source=trigger_method,
-                        target=method_name,
-                        condition_type=condition_type,
-                        is_router_path=False,
-                    )
-                    for trigger_method in trigger_methods
-                    if trigger_method in nodes
-                )
-
-    for router_method_name in router_methods:
-        if router_method_name not in flow._router_paths:
-            flow._router_paths[FlowMethodName(router_method_name)] = []
-
-        inferred_paths: Iterable[FlowMethodName | FlowRouteName] = set(
-            flow._router_paths.get(FlowMethodName(router_method_name), [])
-        )
-
-        for condition_data in flow._listeners.values():
-            trigger_strings: list[str] = []
-
-            if is_simple_flow_condition(condition_data):
-                _, methods = condition_data
-                trigger_strings = [str(m) for m in methods]
-            elif is_flow_condition_dict(condition_data):
-                trigger_strings = _extract_direct_or_triggers(condition_data)
-
-            for trigger_str in trigger_strings:
-                if trigger_str not in nodes:
-                    # This is likely a router path output
-                    inferred_paths.add(trigger_str)  # type: ignore[attr-defined]
-
-        if inferred_paths:
-            flow._router_paths[FlowMethodName(router_method_name)] = list(
-                inferred_paths  # type: ignore[arg-type]
-            )
-            if router_method_name in nodes:
-                nodes[router_method_name]["router_paths"] = list(inferred_paths)
-
     for router_method_name in router_methods:
         if router_method_name not in flow._router_paths:
             continue
@@ -400,7 +340,6 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
                             target=str(listener_name),
                             condition_type=None,
                             is_router_path=True,
-                            router_path_label=str(path),
                         )
                     )
 

lib/crewai/src/crewai/flow/visualization/renderers/interactive.py

@@ -20,7 +20,7 @@ class CSSExtension(Extension):
     Provides {% css 'path/to/file.css' %} tag syntax.
     """
 
-    tags: ClassVar[set[str]] = {"css"}  # type: ignore[misc]
+    tags: ClassVar[set[str]] = {"css"}  # type: ignore[assignment]
 
     def parse(self, parser: Parser) -> nodes.Node:
         """Parse {% css 'styles.css' %} tag.
@@ -53,7 +53,7 @@ class JSExtension(Extension):
     Provides {% js 'path/to/file.js' %} tag syntax.
     """
 
-    tags: ClassVar[set[str]] = {"js"}  # type: ignore[misc]
+    tags: ClassVar[set[str]] = {"js"}  # type: ignore[assignment]
 
     def parse(self, parser: Parser) -> nodes.Node:
         """Parse {% js 'script.js' %} tag.
@@ -91,116 +91,6 @@ TEXT_PRIMARY = "#e6edf3"
 TEXT_SECONDARY = "#7d8590"
 
 
-def calculate_node_positions(
-    dag: FlowStructure,
-) -> dict[str, dict[str, int | float]]:
-    """Calculate hierarchical positions (level, x, y) for each node.
-
-    Args:
-        dag: FlowStructure containing nodes and edges.
-
-    Returns:
-        Dictionary mapping node names to their position data (level, x, y).
-    """
-    children: dict[str, list[str]] = {name: [] for name in dag["nodes"]}
-    parents: dict[str, list[str]] = {name: [] for name in dag["nodes"]}
-
-    for edge in dag["edges"]:
-        source = edge["source"]
-        target = edge["target"]
-        if source in children and target in children:
-            children[source].append(target)
-            parents[target].append(source)
-
-    levels: dict[str, int] = {}
-    queue: list[tuple[str, int]] = []
-
-    for start_method in dag["start_methods"]:
-        if start_method in dag["nodes"]:
-            levels[start_method] = 0
-            queue.append((start_method, 0))
-
-    visited: set[str] = set()
-    while queue:
-        node, level = queue.pop(0)
-        if node in visited:
-            continue
-        visited.add(node)
-
-        if node not in levels or levels[node] < level:
-            levels[node] = level
-
-        for child in children.get(node, []):
-            if child not in visited:
-                child_level = level + 1
-                if child not in levels or levels[child] < child_level:
-                    levels[child] = child_level
-                queue.append((child, child_level))
-
-    for name in dag["nodes"]:
-        if name not in levels:
-            levels[name] = 0
-
-    nodes_by_level: dict[int, list[str]] = {}
-    for node, level in levels.items():
-        if level not in nodes_by_level:
-            nodes_by_level[level] = []
-        nodes_by_level[level].append(node)
-
-    positions: dict[str, dict[str, int | float]] = {}
-    level_separation = 300  # Vertical spacing between levels
-    node_spacing = 400  # Horizontal spacing between nodes
-
-    parent_count: dict[str, int] = {}
-    for node, parent_list in parents.items():
-        parent_count[node] = len(parent_list)
-
-    for level, nodes_at_level in sorted(nodes_by_level.items()):
-        y = level * level_separation
-
-        if level == 0:
-            num_nodes = len(nodes_at_level)
-            for i, node in enumerate(nodes_at_level):
-                x = (i - (num_nodes - 1) / 2) * node_spacing
-                positions[node] = {"level": level, "x": x, "y": y}
-        else:
-            for i, node in enumerate(nodes_at_level):
-                parent_list = parents.get(node, [])
-                parent_positions: list[float] = [
-                    positions[parent]["x"]
-                    for parent in parent_list
-                    if parent in positions
-                ]
-
-                if parent_positions:
-                    if len(parent_positions) > 1 and len(set(parent_positions)) == 1:
-                        base_x = parent_positions[0]
-                        avg_x = base_x + node_spacing * 0.4
-                    else:
-                        avg_x = sum(parent_positions) / len(parent_positions)
-                else:
-                    avg_x = i * node_spacing * 0.5
-
-                positions[node] = {"level": level, "x": avg_x, "y": y}
-
-            nodes_at_level_sorted = sorted(
-                nodes_at_level, key=lambda n: positions[n]["x"]
-            )
-            min_spacing = node_spacing * 0.6  # Minimum horizontal distance
-
-            for i in range(len(nodes_at_level_sorted) - 1):
-                current_node = nodes_at_level_sorted[i]
-                next_node = nodes_at_level_sorted[i + 1]
-
-                current_x = positions[current_node]["x"]
-                next_x = positions[next_node]["x"]
-
-                if next_x - current_x < min_spacing:
-                    positions[next_node]["x"] = current_x + min_spacing
-
-    return positions
-
-
 def render_interactive(
     dag: FlowStructure,
     filename: str = "flow_dag.html",
@@ -220,8 +110,6 @@ def render_interactive(
     Returns:
         Absolute path to generated HTML file in temporary directory.
     """
-    node_positions = calculate_node_positions(dag)
-
     nodes_list: list[dict[str, Any]] = []
     for name, metadata in dag["nodes"].items():
         node_type: str = metadata.get("type", "listen")
@@ -232,37 +120,37 @@ def render_interactive(
 
         if node_type == "start":
             color_config = {
-                "background": "var(--node-bg-start)",
-                "border": "var(--node-border-start)",
-                "highlight": {
-                    "background": "var(--node-bg-start)",
-                    "border": "var(--node-border-start)",
-                },
-            }
-            font_color = "var(--node-text-color)"
-            border_width = 3
-        elif node_type == "router":
-            color_config = {
-                "background": "var(--node-bg-router)",
+                "background": CREWAI_ORANGE,
                 "border": CREWAI_ORANGE,
                 "highlight": {
-                    "background": "var(--node-bg-router)",
+                    "background": CREWAI_ORANGE,
                     "border": CREWAI_ORANGE,
                 },
             }
-            font_color = "var(--node-text-color)"
+            font_color = WHITE
+            border_width = 2
+        elif node_type == "router":
+            color_config = {
+                "background": DARK_GRAY,
+                "border": CREWAI_ORANGE,
+                "highlight": {
+                    "background": DARK_GRAY,
+                    "border": CREWAI_ORANGE,
+                },
+            }
+            font_color = WHITE
             border_width = 3
         else:
             color_config = {
-                "background": "var(--node-bg-listen)",
-                "border": "var(--node-border-listen)",
+                "background": DARK_GRAY,
+                "border": DARK_GRAY,
                 "highlight": {
-                    "background": "var(--node-bg-listen)",
-                    "border": "var(--node-border-listen)",
+                    "background": DARK_GRAY,
+                    "border": DARK_GRAY,
                 },
             }
-            font_color = "var(--node-text-color)"
-            border_width = 3
+            font_color = WHITE
+            border_width = 2
 
         title_parts: list[str] = []
 
@@ -327,34 +215,25 @@ def render_interactive(
         bg_color = color_config["background"]
         border_color = color_config["border"]
 
-        position_data = node_positions.get(name, {"level": 0, "x": 0, "y": 0})
-
-        node_data: dict[str, Any] = {
-            "id": name,
-            "label": name,
-            "title": "".join(title_parts),
-            "shape": "custom",
-            "size": 30,
-            "level": position_data["level"],
-            "nodeStyle": {
-                "name": name,
-                "bgColor": bg_color,
-                "borderColor": border_color,
-                "borderWidth": border_width,
-                "fontColor": font_color,
-            },
-            "opacity": 1.0,
-            "glowSize": 0,
-            "glowColor": None,
-        }
-
-        # Add x,y only for graphs with 3-4 nodes
-        total_nodes = len(dag["nodes"])
-        if 3 <= total_nodes <= 4:
-            node_data["x"] = position_data["x"]
-            node_data["y"] = position_data["y"]
-
-        nodes_list.append(node_data)
+        nodes_list.append(
+            {
+                "id": name,
+                "label": name,
+                "title": "".join(title_parts),
+                "shape": "custom",
+                "size": 30,
+                "nodeStyle": {
+                    "name": name,
+                    "bgColor": bg_color,
+                    "borderColor": border_color,
+                    "borderWidth": border_width,
+                    "fontColor": font_color,
+                },
+                "opacity": 1.0,
+                "glowSize": 0,
+                "glowColor": None,
+            }
+        )
 
     execution_paths: int = calculate_execution_paths(dag)
 
@@ -367,8 +246,6 @@ def render_interactive(
         if edge["is_router_path"]:
             edge_color = CREWAI_ORANGE
             edge_dashes = [15, 10]
-            if "router_path_label" in edge:
-                edge_label = edge["router_path_label"]
         elif edge["condition_type"] == "AND":
             edge_label = "AND"
             edge_color = CREWAI_ORANGE

lib/crewai/src/crewai/flow/visualization/types.py

@@ -10,7 +10,6 @@ class NodeMetadata(TypedDict, total=False):
     is_router: bool
     router_paths: list[str]
     condition_type: str | None
-    trigger_condition_type: str | None
     trigger_methods: list[str]
     trigger_condition: dict[str, Any] | None
     method_signature: dict[str, Any]
@@ -23,14 +22,13 @@ class NodeMetadata(TypedDict, total=False):
     class_line_number: int
 
 
-class StructureEdge(TypedDict, total=False):
+class StructureEdge(TypedDict):
     """Represents a connection in the flow structure."""
 
     source: str
     target: str
     condition_type: str | None
     is_router_path: bool
-    router_path_label: str
 
 
 class FlowStructure(TypedDict):

lib/crewai/src/crewai/lite_agent.py

@@ -1,7 +1,6 @@
 import asyncio
 from collections.abc import Callable
 import inspect
-import json
 from typing import (
     Any,
     Literal,
@@ -59,11 +58,7 @@ from crewai.utilities.agent_utils import (
     process_llm_response,
     render_text_description_and_args,
 )
-from crewai.utilities.converter import (
-    Converter,
-    ConverterError,
-    generate_model_description,
-)
+from crewai.utilities.converter import generate_model_description
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
 from crewai.utilities.i18n import I18N, get_i18n
@@ -246,11 +241,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         """Return the original role for compatibility with tool interfaces."""
         return self.role
 
-    def kickoff(
-        self,
-        messages: str | list[LLMMessage],
-        response_format: type[BaseModel] | None = None,
-    ) -> LiteAgentOutput:
+    def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
         """
         Execute the agent with the given messages.
 
@@ -258,8 +249,6 @@ class LiteAgent(FlowTrackable, BaseModel):
             messages: Either a string query or a list of message dictionaries.
                      If a string is provided, it will be converted to a user message.
                      If a list is provided, each dict should have 'role' and 'content' keys.
-            response_format: Optional Pydantic model for structured output. If provided,
-                           overrides self.response_format for this execution.
 
         Returns:
             LiteAgentOutput: The result of the agent execution.
@@ -280,13 +269,9 @@ class LiteAgent(FlowTrackable, BaseModel):
             self.tools_results = []
 
             # Format messages for the LLM
-            self._messages = self._format_messages(
-                messages, response_format=response_format
-            )
+            self._messages = self._format_messages(messages)
 
-            return self._execute_core(
-                agent_info=agent_info, response_format=response_format
-            )
+            return self._execute_core(agent_info=agent_info)
 
         except Exception as e:
             self._printer.print(
@@ -304,9 +289,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
             raise e
 
-    def _execute_core(
-        self, agent_info: dict[str, Any], response_format: type[BaseModel] | None = None
-    ) -> LiteAgentOutput:
+    def _execute_core(self, agent_info: dict[str, Any]) -> LiteAgentOutput:
         # Emit event for agent execution start
         crewai_event_bus.emit(
             self,
@@ -320,29 +303,15 @@ class LiteAgent(FlowTrackable, BaseModel):
         # Execute the agent using invoke loop
         agent_finish = self._invoke_loop()
         formatted_result: BaseModel | None = None
-
-        active_response_format = response_format or self.response_format
-        if active_response_format:
+        if self.response_format:
             try:
-                model_schema = generate_model_description(active_response_format)
-                schema = json.dumps(model_schema, indent=2)
-                instructions = self.i18n.slice("formatted_task_instructions").format(
-                    output_format=schema
-                )
-
-                converter = Converter(
-                    llm=self.llm,
-                    text=agent_finish.output,
-                    model=active_response_format,
-                    instructions=instructions,
-                )
-
-                result = converter.to_pydantic()
+                # Cast to BaseModel to ensure type safety
+                result = self.response_format.model_validate_json(agent_finish.output)
                 if isinstance(result, BaseModel):
                     formatted_result = result
-            except ConverterError as e:
+            except Exception as e:
                 self._printer.print(
-                    content=f"Failed to parse output into response format after retries: {e.message}",
+                    content=f"Failed to parse output into response format: {e!s}",
                     color="yellow",
                 )
 
@@ -358,7 +327,6 @@ 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
@@ -432,14 +400,8 @@ class LiteAgent(FlowTrackable, BaseModel):
         """
         return await asyncio.to_thread(self.kickoff, messages)
 
-    def _get_default_system_prompt(
-        self, response_format: type[BaseModel] | None = None
-    ) -> str:
-        """Get the default system prompt for the agent.
-
-        Args:
-            response_format: Optional response format to use instead of self.response_format
-        """
+    def _get_default_system_prompt(self) -> str:
+        """Get the default system prompt for the agent."""
         base_prompt = ""
         if self._parsed_tools:
             # Use the prompt template for agents with tools
@@ -460,31 +422,21 @@ class LiteAgent(FlowTrackable, BaseModel):
                 goal=self.goal,
             )
 
-        active_response_format = response_format or self.response_format
-        if active_response_format:
-            model_description = generate_model_description(active_response_format)
-            schema_json = json.dumps(model_description, indent=2)
+        # Add response format instructions if specified
+        if self.response_format:
+            schema = generate_model_description(self.response_format)
             base_prompt += self.i18n.slice("lite_agent_response_format").format(
-                response_format=schema_json
+                response_format=schema
             )
 
         return base_prompt
 
-    def _format_messages(
-        self,
-        messages: str | list[LLMMessage],
-        response_format: type[BaseModel] | None = None,
-    ) -> list[LLMMessage]:
-        """Format messages for the LLM.
-
-        Args:
-            messages: Input messages to format
-            response_format: Optional response format to use instead of self.response_format
-        """
+    def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
+        """Format messages for the LLM."""
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
-        system_prompt = self._get_default_system_prompt(response_format=response_format)
+        system_prompt = self._get_default_system_prompt()
 
         # Add system message at the beginning
         formatted_messages: list[LLMMessage] = [
@@ -554,10 +506,6 @@ class LiteAgent(FlowTrackable, BaseModel):
 
                 self._append_message(formatted_answer.text, role="assistant")
             except OutputParserError as e:  # noqa: PERF203
-                self._printer.print(
-                    content="Failed to parse LLM output. Retrying...",
-                    color="yellow",
-                )
                 formatted_answer = handle_output_parser_exception(
                     e=e,
                     messages=self._messages,

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

@@ -38,13 +38,6 @@ 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,
@@ -330,64 +323,18 @@ class LLM(BaseLLM):
     completion_cost: float | None = None
 
     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 +351,6 @@ 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."""

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/hooks/base.py

@@ -7,14 +7,7 @@ outbound and inbound messages at the transport level.
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Generic, TypeVar
-
-from pydantic_core import core_schema
-
-
-if TYPE_CHECKING:
-    from pydantic import GetCoreSchemaHandler
-    from pydantic_core import CoreSchema
+from typing import Generic, TypeVar
 
 
 T = TypeVar("T")
@@ -32,7 +25,6 @@ class BaseInterceptor(ABC, Generic[T, U]):
         U: Inbound message type (e.g., httpx.Response)
 
     Example:
-        >>> import httpx
         >>> class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
         ...     def on_outbound(self, message: httpx.Request) -> httpx.Request:
         ...         message.headers["X-Custom-Header"] = "value"
@@ -88,46 +80,3 @@ class BaseInterceptor(ABC, Generic[T, U]):
             Modified message object.
         """
         raise NotImplementedError
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for BaseInterceptor.
-
-        This allows the generic BaseInterceptor to be used in Pydantic models
-        without requiring arbitrary_types_allowed=True. The schema validates
-        that the value is an instance of BaseInterceptor.
-
-        Args:
-            _source_type: The source type being validated (unused).
-            _handler: Handler for generating schemas (unused).
-
-        Returns:
-            A Pydantic core schema that validates BaseInterceptor instances.
-        """
-        return core_schema.no_info_plain_validator_function(
-            _validate_interceptor,
-            serialization=core_schema.plain_serializer_function_ser_schema(
-                lambda x: x, return_schema=core_schema.any_schema()
-            ),
-        )
-
-
-def _validate_interceptor(value: Any) -> BaseInterceptor[T, U]:
-    """Validate that the value is a BaseInterceptor instance.
-
-    Args:
-        value: The value to validate.
-
-    Returns:
-        The validated BaseInterceptor instance.
-
-    Raises:
-        ValueError: If the value is not a BaseInterceptor instance.
-    """
-    if not isinstance(value, BaseInterceptor):
-        raise ValueError(
-            f"Expected BaseInterceptor instance, got {type(value).__name__}"
-        )
-    return value

lib/crewai/src/crewai/llms/hooks/transport.py

@@ -6,52 +6,16 @@ to enable request/response modification at the transport level.
 
 from __future__ import annotations
 
-from collections.abc import Iterable
-from typing import TYPE_CHECKING, TypedDict
+from typing import TYPE_CHECKING, Any
 
-from httpx import (
-    AsyncHTTPTransport as _AsyncHTTPTransport,
-    HTTPTransport as _HTTPTransport,
-)
-from typing_extensions import NotRequired, Unpack
+import httpx
 
 
 if TYPE_CHECKING:
-    from ssl import SSLContext
-
-    from httpx import Limits, Request, Response
-    from httpx._types import CertTypes, ProxyTypes
-
     from crewai.llms.hooks.base import BaseInterceptor
 
 
-class HTTPTransportKwargs(TypedDict, total=False):
-    """Typed dictionary for httpx.HTTPTransport initialization parameters.
-
-    These parameters configure the underlying HTTP transport behavior including
-    SSL verification, proxies, connection limits, and low-level socket options.
-    """
-
-    verify: bool | str | SSLContext
-    cert: NotRequired[CertTypes]
-    trust_env: bool
-    http1: bool
-    http2: bool
-    limits: Limits
-    proxy: NotRequired[ProxyTypes]
-    uds: NotRequired[str]
-    local_address: NotRequired[str]
-    retries: int
-    socket_options: NotRequired[
-        Iterable[
-            tuple[int, int, int]
-            | tuple[int, int, bytes | bytearray]
-            | tuple[int, int, None, int]
-        ]
-    ]
-
-
-class HTTPTransport(_HTTPTransport):
+class HTTPTransport(httpx.HTTPTransport):
     """HTTP transport that uses an interceptor for request/response modification.
 
     This transport is used internally when a user provides a BaseInterceptor.
@@ -61,19 +25,19 @@ class HTTPTransport(_HTTPTransport):
 
     def __init__(
         self,
-        interceptor: BaseInterceptor[Request, Response],
-        **kwargs: Unpack[HTTPTransportKwargs],
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response],
+        **kwargs: Any,
     ) -> None:
         """Initialize transport with interceptor.
 
         Args:
             interceptor: HTTP interceptor for modifying raw request/response objects.
-            **kwargs: HTTPTransport configuration parameters (verify, cert, proxy, etc.).
+            **kwargs: Additional arguments passed to httpx.HTTPTransport.
         """
         super().__init__(**kwargs)
         self.interceptor = interceptor
 
-    def handle_request(self, request: Request) -> Response:
+    def handle_request(self, request: httpx.Request) -> httpx.Response:
         """Handle request with interception.
 
         Args:
@@ -87,7 +51,7 @@ class HTTPTransport(_HTTPTransport):
         return self.interceptor.on_inbound(response)
 
 
-class AsyncHTTPTransport(_AsyncHTTPTransport):
+class AsyncHTTPransport(httpx.AsyncHTTPTransport):
     """Async HTTP transport that uses an interceptor for request/response modification.
 
     This transport is used internally when a user provides a BaseInterceptor.
@@ -97,19 +61,19 @@ class AsyncHTTPTransport(_AsyncHTTPTransport):
 
     def __init__(
         self,
-        interceptor: BaseInterceptor[Request, Response],
-        **kwargs: Unpack[HTTPTransportKwargs],
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response],
+        **kwargs: Any,
     ) -> None:
         """Initialize async transport with interceptor.
 
         Args:
             interceptor: HTTP interceptor for modifying raw request/response objects.
-            **kwargs: HTTPTransport configuration parameters (verify, cert, proxy, etc.).
+            **kwargs: Additional arguments passed to httpx.AsyncHTTPTransport.
         """
         super().__init__(**kwargs)
         self.interceptor = interceptor
 
-    async def handle_async_request(self, request: Request) -> Response:
+    async def handle_async_request(self, request: httpx.Request) -> httpx.Response:
         """Handle async request with interception.
 
         Args:

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

@@ -96,18 +96,23 @@ class AnthropicCompletion(BaseLLM):
 
     @property
     def stop(self) -> list[str]:
-        """Get stop sequences sent to the API."""
+        """Get stop sequences.
+
+        Returns:
+            List of stop sequences that will be sent to the Anthropic API.
+        """
         return self.stop_sequences
 
     @stop.setter
     def stop(self, value: list[str] | str | None) -> None:
-        """Set stop sequences.
+        """Set stop sequences and sync with stop_sequences attribute.
 
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Anthropic API.
+        This ensures that when CrewAgentExecutor sets llm.stop, the value
+        is properly synchronized with stop_sequences which is what gets
+        sent to the Anthropic API.
 
         Args:
-            value: Stop sequences as a list, single string, or None
+            value: Stop sequences as a list, single string, or None.
         """
         if value is None:
             self.stop_sequences = []

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

@@ -245,24 +245,29 @@ class BedrockCompletion(BaseLLM):
 
     @property
     def stop(self) -> list[str]:
-        """Get stop sequences sent to the API."""
+        """Get stop sequences.
+
+        Returns:
+            List of stop sequences that will be sent to the Bedrock API.
+        """
         return list(self.stop_sequences)
 
     @stop.setter
-    def stop(self, value: Sequence[str] | str | None) -> None:
-        """Set stop sequences.
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences and sync with stop_sequences attribute.
 
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Bedrock API.
+        This ensures that when CrewAgentExecutor sets llm.stop, the value
+        is properly synchronized with stop_sequences which is what gets
+        sent to the Bedrock API.
 
         Args:
-            value: Stop sequences as a Sequence, single string, or None
+            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, Sequence):
+        elif isinstance(value, list):
             self.stop_sequences = list(value)
         else:
             self.stop_sequences = []

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

@@ -106,18 +106,23 @@ class GeminiCompletion(BaseLLM):
 
     @property
     def stop(self) -> list[str]:
-        """Get stop sequences sent to the API."""
+        """Get stop sequences.
+
+        Returns:
+            List of stop sequences that will be sent to the Gemini API.
+        """
         return self.stop_sequences
 
     @stop.setter
     def stop(self, value: list[str] | str | None) -> None:
-        """Set stop sequences.
+        """Set stop sequences and sync with stop_sequences attribute.
 
-        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
-        are properly sent to the Gemini API.
+        This ensures that when CrewAgentExecutor sets llm.stop, the value
+        is properly synchronized with stop_sequences which is what gets
+        sent to the Gemini API.
 
         Args:
-            value: Stop sequences as a list, single string, or None
+            value: Stop sequences as a list, single string, or None.
         """
         if value is None:
             self.stop_sequences = []

lib/crewai/src/crewai/mcp/__init__.py

@@ -1,37 +0,0 @@
-"""MCP (Model Context Protocol) client support for CrewAI agents.
-
-This module provides native MCP client functionality, allowing CrewAI agents
-to connect to any MCP-compliant server using various transport types.
-"""
-
-from crewai.mcp.client import MCPClient
-from crewai.mcp.config import (
-    MCPServerConfig,
-    MCPServerHTTP,
-    MCPServerSSE,
-    MCPServerStdio,
-)
-from crewai.mcp.filters import (
-    StaticToolFilter,
-    ToolFilter,
-    ToolFilterContext,
-    create_dynamic_tool_filter,
-    create_static_tool_filter,
-)
-from crewai.mcp.transports.base import BaseTransport, TransportType
-
-
-__all__ = [
-    "BaseTransport",
-    "MCPClient",
-    "MCPServerConfig",
-    "MCPServerHTTP",
-    "MCPServerSSE",
-    "MCPServerStdio",
-    "StaticToolFilter",
-    "ToolFilter",
-    "ToolFilterContext",
-    "TransportType",
-    "create_dynamic_tool_filter",
-    "create_static_tool_filter",
-]

lib/crewai/src/crewai/mcp/client.py

@@ -1,742 +0,0 @@
-"""MCP client with session management for CrewAI agents."""
-
-import asyncio
-from collections.abc import Callable
-from contextlib import AsyncExitStack
-from datetime import datetime
-import logging
-import time
-from typing import Any
-
-from typing_extensions import Self
-
-
-# BaseExceptionGroup is available in Python 3.11+
-try:
-    from builtins import BaseExceptionGroup
-except ImportError:
-    # Fallback for Python < 3.11 (shouldn't happen in practice)
-    BaseExceptionGroup = Exception
-
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.mcp_events import (
-    MCPConnectionCompletedEvent,
-    MCPConnectionFailedEvent,
-    MCPConnectionStartedEvent,
-    MCPToolExecutionCompletedEvent,
-    MCPToolExecutionFailedEvent,
-    MCPToolExecutionStartedEvent,
-)
-from crewai.mcp.transports.base import BaseTransport
-from crewai.mcp.transports.http import HTTPTransport
-from crewai.mcp.transports.sse import SSETransport
-from crewai.mcp.transports.stdio import StdioTransport
-
-
-# MCP Connection timeout constants (in seconds)
-MCP_CONNECTION_TIMEOUT = 30  # Increased for slow servers
-MCP_TOOL_EXECUTION_TIMEOUT = 30
-MCP_DISCOVERY_TIMEOUT = 30  # Increased for slow servers
-MCP_MAX_RETRIES = 3
-
-# Simple in-memory cache for MCP tool schemas (duration: 5 minutes)
-_mcp_schema_cache: dict[str, tuple[dict[str, Any], float]] = {}
-_cache_ttl = 300  # 5 minutes
-
-
-class MCPClient:
-    """MCP client with session management.
-
-    This client manages connections to MCP servers and provides a high-level
-    interface for interacting with MCP tools, prompts, and resources.
-
-    Example:
-        ```python
-        transport = StdioTransport(command="python", args=["server.py"])
-        client = MCPClient(transport)
-        async with client:
-            tools = await client.list_tools()
-            result = await client.call_tool("tool_name", {"arg": "value"})
-        ```
-    """
-
-    def __init__(
-        self,
-        transport: BaseTransport,
-        connect_timeout: int = MCP_CONNECTION_TIMEOUT,
-        execution_timeout: int = MCP_TOOL_EXECUTION_TIMEOUT,
-        discovery_timeout: int = MCP_DISCOVERY_TIMEOUT,
-        max_retries: int = MCP_MAX_RETRIES,
-        cache_tools_list: bool = False,
-        logger: logging.Logger | None = None,
-    ) -> None:
-        """Initialize MCP client.
-
-        Args:
-            transport: Transport instance for MCP server connection.
-            connect_timeout: Connection timeout in seconds.
-            execution_timeout: Tool execution timeout in seconds.
-            discovery_timeout: Tool discovery timeout in seconds.
-            max_retries: Maximum retry attempts for operations.
-            cache_tools_list: Whether to cache tool list results.
-            logger: Optional logger instance.
-        """
-        self.transport = transport
-        self.connect_timeout = connect_timeout
-        self.execution_timeout = execution_timeout
-        self.discovery_timeout = discovery_timeout
-        self.max_retries = max_retries
-        self.cache_tools_list = cache_tools_list
-        # self._logger = logger or logging.getLogger(__name__)
-        self._session: Any = None
-        self._initialized = False
-        self._exit_stack = AsyncExitStack()
-        self._was_connected = False
-
-    @property
-    def connected(self) -> bool:
-        """Check if client is connected to server."""
-        return self.transport.connected and self._initialized
-
-    @property
-    def session(self) -> Any:
-        """Get the MCP session."""
-        if self._session is None:
-            raise RuntimeError("Client not connected. Call connect() first.")
-        return self._session
-
-    def _get_server_info(self) -> tuple[str, str | None, str | None]:
-        """Get server information for events.
-
-        Returns:
-            Tuple of (server_name, server_url, transport_type).
-        """
-        if isinstance(self.transport, StdioTransport):
-            server_name = f"{self.transport.command} {' '.join(self.transport.args)}"
-            server_url = None
-            transport_type = self.transport.transport_type.value
-        elif isinstance(self.transport, HTTPTransport):
-            server_name = self.transport.url
-            server_url = self.transport.url
-            transport_type = self.transport.transport_type.value
-        elif isinstance(self.transport, SSETransport):
-            server_name = self.transport.url
-            server_url = self.transport.url
-            transport_type = self.transport.transport_type.value
-        else:
-            server_name = "Unknown MCP Server"
-            server_url = None
-            transport_type = (
-                self.transport.transport_type.value
-                if hasattr(self.transport, "transport_type")
-                else None
-            )
-
-        return server_name, server_url, transport_type
-
-    async def connect(self) -> Self:
-        """Connect to MCP server and initialize session.
-
-        Returns:
-            Self for method chaining.
-
-        Raises:
-            ConnectionError: If connection fails.
-            ImportError: If MCP SDK not available.
-        """
-        if self.connected:
-            return self
-
-        # Get server info for events
-        server_name, server_url, transport_type = self._get_server_info()
-        is_reconnect = self._was_connected
-
-        # Emit connection started event
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            MCPConnectionStartedEvent(
-                server_name=server_name,
-                server_url=server_url,
-                transport_type=transport_type,
-                is_reconnect=is_reconnect,
-                connect_timeout=self.connect_timeout,
-            ),
-        )
-
-        try:
-            from mcp import ClientSession
-
-            # Use AsyncExitStack to manage transport and session contexts together
-            # This ensures they're in the same async scope and prevents cancel scope errors
-            # Always enter transport context via exit stack (it handles already-connected state)
-            await self._exit_stack.enter_async_context(self.transport)
-
-            # Create ClientSession with transport streams
-            self._session = ClientSession(
-                self.transport.read_stream,
-                self.transport.write_stream,
-            )
-
-            # Enter the session's async context manager via exit stack
-            await self._exit_stack.enter_async_context(self._session)
-
-            # Initialize the session (required by MCP protocol)
-            try:
-                await asyncio.wait_for(
-                    self._session.initialize(),
-                    timeout=self.connect_timeout,
-                )
-            except asyncio.CancelledError:
-                # If initialization was cancelled (e.g., event loop closing),
-                # cleanup and re-raise - don't suppress cancellation
-                await self._cleanup_on_error()
-                raise
-            except BaseExceptionGroup as eg:
-                # Handle exception groups from anyio task groups
-                # Extract the actual meaningful error (not GeneratorExit)
-                actual_error = None
-                for exc in eg.exceptions:
-                    if isinstance(exc, Exception) and not isinstance(
-                        exc, GeneratorExit
-                    ):
-                        # Check if it's an HTTP error (like 401)
-                        error_msg = str(exc).lower()
-                        if "401" in error_msg or "unauthorized" in error_msg:
-                            actual_error = exc
-                            break
-                        if "cancel scope" not in error_msg and "task" not in error_msg:
-                            actual_error = exc
-                            break
-
-                await self._cleanup_on_error()
-                if actual_error:
-                    raise ConnectionError(
-                        f"Failed to connect to MCP server: {actual_error}"
-                    ) from actual_error
-                raise ConnectionError(f"Failed to connect to MCP server: {eg}") from eg
-
-            self._initialized = True
-            self._was_connected = True
-
-            completed_at = datetime.now()
-            connection_duration_ms = (completed_at - started_at).total_seconds() * 1000
-            crewai_event_bus.emit(
-                self,
-                MCPConnectionCompletedEvent(
-                    server_name=server_name,
-                    server_url=server_url,
-                    transport_type=transport_type,
-                    started_at=started_at,
-                    completed_at=completed_at,
-                    connection_duration_ms=connection_duration_ms,
-                    is_reconnect=is_reconnect,
-                ),
-            )
-
-            return self
-        except ImportError as e:
-            await self._cleanup_on_error()
-            error_msg = (
-                "MCP library not available. Please install with: pip install mcp"
-            )
-            self._emit_connection_failed(
-                server_name,
-                server_url,
-                transport_type,
-                error_msg,
-                "import_error",
-                started_at,
-            )
-            raise ImportError(error_msg) from e
-        except asyncio.TimeoutError as e:
-            await self._cleanup_on_error()
-            error_msg = f"MCP connection timed out after {self.connect_timeout} seconds. The server may be slow or unreachable."
-            self._emit_connection_failed(
-                server_name,
-                server_url,
-                transport_type,
-                error_msg,
-                "timeout",
-                started_at,
-            )
-            raise ConnectionError(error_msg) from e
-        except asyncio.CancelledError:
-            # Re-raise cancellation - don't suppress it
-            await self._cleanup_on_error()
-            self._emit_connection_failed(
-                server_name,
-                server_url,
-                transport_type,
-                "Connection cancelled",
-                "cancelled",
-                started_at,
-            )
-            raise
-        except BaseExceptionGroup as eg:
-            # Handle exception groups from anyio task groups at outer level
-            actual_error = None
-            for exc in eg.exceptions:
-                if isinstance(exc, Exception) and not isinstance(exc, GeneratorExit):
-                    error_msg = str(exc).lower()
-                    if "401" in error_msg or "unauthorized" in error_msg:
-                        actual_error = exc
-                        break
-                    if "cancel scope" not in error_msg and "task" not in error_msg:
-                        actual_error = exc
-                        break
-
-            await self._cleanup_on_error()
-            error_type = (
-                "authentication"
-                if actual_error
-                and (
-                    "401" in str(actual_error).lower()
-                    or "unauthorized" in str(actual_error).lower()
-                )
-                else "network"
-            )
-            error_msg = str(actual_error) if actual_error else str(eg)
-            self._emit_connection_failed(
-                server_name,
-                server_url,
-                transport_type,
-                error_msg,
-                error_type,
-                started_at,
-            )
-            if actual_error:
-                raise ConnectionError(
-                    f"Failed to connect to MCP server: {actual_error}"
-                ) from actual_error
-            raise ConnectionError(f"Failed to connect to MCP server: {eg}") from eg
-        except Exception as e:
-            await self._cleanup_on_error()
-            error_type = (
-                "authentication"
-                if "401" in str(e).lower() or "unauthorized" in str(e).lower()
-                else "network"
-            )
-            self._emit_connection_failed(
-                server_name, server_url, transport_type, str(e), error_type, started_at
-            )
-            raise ConnectionError(f"Failed to connect to MCP server: {e}") from e
-
-    def _emit_connection_failed(
-        self,
-        server_name: str,
-        server_url: str | None,
-        transport_type: str | None,
-        error: str,
-        error_type: str,
-        started_at: datetime,
-    ) -> None:
-        """Emit connection failed event."""
-        failed_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            MCPConnectionFailedEvent(
-                server_name=server_name,
-                server_url=server_url,
-                transport_type=transport_type,
-                error=error,
-                error_type=error_type,
-                started_at=started_at,
-                failed_at=failed_at,
-            ),
-        )
-
-    async def _cleanup_on_error(self) -> None:
-        """Cleanup resources when an error occurs during connection."""
-        try:
-            await self._exit_stack.aclose()
-
-        except Exception as e:
-            # Best effort cleanup - ignore all other errors
-            raise RuntimeError(f"Error during MCP client cleanup: {e}") from e
-        finally:
-            self._session = None
-            self._initialized = False
-            self._exit_stack = AsyncExitStack()
-
-    async def disconnect(self) -> None:
-        """Disconnect from MCP server and cleanup resources."""
-        if not self.connected:
-            return
-
-        try:
-            await self._exit_stack.aclose()
-        except Exception as e:
-            raise RuntimeError(f"Error during MCP client disconnect: {e}") from e
-        finally:
-            self._session = None
-            self._initialized = False
-            self._exit_stack = AsyncExitStack()
-
-    async def list_tools(self, use_cache: bool | None = None) -> list[dict[str, Any]]:
-        """List available tools from MCP server.
-
-        Args:
-            use_cache: Whether to use cached results. If None, uses
-                client's cache_tools_list setting.
-
-        Returns:
-            List of tool definitions with name, description, and inputSchema.
-        """
-        if not self.connected:
-            await self.connect()
-
-        # Check cache if enabled
-        use_cache = use_cache if use_cache is not None else self.cache_tools_list
-        if use_cache:
-            cache_key = self._get_cache_key("tools")
-            if cache_key in _mcp_schema_cache:
-                cached_data, cache_time = _mcp_schema_cache[cache_key]
-                if time.time() - cache_time < _cache_ttl:
-                    # Logger removed - return cached data
-                    return cached_data
-
-        # List tools with timeout and retries
-        tools = await self._retry_operation(
-            self._list_tools_impl,
-            timeout=self.discovery_timeout,
-        )
-
-        # Cache results if enabled
-        if use_cache:
-            cache_key = self._get_cache_key("tools")
-            _mcp_schema_cache[cache_key] = (tools, time.time())
-
-        return tools
-
-    async def _list_tools_impl(self) -> list[dict[str, Any]]:
-        """Internal implementation of list_tools."""
-        tools_result = await asyncio.wait_for(
-            self.session.list_tools(),
-            timeout=self.discovery_timeout,
-        )
-
-        return [
-            {
-                "name": tool.name,
-                "description": getattr(tool, "description", ""),
-                "inputSchema": getattr(tool, "inputSchema", {}),
-            }
-            for tool in tools_result.tools
-        ]
-
-    async def call_tool(
-        self, tool_name: str, arguments: dict[str, Any] | None = None
-    ) -> Any:
-        """Call a tool on the MCP server.
-
-        Args:
-            tool_name: Name of the tool to call.
-            arguments: Tool arguments.
-
-        Returns:
-            Tool execution result.
-        """
-        if not self.connected:
-            await self.connect()
-
-        arguments = arguments or {}
-        cleaned_arguments = self._clean_tool_arguments(arguments)
-
-        # Get server info for events
-        server_name, server_url, transport_type = self._get_server_info()
-
-        # Emit tool execution started event
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            MCPToolExecutionStartedEvent(
-                server_name=server_name,
-                server_url=server_url,
-                transport_type=transport_type,
-                tool_name=tool_name,
-                tool_args=cleaned_arguments,
-            ),
-        )
-
-        try:
-            result = await self._retry_operation(
-                lambda: self._call_tool_impl(tool_name, cleaned_arguments),
-                timeout=self.execution_timeout,
-            )
-
-            completed_at = datetime.now()
-            execution_duration_ms = (completed_at - started_at).total_seconds() * 1000
-            crewai_event_bus.emit(
-                self,
-                MCPToolExecutionCompletedEvent(
-                    server_name=server_name,
-                    server_url=server_url,
-                    transport_type=transport_type,
-                    tool_name=tool_name,
-                    tool_args=cleaned_arguments,
-                    result=result,
-                    started_at=started_at,
-                    completed_at=completed_at,
-                    execution_duration_ms=execution_duration_ms,
-                ),
-            )
-
-            return result
-        except Exception as e:
-            failed_at = datetime.now()
-            error_type = (
-                "timeout"
-                if isinstance(e, (asyncio.TimeoutError, ConnectionError))
-                and "timeout" in str(e).lower()
-                else "server_error"
-            )
-            crewai_event_bus.emit(
-                self,
-                MCPToolExecutionFailedEvent(
-                    server_name=server_name,
-                    server_url=server_url,
-                    transport_type=transport_type,
-                    tool_name=tool_name,
-                    tool_args=cleaned_arguments,
-                    error=str(e),
-                    error_type=error_type,
-                    started_at=started_at,
-                    failed_at=failed_at,
-                ),
-            )
-            raise
-
-    def _clean_tool_arguments(self, arguments: dict[str, Any]) -> dict[str, Any]:
-        """Clean tool arguments by removing None values and fixing formats.
-
-        Args:
-            arguments: Raw tool arguments.
-
-        Returns:
-            Cleaned arguments ready for MCP server.
-        """
-        cleaned = {}
-
-        for key, value in arguments.items():
-            # Skip None values
-            if value is None:
-                continue
-
-            # Fix sources array format: convert ["web"] to [{"type": "web"}]
-            if key == "sources" and isinstance(value, list):
-                fixed_sources = []
-                for item in value:
-                    if isinstance(item, str):
-                        # Convert string to object format
-                        fixed_sources.append({"type": item})
-                    elif isinstance(item, dict):
-                        # Already in correct format
-                        fixed_sources.append(item)
-                    else:
-                        # Keep as is if unknown format
-                        fixed_sources.append(item)
-                if fixed_sources:
-                    cleaned[key] = fixed_sources
-                continue
-
-            # Recursively clean nested dictionaries
-            if isinstance(value, dict):
-                nested_cleaned = self._clean_tool_arguments(value)
-                if nested_cleaned:  # Only add if not empty
-                    cleaned[key] = nested_cleaned
-            elif isinstance(value, list):
-                # Clean list items
-                cleaned_list = []
-                for item in value:
-                    if isinstance(item, dict):
-                        cleaned_item = self._clean_tool_arguments(item)
-                        if cleaned_item:
-                            cleaned_list.append(cleaned_item)
-                    elif item is not None:
-                        cleaned_list.append(item)
-                if cleaned_list:
-                    cleaned[key] = cleaned_list
-            else:
-                # Keep primitive values
-                cleaned[key] = value
-
-        return cleaned
-
-    async def _call_tool_impl(self, tool_name: str, arguments: dict[str, Any]) -> Any:
-        """Internal implementation of call_tool."""
-        result = await asyncio.wait_for(
-            self.session.call_tool(tool_name, arguments),
-            timeout=self.execution_timeout,
-        )
-
-        # Extract result content
-        if hasattr(result, "content") and result.content:
-            if isinstance(result.content, list) and len(result.content) > 0:
-                content_item = result.content[0]
-                if hasattr(content_item, "text"):
-                    return str(content_item.text)
-                return str(content_item)
-            return str(result.content)
-
-        return str(result)
-
-    async def list_prompts(self) -> list[dict[str, Any]]:
-        """List available prompts from MCP server.
-
-        Returns:
-            List of prompt definitions.
-        """
-        if not self.connected:
-            await self.connect()
-
-        return await self._retry_operation(
-            self._list_prompts_impl,
-            timeout=self.discovery_timeout,
-        )
-
-    async def _list_prompts_impl(self) -> list[dict[str, Any]]:
-        """Internal implementation of list_prompts."""
-        prompts_result = await asyncio.wait_for(
-            self.session.list_prompts(),
-            timeout=self.discovery_timeout,
-        )
-
-        return [
-            {
-                "name": prompt.name,
-                "description": getattr(prompt, "description", ""),
-                "arguments": getattr(prompt, "arguments", []),
-            }
-            for prompt in prompts_result.prompts
-        ]
-
-    async def get_prompt(
-        self, prompt_name: str, arguments: dict[str, Any] | None = None
-    ) -> dict[str, Any]:
-        """Get a prompt from the MCP server.
-
-        Args:
-            prompt_name: Name of the prompt to get.
-            arguments: Optional prompt arguments.
-
-        Returns:
-            Prompt content and metadata.
-        """
-        if not self.connected:
-            await self.connect()
-
-        arguments = arguments or {}
-
-        return await self._retry_operation(
-            lambda: self._get_prompt_impl(prompt_name, arguments),
-            timeout=self.execution_timeout,
-        )
-
-    async def _get_prompt_impl(
-        self, prompt_name: str, arguments: dict[str, Any]
-    ) -> dict[str, Any]:
-        """Internal implementation of get_prompt."""
-        result = await asyncio.wait_for(
-            self.session.get_prompt(prompt_name, arguments),
-            timeout=self.execution_timeout,
-        )
-
-        return {
-            "name": prompt_name,
-            "messages": [
-                {
-                    "role": msg.role,
-                    "content": msg.content,
-                }
-                for msg in result.messages
-            ],
-            "arguments": arguments,
-        }
-
-    async def _retry_operation(
-        self,
-        operation: Callable[[], Any],
-        timeout: int | None = None,
-    ) -> Any:
-        """Retry an operation with exponential backoff.
-
-        Args:
-            operation: Async operation to retry.
-            timeout: Operation timeout in seconds.
-
-        Returns:
-            Operation result.
-        """
-        last_error = None
-        timeout = timeout or self.execution_timeout
-
-        for attempt in range(self.max_retries):
-            try:
-                if timeout:
-                    return await asyncio.wait_for(operation(), timeout=timeout)
-                return await operation()
-
-            except asyncio.TimeoutError as e:  # noqa: PERF203
-                last_error = f"Operation timed out after {timeout} seconds"
-                if attempt < self.max_retries - 1:
-                    wait_time = 2**attempt
-                    await asyncio.sleep(wait_time)
-                else:
-                    raise ConnectionError(last_error) from e
-
-            except Exception as e:
-                error_str = str(e).lower()
-
-                # Classify errors as retryable or non-retryable
-                if "authentication" in error_str or "unauthorized" in error_str:
-                    raise ConnectionError(f"Authentication failed: {e}") from e
-
-                if "not found" in error_str:
-                    raise ValueError(f"Resource not found: {e}") from e
-
-                # Retryable errors
-                last_error = str(e)
-                if attempt < self.max_retries - 1:
-                    wait_time = 2**attempt
-                    await asyncio.sleep(wait_time)
-                else:
-                    raise ConnectionError(
-                        f"Operation failed after {self.max_retries} attempts: {last_error}"
-                    ) from e
-
-        raise ConnectionError(f"Operation failed: {last_error}")
-
-    def _get_cache_key(self, resource_type: str) -> str:
-        """Generate cache key for resource.
-
-        Args:
-            resource_type: Type of resource (e.g., "tools", "prompts").
-
-        Returns:
-            Cache key string.
-        """
-        # Use transport type and URL/command as cache key
-        if isinstance(self.transport, StdioTransport):
-            key = f"stdio:{self.transport.command}:{':'.join(self.transport.args)}"
-        elif isinstance(self.transport, HTTPTransport):
-            key = f"http:{self.transport.url}"
-        elif isinstance(self.transport, SSETransport):
-            key = f"sse:{self.transport.url}"
-        else:
-            key = f"{self.transport.transport_type}:unknown"
-
-        return f"mcp:{key}:{resource_type}"
-
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        return await self.connect()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: Any,
-    ) -> None:
-        """Async context manager exit."""
-        await self.disconnect()

lib/crewai/src/crewai/mcp/config.py

@@ -1,124 +0,0 @@
-"""MCP server configuration models for CrewAI agents.
-
-This module provides Pydantic models for configuring MCP servers with
-various transport types, similar to OpenAI's Agents SDK.
-"""
-
-from pydantic import BaseModel, Field
-
-from crewai.mcp.filters import ToolFilter
-
-
-class MCPServerStdio(BaseModel):
-    """Stdio MCP server configuration.
-
-    This configuration is used for connecting to local MCP servers
-    that run as processes and communicate via standard input/output.
-
-    Example:
-        ```python
-        mcp_server = MCPServerStdio(
-            command="python",
-            args=["path/to/server.py"],
-            env={"API_KEY": "..."},
-            tool_filter=create_static_tool_filter(
-                allowed_tool_names=["read_file", "write_file"]
-            ),
-        )
-        ```
-    """
-
-    command: str = Field(
-        ...,
-        description="Command to execute (e.g., 'python', 'node', 'npx', 'uvx').",
-    )
-    args: list[str] = Field(
-        default_factory=list,
-        description="Command arguments (e.g., ['server.py'] or ['-y', '@mcp/server']).",
-    )
-    env: dict[str, str] | None = Field(
-        default=None,
-        description="Environment variables to pass to the process.",
-    )
-    tool_filter: ToolFilter | None = Field(
-        default=None,
-        description="Optional tool filter for filtering available tools.",
-    )
-    cache_tools_list: bool = Field(
-        default=False,
-        description="Whether to cache the tool list for faster subsequent access.",
-    )
-
-
-class MCPServerHTTP(BaseModel):
-    """HTTP/Streamable HTTP MCP server configuration.
-
-    This configuration is used for connecting to remote MCP servers
-    over HTTP/HTTPS using streamable HTTP transport.
-
-    Example:
-        ```python
-        mcp_server = MCPServerHTTP(
-            url="https://api.example.com/mcp",
-            headers={"Authorization": "Bearer ..."},
-            cache_tools_list=True,
-        )
-        ```
-    """
-
-    url: str = Field(
-        ..., description="Server URL (e.g., 'https://api.example.com/mcp')."
-    )
-    headers: dict[str, str] | None = Field(
-        default=None,
-        description="Optional HTTP headers for authentication or other purposes.",
-    )
-    streamable: bool = Field(
-        default=True,
-        description="Whether to use streamable HTTP transport (default: True).",
-    )
-    tool_filter: ToolFilter | None = Field(
-        default=None,
-        description="Optional tool filter for filtering available tools.",
-    )
-    cache_tools_list: bool = Field(
-        default=False,
-        description="Whether to cache the tool list for faster subsequent access.",
-    )
-
-
-class MCPServerSSE(BaseModel):
-    """Server-Sent Events (SSE) MCP server configuration.
-
-    This configuration is used for connecting to remote MCP servers
-    using Server-Sent Events for real-time streaming communication.
-
-    Example:
-        ```python
-        mcp_server = MCPServerSSE(
-            url="https://api.example.com/mcp/sse",
-            headers={"Authorization": "Bearer ..."},
-        )
-        ```
-    """
-
-    url: str = Field(
-        ...,
-        description="Server URL (e.g., 'https://api.example.com/mcp/sse').",
-    )
-    headers: dict[str, str] | None = Field(
-        default=None,
-        description="Optional HTTP headers for authentication or other purposes.",
-    )
-    tool_filter: ToolFilter | None = Field(
-        default=None,
-        description="Optional tool filter for filtering available tools.",
-    )
-    cache_tools_list: bool = Field(
-        default=False,
-        description="Whether to cache the tool list for faster subsequent access.",
-    )
-
-
-# Type alias for all MCP server configurations
-MCPServerConfig = MCPServerStdio | MCPServerHTTP | MCPServerSSE

lib/crewai/src/crewai/mcp/filters.py

@@ -1,166 +0,0 @@
-"""Tool filtering support for MCP servers.
-
-This module provides utilities for filtering tools from MCP servers,
-including static allow/block lists and dynamic context-aware filtering.
-"""
-
-from collections.abc import Callable
-from typing import TYPE_CHECKING, Any
-
-from pydantic import BaseModel, Field
-
-
-if TYPE_CHECKING:
-    pass
-
-
-class ToolFilterContext(BaseModel):
-    """Context for dynamic tool filtering.
-
-    This context is passed to dynamic tool filters to provide
-    information about the agent, run context, and server.
-    """
-
-    agent: Any = Field(..., description="The agent requesting tools.")
-    server_name: str = Field(..., description="Name of the MCP server.")
-    run_context: dict[str, Any] | None = Field(
-        default=None,
-        description="Optional run context for additional filtering logic.",
-    )
-
-
-# Type alias for tool filter functions
-ToolFilter = (
-    Callable[[ToolFilterContext, dict[str, Any]], bool]
-    | Callable[[dict[str, Any]], bool]
-)
-
-
-class StaticToolFilter:
-    """Static tool filter with allow/block lists.
-
-    This filter provides simple allow/block list filtering based on
-    tool names. Useful for restricting which tools are available
-    from an MCP server.
-
-    Example:
-        ```python
-        filter = StaticToolFilter(
-            allowed_tool_names=["read_file", "write_file"],
-            blocked_tool_names=["delete_file"],
-        )
-        ```
-    """
-
-    def __init__(
-        self,
-        allowed_tool_names: list[str] | None = None,
-        blocked_tool_names: list[str] | None = None,
-    ) -> None:
-        """Initialize static tool filter.
-
-        Args:
-            allowed_tool_names: List of tool names to allow. If None,
-                all tools are allowed (unless blocked).
-            blocked_tool_names: List of tool names to block. Blocked tools
-                take precedence over allowed tools.
-        """
-        self.allowed_tool_names = set(allowed_tool_names or [])
-        self.blocked_tool_names = set(blocked_tool_names or [])
-
-    def __call__(self, tool: dict[str, Any]) -> bool:
-        """Filter tool based on allow/block lists.
-
-        Args:
-            tool: Tool definition dictionary with at least 'name' key.
-
-        Returns:
-            True if tool should be included, False otherwise.
-        """
-        tool_name = tool.get("name", "")
-
-        # Blocked tools take precedence
-        if self.blocked_tool_names and tool_name in self.blocked_tool_names:
-            return False
-
-        # If allow list exists, tool must be in it
-        if self.allowed_tool_names:
-            return tool_name in self.allowed_tool_names
-
-        # No restrictions - allow all
-        return True
-
-
-def create_static_tool_filter(
-    allowed_tool_names: list[str] | None = None,
-    blocked_tool_names: list[str] | None = None,
-) -> Callable[[dict[str, Any]], bool]:
-    """Create a static tool filter function.
-
-    This is a convenience function for creating static tool filters
-    with allow/block lists.
-
-    Args:
-        allowed_tool_names: List of tool names to allow. If None,
-            all tools are allowed (unless blocked).
-        blocked_tool_names: List of tool names to block. Blocked tools
-            take precedence over allowed tools.
-
-    Returns:
-        Tool filter function that returns True for allowed tools.
-
-    Example:
-        ```python
-        filter_fn = create_static_tool_filter(
-            allowed_tool_names=["read_file", "write_file"],
-            blocked_tool_names=["delete_file"],
-        )
-
-        # Use in MCPServerStdio
-        mcp_server = MCPServerStdio(
-            command="npx",
-            args=["-y", "@modelcontextprotocol/server-filesystem"],
-            tool_filter=filter_fn,
-        )
-        ```
-    """
-    return StaticToolFilter(
-        allowed_tool_names=allowed_tool_names,
-        blocked_tool_names=blocked_tool_names,
-    )
-
-
-def create_dynamic_tool_filter(
-    filter_func: Callable[[ToolFilterContext, dict[str, Any]], bool],
-) -> Callable[[ToolFilterContext, dict[str, Any]], bool]:
-    """Create a dynamic tool filter function.
-
-    This function wraps a dynamic filter function that has access
-    to the tool filter context (agent, server, run context).
-
-    Args:
-        filter_func: Function that takes (context, tool) and returns bool.
-
-    Returns:
-        Tool filter function that can be used with MCP server configs.
-
-    Example:
-        ```python
-        async def context_aware_filter(
-            context: ToolFilterContext, tool: dict[str, Any]
-        ) -> bool:
-            # Block dangerous tools for code reviewers
-            if context.agent.role == "Code Reviewer":
-                if tool["name"].startswith("danger_"):
-                    return False
-            return True
-
-
-        filter_fn = create_dynamic_tool_filter(context_aware_filter)
-
-        mcp_server = MCPServerStdio(
-            command="python", args=["server.py"], tool_filter=filter_fn
-        )
-        ```
-    """
-    return filter_func

lib/crewai/src/crewai/mcp/transports/__init__.py

@@ -1,15 +0,0 @@
-"""MCP transport implementations for various connection types."""
-
-from crewai.mcp.transports.base import BaseTransport, TransportType
-from crewai.mcp.transports.http import HTTPTransport
-from crewai.mcp.transports.sse import SSETransport
-from crewai.mcp.transports.stdio import StdioTransport
-
-
-__all__ = [
-    "BaseTransport",
-    "HTTPTransport",
-    "SSETransport",
-    "StdioTransport",
-    "TransportType",
-]

lib/crewai/src/crewai/mcp/transports/base.py

@@ -1,125 +0,0 @@
-"""Base transport interface for MCP connections."""
-
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import Any, Protocol
-
-from typing_extensions import Self
-
-
-class TransportType(str, Enum):
-    """MCP transport types."""
-
-    STDIO = "stdio"
-    HTTP = "http"
-    STREAMABLE_HTTP = "streamable-http"
-    SSE = "sse"
-
-
-class ReadStream(Protocol):
-    """Protocol for read streams."""
-
-    async def read(self, n: int = -1) -> bytes:
-        """Read bytes from stream."""
-        ...
-
-
-class WriteStream(Protocol):
-    """Protocol for write streams."""
-
-    async def write(self, data: bytes) -> None:
-        """Write bytes to stream."""
-        ...
-
-
-class BaseTransport(ABC):
-    """Base class for MCP transport implementations.
-
-    This abstract base class defines the interface that all transport
-    implementations must follow. Transports handle the low-level communication
-    with MCP servers.
-    """
-
-    def __init__(self, **kwargs: Any) -> None:
-        """Initialize the transport.
-
-        Args:
-            **kwargs: Transport-specific configuration options.
-        """
-        self._read_stream: ReadStream | None = None
-        self._write_stream: WriteStream | None = None
-        self._connected = False
-
-    @property
-    @abstractmethod
-    def transport_type(self) -> TransportType:
-        """Return the transport type."""
-        ...
-
-    @property
-    def connected(self) -> bool:
-        """Check if transport is connected."""
-        return self._connected
-
-    @property
-    def read_stream(self) -> ReadStream:
-        """Get the read stream."""
-        if self._read_stream is None:
-            raise RuntimeError("Transport not connected. Call connect() first.")
-        return self._read_stream
-
-    @property
-    def write_stream(self) -> WriteStream:
-        """Get the write stream."""
-        if self._write_stream is None:
-            raise RuntimeError("Transport not connected. Call connect() first.")
-        return self._write_stream
-
-    @abstractmethod
-    async def connect(self) -> Self:
-        """Establish connection to MCP server.
-
-        Returns:
-            Self for method chaining.
-
-        Raises:
-            ConnectionError: If connection fails.
-        """
-        ...
-
-    @abstractmethod
-    async def disconnect(self) -> None:
-        """Close connection to MCP server."""
-        ...
-
-    @abstractmethod
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        ...
-
-    @abstractmethod
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: Any,
-    ) -> None:
-        """Async context manager exit."""
-        ...
-
-    def _set_streams(self, read: ReadStream, write: WriteStream) -> None:
-        """Set the read and write streams.
-
-        Args:
-            read: Read stream.
-            write: Write stream.
-        """
-        self._read_stream = read
-        self._write_stream = write
-        self._connected = True
-
-    def _clear_streams(self) -> None:
-        """Clear the read and write streams."""
-        self._read_stream = None
-        self._write_stream = None
-        self._connected = False

lib/crewai/src/crewai/mcp/transports/http.py

@@ -1,174 +0,0 @@
-"""HTTP and Streamable HTTP transport for MCP servers."""
-
-import asyncio
-from typing import Any
-
-from typing_extensions import Self
-
-
-# BaseExceptionGroup is available in Python 3.11+
-try:
-    from builtins import BaseExceptionGroup
-except ImportError:
-    # Fallback for Python < 3.11 (shouldn't happen in practice)
-    BaseExceptionGroup = Exception
-
-from crewai.mcp.transports.base import BaseTransport, TransportType
-
-
-class HTTPTransport(BaseTransport):
-    """HTTP/Streamable HTTP transport for connecting to remote MCP servers.
-
-    This transport connects to MCP servers over HTTP/HTTPS using the
-    streamable HTTP client from the MCP SDK.
-
-    Example:
-        ```python
-        transport = HTTPTransport(
-            url="https://api.example.com/mcp",
-            headers={"Authorization": "Bearer ..."}
-        )
-        async with transport:
-            # Use transport...
-        ```
-    """
-
-    def __init__(
-        self,
-        url: str,
-        headers: dict[str, str] | None = None,
-        streamable: bool = True,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize HTTP transport.
-
-        Args:
-            url: Server URL (e.g., "https://api.example.com/mcp").
-            headers: Optional HTTP headers.
-            streamable: Whether to use streamable HTTP (default: True).
-            **kwargs: Additional transport options.
-        """
-        super().__init__(**kwargs)
-        self.url = url
-        self.headers = headers or {}
-        self.streamable = streamable
-        self._transport_context: Any = None
-
-    @property
-    def transport_type(self) -> TransportType:
-        """Return the transport type."""
-        return TransportType.STREAMABLE_HTTP if self.streamable else TransportType.HTTP
-
-    async def connect(self) -> Self:
-        """Establish HTTP connection to MCP server.
-
-        Returns:
-            Self for method chaining.
-
-        Raises:
-            ConnectionError: If connection fails.
-            ImportError: If MCP SDK not available.
-        """
-        if self._connected:
-            return self
-
-        try:
-            from mcp.client.streamable_http import streamablehttp_client
-
-            self._transport_context = streamablehttp_client(
-                self.url,
-                headers=self.headers if self.headers else None,
-                terminate_on_close=True,
-            )
-
-            try:
-                read, write, _ = await asyncio.wait_for(
-                    self._transport_context.__aenter__(), timeout=30.0
-                )
-            except asyncio.TimeoutError as e:
-                self._transport_context = None
-                raise ConnectionError(
-                    "Transport context entry timed out after 30 seconds. "
-                    "Server may be slow or unreachable."
-                ) from e
-            except Exception as e:
-                self._transport_context = None
-                raise ConnectionError(f"Failed to enter transport context: {e}") from e
-            self._set_streams(read=read, write=write)
-            return self
-
-        except ImportError as e:
-            raise ImportError(
-                "MCP library not available. Please install with: pip install mcp"
-            ) from e
-        except Exception as e:
-            self._clear_streams()
-            if self._transport_context is not None:
-                self._transport_context = None
-            raise ConnectionError(f"Failed to connect to MCP server: {e}") from e
-
-    async def disconnect(self) -> None:
-        """Close HTTP connection."""
-        if not self._connected:
-            return
-
-        try:
-            # Clear streams first
-            self._clear_streams()
-            # await self._exit_stack.aclose()
-
-            # Exit transport context - this will clean up background tasks
-            # Give a small delay to allow background tasks to complete
-            if self._transport_context is not None:
-                try:
-                    # Wait a tiny bit for any pending operations
-                    await asyncio.sleep(0.1)
-                    await self._transport_context.__aexit__(None, None, None)
-                except (RuntimeError, asyncio.CancelledError) as e:
-                    # Ignore "exit cancel scope in different task" errors and cancellation
-                    # These happen when asyncio.run() closes the event loop
-                    # while background tasks are still running
-                    error_msg = str(e).lower()
-                    if "cancel scope" not in error_msg and "task" not in error_msg:
-                        # Only suppress cancel scope/task errors, re-raise others
-                        if isinstance(e, RuntimeError):
-                            raise
-                        # For CancelledError, just suppress it
-                except BaseExceptionGroup as eg:
-                    # Handle exception groups from anyio task groups
-                    # Suppress if they contain cancel scope errors
-                    should_suppress = False
-                    for exc in eg.exceptions:
-                        error_msg = str(exc).lower()
-                        if "cancel scope" in error_msg or "task" in error_msg:
-                            should_suppress = True
-                            break
-                    if not should_suppress:
-                        raise
-                except Exception as e:
-                    raise RuntimeError(
-                        f"Error during HTTP transport disconnect: {e}"
-                    ) from e
-
-            self._connected = False
-
-        except Exception as e:
-            # Log but don't raise - cleanup should be best effort
-            import logging
-
-            logger = logging.getLogger(__name__)
-            logger.warning(f"Error during HTTP transport disconnect: {e}")
-
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        return await self.connect()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: Any,
-    ) -> None:
-        """Async context manager exit."""
-
-        await self.disconnect()

lib/crewai/src/crewai/mcp/transports/sse.py

@@ -1,113 +0,0 @@
-"""Server-Sent Events (SSE) transport for MCP servers."""
-
-from typing import Any
-
-from typing_extensions import Self
-
-from crewai.mcp.transports.base import BaseTransport, TransportType
-
-
-class SSETransport(BaseTransport):
-    """SSE transport for connecting to remote MCP servers.
-
-    This transport connects to MCP servers using Server-Sent Events (SSE)
-    for real-time streaming communication.
-
-    Example:
-        ```python
-        transport = SSETransport(
-            url="https://api.example.com/mcp/sse",
-            headers={"Authorization": "Bearer ..."}
-        )
-        async with transport:
-            # Use transport...
-        ```
-    """
-
-    def __init__(
-        self,
-        url: str,
-        headers: dict[str, str] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize SSE transport.
-
-        Args:
-            url: Server URL (e.g., "https://api.example.com/mcp/sse").
-            headers: Optional HTTP headers.
-            **kwargs: Additional transport options.
-        """
-        super().__init__(**kwargs)
-        self.url = url
-        self.headers = headers or {}
-        self._transport_context: Any = None
-
-    @property
-    def transport_type(self) -> TransportType:
-        """Return the transport type."""
-        return TransportType.SSE
-
-    async def connect(self) -> Self:
-        """Establish SSE connection to MCP server.
-
-        Returns:
-            Self for method chaining.
-
-        Raises:
-            ConnectionError: If connection fails.
-            ImportError: If MCP SDK not available.
-        """
-        if self._connected:
-            return self
-
-        try:
-            from mcp.client.sse import sse_client
-
-            self._transport_context = sse_client(
-                self.url,
-                headers=self.headers if self.headers else None,
-                terminate_on_close=True,
-            )
-
-            read, write = await self._transport_context.__aenter__()
-
-            self._set_streams(read=read, write=write)
-
-            return self
-
-        except ImportError as e:
-            raise ImportError(
-                "MCP library not available. Please install with: pip install mcp"
-            ) from e
-        except Exception as e:
-            self._clear_streams()
-            raise ConnectionError(f"Failed to connect to SSE MCP server: {e}") from e
-
-    async def disconnect(self) -> None:
-        """Close SSE connection."""
-        if not self._connected:
-            return
-
-        try:
-            self._clear_streams()
-            if self._transport_context is not None:
-                await self._transport_context.__aexit__(None, None, None)
-
-        except Exception as e:
-            import logging
-
-            logger = logging.getLogger(__name__)
-            logger.warning(f"Error during SSE transport disconnect: {e}")
-
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        return await self.connect()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: Any,
-    ) -> None:
-        """Async context manager exit."""
-        await self.disconnect()

lib/crewai/src/crewai/mcp/transports/stdio.py

@@ -1,153 +0,0 @@
-"""Stdio transport for MCP servers running as local processes."""
-
-import asyncio
-import os
-import subprocess
-from typing import Any
-
-from typing_extensions import Self
-
-from crewai.mcp.transports.base import BaseTransport, TransportType
-
-
-class StdioTransport(BaseTransport):
-    """Stdio transport for connecting to local MCP servers.
-
-    This transport connects to MCP servers running as local processes,
-    communicating via standard input/output streams. Supports Python,
-    Node.js, and other command-line servers.
-
-    Example:
-        ```python
-        transport = StdioTransport(
-            command="python",
-            args=["path/to/server.py"],
-            env={"API_KEY": "..."}
-        )
-        async with transport:
-            # Use transport...
-        ```
-    """
-
-    def __init__(
-        self,
-        command: str,
-        args: list[str] | None = None,
-        env: dict[str, str] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize stdio transport.
-
-        Args:
-            command: Command to execute (e.g., "python", "node", "npx").
-            args: Command arguments (e.g., ["server.py"] or ["-y", "@mcp/server"]).
-            env: Environment variables to pass to the process.
-            **kwargs: Additional transport options.
-        """
-        super().__init__(**kwargs)
-        self.command = command
-        self.args = args or []
-        self.env = env or {}
-        self._process: subprocess.Popen[bytes] | None = None
-        self._transport_context: Any = None
-
-    @property
-    def transport_type(self) -> TransportType:
-        """Return the transport type."""
-        return TransportType.STDIO
-
-    async def connect(self) -> Self:
-        """Start the MCP server process and establish connection.
-
-        Returns:
-            Self for method chaining.
-
-        Raises:
-            ConnectionError: If process fails to start.
-            ImportError: If MCP SDK not available.
-        """
-        if self._connected:
-            return self
-
-        try:
-            from mcp import StdioServerParameters
-            from mcp.client.stdio import stdio_client
-
-            process_env = os.environ.copy()
-            process_env.update(self.env)
-
-            server_params = StdioServerParameters(
-                command=self.command,
-                args=self.args,
-                env=process_env if process_env else None,
-            )
-            self._transport_context = stdio_client(server_params)
-
-            try:
-                read, write = await self._transport_context.__aenter__()
-            except Exception as e:
-                import traceback
-
-                traceback.print_exc()
-                self._transport_context = None
-                raise ConnectionError(
-                    f"Failed to enter stdio transport context: {e}"
-                ) from e
-
-            self._set_streams(read=read, write=write)
-
-            return self
-
-        except ImportError as e:
-            raise ImportError(
-                "MCP library not available. Please install with: pip install mcp"
-            ) from e
-        except Exception as e:
-            self._clear_streams()
-            if self._transport_context is not None:
-                self._transport_context = None
-            raise ConnectionError(f"Failed to start MCP server process: {e}") from e
-
-    async def disconnect(self) -> None:
-        """Terminate the MCP server process and close connection."""
-        if not self._connected:
-            return
-
-        try:
-            self._clear_streams()
-
-            if self._transport_context is not None:
-                await self._transport_context.__aexit__(None, None, None)
-
-            if self._process is not None:
-                try:
-                    self._process.terminate()
-                    try:
-                        await asyncio.wait_for(self._process.wait(), timeout=5.0)
-                    except asyncio.TimeoutError:
-                        self._process.kill()
-                        await self._process.wait()
-                # except ProcessLookupError:
-                #     pass
-                finally:
-                    self._process = None
-
-        except Exception as e:
-            # Log but don't raise - cleanup should be best effort
-            import logging
-
-            logger = logging.getLogger(__name__)
-            logger.warning(f"Error during stdio transport disconnect: {e}")
-
-    async def __aenter__(self) -> Self:
-        """Async context manager entry."""
-        return await self.connect()
-
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: Any,
-    ) -> None:
-        """Async context manager exit."""
-        await self.disconnect()

lib/crewai/src/crewai/rag/chromadb/utils.py

@@ -67,44 +67,31 @@ def _prepare_documents_for_chromadb(
     ids: list[str] = []
     texts: list[str] = []
     metadatas: list[Mapping[str, str | int | float | bool]] = []
-    seen_ids: dict[str, int] = {}
-
-    try:
-        for doc in documents:
-            if "doc_id" in doc:
-                doc_id = str(doc["doc_id"])
-            else:
-                metadata = doc.get("metadata")
-                if metadata and isinstance(metadata, dict) and "doc_id" in metadata:
-                    doc_id = str(metadata["doc_id"])
-                else:
-                    content_for_hash = doc["content"]
-                    if metadata:
-                        metadata_str = json.dumps(metadata, sort_keys=True)
-                        content_for_hash = f"{content_for_hash}|{metadata_str}"
-                    doc_id = hashlib.sha256(content_for_hash.encode()).hexdigest()
 
+    for doc in documents:
+        if "doc_id" in doc:
+            ids.append(doc["doc_id"])
+        else:
+            content_for_hash = doc["content"]
             metadata = doc.get("metadata")
             if metadata:
-                if isinstance(metadata, list):
-                    processed_metadata = metadata[0] if metadata and metadata[0] else {}
-                else:
-                    processed_metadata = metadata
-            else:
-                processed_metadata = {}
+                metadata_str = json.dumps(metadata, sort_keys=True)
+                content_for_hash = f"{content_for_hash}|{metadata_str}"
 
-            if doc_id in seen_ids:
-                idx = seen_ids[doc_id]
-                texts[idx] = doc["content"]
-                metadatas[idx] = processed_metadata
+            content_hash = hashlib.blake2b(
+                content_for_hash.encode(), digest_size=32
+            ).hexdigest()
+            ids.append(content_hash)
+
+        texts.append(doc["content"])
+        metadata = doc.get("metadata")
+        if metadata:
+            if isinstance(metadata, list):
+                metadatas.append(metadata[0] if metadata and metadata[0] else {})
             else:
-                idx = len(ids)
-                ids.append(doc_id)
-                texts.append(doc["content"])
-                metadatas.append(processed_metadata)
-                seen_ids[doc_id] = idx
-    except Exception as e:
-        raise ValueError(f"Error preparing documents for ChromaDB: {e}") from e
+                metadatas.append(metadata)
+        else:
+            metadatas.append({})
 
     return PreparedDocuments(ids, texts, metadatas)
 

lib/crewai/src/crewai/task.py

@@ -525,11 +525,7 @@ class Task(BaseModel):
                 tools=tools,
             )
 
-            if not self._guardrails and not self._guardrail:
-                pydantic_output, json_output = self._export_output(result)
-            else:
-                pydantic_output, json_output = None, None
-
+            pydantic_output, json_output = self._export_output(result)
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,
@@ -539,7 +535,6 @@ 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 +945,6 @@ 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/tools/mcp_native_tool.py

@@ -1,162 +0,0 @@
-"""Native MCP tool wrapper for CrewAI agents.
-
-This module provides a tool wrapper that reuses existing MCP client sessions
-for better performance and connection management.
-"""
-
-import asyncio
-from typing import Any
-
-from crewai.tools import BaseTool
-
-
-class MCPNativeTool(BaseTool):
-    """Native MCP tool that reuses client sessions.
-
-    This tool wrapper is used when agents connect to MCP servers using
-    structured configurations. It reuses existing client sessions for
-    better performance and proper connection lifecycle management.
-
-    Unlike MCPToolWrapper which connects on-demand, this tool uses
-    a shared MCP client instance that maintains a persistent connection.
-    """
-
-    def __init__(
-        self,
-        mcp_client: Any,
-        tool_name: str,
-        tool_schema: dict[str, Any],
-        server_name: str,
-    ) -> None:
-        """Initialize native MCP tool.
-
-        Args:
-            mcp_client: MCPClient instance with active session.
-            tool_name: Original name of the tool on the MCP server.
-            tool_schema: Schema information for the tool.
-            server_name: Name of the MCP server for prefixing.
-        """
-        # Create tool name with server prefix to avoid conflicts
-        prefixed_name = f"{server_name}_{tool_name}"
-
-        # Handle args_schema properly - BaseTool expects a BaseModel subclass
-        args_schema = tool_schema.get("args_schema")
-
-        # Only pass args_schema if it's provided
-        kwargs = {
-            "name": prefixed_name,
-            "description": tool_schema.get(
-                "description", f"Tool {tool_name} from {server_name}"
-            ),
-        }
-
-        if args_schema is not None:
-            kwargs["args_schema"] = args_schema
-
-        super().__init__(**kwargs)
-
-        # Set instance attributes after super().__init__
-        self._mcp_client = mcp_client
-        self._original_tool_name = tool_name
-        self._server_name = server_name
-        # self._logger = logging.getLogger(__name__)
-
-    @property
-    def mcp_client(self) -> Any:
-        """Get the MCP client instance."""
-        return self._mcp_client
-
-    @property
-    def original_tool_name(self) -> str:
-        """Get the original tool name."""
-        return self._original_tool_name
-
-    @property
-    def server_name(self) -> str:
-        """Get the server name."""
-        return self._server_name
-
-    def _run(self, **kwargs) -> str:
-        """Execute tool using the MCP client session.
-
-        Args:
-            **kwargs: Arguments to pass to the MCP tool.
-
-        Returns:
-            Result from the MCP tool execution.
-        """
-        try:
-            try:
-                asyncio.get_running_loop()
-
-                import concurrent.futures
-
-                with concurrent.futures.ThreadPoolExecutor() as executor:
-                    coro = self._run_async(**kwargs)
-                    future = executor.submit(asyncio.run, coro)
-                    return future.result()
-            except RuntimeError:
-                return asyncio.run(self._run_async(**kwargs))
-
-        except Exception as e:
-            raise RuntimeError(
-                f"Error executing MCP tool {self.original_tool_name}: {e!s}"
-            ) from e
-
-    async def _run_async(self, **kwargs) -> str:
-        """Async implementation of tool execution.
-
-        Args:
-            **kwargs: Arguments to pass to the MCP tool.
-
-        Returns:
-            Result from the MCP tool execution.
-        """
-        # Note: Since we use asyncio.run() which creates a new event loop each time,
-        # Always reconnect on-demand because asyncio.run() creates new event loops per call
-        # All MCP transport context managers (stdio, streamablehttp_client, sse_client)
-        # use anyio.create_task_group() which can't span different event loops
-        if self._mcp_client.connected:
-            await self._mcp_client.disconnect()
-
-        await self._mcp_client.connect()
-
-        try:
-            result = await self._mcp_client.call_tool(self.original_tool_name, kwargs)
-
-        except Exception as e:
-            error_str = str(e).lower()
-            if (
-                "not connected" in error_str
-                or "connection" in error_str
-                or "send" in error_str
-            ):
-                await self._mcp_client.disconnect()
-                await self._mcp_client.connect()
-                # Retry the call
-                result = await self._mcp_client.call_tool(
-                    self.original_tool_name, kwargs
-                )
-            else:
-                raise
-
-        finally:
-            # Always disconnect after tool call to ensure clean context manager lifecycle
-            # This prevents "exit cancel scope in different task" errors
-            # All transport context managers must be exited in the same event loop they were entered
-            await self._mcp_client.disconnect()
-
-        # Extract result content
-        if isinstance(result, str):
-            return result
-
-        # Handle various result formats
-        if hasattr(result, "content") and result.content:
-            if isinstance(result.content, list) and len(result.content) > 0:
-                content_item = result.content[0]
-                if hasattr(content_item, "text"):
-                    return str(content_item.text)
-                return str(content_item)
-            return str(result.content)
-
-        return str(result)

lib/crewai/src/crewai/tools/tool_usage.py

@@ -526,29 +526,6 @@ class ToolUsage:
                 )
             return self._tool_calling(tool_string)
 
-    def _coerce_args_dict(self, val: Any) -> dict[str, Any] | None:
-        """Coerce parsed arguments to a dictionary format.
-
-        Handles both GPT-4 format (flat dict) and GPT-5 format (array-wrapped dict).
-        GPT-5 wraps arguments in an array like: [{"arg": "value"}, []]
-        while GPT-4 uses a flat dict: {"arg": "value"}
-
-        Args:
-            val: The parsed value to coerce
-
-        Returns:
-            Dictionary if coercion is successful, None otherwise
-        """
-        if isinstance(val, dict):
-            return val
-
-        if isinstance(val, list) and val and isinstance(val[0], dict):
-            trailing_elements = val[1:]
-            if all(not x for x in trailing_elements):
-                return val[0]
-
-        return None
-
     def _validate_tool_input(self, tool_input: str | None) -> dict[str, Any]:
         if tool_input is None:
             return {}
@@ -561,18 +538,16 @@ class ToolUsage:
         # Attempt 1: Parse as JSON
         try:
             arguments = json.loads(tool_input)
-            coerced = self._coerce_args_dict(arguments)
-            if coerced is not None:
-                return coerced
+            if isinstance(arguments, dict):
+                return arguments
         except (JSONDecodeError, TypeError):
             pass  # Continue to the next parsing attempt
 
         # Attempt 2: Parse as Python literal
         try:
             arguments = ast.literal_eval(tool_input)
-            coerced = self._coerce_args_dict(arguments)
-            if coerced is not None:
-                return coerced
+            if isinstance(arguments, dict):
+                return arguments
         except (ValueError, SyntaxError):
             repaired_input = repair_json(tool_input)
             # Continue to the next parsing attempt
@@ -580,9 +555,8 @@ class ToolUsage:
         # Attempt 3: Parse as JSON5
         try:
             arguments = json5.loads(tool_input)
-            coerced = self._coerce_args_dict(arguments)
-            if coerced is not None:
-                return coerced
+            if isinstance(arguments, dict):
+                return arguments
         except (JSONDecodeError, ValueError, TypeError):
             pass  # Continue to the next parsing attempt
 
@@ -593,9 +567,8 @@ class ToolUsage:
                 content=f"Repaired JSON: {repaired_input}", color="blue"
             )
             arguments = json.loads(repaired_input)
-            coerced = self._coerce_args_dict(arguments)
-            if coerced is not None:
-                return coerced
+            if isinstance(arguments, dict):
+                return arguments
         except Exception as e:
             error = f"Failed to repair JSON: {e}"
             self._printer.print(content=error, color="red")

lib/crewai/src/crewai/translations/en.json

@@ -22,12 +22,12 @@
     "summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
-    "formatted_task_instructions": "Ensure your final answer strictly adheres to the following OpenAPI schema: {output_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
+    "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
     "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "lite_agent_response_format": "Ensure your final answer strictly adheres to the following OpenAPI schema: {response_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
+    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
     "knowledge_search_query": "The original query is: {task_prompt}.",
     "knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
   },

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

@@ -127,7 +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:
@@ -139,7 +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.",
@@ -157,7 +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,
     )
 
@@ -168,16 +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(
@@ -257,10 +249,10 @@ def get_llm_response(
     """
     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:
@@ -302,8 +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.
 
@@ -489,7 +481,7 @@ def summarize_messages(
             ),
         ]
         summary = llm.call(
-            messages,
+            messages,  # type: ignore[arg-type]
             callbacks=callbacks,
         )
         summarized_contents.append({"content": str(summary)})

lib/crewai/src/crewai/utilities/converter.py

@@ -10,9 +10,9 @@ from pydantic import BaseModel, ValidationError
 from typing_extensions import Unpack
 
 from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
-from crewai.utilities.i18n import get_i18n
 from crewai.utilities.internal_instructor import InternalInstructor
 from crewai.utilities.printer import Printer
+from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 
 if TYPE_CHECKING:
@@ -22,7 +22,6 @@ if TYPE_CHECKING:
     from crewai.llms.base_llm import BaseLLM
 
 _JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
-_I18N = get_i18n()
 
 
 class ConverterError(Exception):
@@ -88,7 +87,8 @@ class Converter(OutputConverter):
                             result = self.model.model_validate(result)
                         elif isinstance(result, str):
                             try:
-                                result = self.model.model_validate_json(result)
+                                parsed = json.loads(result)
+                                result = self.model.model_validate(parsed)
                             except Exception as parse_err:
                                 raise ConverterError(
                                     f"Failed to convert partial JSON result into Pydantic: {parse_err}"
@@ -172,16 +172,6 @@ def convert_to_model(
     model = output_pydantic or output_json
     if model is None:
         return result
-
-    if converter_cls:
-        return convert_with_instructions(
-            result=result,
-            model=model,
-            is_json_output=bool(output_json),
-            agent=agent,
-            converter_cls=converter_cls,
-        )
-
     try:
         escaped_result = json.dumps(json.loads(result, strict=False))
         return validate_model(
@@ -261,7 +251,7 @@ def handle_partial_json(
         except json.JSONDecodeError:
             pass
         except ValidationError:
-            raise
+            pass
         except Exception as e:
             Printer().print(
                 content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
@@ -345,26 +335,25 @@ def get_conversion_instructions(
     Returns:
 
     """
-    instructions = ""
+    instructions = "Please convert the following text into valid JSON."
     if (
         llm
         and not isinstance(llm, str)
         and hasattr(llm, "supports_function_calling")
         and llm.supports_function_calling()
     ):
-        schema_dict = generate_model_description(model)
-        schema = json.dumps(schema_dict, indent=2)
-        formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
-            output_format=schema
+        model_schema = PydanticSchemaParser(model=model).get_schema()
+        instructions += (
+            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
+            f"Use this format exactly:\n```json\n{model_schema}\n```"
         )
-        instructions += formatted_task_instructions
     else:
         model_description = generate_model_description(model)
-        schema_json = json.dumps(model_description, indent=2)
-        formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
-            output_format=schema_json
+        schema_json = json.dumps(model_description["json_schema"]["schema"], indent=2)
+        instructions += (
+            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
+            f"Use this format exactly:\n```json\n{schema_json}\n```"
         )
-        instructions += formatted_task_instructions
     return instructions
 
 

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_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"])
@@ -2157,14 +2117,15 @@ def test_agent_with_only_crewai_knowledge():
         goal="Provide information based on knowledge sources",
         backstory="You have access to specific knowledge sources.",
         llm=LLM(
-            model="gpt-4o-mini",
+            model="openrouter/openai/gpt-4o-mini",
+            api_key=os.getenv("OPENROUTER_API_KEY"),
         ),
     )
 
     # Create a task that requires the agent to use the knowledge
     task = Task(
         description="What is Vidit's favorite color?",
-        expected_output="Vidit's favorite color.",
+        expected_output="Vidit's favorclearite color.",
         agent=agent,
     )
 

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,
-    )
-
-    result = agent.kickoff("What is the population of Tokyo?")
-
-    assert isinstance(result, LiteAgentOutput)
-    assert hasattr(result, "messages")
-    assert isinstance(result.messages, list)
-    assert len(result.messages) > 0
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_lite_agent_returns_usage_metrics_async():
@@ -403,8 +382,8 @@ def test_guardrail_is_called_using_string():
     assert not guardrail_events["completed"][0].success
     assert guardrail_events["completed"][1].success
     assert (
-        "top 10 best Brazilian soccer players" in result.raw or
-        "Brazilian players" in result.raw
+        "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
+        in result.raw
     )
 
 

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-        https://*.google.com https://app.hubspot.com/ https://td.doubleclick.net https://www.googletagmanager.com/
-        https://www.youtube.com https://share.descript.com'
-      expires:
-      - '0'
-      permissions-policy:
-      - camera=(), microphone=(self), geolocation=()
-      pragma:
-      - no-cache
-      referrer-policy:
-      - strict-origin-when-cross-origin
-      strict-transport-security:
-      - max-age=63072000; includeSubDomains
-      vary:
-      - Accept
-      x-content-type-options:
-      - nosniff
-      x-frame-options:
-      - SAMEORIGIN
-      x-permitted-cross-domain-policies:
-      - none
-      x-request-id:
-      - ba6636f8-2374-4a67-8176-88341f2999ed
-      x-runtime:
-      - '0.081473'
-      x-xss-protection:
-      - 1; mode=block
-    status:
-      code: 401
-      message: Unauthorized
 version: 1

lib/crewai/tests/knowledge/test_knowledge.py

@@ -601,81 +601,3 @@ def test_file_path_validation():
         match="file_path/file_paths must be a Path, str, or a list of these types",
     ):
         PDFKnowledgeSource()
-
-
-def test_hash_based_id_generation_without_doc_id(mock_vector_db):
-    """Test that documents without doc_id generate hash-based IDs. Duplicates are deduplicated before upsert."""
-    import hashlib
-    import json
-    from crewai.rag.chromadb.utils import _prepare_documents_for_chromadb
-    from crewai.rag.types import BaseRecord
-
-    documents: list[BaseRecord] = [
-        {"content": "First document content", "metadata": {"source": "test1", "category": "research"}},
-        {"content": "Second document content", "metadata": {"source": "test2", "category": "research"}},
-        {"content": "Third document content"},  # No metadata
-    ]
-
-    result = _prepare_documents_for_chromadb(documents)
-
-    assert len(result.ids) == 3
-
-    # Unique documents should get 64-character hex hashes (no suffix)
-    for doc_id in result.ids:
-        assert len(doc_id) == 64, f"ID should be 64 characters: {doc_id}"
-        assert all(c in "0123456789abcdef" for c in doc_id), f"ID should be hex: {doc_id}"
-
-    # Different documents should have different hashes
-    assert result.ids[0] != result.ids[1] != result.ids[2]
-
-    # Verify hashes match expected values
-    expected_hash_1 = hashlib.sha256(
-        f"First document content|{json.dumps({'category': 'research', 'source': 'test1'}, sort_keys=True)}".encode()
-    ).hexdigest()
-    assert result.ids[0] == expected_hash_1, "First document hash should match expected"
-
-    expected_hash_3 = hashlib.sha256("Third document content".encode()).hexdigest()
-    assert result.ids[2] == expected_hash_3, "Third document hash should match expected"
-
-    # Test that duplicate documents are deduplicated (same ID, only one sent)
-    duplicate_documents: list[BaseRecord] = [
-        {"content": "Same content", "metadata": {"source": "test"}},
-        {"content": "Same content", "metadata": {"source": "test"}},
-        {"content": "Same content", "metadata": {"source": "test"}},
-    ]
-    duplicate_result = _prepare_documents_for_chromadb(duplicate_documents)
-    # Duplicates should be deduplicated - only one ID should remain
-    assert len(duplicate_result.ids) == 1, "Duplicate documents should be deduplicated"
-    assert len(duplicate_result.ids[0]) == 64, "Deduplicated ID should be clean hash"
-    # Verify it's the expected hash
-    expected_hash = hashlib.sha256(
-        f"Same content|{json.dumps({'source': 'test'}, sort_keys=True)}".encode()
-    ).hexdigest()
-    assert duplicate_result.ids[0] == expected_hash, "Deduplicated ID should match expected hash"
-
-
-def test_hash_based_id_generation_with_doc_id_in_metadata(mock_vector_db):
-    """Test that documents with doc_id in metadata use the doc_id directly, not hash-based."""
-    from crewai.rag.chromadb.utils import _prepare_documents_for_chromadb
-    from crewai.rag.types import BaseRecord
-
-    documents_with_doc_id: list[BaseRecord] = [
-        {"content": "First document", "metadata": {"doc_id": "custom-id-1", "source": "test1"}},
-        {"content": "Second document", "metadata": {"doc_id": "custom-id-2"}},
-    ]
-
-    documents_without_doc_id: list[BaseRecord] = [
-        {"content": "First document", "metadata": {"source": "test1"}},
-        {"content": "Second document"},
-    ]
-
-    result_with_doc_id = _prepare_documents_for_chromadb(documents_with_doc_id)
-    result_without_doc_id = _prepare_documents_for_chromadb(documents_without_doc_id)
-
-    assert result_with_doc_id.ids == ["custom-id-1", "custom-id-2"]
-
-    assert len(result_without_doc_id.ids) == 2
-    # Unique documents get 64-character hashes
-    for doc_id in result_without_doc_id.ids:
-        assert len(doc_id) == 64, "ID should be 64 characters"
-        assert all(c in "0123456789abcdef" for c in doc_id), "ID should be hex"

lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -36,7 +36,7 @@ def test_anthropic_completion_is_used_when_claude_provider():
 
     from crewai.llms.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
-    assert llm.provider == "anthropic"
+    assert llm.provider == "claude"
     assert llm.model == "claude-3-5-sonnet-20241022"
 
 
@@ -664,37 +664,3 @@ def test_anthropic_token_usage_tracking():
         assert usage["input_tokens"] == 50
         assert usage["output_tokens"] == 25
         assert usage["total_tokens"] == 75
-
-
-def test_anthropic_stop_sequences_sync():
-    """Test that stop and stop_sequences attributes stay synchronized."""
-    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
-
-    # Test setting stop as a list
-    llm.stop = ["\nObservation:", "\nThought:"]
-    assert llm.stop_sequences == ["\nObservation:", "\nThought:"]
-    assert llm.stop == ["\nObservation:", "\nThought:"]
-
-    # Test setting stop as a string
-    llm.stop = "\nFinal Answer:"
-    assert llm.stop_sequences == ["\nFinal Answer:"]
-    assert llm.stop == ["\nFinal Answer:"]
-
-    # Test setting stop as None
-    llm.stop = None
-    assert llm.stop_sequences == []
-    assert llm.stop == []
-
-
-@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
-def test_anthropic_stop_sequences_sent_to_api():
-    """Test that stop_sequences are properly sent to the Anthropic API."""
-    llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
-
-    llm.stop = ["\nObservation:", "\nThought:"]
-
-    result = llm.call("Say hello in one word")
-
-    assert result is not None
-    assert isinstance(result, str)
-    assert len(result) > 0

lib/crewai/tests/llms/azure/test_azure.py

@@ -39,7 +39,7 @@ def test_azure_completion_is_used_when_azure_openai_provider():
 
     from crewai.llms.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
-    assert llm.provider == "azure"
+    assert llm.provider == "azure_openai"
     assert llm.model == "gpt-4"
 
 

lib/crewai/tests/llms/bedrock/test_bedrock.py

@@ -736,56 +736,3 @@ def test_bedrock_client_error_handling():
         with pytest.raises(RuntimeError) as exc_info:
             llm.call("Hello")
         assert "throttled" in str(exc_info.value).lower()
-
-
-def test_bedrock_stop_sequences_sync():
-    """Test that stop and stop_sequences attributes stay synchronized."""
-    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-    # Test setting stop as a list
-    llm.stop = ["\nObservation:", "\nThought:"]
-    assert list(llm.stop_sequences) == ["\nObservation:", "\nThought:"]
-    assert llm.stop == ["\nObservation:", "\nThought:"]
-
-    # Test setting stop as a string
-    llm.stop = "\nFinal Answer:"
-    assert list(llm.stop_sequences) == ["\nFinal Answer:"]
-    assert llm.stop == ["\nFinal Answer:"]
-
-    # Test setting stop as None
-    llm.stop = None
-    assert list(llm.stop_sequences) == []
-    assert llm.stop == []
-
-
-def test_bedrock_stop_sequences_sent_to_api():
-    """Test that stop_sequences are properly sent to the Bedrock API."""
-    llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-    # Set stop sequences via the stop attribute (simulating CrewAgentExecutor)
-    llm.stop = ["\nObservation:", "\nThought:"]
-
-    # Patch the API call to capture parameters without making real call
-    with patch.object(llm.client, 'converse') as mock_converse:
-        mock_response = {
-            'output': {
-                'message': {
-                    'role': 'assistant',
-                    'content': [{'text': 'Hello'}]
-                }
-            },
-            'usage': {
-                'inputTokens': 10,
-                'outputTokens': 5,
-                'totalTokens': 15
-            }
-        }
-        mock_converse.return_value = mock_response
-
-        llm.call("Say hello in one word")
-
-        # Verify stop_sequences were passed to the API in the inference config
-        call_kwargs = mock_converse.call_args[1]
-        assert "inferenceConfig" in call_kwargs
-        assert "stopSequences" in call_kwargs["inferenceConfig"]
-        assert call_kwargs["inferenceConfig"]["stopSequences"] == ["\nObservation:", "\nThought:"]

lib/crewai/tests/llms/google/test_google.py

@@ -24,7 +24,7 @@ def test_gemini_completion_is_used_when_google_provider():
     llm = LLM(model="google/gemini-2.0-flash-001")
 
     assert llm.__class__.__name__ == "GeminiCompletion"
-    assert llm.provider == "gemini"
+    assert llm.provider == "google"
     assert llm.model == "gemini-2.0-flash-001"
 
 
@@ -648,55 +648,3 @@ def test_gemini_token_usage_tracking():
         assert usage["candidates_token_count"] == 25
         assert usage["total_token_count"] == 75
         assert usage["total_tokens"] == 75
-
-
-def test_gemini_stop_sequences_sync():
-    """Test that stop and stop_sequences attributes stay synchronized."""
-    llm = LLM(model="google/gemini-2.0-flash-001")
-
-    # Test setting stop as a list
-    llm.stop = ["\nObservation:", "\nThought:"]
-    assert llm.stop_sequences == ["\nObservation:", "\nThought:"]
-    assert llm.stop == ["\nObservation:", "\nThought:"]
-
-    # Test setting stop as a string
-    llm.stop = "\nFinal Answer:"
-    assert llm.stop_sequences == ["\nFinal Answer:"]
-    assert llm.stop == ["\nFinal Answer:"]
-
-    # Test setting stop as None
-    llm.stop = None
-    assert llm.stop_sequences == []
-    assert llm.stop == []
-
-
-def test_gemini_stop_sequences_sent_to_api():
-    """Test that stop_sequences are properly sent to the Gemini API."""
-    llm = LLM(model="google/gemini-2.0-flash-001")
-
-    # Set stop sequences via the stop attribute (simulating CrewAgentExecutor)
-    llm.stop = ["\nObservation:", "\nThought:"]
-
-    # Patch the API call to capture parameters without making real call
-    with patch.object(llm.client.models, 'generate_content') as mock_generate:
-        mock_response = MagicMock()
-        mock_response.text = "Hello"
-        mock_response.candidates = []
-        mock_response.usage_metadata = MagicMock(
-            prompt_token_count=10,
-            candidates_token_count=5,
-            total_token_count=15
-        )
-        mock_generate.return_value = mock_response
-
-        llm.call("Say hello in one word")
-
-        # Verify stop_sequences were passed to the API in the config
-        call_kwargs = mock_generate.call_args[1]
-        assert "config" in call_kwargs
-        # The config object should have stop_sequences set
-        config = call_kwargs["config"]
-        # Check if the config has stop_sequences attribute
-        assert hasattr(config, 'stop_sequences') or 'stop_sequences' in config.__dict__
-        if hasattr(config, 'stop_sequences'):
-            assert config.stop_sequences == ["\nObservation:", "\nThought:"]

lib/crewai/tests/llms/hooks/test_transport.py

@@ -6,7 +6,7 @@ import httpx
 import pytest
 
 from crewai.llms.hooks.base import BaseInterceptor
-from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
+from crewai.llms.hooks.transport import AsyncHTTPransport, HTTPTransport
 
 
 class TrackingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
@@ -128,7 +128,7 @@ class TestAsyncHTTPTransport:
     def test_async_transport_instantiation(self) -> None:
         """Test that async transport can be instantiated with interceptor."""
         interceptor = TrackingInterceptor()
-        transport = AsyncHTTPTransport(interceptor=interceptor)
+        transport = AsyncHTTPransport(interceptor=interceptor)
 
         assert transport.interceptor is interceptor
 
@@ -136,13 +136,13 @@ class TestAsyncHTTPTransport:
         """Test that async transport requires interceptor parameter."""
         # AsyncHTTPransport requires an interceptor parameter
         with pytest.raises(TypeError):
-            AsyncHTTPTransport()
+            AsyncHTTPransport()
 
     @pytest.mark.asyncio
     async def test_async_interceptor_called_on_request(self) -> None:
         """Test that async interceptor hooks are called during request handling."""
         interceptor = TrackingInterceptor()
-        transport = AsyncHTTPTransport(interceptor=interceptor)
+        transport = AsyncHTTPransport(interceptor=interceptor)
 
         # Create a mock parent transport that returns a response
         mock_response = httpx.Response(200, json={"success": True})
@@ -217,7 +217,7 @@ class TestTransportIntegration:
     async def test_multiple_async_requests_same_interceptor(self) -> None:
         """Test that multiple async requests through same interceptor are tracked."""
         interceptor = TrackingInterceptor()
-        transport = AsyncHTTPTransport(interceptor=interceptor)
+        transport = AsyncHTTPransport(interceptor=interceptor)
 
         mock_response = httpx.Response(200)
 

lib/crewai/tests/llms/hooks/test_unsupported_providers.py

@@ -154,7 +154,7 @@ class TestGeminiProviderInterceptor:
         # Gemini provider should raise NotImplementedError
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-2.5-pro",
+                model="gemini/gemini-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -169,7 +169,7 @@ class TestGeminiProviderInterceptor:
 
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-2.5-pro",
+                model="gemini/gemini-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -181,7 +181,7 @@ class TestGeminiProviderInterceptor:
     def test_gemini_without_interceptor_works(self) -> None:
         """Test that Gemini LLM works without interceptor."""
         llm = LLM(
-            model="gemini/gemini-2.5-pro",
+            model="gemini/gemini-pro",
             api_key="test-gemini-key",
         )
 
@@ -231,7 +231,7 @@ class TestUnsupportedProviderMessages:
 
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-2.5-pro",
+                model="gemini/gemini-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -282,7 +282,7 @@ class TestProviderSupportMatrix:
         # Gemini - NOT SUPPORTED
         with pytest.raises(NotImplementedError):
             LLM(
-                model="gemini/gemini-2.5-pro",
+                model="gemini/gemini-pro",
                 interceptor=interceptor,
                 api_key="test",
             )
@@ -315,5 +315,5 @@ class TestProviderSupportMatrix:
         assert not hasattr(bedrock_llm, 'interceptor') or bedrock_llm.interceptor is None
 
         # Gemini - doesn't have interceptor attribute
-        gemini_llm = LLM(model="gemini/gemini-2.5-pro", api_key="test")
-        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None
+        gemini_llm = LLM(model="gemini/gemini-pro", api_key="test")
+        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None

lib/crewai/tests/llms/openai/test_openai.py

@@ -16,7 +16,7 @@ def test_openai_completion_is_used_when_openai_provider():
     """
     Test that OpenAICompletion from completion.py is used when LLM uses provider 'openai'
     """
-    llm = LLM(model="gpt-4o")
+    llm = LLM(model="openai/gpt-4o")
 
     assert llm.__class__.__name__ == "OpenAICompletion"
     assert llm.provider == "openai"
@@ -70,7 +70,7 @@ def test_openai_completion_module_is_imported():
         del sys.modules[module_name]
 
     # Create LLM instance - this should trigger the import
-    LLM(model="gpt-4o")
+    LLM(model="openai/gpt-4o")
 
     # Verify the module was imported
     assert module_name in sys.modules
@@ -97,7 +97,7 @@ def test_native_openai_raises_error_when_initialization_fails():
 
         # This should raise ImportError, not fall back to LiteLLM
         with pytest.raises(ImportError) as excinfo:
-            LLM(model="gpt-4o")
+            LLM(model="openai/gpt-4o")
 
         assert "Error importing native provider" in str(excinfo.value)
         assert "Native SDK failed" in str(excinfo.value)
@@ -108,7 +108,7 @@ def test_openai_completion_initialization_parameters():
     Test that OpenAICompletion is initialized with correct parameters
     """
     llm = LLM(
-        model="gpt-4o",
+        model="openai/gpt-4o",
         temperature=0.7,
         max_tokens=1000,
         api_key="test-key"
@@ -311,7 +311,7 @@ def test_openai_completion_call_returns_usage_metrics():
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant.",
-        llm=LLM(model="gpt-4o"),
+        llm=LLM(model="openai/gpt-4o"),
         verbose=True,
     )
 
@@ -331,7 +331,6 @@ def test_openai_completion_call_returns_usage_metrics():
     assert result.token_usage.cached_prompt_tokens == 0
 
 
-@pytest.mark.skip(reason="Allow for litellm")
 def test_openai_raises_error_when_model_not_supported():
     """Test that OpenAICompletion raises ValueError when model not supported"""
 
@@ -355,7 +354,7 @@ def test_openai_client_setup_with_extra_arguments():
     Test that OpenAICompletion is initialized with correct parameters
     """
     llm = LLM(
-        model="gpt-4o",
+        model="openai/gpt-4o",
         temperature=0.7,
         max_tokens=1000,
         top_p=0.5,
@@ -392,7 +391,7 @@ def test_extra_arguments_are_passed_to_openai_completion():
     """
     Test that extra arguments are passed to OpenAICompletion
     """
-    llm = LLM(model="gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
+    llm = LLM(model="openai/gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
 
     with patch.object(llm.client.chat.completions, 'create') as mock_create:
         mock_create.return_value = MagicMock(

lib/crewai/tests/llms/test_stop_sequences_sync.py

@@ -0,0 +1,285 @@
+"""Tests for stop sequences synchronization across LLM providers.
+
+This test module verifies that the stop sequences are properly synchronized
+between the `stop` attribute (set by CrewAgentExecutor) and the provider-specific
+`stop_sequences` attribute (sent to the API) for Anthropic, Bedrock, and Gemini providers.
+
+Issue: https://github.com/crewAIInc/crewAI/issues/3836
+"""
+
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+class TestAnthropicStopSequencesSync:
+    """Test stop sequences synchronization for AnthropicCompletion."""
+
+    @pytest.fixture
+    def mock_anthropic_client(self):
+        """Mock Anthropic client to avoid network calls."""
+        with patch("crewai.llms.providers.anthropic.completion.Anthropic") as mock:
+            yield mock
+
+    def test_stop_property_getter_returns_stop_sequences(self, mock_anthropic_client):
+        """Test that getting stop returns stop_sequences."""
+        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            llm = AnthropicCompletion(
+                model="claude-3-5-sonnet-20241022",
+                stop_sequences=[r"\nObservation:"],
+            )
+
+            assert llm.stop == [r"\nObservation:"]
+            assert llm.stop_sequences == [r"\nObservation:"]
+            assert llm.stop is llm.stop_sequences
+
+    def test_stop_property_setter_syncs_with_stop_sequences(self, mock_anthropic_client):
+        """Test that setting stop updates stop_sequences."""
+        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            llm = AnthropicCompletion(model="claude-3-5-sonnet-20241022")
+
+            llm.stop = [r"\nObservation:", r"\nFinal Answer:"]
+            assert llm.stop_sequences == [r"\nObservation:", r"\nFinal Answer:"]
+            assert llm.stop == llm.stop_sequences
+
+    def test_stop_property_setter_handles_string(self, mock_anthropic_client):
+        """Test that setting stop with a string converts to list."""
+        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            llm = AnthropicCompletion(model="claude-3-5-sonnet-20241022")
+
+            llm.stop = r"\nObservation:"
+            assert llm.stop_sequences == [r"\nObservation:"]
+            assert llm.stop == [r"\nObservation:"]
+
+    def test_stop_property_setter_handles_none(self, mock_anthropic_client):
+        """Test that setting stop to None clears stop_sequences."""
+        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            llm = AnthropicCompletion(
+                model="claude-3-5-sonnet-20241022",
+                stop_sequences=[r"\nObservation:"],
+            )
+
+            llm.stop = None
+            assert llm.stop_sequences == []
+            assert llm.stop == []
+
+    def test_crew_agent_executor_pattern(self, mock_anthropic_client):
+        """Test the pattern used by CrewAgentExecutor to set stop sequences."""
+        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            llm = AnthropicCompletion(model="claude-3-5-sonnet-20241022")
+
+            existing_stop = getattr(llm, "stop", [])
+            new_stops = [r"\nObservation:"]
+            llm.stop = list(
+                set(existing_stop + new_stops if isinstance(existing_stop, list) else new_stops)
+            )
+
+            assert llm.stop_sequences == [r"\nObservation:"]
+            assert llm.stop == llm.stop_sequences
+
+    def test_prepare_completion_params_includes_stop_sequences(
+        self, mock_anthropic_client
+    ):
+        """Test that _prepare_completion_params includes stop_sequences in params."""
+        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            llm = AnthropicCompletion(model="claude-3-5-sonnet-20241022")
+
+            llm.stop = [r"\nObservation:"]
+
+            params = llm._prepare_completion_params(
+                messages=[{"role": "user", "content": "test"}]
+            )
+
+            assert "stop_sequences" in params
+            assert params["stop_sequences"] == [r"\nObservation:"]
+
+
+class TestBedrockStopSequencesSync:
+    """Test stop sequences synchronization for BedrockCompletion."""
+
+    @pytest.fixture
+    def mock_bedrock_session(self):
+        """Mock boto3 Session to avoid AWS calls."""
+        with patch("crewai.llms.providers.bedrock.completion.Session") as mock:
+            mock_client = MagicMock()
+            mock.return_value.client.return_value = mock_client
+            yield mock
+
+    def test_stop_property_getter_returns_stop_sequences(self, mock_bedrock_session):
+        """Test that getting stop returns stop_sequences."""
+        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+
+        llm = BedrockCompletion(
+            model="anthropic.claude-3-5-sonnet-20241022-v2:0",
+            stop_sequences=[r"\nObservation:"],
+        )
+
+        assert llm.stop == [r"\nObservation:"]
+        assert llm.stop_sequences == [r"\nObservation:"]
+        assert llm.stop is llm.stop_sequences
+
+    def test_stop_property_setter_syncs_with_stop_sequences(self, mock_bedrock_session):
+        """Test that setting stop updates stop_sequences."""
+        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+
+        llm = BedrockCompletion(model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+        llm.stop = [r"\nObservation:", r"\nFinal Answer:"]
+        assert llm.stop_sequences == [r"\nObservation:", r"\nFinal Answer:"]
+        assert llm.stop == llm.stop_sequences
+
+    def test_stop_property_setter_handles_string(self, mock_bedrock_session):
+        """Test that setting stop with a string converts to list."""
+        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+
+        llm = BedrockCompletion(model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+        llm.stop = r"\nObservation:"
+        assert llm.stop_sequences == [r"\nObservation:"]
+        assert llm.stop == [r"\nObservation:"]
+
+    def test_stop_property_setter_handles_none(self, mock_bedrock_session):
+        """Test that setting stop to None clears stop_sequences."""
+        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+
+        llm = BedrockCompletion(
+            model="anthropic.claude-3-5-sonnet-20241022-v2:0",
+            stop_sequences=[r"\nObservation:"],
+        )
+
+        llm.stop = None
+        assert llm.stop_sequences == []
+        assert llm.stop == []
+
+    def test_crew_agent_executor_pattern(self, mock_bedrock_session):
+        """Test the pattern used by CrewAgentExecutor to set stop sequences."""
+        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+
+        llm = BedrockCompletion(model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+        existing_stop = getattr(llm, "stop", [])
+        new_stops = [r"\nObservation:"]
+        llm.stop = list(
+            set(existing_stop + new_stops if isinstance(existing_stop, list) else new_stops)
+        )
+
+        assert llm.stop_sequences == [r"\nObservation:"]
+        assert llm.stop == llm.stop_sequences
+
+    def test_get_inference_config_includes_stop_sequences(self, mock_bedrock_session):
+        """Test that _get_inference_config includes stopSequences."""
+        from crewai.llms.providers.bedrock.completion import BedrockCompletion
+
+        llm = BedrockCompletion(model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+        llm.stop = [r"\nObservation:"]
+
+        config = llm._get_inference_config()
+
+        assert "stopSequences" in config
+        assert config["stopSequences"] == [r"\nObservation:"]
+
+
+class TestGeminiStopSequencesSync:
+    """Test stop sequences synchronization for GeminiCompletion."""
+
+    @pytest.fixture
+    def mock_gemini_client(self):
+        """Mock Google Gen AI client to avoid network calls."""
+        with patch("crewai.llms.providers.gemini.completion.genai") as mock:
+            mock_client = MagicMock()
+            mock.Client.return_value = mock_client
+            yield mock
+
+    def test_stop_property_getter_returns_stop_sequences(self, mock_gemini_client):
+        """Test that getting stop returns stop_sequences."""
+        from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+            llm = GeminiCompletion(
+                model="gemini-2.0-flash-001",
+                stop_sequences=[r"\nObservation:"],
+            )
+
+            assert llm.stop == [r"\nObservation:"]
+            assert llm.stop_sequences == [r"\nObservation:"]
+            assert llm.stop is llm.stop_sequences
+
+    def test_stop_property_setter_syncs_with_stop_sequences(self, mock_gemini_client):
+        """Test that setting stop updates stop_sequences."""
+        from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+            llm = GeminiCompletion(model="gemini-2.0-flash-001")
+
+            llm.stop = [r"\nObservation:", r"\nFinal Answer:"]
+            assert llm.stop_sequences == [r"\nObservation:", r"\nFinal Answer:"]
+            assert llm.stop == llm.stop_sequences
+
+    def test_stop_property_setter_handles_string(self, mock_gemini_client):
+        """Test that setting stop with a string converts to list."""
+        from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+            llm = GeminiCompletion(model="gemini-2.0-flash-001")
+
+            llm.stop = r"\nObservation:"
+            assert llm.stop_sequences == [r"\nObservation:"]
+            assert llm.stop == [r"\nObservation:"]
+
+    def test_stop_property_setter_handles_none(self, mock_gemini_client):
+        """Test that setting stop to None clears stop_sequences."""
+        from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+            llm = GeminiCompletion(
+                model="gemini-2.0-flash-001",
+                stop_sequences=[r"\nObservation:"],
+            )
+
+            llm.stop = None
+            assert llm.stop_sequences == []
+            assert llm.stop == []
+
+    def test_crew_agent_executor_pattern(self, mock_gemini_client):
+        """Test the pattern used by CrewAgentExecutor to set stop sequences."""
+        from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+            llm = GeminiCompletion(model="gemini-2.0-flash-001")
+
+            existing_stop = getattr(llm, "stop", [])
+            new_stops = [r"\nObservation:"]
+            llm.stop = list(
+                set(existing_stop + new_stops if isinstance(existing_stop, list) else new_stops)
+            )
+
+            assert llm.stop_sequences == [r"\nObservation:"]
+            assert llm.stop == llm.stop_sequences
+
+    def test_prepare_generation_config_includes_stop_sequences(self, mock_gemini_client):
+        """Test that _prepare_generation_config includes stop_sequences."""
+        from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+        with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+            llm = GeminiCompletion(model="gemini-2.0-flash-001")
+
+            llm.stop = [r"\nObservation:"]
+
+            config = llm._prepare_generation_config()
+
+            assert "stop_sequences" in config
+            assert config["stop_sequences"] == [r"\nObservation:"]

lib/crewai/tests/mcp/__init__.py

@@ -1,4 +0,0 @@
-"""Tests for MCP (Model Context Protocol) integration."""
-
-
-

lib/crewai/tests/mcp/test_mcp_config.py

@@ -1,200 +0,0 @@
-import asyncio
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from crewai.agent.core import Agent
-from crewai.mcp.config import MCPServerHTTP, MCPServerSSE, MCPServerStdio
-from crewai.tools.base_tool import BaseTool
-
-
-@pytest.fixture
-def mock_tool_definitions():
-    """Create mock MCP tool definitions (as returned by list_tools)."""
-    return [
-        {
-            "name": "test_tool_1",
-            "description": "Test tool 1 description",
-            "inputSchema": {
-                "type": "object",
-                "properties": {
-                    "query": {"type": "string", "description": "Search query"}
-                },
-                "required": ["query"]
-            }
-        },
-        {
-            "name": "test_tool_2",
-            "description": "Test tool 2 description",
-            "inputSchema": {}
-        }
-    ]
-
-
-def test_agent_with_stdio_mcp_config(mock_tool_definitions):
-    """Test agent setup with MCPServerStdio configuration."""
-    stdio_config = MCPServerStdio(
-        command="python",
-        args=["server.py"],
-        env={"API_KEY": "test_key"},
-    )
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test goal",
-        backstory="Test backstory",
-        mcps=[stdio_config],
-    )
-
-
-    with patch("crewai.agent.core.MCPClient") as mock_client_class:
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        mock_client.connected = False  # Will trigger connect
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client_class.return_value = mock_client
-
-        tools = agent.get_mcp_tools([stdio_config])
-
-        assert len(tools) == 2
-        assert all(isinstance(tool, BaseTool) for tool in tools)
-
-        mock_client_class.assert_called_once()
-        call_args = mock_client_class.call_args
-        transport = call_args.kwargs["transport"]
-        assert transport.command == "python"
-        assert transport.args == ["server.py"]
-        assert transport.env == {"API_KEY": "test_key"}
-
-
-def test_agent_with_http_mcp_config(mock_tool_definitions):
-    """Test agent setup with MCPServerHTTP configuration."""
-    http_config = MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer test_token"},
-        streamable=True,
-    )
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test goal",
-        backstory="Test backstory",
-        mcps=[http_config],
-    )
-
-    with patch("crewai.agent.core.MCPClient") as mock_client_class:
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        mock_client.connected = False  # Will trigger connect
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client_class.return_value = mock_client
-
-        tools = agent.get_mcp_tools([http_config])
-
-        assert len(tools) == 2
-        assert all(isinstance(tool, BaseTool) for tool in tools)
-
-        mock_client_class.assert_called_once()
-        call_args = mock_client_class.call_args
-        transport = call_args.kwargs["transport"]
-        assert transport.url == "https://api.example.com/mcp"
-        assert transport.headers == {"Authorization": "Bearer test_token"}
-        assert transport.streamable is True
-
-
-def test_agent_with_sse_mcp_config(mock_tool_definitions):
-    """Test agent setup with MCPServerSSE configuration."""
-    sse_config = MCPServerSSE(
-        url="https://api.example.com/mcp/sse",
-        headers={"Authorization": "Bearer test_token"},
-    )
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test goal",
-        backstory="Test backstory",
-        mcps=[sse_config],
-    )
-
-    with patch("crewai.agent.core.MCPClient") as mock_client_class:
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        mock_client.connected = False
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client_class.return_value = mock_client
-
-        tools = agent.get_mcp_tools([sse_config])
-
-        assert len(tools) == 2
-        assert all(isinstance(tool, BaseTool) for tool in tools)
-
-        mock_client_class.assert_called_once()
-        call_args = mock_client_class.call_args
-        transport = call_args.kwargs["transport"]
-        assert transport.url == "https://api.example.com/mcp/sse"
-        assert transport.headers == {"Authorization": "Bearer test_token"}
-
-
-def test_mcp_tool_execution_in_sync_context(mock_tool_definitions):
-    """Test MCPNativeTool execution in synchronous context (normal crew execution)."""
-    http_config = MCPServerHTTP(url="https://api.example.com/mcp")
-
-    with patch("crewai.agent.core.MCPClient") as mock_client_class:
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        mock_client.connected = False
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client.call_tool = AsyncMock(return_value="test result")
-        mock_client_class.return_value = mock_client
-
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            mcps=[http_config],
-        )
-
-        tools = agent.get_mcp_tools([http_config])
-        assert len(tools) == 2
-
-
-        tool = tools[0]
-        result = tool.run(query="test query")
-
-        assert result == "test result"
-        mock_client.call_tool.assert_called()
-
-
-@pytest.mark.asyncio
-async def test_mcp_tool_execution_in_async_context(mock_tool_definitions):
-    """Test MCPNativeTool execution in async context (e.g., from a Flow)."""
-    http_config = MCPServerHTTP(url="https://api.example.com/mcp")
-
-    with patch("crewai.agent.core.MCPClient") as mock_client_class:
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(return_value=mock_tool_definitions)
-        mock_client.connected = False
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client.call_tool = AsyncMock(return_value="test result")
-        mock_client_class.return_value = mock_client
-
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            mcps=[http_config],
-        )
-
-        tools = agent.get_mcp_tools([http_config])
-        assert len(tools) == 2
-
-
-        tool = tools[0]
-        result = tool.run(query="test query")
-
-        assert result == "test result"
-        mock_client.call_tool.assert_called()

lib/crewai/tests/test_crew.py

@@ -340,7 +340,7 @@ def test_sync_task_execution(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -412,7 +412,7 @@ def test_manager_agent_delegating_to_assigned_task_agent(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -513,7 +513,7 @@ def test_manager_agent_delegates_with_varied_role_cases():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
     task.output = mock_task_output
 
@@ -611,7 +611,7 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer)
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -669,7 +669,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -788,7 +788,7 @@ def test_task_tools_override_agent_tools_with_allow_delegation(researcher, write
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # We mock execute_sync to verify which tools get used at runtime
@@ -1225,7 +1225,7 @@ async def test_async_task_execution_call_count(researcher, writer):
 
     # Create a valid TaskOutput instance to mock the return value
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Create a MagicMock Future instance
@@ -1784,7 +1784,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
         Task,
         "execute_sync",
         return_value=TaskOutput(
-            description="dummy", raw="Hello", agent=researcher.role, messages=[]
+            description="dummy", raw="Hello", agent=researcher.role
         ),
     ):
         crew.kickoff()
@@ -1828,7 +1828,7 @@ def test_hierarchical_crew_creation_tasks_with_agents(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -1881,7 +1881,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(researcher, write
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Create a mock Future that returns our TaskOutput
@@ -2246,13 +2246,11 @@ def test_conditional_task_uses_last_output(researcher, writer):
         description="First task output",
         raw="First success output",  # Will be used by third task's condition
         agent=researcher.role,
-        messages=[],
     )
     mock_third = TaskOutput(
         description="Third task output",
         raw="Third task executed",  # Output when condition succeeds using first task output
         agent=writer.role,
-        messages=[],
     )
 
     # Set up mocks for task execution and conditional logic
@@ -2320,13 +2318,11 @@ def test_conditional_tasks_result_collection(researcher, writer):
         description="Success output",
         raw="Success output",  # Triggers third task's condition
         agent=researcher.role,
-        messages=[],
     )
     mock_conditional = TaskOutput(
         description="Conditional output",
         raw="Conditional task executed",
         agent=writer.role,
-        messages=[],
     )
 
     # Set up mocks for task execution and conditional logic
@@ -2403,7 +2399,6 @@ def test_multiple_conditional_tasks(researcher, writer):
         description="Mock success",
         raw="Success and proceed output",
         agent=researcher.role,
-        messages=[],
     )
 
     # Set up mocks for task execution
@@ -2811,7 +2806,7 @@ def test_manager_agent(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -3006,7 +3001,6 @@ def test_replay_feature(researcher, writer):
             output_format=OutputFormat.RAW,
             pydantic=None,
             summary="Mocked output for list of ideas",
-            messages=[],
         )
 
         crew.kickoff()
@@ -3058,7 +3052,6 @@ def test_crew_task_db_init():
             output_format=OutputFormat.RAW,
             pydantic=None,
             summary="Write about AI in healthcare...",
-            messages=[],
         )
 
         crew.kickoff()
@@ -3121,7 +3114,6 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Detailed report on AI advancements...",
-        messages=[],
     )
     mock_task_output2 = TaskOutput(
         description="Summarize the AI advancements report.",
@@ -3131,7 +3123,6 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Summary of the AI advancements report...",
-        messages=[],
     )
     mock_task_output3 = TaskOutput(
         description="Write an article based on the AI advancements summary.",
@@ -3141,7 +3132,6 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Article on AI advancements...",
-        messages=[],
     )
     mock_task_output4 = TaskOutput(
         description="Create a presentation based on the AI advancements article.",
@@ -3151,7 +3141,6 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Presentation on AI advancements...",
-        messages=[],
     )
 
     with patch.object(Task, "execute_sync") as mock_execute_task:
@@ -3175,70 +3164,6 @@ def test_replay_task_with_context():
         db_handler.reset()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_replay_preserves_messages():
-    """Test that replay preserves messages from stored task outputs."""
-    from crewai.utilities.types import LLMMessage
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test goal",
-        backstory="Test backstory",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Say hello",
-        expected_output="A greeting",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task], process=Process.sequential)
-
-    mock_messages: list[LLMMessage] = [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Say hello"},
-        {"role": "assistant", "content": "Hello!"},
-    ]
-
-    mock_task_output = TaskOutput(
-        description="Say hello",
-        raw="Hello!",
-        agent="Test Agent",
-        messages=mock_messages,
-    )
-
-    with patch.object(Task, "execute_sync", return_value=mock_task_output):
-        crew.kickoff()
-
-    # Verify the task output was stored with messages
-    db_handler = TaskOutputStorageHandler()
-    stored_outputs = db_handler.load()
-    assert stored_outputs is not None
-    assert len(stored_outputs) > 0
-
-    # Verify messages are in the stored output
-    stored_output = stored_outputs[0]["output"]
-    assert "messages" in stored_output
-    assert len(stored_output["messages"]) == 3
-    assert stored_output["messages"][0]["role"] == "system"
-    assert stored_output["messages"][1]["role"] == "user"
-    assert stored_output["messages"][2]["role"] == "assistant"
-
-    # Replay the task and verify messages are preserved
-    with patch.object(Task, "execute_sync", return_value=mock_task_output):
-        replayed_output = crew.replay(str(task.id))
-
-    # Verify the replayed task output has messages
-    assert len(replayed_output.tasks_output) > 0
-    replayed_task_output = replayed_output.tasks_output[0]
-    assert hasattr(replayed_task_output, "messages")
-    assert isinstance(replayed_task_output.messages, list)
-    assert len(replayed_task_output.messages) == 3
-
-    db_handler.reset()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_replay_with_context():
     agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
@@ -3256,7 +3181,6 @@ def test_replay_with_context():
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
-        messages=[],
     )
     task1.output = context_output
 
@@ -3317,7 +3241,6 @@ def test_replay_with_context_set_to_nullable():
             description="Test Task Output",
             raw="test raw output",
             agent="test_agent",
-            messages=[],
         )
         crew.kickoff()
 
@@ -3341,7 +3264,6 @@ def test_replay_with_invalid_task_id():
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
-        messages=[],
     )
     task1.output = context_output
 
@@ -3406,7 +3328,6 @@ def test_replay_interpolates_inputs_properly(mock_interpolate_inputs):
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
-        messages=[],
     )
     task1.output = context_output
 
@@ -3465,7 +3386,6 @@ def test_replay_setup_context():
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
-        messages=[],
     )
     task1.output = context_output
     crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
@@ -3699,7 +3619,6 @@ def test_conditional_should_skip(researcher, writer):
             description="Task 1 description",
             raw="Task 1 output",
             agent="Researcher",
-            messages=[],
         )
 
         result = crew_met.kickoff()
@@ -3734,7 +3653,6 @@ def test_conditional_should_execute(researcher, writer):
             description="Task 1 description",
             raw="Task 1 output",
             agent="Researcher",
-            messages=[],
         )
 
         crew_met.kickoff()
@@ -3906,7 +3824,7 @@ def test_task_tools_preserve_code_execution_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     with patch.object(
@@ -3960,7 +3878,7 @@ def test_multimodal_flag_adds_multimodal_tools():
     crew = Crew(agents=[multimodal_agent], tasks=[task], process=Process.sequential)
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
+        description="Mock description", raw="mocked output", agent="mocked agent"
     )
 
     # Mock execute_sync to verify the tools passed at runtime
@@ -4024,7 +3942,6 @@ def test_multimodal_agent_image_tool_handling():
         description="Mock description",
         raw="A detailed analysis of the image",
         agent="Image Analyst",
-        messages=[],
     )
 
     with patch.object(Task, "execute_sync") as mock_execute_sync:

lib/crewai/tests/test_flow.py

@@ -3,7 +3,6 @@
 import asyncio
 import threading
 from datetime import datetime
-from typing import Optional
 
 import pytest
 from pydantic import BaseModel
@@ -1385,110 +1384,3 @@ def test_mixed_sync_async_execution_order():
     ]
 
     assert execution_order == expected_order
-
-
-def test_flow_copy_state_with_unpickleable_objects():
-    """Test that _copy_state handles unpickleable objects like RLock.
-
-    Regression test for issue #3828: Flow should not crash when state contains
-    objects that cannot be deep copied (like threading.RLock).
-    """
-
-    class StateWithRLock(BaseModel):
-        counter: int = 0
-        lock: Optional[threading.RLock] = None
-
-    class FlowWithRLock(Flow[StateWithRLock]):
-        @start()
-        def step_1(self):
-            self.state.counter += 1
-
-        @listen(step_1)
-        def step_2(self):
-            self.state.counter += 1
-
-    flow = FlowWithRLock(initial_state=StateWithRLock())
-    flow._state.lock = threading.RLock()
-
-    copied_state = flow._copy_state()
-    assert copied_state.counter == 0
-    assert copied_state.lock is not None
-
-
-def test_flow_copy_state_with_nested_unpickleable_objects():
-    """Test that _copy_state handles unpickleable objects nested in containers.
-
-    Regression test for issue #3828: Verifies that unpickleable objects
-    nested inside dicts/lists in state don't cause crashes.
-    """
-
-    class NestedState(BaseModel):
-        data: dict = {}
-        items: list = []
-
-    class FlowWithNestedUnpickleable(Flow[NestedState]):
-        @start()
-        def step_1(self):
-            self.state.data["lock"] = threading.RLock()
-            self.state.data["value"] = 42
-
-        @listen(step_1)
-        def step_2(self):
-            self.state.items.append(threading.Lock())
-            self.state.items.append("normal_value")
-
-    flow = FlowWithNestedUnpickleable(initial_state=NestedState())
-    flow.kickoff()
-
-    assert flow.state.data["value"] == 42
-    assert len(flow.state.items) == 2
-
-
-def test_flow_copy_state_without_unpickleable_objects():
-    """Test that _copy_state still works normally with pickleable objects.
-
-    Ensures that the fallback logic doesn't break normal deep copy behavior.
-    """
-
-    class NormalState(BaseModel):
-        counter: int = 0
-        data: str = ""
-        nested: dict = {}
-
-    class NormalFlow(Flow[NormalState]):
-        @start()
-        def step_1(self):
-            self.state.counter = 5
-            self.state.data = "test"
-            self.state.nested = {"key": "value"}
-
-    flow = NormalFlow(initial_state=NormalState())
-    flow.state.counter = 10
-    flow.state.data = "modified"
-    flow.state.nested["key"] = "modified"
-
-    copied_state = flow._copy_state()
-    assert copied_state.counter == 10
-    assert copied_state.data == "modified"
-    assert copied_state.nested["key"] == "modified"
-
-    flow.state.nested["key"] = "changed_after_copy"
-    assert copied_state.nested["key"] == "modified"
-
-
-def test_flow_copy_state_with_dict_state():
-    """Test that _copy_state works with dict-based states."""
-
-    class DictFlow(Flow[dict]):
-        @start()
-        def step_1(self):
-            self.state["counter"] = 1
-
-    flow = DictFlow()
-    flow.state["test"] = "value"
-
-    copied_state = flow._copy_state()
-    assert copied_state["test"] == "value"
-
-    flow.state["test"] = "modified"
-    assert copied_state["test"] == "value"

lib/crewai/tests/test_llm.py

@@ -710,7 +710,7 @@ def test_native_provider_raises_error_when_supported_but_fails():
             mock_get_native.return_value = mock_provider
 
             with pytest.raises(ImportError) as excinfo:
-                LLM(model="gpt-4", is_litellm=False)
+                LLM(model="openai/gpt-4", is_litellm=False)
 
             assert "Error importing native provider" in str(excinfo.value)
             assert "Native provider initialization failed" in str(excinfo.value)
@@ -725,113 +725,3 @@ def test_native_provider_falls_back_to_litellm_when_not_in_supported_list():
         # Should fall back to LiteLLM
         assert llm.is_litellm is True
         assert llm.model == "groq/llama-3.1-70b-versatile"
-
-
-def test_prefixed_models_with_valid_constants_use_native_sdk():
-    """Test that models with native provider prefixes use native SDK when model is in constants."""
-    # Test openai/ prefix with actual OpenAI model in constants → Native SDK
-    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
-        llm = LLM(model="openai/gpt-4o", is_litellm=False)
-        assert llm.is_litellm is False
-        assert llm.provider == "openai"
-
-    # Test anthropic/ prefix with Claude model in constants → Native SDK
-    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-        llm2 = LLM(model="anthropic/claude-opus-4-0", is_litellm=False)
-        assert llm2.is_litellm is False
-        assert llm2.provider == "anthropic"
-
-    # Test gemini/ prefix with Gemini model in constants → Native SDK
-    with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
-        llm3 = LLM(model="gemini/gemini-2.5-pro", is_litellm=False)
-        assert llm3.is_litellm is False
-        assert llm3.provider == "gemini"
-
-
-def test_prefixed_models_with_invalid_constants_use_litellm():
-    """Test that models with native provider prefixes use LiteLLM when model is NOT in constants."""
-    # Test openai/ prefix with non-OpenAI model (not in OPENAI_MODELS) → LiteLLM
-    llm = LLM(model="openai/gemini-2.5-flash", is_litellm=False)
-    assert llm.is_litellm is True
-    assert llm.model == "openai/gemini-2.5-flash"
-
-    # Test openai/ prefix with unknown future model → LiteLLM
-    llm2 = LLM(model="openai/gpt-future-6", is_litellm=False)
-    assert llm2.is_litellm is True
-    assert llm2.model == "openai/gpt-future-6"
-
-    # Test anthropic/ prefix with non-Anthropic model → LiteLLM
-    llm3 = LLM(model="anthropic/gpt-4o", is_litellm=False)
-    assert llm3.is_litellm is True
-    assert llm3.model == "anthropic/gpt-4o"
-
-
-def test_prefixed_models_with_non_native_providers_use_litellm():
-    """Test that models with non-native provider prefixes always use LiteLLM."""
-    # Test groq/ prefix (not a native provider) → LiteLLM
-    llm = LLM(model="groq/llama-3.3-70b", is_litellm=False)
-    assert llm.is_litellm is True
-    assert llm.model == "groq/llama-3.3-70b"
-
-    # Test together/ prefix (not a native provider) → LiteLLM
-    llm2 = LLM(model="together/qwen-2.5-72b", is_litellm=False)
-    assert llm2.is_litellm is True
-    assert llm2.model == "together/qwen-2.5-72b"
-
-
-def test_unprefixed_models_use_native_sdk():
-    """Test that unprefixed models use native SDK when model is in constants."""
-    # gpt-4o is in OPENAI_MODELS → Native OpenAI SDK
-    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
-        llm = LLM(model="gpt-4o", is_litellm=False)
-        assert llm.is_litellm is False
-        assert llm.provider == "openai"
-
-    # claude-opus-4-0 is in ANTHROPIC_MODELS → Native Anthropic SDK
-    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
-        llm2 = LLM(model="claude-opus-4-0", is_litellm=False)
-        assert llm2.is_litellm is False
-        assert llm2.provider == "anthropic"
-
-    # gemini-2.5-pro is in GEMINI_MODELS → Native Gemini SDK
-    with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
-        llm3 = LLM(model="gemini-2.5-pro", is_litellm=False)
-        assert llm3.is_litellm is False
-        assert llm3.provider == "gemini"
-
-
-def test_explicit_provider_kwarg_takes_priority():
-    """Test that explicit provider kwarg takes priority over model name inference."""
-    # Explicit provider=openai should use OpenAI even if model name suggests otherwise
-    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
-        llm = LLM(model="gpt-4o", provider="openai", is_litellm=False)
-        assert llm.is_litellm is False
-        assert llm.provider == "openai"
-
-    # Explicit provider for a model with "/" should still use that provider
-    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
-        llm2 = LLM(model="gpt-4o", provider="openai", is_litellm=False)
-        assert llm2.is_litellm is False
-        assert llm2.provider == "openai"
-
-
-def test_validate_model_in_constants():
-    """Test the _validate_model_in_constants method."""
-    # OpenAI models
-    assert LLM._validate_model_in_constants("gpt-4o", "openai") is True
-    assert LLM._validate_model_in_constants("gpt-future-6", "openai") is False
-
-    # Anthropic models
-    assert LLM._validate_model_in_constants("claude-opus-4-0", "claude") is True
-    assert LLM._validate_model_in_constants("claude-future-5", "claude") is False
-
-    # Gemini models
-    assert LLM._validate_model_in_constants("gemini-2.5-pro", "gemini") is True
-    assert LLM._validate_model_in_constants("gemini-future", "gemini") is False
-
-    # Azure models
-    assert LLM._validate_model_in_constants("gpt-4o", "azure") is True
-    assert LLM._validate_model_in_constants("gpt-35-turbo", "azure") is True
-
-    # Bedrock models
-    assert LLM._validate_model_in_constants("anthropic.claude-opus-4-1-20250805-v1:0", "bedrock") is True

lib/crewai/tests/test_task.py

@@ -162,7 +162,6 @@ def test_task_callback_returns_task_output():
             "name": task.name or task.description,
             "expected_output": "Bullet point list of 5 interesting ideas.",
             "output_format": OutputFormat.RAW,
-            "messages": [],
         }
         assert output_dict == expected_output
 
@@ -341,7 +340,7 @@ def test_output_pydantic_hierarchical():
     )
     result = crew.kickoff()
     assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert result.to_dict() == {"score": 0}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -600,7 +599,7 @@ def test_output_pydantic_to_another_task():
     assert isinstance(pydantic_result, ScoreOutput), (
         "Expected pydantic result to be of type ScoreOutput"
     )
-    assert pydantic_result.score == 5
+    assert pydantic_result.score == 4
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -631,7 +630,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 3}' == result.json
+    assert '{"score": 4}' == result.json
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1681,44 +1680,3 @@ def test_task_copy_with_list_context():
     assert isinstance(copied_task2.context, list)
     assert len(copied_task2.context) == 1
     assert copied_task2.context[0] is task1
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_output_includes_messages():
-    """Test that TaskOutput includes messages from agent execution."""
-    researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=False,
-    )
-
-    task1 = Task(
-        description="Give me a list of 3 interesting ideas about AI.",
-        expected_output="Bullet point list of 3 ideas.",
-        agent=researcher,
-    )
-
-    task2 = Task(
-        description="Summarize the ideas from the previous task.",
-        expected_output="A summary of the ideas.",
-        agent=researcher,
-    )
-
-    crew = Crew(agents=[researcher], tasks=[task1, task2], process=Process.sequential)
-    result = crew.kickoff()
-
-    # Verify both tasks have messages
-    assert len(result.tasks_output) == 2
-
-    # Check first task output has messages
-    task1_output = result.tasks_output[0]
-    assert hasattr(task1_output, "messages")
-    assert isinstance(task1_output.messages, list)
-    assert len(task1_output.messages) > 0
-
-    # Check second task output has messages
-    task2_output = result.tasks_output[1]
-    assert hasattr(task2_output, "messages")
-    assert isinstance(task2_output.messages, list)
-    assert len(task2_output.messages) > 0

lib/crewai/tests/test_task_guardrails.py

@@ -38,7 +38,6 @@ def test_task_without_guardrail():
     agent.role = "test_agent"
     agent.execute_task.return_value = "test result"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(description="Test task", expected_output="Output")
 
@@ -57,7 +56,6 @@ def test_task_with_successful_guardrail_func():
     agent.role = "test_agent"
     agent.execute_task.return_value = "test result"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task", expected_output="Output", guardrail=guardrail
@@ -78,7 +76,6 @@ def test_task_with_failing_guardrail():
     agent.role = "test_agent"
     agent.execute_task.side_effect = ["bad result", "good result"]
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task",
@@ -106,7 +103,6 @@ def test_task_with_guardrail_retries():
     agent.role = "test_agent"
     agent.execute_task.return_value = "bad result"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task",
@@ -132,7 +128,6 @@ def test_guardrail_error_in_context():
     agent = Mock()
     agent.role = "test_agent"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task",
@@ -186,7 +181,7 @@ def test_task_guardrail_process_output(task_output):
     result = guardrail(task_output)
     assert result[0] is False
 
-    assert result[1] == "The task result contains more than 10 words, violating the guardrail. The text provided contains about 21 words."
+    assert "exceeding the guardrail limit of fewer than" in result[1].lower()
 
     guardrail = LLMGuardrail(
         description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
@@ -257,7 +252,10 @@ def test_guardrail_emits_events(sample_agent):
             {
                 "success": False,
                 "result": None,
-                "error": "The output indicates that none of the authors mentioned are from Italy, while the guardrail requires authors to be from Italy. Therefore, the output does not comply with the guardrail.",
+                "error": "The task result does not comply with the guardrail because none of "
+                "the listed authors are from Italy. All authors mentioned are from "
+                "different countries, including Germany, the UK, the USA, and others, "
+                "which violates the requirement that authors must be Italian.",
                 "retry_count": 0,
             },
             {"success": True, "result": result.raw, "error": None, "retry_count": 1},
@@ -300,7 +298,6 @@ def test_hallucination_guardrail_integration():
     agent.role = "test_agent"
     agent.execute_task.return_value = "test result"
     agent.crew = None
-    agent.last_messages = []
 
     mock_llm = Mock(spec=LLM)
     guardrail = HallucinationGuardrail(
@@ -348,7 +345,6 @@ def test_multiple_guardrails_sequential_processing():
     agent.role = "sequential_agent"
     agent.execute_task.return_value = "original text"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test sequential guardrails",
@@ -398,7 +394,6 @@ def test_multiple_guardrails_with_validation_failure():
     agent.role = "validation_agent"
     agent.execute_task = mock_execute_task
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test guardrails with validation",
@@ -440,7 +435,6 @@ def test_multiple_guardrails_with_mixed_string_and_taskoutput():
     agent.role = "mixed_agent"
     agent.execute_task.return_value = "original"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test mixed return types",
@@ -478,7 +472,6 @@ def test_multiple_guardrails_with_retry_on_middle_guardrail():
     agent.role = "retry_agent"
     agent.execute_task.return_value = "base"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test retry in middle guardrail",
@@ -510,7 +503,6 @@ def test_multiple_guardrails_with_max_retries_exceeded():
     agent.role = "failing_agent"
     agent.execute_task.return_value = "test"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test max retries with multiple guardrails",
@@ -534,7 +526,6 @@ def test_multiple_guardrails_empty_list():
     agent.role = "empty_agent"
     agent.execute_task.return_value = "no guardrails"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test empty guardrails list",
@@ -594,7 +585,6 @@ def test_multiple_guardrails_processing_order():
     agent.role = "order_agent"
     agent.execute_task.return_value = "base"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test processing order",
@@ -638,7 +628,6 @@ def test_multiple_guardrails_with_pydantic_output():
     agent.role = "pydantic_agent"
     agent.execute_task.return_value = "test content"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test guardrails with Pydantic",
@@ -672,7 +661,6 @@ def test_guardrails_vs_single_guardrail_mutual_exclusion():
     agent.role = "exclusion_agent"
     agent.execute_task.return_value = "test"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test mutual exclusion",
@@ -715,7 +703,6 @@ def test_per_guardrail_independent_retry_tracking():
     agent.role = "independent_retry_agent"
     agent.execute_task.return_value = "base"
     agent.crew = None
-    agent.last_messages = []
 
     task = create_smart_task(
         description="Test independent retry tracking",