chore(deps-dev): bump pre-commit from 4.5.0 to 4.5.1

Bumps [pre-commit](https://github.com/pre-commit/pre-commit) from 4.5.0 to 4.5.1.
- [Release notes](https://github.com/pre-commit/pre-commit/releases)
- [Changelog](https://github.com/pre-commit/pre-commit/blob/main/CHANGELOG.md)
- [Commits](https://github.com/pre-commit/pre-commit/compare/v4.5.0...v4.5.1)

---
updated-dependencies:
- dependency-name: pre-commit
  dependency-version: 4.5.1
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

lib/crewai-tools/src/crewai_tools/adapters/mcp_adapter.py

@@ -2,29 +2,95 @@
 
 from __future__ import annotations
 
+from collections.abc import Callable
 import logging
 from typing import TYPE_CHECKING, Any
 
 from crewai.tools import BaseTool
+from crewai.utilities.pydantic_schema_utils import create_model_from_schema
+from crewai.utilities.string_utils import sanitize_tool_name
+from pydantic import BaseModel
 
 from crewai_tools.adapters.tool_collection import ToolCollection
 
 
-logger = logging.getLogger(__name__)
-
 if TYPE_CHECKING:
     from mcp import StdioServerParameters
-    from mcpadapt.core import MCPAdapt
-    from mcpadapt.crewai_adapter import CrewAIAdapter
+    from mcp.types import CallToolResult, TextContent, Tool
+    from mcpadapt.core import MCPAdapt, ToolAdapter
+
+
+logger = logging.getLogger(__name__)
 
 
 try:
     from mcp import StdioServerParameters
-    from mcpadapt.core import MCPAdapt
-    from mcpadapt.crewai_adapter import CrewAIAdapter
+    from mcp.types import CallToolResult, TextContent, Tool
+    from mcpadapt.core import MCPAdapt, ToolAdapter
+
+    class CrewAIToolAdapter(ToolAdapter):
+        """Adapter that creates CrewAI tools with properly normalized JSON schemas.
+
+        This adapter bypasses mcpadapt's model creation which adds invalid null values
+        to field schemas, instead using CrewAI's own schema utilities.
+        """
+
+        def adapt(
+            self,
+            func: Callable[[dict[str, Any] | None], CallToolResult],
+            mcp_tool: Tool,
+        ) -> BaseTool:
+            """Adapt a MCP tool to a CrewAI tool.
+
+            Args:
+                func: The function to call when the tool is invoked.
+                mcp_tool: The MCP tool definition to adapt.
+
+            Returns:
+                A CrewAI BaseTool instance.
+            """
+            tool_name = sanitize_tool_name(mcp_tool.name)
+            tool_description = mcp_tool.description or ""
+            args_model = create_model_from_schema(mcp_tool.inputSchema)
+
+            class CrewAIMCPTool(BaseTool):
+                name: str = tool_name
+                description: str = tool_description
+                args_schema: type[BaseModel] = args_model
+
+                def _run(self, **kwargs: Any) -> Any:
+                    result = func(kwargs)
+                    if len(result.content) == 1:
+                        first_content = result.content[0]
+                        if isinstance(first_content, TextContent):
+                            return first_content.text
+                        return str(first_content)
+                    return str(
+                        [
+                            content.text
+                            for content in result.content
+                            if isinstance(content, TextContent)
+                        ]
+                    )
+
+                def _generate_description(self) -> None:
+                    schema = self.args_schema.model_json_schema()
+                    schema.pop("$defs", None)
+                    self.description = (
+                        f"Tool Name: {self.name}\n"
+                        f"Tool Arguments: {schema}\n"
+                        f"Tool Description: {self.description}"
+                    )
+
+            return CrewAIMCPTool()
+
+        async def async_adapt(self, afunc: Any, mcp_tool: Tool) -> Any:
+            """Async adaptation is not supported by CrewAI."""
+            raise NotImplementedError("async is not supported by the CrewAI framework.")
 
     MCP_AVAILABLE = True
-except ImportError:
+except ImportError as e:
+    logger.debug(f"MCP packages not available: {e}")
     MCP_AVAILABLE = False
 
 
@@ -34,9 +100,6 @@ class MCPServerAdapter:
     Note: tools can only be accessed after the server has been started with the
         `start()` method.
 
-    Attributes:
-        tools: The CrewAI tools available from the MCP server.
-
     Usage:
         # context manager + stdio
         with MCPServerAdapter(...) as tools:
@@ -89,7 +152,9 @@ class MCPServerAdapter:
         super().__init__()
         self._adapter = None
         self._tools = None
-        self._tool_names = list(tool_names) if tool_names else None
+        self._tool_names = (
+            [sanitize_tool_name(name) for name in tool_names] if tool_names else None
+        )
 
         if not MCP_AVAILABLE:
             import click
@@ -100,7 +165,7 @@ class MCPServerAdapter:
                 import subprocess
 
                 try:
-                    subprocess.run(["uv", "add", "mcp crewai-tools[mcp]"], check=True)  # noqa: S607
+                    subprocess.run(["uv", "add", "mcp crewai-tools'[mcp]'"], check=True)  # noqa: S607
 
                 except subprocess.CalledProcessError as e:
                     raise ImportError("Failed to install mcp package") from e
@@ -112,7 +177,7 @@ class MCPServerAdapter:
         try:
             self._serverparams = serverparams
             self._adapter = MCPAdapt(
-                self._serverparams, CrewAIAdapter(), connect_timeout
+                self._serverparams, CrewAIToolAdapter(), connect_timeout
             )
             self.start()
 
@@ -124,13 +189,13 @@ class MCPServerAdapter:
                     logger.error(f"Error during stop cleanup: {stop_e}")
             raise RuntimeError(f"Failed to initialize MCP Adapter: {e}") from e
 
-    def start(self):
+    def start(self) -> None:
         """Start the MCP server and initialize the tools."""
-        self._tools = self._adapter.__enter__()
+        self._tools = self._adapter.__enter__()  # type: ignore[union-attr]
 
-    def stop(self):
+    def stop(self) -> None:
         """Stop the MCP server."""
-        self._adapter.__exit__(None, None, None)
+        self._adapter.__exit__(None, None, None)  # type: ignore[union-attr]
 
     @property
     def tools(self) -> ToolCollection[BaseTool]:
@@ -152,12 +217,19 @@ class MCPServerAdapter:
             return tools_collection.filter_by_names(self._tool_names)
         return tools_collection
 
-    def __enter__(self):
-        """Enter the context manager. Note that `__init__()` already starts the MCP server.
-        So tools should already be available.
+    def __enter__(self) -> ToolCollection[BaseTool]:
+        """Enter the context manager.
+
+        Note that `__init__()` already starts the MCP server,
+        so tools should already be available.
         """
         return self.tools
 
-    def __exit__(self, exc_type, exc_value, traceback):
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: Any,
+    ) -> None:
         """Exit the context manager."""
-        return self._adapter.__exit__(exc_type, exc_value, traceback)
+        self._adapter.__exit__(exc_type, exc_value, traceback)  # type: ignore[union-attr]

lib/crewai-tools/tool.specs.json

@@ -197,7 +197,7 @@
       }
     },
     {
-      "description": "A tool that can be used to search the internet with a search_query.",
+      "description": "A tool that performs web searches using the Brave Search API. Results are returned as structured JSON data.",
       "env_vars": [
         {
           "default": null,
@@ -206,7 +206,7 @@
           "required": true
         }
       ],
-      "humanized_name": "Brave Web Search the internet",
+      "humanized_name": "Brave Search",
       "init_params_schema": {
         "$defs": {
           "EnvVar": {
@@ -245,20 +245,8 @@
             "type": "object"
           }
         },
-        "description": "BraveSearchTool - A tool for performing web searches using the Brave Search API.\n\nThis module provides functionality to search the internet using Brave's Search API,\nsupporting customizable result counts and country-specific searches.\n\nDependencies:\n    - requests\n    - pydantic\n    - python-dotenv (for API key management)",
+        "description": "A tool that performs web searches using the Brave Search API.",
         "properties": {
-          "country": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": "",
-            "title": "Country"
-          },
           "n_results": {
             "default": 10,
             "title": "N Results",
@@ -281,16 +269,161 @@
       "name": "BraveSearchTool",
       "package_dependencies": [],
       "run_params_schema": {
-        "description": "Input for BraveSearchTool.",
+        "description": "Input for BraveSearchTool",
         "properties": {
-          "search_query": {
-            "description": "Mandatory search query you want to use to search the internet",
-            "title": "Search Query",
+          "count": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "The maximum number of results to return. Actual number may be less.",
+            "title": "Count"
+          },
+          "country": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Country code for geo-targeting (e.g., 'US', 'BR').",
+            "title": "Country"
+          },
+          "extra_snippets": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Include up to 5 text snippets for each page if possible.",
+            "title": "Extra Snippets"
+          },
+          "freshness": {
+            "anyOf": [
+              {
+                "enum": [
+                  "pd",
+                  "pw",
+                  "pm",
+                  "py"
+                ],
+                "type": "string"
+              },
+              {
+                "pattern": "^\\d{4}-\\d{2}-\\d{2}to\\d{4}-\\d{2}-\\d{2}$",
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Enforce freshness of results. Options: pd/pw/pm/py, or YYYY-MM-DDtoYYYY-MM-DD",
+            "title": "Freshness"
+          },
+          "offset": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Skip the first N result sets/pages. Max is 9.",
+            "title": "Offset"
+          },
+          "operators": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Whether to apply search operators (e.g., site:example.com).",
+            "title": "Operators"
+          },
+          "query": {
+            "description": "Search query to perform",
+            "title": "Query",
             "type": "string"
+          },
+          "safesearch": {
+            "anyOf": [
+              {
+                "enum": [
+                  "off",
+                  "moderate",
+                  "strict"
+                ],
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Filter out explicit content. Options: off/moderate/strict",
+            "title": "Safesearch"
+          },
+          "search_language": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Language code for the search results (e.g., 'en', 'es').",
+            "title": "Search Language"
+          },
+          "spellcheck": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Attempt to correct spelling errors in the search query.",
+            "title": "Spellcheck"
+          },
+          "text_decorations": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Include markup to highlight search terms in the results.",
+            "title": "Text Decorations"
           }
         },
         "required": [
-          "search_query"
+          "query"
         ],
         "title": "BraveSearchToolSchema",
         "type": "object"
@@ -3741,10 +3874,6 @@
             "title": "Bucket Name",
             "type": "string"
           },
-          "cluster": {
-            "description": "An instance of the Couchbase Cluster connected to the desired Couchbase server.",
-            "title": "Cluster"
-          },
           "collection_name": {
             "description": "The name of the Couchbase collection to search",
             "title": "Collection Name",
@@ -3793,7 +3922,6 @@
           }
         },
         "required": [
-          "cluster",
           "collection_name",
           "scope_name",
           "bucket_name",
@@ -12537,13 +12665,9 @@
         "properties": {
           "config": {
             "$ref": "#/$defs/OxylabsAmazonProductScraperConfig"
-          },
-          "oxylabs_api": {
-            "title": "Oxylabs Api"
           }
         },
         "required": [
-          "oxylabs_api",
           "config"
         ],
         "title": "OxylabsAmazonProductScraperTool",
@@ -12766,13 +12890,9 @@
         "properties": {
           "config": {
             "$ref": "#/$defs/OxylabsAmazonSearchScraperConfig"
-          },
-          "oxylabs_api": {
-            "title": "Oxylabs Api"
           }
         },
         "required": [
-          "oxylabs_api",
           "config"
         ],
         "title": "OxylabsAmazonSearchScraperTool",
@@ -13008,13 +13128,9 @@
         "properties": {
           "config": {
             "$ref": "#/$defs/OxylabsGoogleSearchScraperConfig"
-          },
-          "oxylabs_api": {
-            "title": "Oxylabs Api"
           }
         },
         "required": [
-          "oxylabs_api",
           "config"
         ],
         "title": "OxylabsGoogleSearchScraperTool",
@@ -13198,13 +13314,9 @@
         "properties": {
           "config": {
             "$ref": "#/$defs/OxylabsUniversalScraperConfig"
-          },
-          "oxylabs_api": {
-            "title": "Oxylabs Api"
           }
         },
         "required": [
-          "oxylabs_api",
           "config"
         ],
         "title": "OxylabsUniversalScraperTool",
@@ -20005,6 +20117,18 @@
       "humanized_name": "Web Automation Tool",
       "init_params_schema": {
         "$defs": {
+          "AvailableModel": {
+            "enum": [
+              "gpt-4o",
+              "gpt-4o-mini",
+              "claude-3-5-sonnet-latest",
+              "claude-3-7-sonnet-latest",
+              "computer-use-preview",
+              "gemini-2.0-flash"
+            ],
+            "title": "AvailableModel",
+            "type": "string"
+          },
           "EnvVar": {
             "properties": {
               "default": {
@@ -20082,6 +20206,17 @@
             "default": null,
             "title": "Model Api Key"
           },
+          "model_name": {
+            "anyOf": [
+              {
+                "$ref": "#/$defs/AvailableModel"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": "claude-3-7-sonnet-latest"
+          },
           "project_id": {
             "anyOf": [
               {
@@ -21306,26 +21441,6 @@
             "description": "The Tavily API key. If not provided, it will be loaded from the environment variable TAVILY_API_KEY.",
             "title": "Api Key"
           },
-          "async_client": {
-            "anyOf": [
-              {},
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Async Client"
-          },
-          "client": {
-            "anyOf": [
-              {},
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Client"
-          },
           "extract_depth": {
             "default": "basic",
             "description": "The depth of extraction. 'basic' for basic extraction, 'advanced' for advanced extraction.",
@@ -21461,26 +21576,6 @@
             "description": "The Tavily API key. If not provided, it will be loaded from the environment variable TAVILY_API_KEY.",
             "title": "Api Key"
           },
-          "async_client": {
-            "anyOf": [
-              {},
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Async Client"
-          },
-          "client": {
-            "anyOf": [
-              {},
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Client"
-          },
           "days": {
             "default": 7,
             "description": "The number of days to search back.",

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

@@ -118,6 +118,8 @@ MCP_TOOL_EXECUTION_TIMEOUT: Final[int] = 30
 MCP_DISCOVERY_TIMEOUT: Final[int] = 15
 MCP_MAX_RETRIES: Final[int] = 3
 
+_passthrough_exceptions: tuple[type[Exception], ...] = ()
+
 # Simple in-memory cache for MCP tool schemas (duration: 5 minutes)
 _mcp_schema_cache: dict[str, Any] = {}
 _cache_ttl: Final[int] = 300  # 5 minutes
@@ -479,6 +481,8 @@ class Agent(BaseAgent):
                     ),
                 )
                 raise e
+            if isinstance(e, _passthrough_exceptions):
+                raise
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 crewai_event_bus.emit(
@@ -711,6 +715,8 @@ class Agent(BaseAgent):
                     ),
                 )
                 raise e
+            if isinstance(e, _passthrough_exceptions):
+                raise
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 crewai_event_bus.emit(

lib/crewai/src/crewai/agents/agent_adapters/base_agent_adapter.py

@@ -37,9 +37,10 @@ class BaseAgentAdapter(BaseAgent, ABC):
             tools: Optional list of BaseTool instances to be configured
         """
 
-    def configure_structured_output(self, structured_output: Any) -> None:
+    @abstractmethod
+    def configure_structured_output(self, task: Any) -> None:
         """Configure the structured output for the specific agent implementation.
 
         Args:
-            structured_output: The structured output to be configured
+            task: The task object containing output format specifications.
         """

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

@@ -4,7 +4,6 @@ import time
 from typing import TYPE_CHECKING
 
 from crewai.agents.parser import AgentFinish
-from crewai.events.event_listener import event_listener
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.utilities.converter import ConverterError
@@ -138,52 +137,3 @@ class CrewAgentExecutorMixin:
                     content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
                     color="bold_yellow",
                 )
-
-    def _ask_human_input(self, final_answer: str) -> str:
-        """Prompt human input with mode-appropriate messaging.
-
-        Note: The final answer is already displayed via the AgentLogsExecutionEvent
-        panel, so we only show the feedback prompt here.
-        """
-        from rich.panel import Panel
-        from rich.text import Text
-
-        formatter = event_listener.formatter
-        formatter.pause_live_updates()
-
-        try:
-            # Training mode prompt (single iteration)
-            if self.crew and getattr(self.crew, "_train", False):
-                prompt_text = (
-                    "TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
-                    "This will be used to train better versions of the agent.\n"
-                    "Please provide detailed feedback about the result quality and reasoning process."
-                )
-                title = "🎓 Training Feedback Required"
-            # Regular human-in-the-loop prompt (multiple iterations)
-            else:
-                prompt_text = (
-                    "Provide feedback on the Final Result above.\n\n"
-                    "• If you are happy with the result, simply hit Enter without typing anything.\n"
-                    "• Otherwise, provide specific improvement requests.\n"
-                    "• You can provide multiple rounds of feedback until satisfied."
-                )
-                title = "💬 Human Feedback Required"
-
-            content = Text()
-            content.append(prompt_text, style="yellow")
-
-            prompt_panel = Panel(
-                content,
-                title=title,
-                border_style="yellow",
-                padding=(1, 2),
-            )
-            formatter.console.print(prompt_panel)
-
-            response = input()
-            if response.strip() != "":
-                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
-            return response
-        finally:
-            formatter.resume_live_updates()

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

@@ -19,6 +19,7 @@ from crewai.agents.parser import (
     AgentFinish,
     OutputParserError,
 )
+from crewai.core.providers.human_input import ExecutorContext, get_provider
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.logging_events import (
     AgentLogsExecutionEvent,
@@ -175,15 +176,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         return self.llm.supports_stop_words() if self.llm else False
 
-    def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
-        """Execute the agent with given inputs.
+    def _setup_messages(self, inputs: dict[str, Any]) -> None:
+        """Set up messages for the agent execution.
 
         Args:
             inputs: Input dictionary containing prompt variables.
-
-        Returns:
-            Dictionary with agent output.
         """
+        provider = get_provider()
+        if provider.setup_messages(cast(ExecutorContext, cast(object, self))):
+            return
+
         if "system" in self.prompt:
             system_prompt = self._format_prompt(
                 cast(str, self.prompt.get("system", "")), inputs
@@ -197,6 +199,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
             self.messages.append(format_message_for_llm(user_prompt))
 
+        provider.post_setup_messages(cast(ExecutorContext, cast(object, self)))
+
+    def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
+        """Execute the agent with given inputs.
+
+        Args:
+            inputs: Input dictionary containing prompt variables.
+
+        Returns:
+            Dictionary with agent output.
+        """
+        self._setup_messages(inputs)
+
         self._inject_multimodal_files(inputs)
 
         self._show_start_logs()
@@ -799,6 +814,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 agent_key=agent_key,
             ),
         )
+        error_event_emitted = False
 
         track_delegation_if_needed(func_name, args_dict, self.task)
 
@@ -881,6 +897,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                             error=e,
                         ),
                     )
+                    error_event_emitted = True
         elif max_usage_reached and original_tool:
             # Return error message when max usage limit is reached
             result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
@@ -908,20 +925,20 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     color="red",
                 )
 
-        # Emit tool usage finished event
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageFinishedEvent(
-                output=result,
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                agent_key=agent_key,
-                started_at=started_at,
-                finished_at=datetime.now(),
-            ),
-        )
+        if not error_event_emitted:
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageFinishedEvent(
+                    output=result,
+                    tool_name=func_name,
+                    tool_args=args_dict,
+                    from_agent=self.agent,
+                    from_task=self.task,
+                    agent_key=agent_key,
+                    started_at=started_at,
+                    finished_at=datetime.now(),
+                ),
+            )
 
         # Append tool result message
         tool_message: LLMMessage = {
@@ -970,18 +987,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Dictionary with agent output.
         """
-        if "system" in self.prompt:
-            system_prompt = self._format_prompt(
-                cast(str, self.prompt.get("system", "")), inputs
-            )
-            user_prompt = self._format_prompt(
-                cast(str, self.prompt.get("user", "")), inputs
-            )
-            self.messages.append(format_message_for_llm(system_prompt, role="system"))
-            self.messages.append(format_message_for_llm(user_prompt))
-        else:
-            user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
-            self.messages.append(format_message_for_llm(user_prompt))
+        self._setup_messages(inputs)
 
         await self._ainject_multimodal_files(inputs)
 
@@ -1003,7 +1009,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             raise
 
         if self.ask_for_human_input:
-            formatted_answer = self._handle_human_feedback(formatted_answer)
+            formatted_answer = await self._ahandle_human_feedback(formatted_answer)
 
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
@@ -1491,7 +1497,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return prompt.replace("{tools}", inputs["tools"])
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
-        """Process human feedback.
+        """Process human feedback via the configured provider.
 
         Args:
             formatted_answer: Initial agent result.
@@ -1499,17 +1505,22 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             Final answer after feedback.
         """
-        output_str = (
-            formatted_answer.output
-            if isinstance(formatted_answer.output, str)
-            else formatted_answer.output.model_dump_json()
-        )
-        human_feedback = self._ask_human_input(output_str)
+        provider = get_provider()
+        return provider.handle_feedback(formatted_answer, self)
 
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
+    async def _ahandle_human_feedback(
+        self, formatted_answer: AgentFinish
+    ) -> AgentFinish:
+        """Process human feedback asynchronously via the configured provider.
 
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
+        Args:
+            formatted_answer: Initial agent result.
+
+        Returns:
+            Final answer after feedback.
+        """
+        provider = get_provider()
+        return await provider.handle_feedback_async(formatted_answer, self)
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -1519,74 +1530,18 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         """
         return bool(self.crew and self.crew._train)
 
-    def _handle_training_feedback(
-        self, initial_answer: AgentFinish, feedback: str
-    ) -> AgentFinish:
-        """Process training feedback.
+    def _format_feedback_message(self, feedback: str) -> LLMMessage:
+        """Format feedback as a message for the LLM.
 
         Args:
-            initial_answer: Initial agent output.
-            feedback: Training feedback.
+            feedback: User feedback string.
 
         Returns:
-            Improved answer.
+            Formatted message dict.
         """
-        self._handle_crew_training_output(initial_answer, feedback)
-        self.messages.append(
-            format_message_for_llm(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
+        return format_message_for_llm(
+            self._i18n.slice("feedback_instructions").format(feedback=feedback)
         )
-        improved_answer = self._invoke_loop()
-        self._handle_crew_training_output(improved_answer)
-        self.ask_for_human_input = False
-        return improved_answer
-
-    def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
-    ) -> AgentFinish:
-        """Process regular feedback iteratively.
-
-        Args:
-            current_answer: Current agent output.
-            initial_feedback: Initial user feedback.
-
-        Returns:
-            Final answer after iterations.
-        """
-        feedback = initial_feedback
-        answer = current_answer
-
-        while self.ask_for_human_input:
-            # If the user provides a blank response, assume they are happy with the result
-            if feedback.strip() == "":
-                self.ask_for_human_input = False
-            else:
-                answer = self._process_feedback_iteration(feedback)
-                output_str = (
-                    answer.output
-                    if isinstance(answer.output, str)
-                    else answer.output.model_dump_json()
-                )
-                feedback = self._ask_human_input(output_str)
-
-        return answer
-
-    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process single feedback iteration.
-
-        Args:
-            feedback: User feedback.
-
-        Returns:
-            Updated agent response.
-        """
-        self.messages.append(
-            format_message_for_llm(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-        return self._invoke_loop()
 
     @classmethod
     def __get_pydantic_core_schema__(

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

@@ -1,6 +1,8 @@
+import os
 from typing import Any
 from urllib.parse import urljoin
-import os
+
+import httpx
 import requests
 
 from crewai.cli.config import Settings
@@ -33,7 +35,11 @@ class PlusAPI:
         if settings.org_uuid:
             self.headers["X-Crewai-Organization-Id"] = settings.org_uuid
 
-        self.base_url = os.getenv("CREWAI_PLUS_URL") or str(settings.enterprise_base_url) or DEFAULT_CREWAI_ENTERPRISE_URL
+        self.base_url = (
+            os.getenv("CREWAI_PLUS_URL")
+            or str(settings.enterprise_base_url)
+            or DEFAULT_CREWAI_ENTERPRISE_URL
+        )
 
     def _make_request(
         self, method: str, endpoint: str, **kwargs: Any
@@ -49,8 +55,10 @@ class PlusAPI:
     def get_tool(self, handle: str) -> requests.Response:
         return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
 
-    def get_agent(self, handle: str) -> requests.Response:
-        return self._make_request("GET", f"{self.AGENTS_RESOURCE}/{handle}")
+    async def get_agent(self, handle: str) -> httpx.Response:
+        url = urljoin(self.base_url, f"{self.AGENTS_RESOURCE}/{handle}")
+        async with httpx.AsyncClient() as client:
+            return await client.get(url, headers=self.headers)
 
     def publish_tool(
         self,

lib/crewai/src/crewai/context.py

@@ -43,3 +43,23 @@ def platform_context(integration_token: str) -> Generator[None, Any, None]:
         yield
     finally:
         _platform_integration_token.reset(token)
+
+
+_current_task_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
+    "current_task_id", default=None
+)
+
+
+def set_current_task_id(task_id: str | None) -> contextvars.Token[str | None]:
+    """Set the current task ID in the context. Returns a token for reset."""
+    return _current_task_id.set(task_id)
+
+
+def reset_current_task_id(token: contextvars.Token[str | None]) -> None:
+    """Reset the current task ID to its previous value."""
+    _current_task_id.reset(token)
+
+
+def get_current_task_id() -> str | None:
+    """Get the current task ID from the context."""
+    return _current_task_id.get()

lib/crewai/src/crewai/core/__init__.py

@@ -0,0 +1 @@
+"""Core crewAI components and interfaces."""

lib/crewai/src/crewai/core/providers/__init__.py

@@ -0,0 +1 @@
+"""Provider interfaces for extensible crewAI components."""

lib/crewai/src/crewai/core/providers/content_processor.py

@@ -0,0 +1,78 @@
+"""Content processor provider for extensible content processing."""
+
+from contextvars import ContextVar
+from typing import Any, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class ContentProcessorProvider(Protocol):
+    """Protocol for content processing during task execution."""
+
+    def process(self, content: str, context: dict[str, Any] | None = None) -> str:
+        """Process content before use.
+
+        Args:
+            content: The content to process.
+            context: Optional context information.
+
+        Returns:
+            The processed content.
+        """
+        ...
+
+
+class NoOpContentProcessor:
+    """Default processor that returns content unchanged."""
+
+    def process(self, content: str, context: dict[str, Any] | None = None) -> str:
+        """Return content unchanged.
+
+        Args:
+            content: The content to process.
+            context: Optional context information (unused).
+
+        Returns:
+            The original content unchanged.
+        """
+        return content
+
+
+_content_processor: ContextVar[ContentProcessorProvider | None] = ContextVar(
+    "_content_processor", default=None
+)
+
+_default_processor = NoOpContentProcessor()
+
+
+def get_processor() -> ContentProcessorProvider:
+    """Get the current content processor.
+
+    Returns:
+        The registered content processor or the default no-op processor.
+    """
+    processor = _content_processor.get()
+    if processor is not None:
+        return processor
+    return _default_processor
+
+
+def set_processor(processor: ContentProcessorProvider) -> None:
+    """Set the content processor for the current context.
+
+    Args:
+        processor: The content processor to use.
+    """
+    _content_processor.set(processor)
+
+
+def process_content(content: str, context: dict[str, Any] | None = None) -> str:
+    """Process content using the registered processor.
+
+    Args:
+        content: The content to process.
+        context: Optional context information.
+
+    Returns:
+        The processed content.
+    """
+    return get_processor().process(content, context)

lib/crewai/src/crewai/core/providers/human_input.py

@@ -0,0 +1,489 @@
+"""Human input provider for HITL (Human-in-the-Loop) flows."""
+
+from __future__ import annotations
+
+import asyncio
+from contextvars import ContextVar, Token
+import sys
+from typing import TYPE_CHECKING, Protocol, runtime_checkable
+
+
+if TYPE_CHECKING:
+    from crewai.agent.core import Agent
+    from crewai.agents.parser import AgentFinish
+    from crewai.crew import Crew
+    from crewai.llms.base_llm import BaseLLM
+    from crewai.task import Task
+    from crewai.utilities.types import LLMMessage
+
+
+class ExecutorContext(Protocol):
+    """Context interface for human input providers to interact with executor."""
+
+    task: Task | None
+    crew: Crew | None
+    messages: list[LLMMessage]
+    ask_for_human_input: bool
+    llm: BaseLLM
+    agent: Agent
+
+    def _invoke_loop(self) -> AgentFinish:
+        """Invoke the agent loop and return the result."""
+        ...
+
+    def _is_training_mode(self) -> bool:
+        """Check if training mode is active."""
+        ...
+
+    def _handle_crew_training_output(
+        self,
+        result: AgentFinish,
+        human_feedback: str | None = None,
+    ) -> None:
+        """Handle training output."""
+        ...
+
+    def _format_feedback_message(self, feedback: str) -> LLMMessage:
+        """Format feedback as a message."""
+        ...
+
+
+class AsyncExecutorContext(ExecutorContext, Protocol):
+    """Extended context for executors that support async invocation."""
+
+    async def _ainvoke_loop(self) -> AgentFinish:
+        """Invoke the agent loop asynchronously and return the result."""
+        ...
+
+
+@runtime_checkable
+class HumanInputProvider(Protocol):
+    """Protocol for human input handling.
+
+    Implementations handle the full feedback flow:
+    - Sync: prompt user, loop until satisfied
+    - Async: use non-blocking I/O and async invoke loop
+    """
+
+    def setup_messages(self, context: ExecutorContext) -> bool:
+        """Set up messages for execution.
+
+        Called before standard message setup. Allows providers to handle
+        conversation resumption or other custom message initialization.
+
+        Args:
+            context: Executor context with messages list to modify.
+
+        Returns:
+            True if messages were set up (skip standard setup),
+            False to use standard setup.
+        """
+        ...
+
+    def post_setup_messages(self, context: ExecutorContext) -> None:
+        """Called after standard message setup.
+
+        Allows providers to modify messages after standard setup completes.
+        Only called when setup_messages returned False.
+
+        Args:
+            context: Executor context with messages list to modify.
+        """
+        ...
+
+    def handle_feedback(
+        self,
+        formatted_answer: AgentFinish,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Handle the full human feedback flow synchronously.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+
+        Raises:
+            Exception: Async implementations may raise to signal external handling.
+        """
+        ...
+
+    async def handle_feedback_async(
+        self,
+        formatted_answer: AgentFinish,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Handle the full human feedback flow asynchronously.
+
+        Uses non-blocking I/O for user prompts and async invoke loop
+        for agent re-execution.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Async executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+        """
+        ...
+
+    @staticmethod
+    def _get_output_string(answer: AgentFinish) -> str:
+        """Extract output string from answer.
+
+        Args:
+            answer: The agent's finished answer.
+
+        Returns:
+            String representation of the output.
+        """
+        if isinstance(answer.output, str):
+            return answer.output
+        return answer.output.model_dump_json()
+
+
+class SyncHumanInputProvider(HumanInputProvider):
+    """Default human input provider with sync and async support."""
+
+    def setup_messages(self, context: ExecutorContext) -> bool:
+        """Use standard message setup.
+
+        Args:
+            context: Executor context (unused).
+
+        Returns:
+            False to use standard setup.
+        """
+        return False
+
+    def post_setup_messages(self, context: ExecutorContext) -> None:
+        """No-op for sync provider.
+
+        Args:
+            context: Executor context (unused).
+        """
+
+    def handle_feedback(
+        self,
+        formatted_answer: AgentFinish,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Handle feedback synchronously with terminal prompts.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+        """
+        feedback = self._prompt_input(context.crew)
+
+        if context._is_training_mode():
+            return self._handle_training_feedback(formatted_answer, feedback, context)
+
+        return self._handle_regular_feedback(formatted_answer, feedback, context)
+
+    async def handle_feedback_async(
+        self,
+        formatted_answer: AgentFinish,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Handle feedback asynchronously without blocking the event loop.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Async executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+        """
+        feedback = await self._prompt_input_async(context.crew)
+
+        if context._is_training_mode():
+            return await self._handle_training_feedback_async(
+                formatted_answer, feedback, context
+            )
+
+        return await self._handle_regular_feedback_async(
+            formatted_answer, feedback, context
+        )
+
+    # ── Sync helpers ──────────────────────────────────────────────────
+
+    @staticmethod
+    def _handle_training_feedback(
+        initial_answer: AgentFinish,
+        feedback: str,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Process training feedback (single iteration).
+
+        Args:
+            initial_answer: The agent's initial answer.
+            feedback: Human feedback string.
+            context: Executor context for callbacks.
+
+        Returns:
+            Improved answer after processing feedback.
+        """
+        context._handle_crew_training_output(initial_answer, feedback)
+        context.messages.append(context._format_feedback_message(feedback))
+        improved_answer = context._invoke_loop()
+        context._handle_crew_training_output(improved_answer)
+        context.ask_for_human_input = False
+        return improved_answer
+
+    def _handle_regular_feedback(
+        self,
+        current_answer: AgentFinish,
+        initial_feedback: str,
+        context: ExecutorContext,
+    ) -> AgentFinish:
+        """Process regular feedback with iteration loop.
+
+        Args:
+            current_answer: The agent's current answer.
+            initial_feedback: Initial human feedback string.
+            context: Executor context for callbacks.
+
+        Returns:
+            Final answer after all feedback iterations.
+        """
+        feedback = initial_feedback
+        answer = current_answer
+
+        while context.ask_for_human_input:
+            if feedback.strip() == "":
+                context.ask_for_human_input = False
+            else:
+                context.messages.append(context._format_feedback_message(feedback))
+                answer = context._invoke_loop()
+                feedback = self._prompt_input(context.crew)
+
+        return answer
+
+    # ── Async helpers ─────────────────────────────────────────────────
+
+    @staticmethod
+    async def _handle_training_feedback_async(
+        initial_answer: AgentFinish,
+        feedback: str,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Process training feedback asynchronously (single iteration).
+
+        Args:
+            initial_answer: The agent's initial answer.
+            feedback: Human feedback string.
+            context: Async executor context for callbacks.
+
+        Returns:
+            Improved answer after processing feedback.
+        """
+        context._handle_crew_training_output(initial_answer, feedback)
+        context.messages.append(context._format_feedback_message(feedback))
+        improved_answer = await context._ainvoke_loop()
+        context._handle_crew_training_output(improved_answer)
+        context.ask_for_human_input = False
+        return improved_answer
+
+    async def _handle_regular_feedback_async(
+        self,
+        current_answer: AgentFinish,
+        initial_feedback: str,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Process regular feedback with async iteration loop.
+
+        Args:
+            current_answer: The agent's current answer.
+            initial_feedback: Initial human feedback string.
+            context: Async executor context for callbacks.
+
+        Returns:
+            Final answer after all feedback iterations.
+        """
+        feedback = initial_feedback
+        answer = current_answer
+
+        while context.ask_for_human_input:
+            if feedback.strip() == "":
+                context.ask_for_human_input = False
+            else:
+                context.messages.append(context._format_feedback_message(feedback))
+                answer = await context._ainvoke_loop()
+                feedback = await self._prompt_input_async(context.crew)
+
+        return answer
+
+    # ── I/O ───────────────────────────────────────────────────────────
+
+    @staticmethod
+    def _prompt_input(crew: Crew | None) -> str:
+        """Show rich panel and prompt for input.
+
+        Args:
+            crew: The crew instance for context.
+
+        Returns:
+            User input string from terminal.
+        """
+        from rich.panel import Panel
+        from rich.text import Text
+
+        from crewai.events.event_listener import event_listener
+
+        formatter = event_listener.formatter
+        formatter.pause_live_updates()
+
+        try:
+            if crew and getattr(crew, "_train", False):
+                prompt_text = (
+                    "TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
+                    "This will be used to train better versions of the agent.\n"
+                    "Please provide detailed feedback about the result quality and reasoning process."
+                )
+                title = "🎓 Training Feedback Required"
+            else:
+                prompt_text = (
+                    "Provide feedback on the Final Result above.\n\n"
+                    "• If you are happy with the result, simply hit Enter without typing anything.\n"
+                    "• Otherwise, provide specific improvement requests.\n"
+                    "• You can provide multiple rounds of feedback until satisfied."
+                )
+                title = "💬 Human Feedback Required"
+
+            content = Text()
+            content.append(prompt_text, style="yellow")
+
+            prompt_panel = Panel(
+                content,
+                title=title,
+                border_style="yellow",
+                padding=(1, 2),
+            )
+            formatter.console.print(prompt_panel)
+
+            response = input()
+            if response.strip() != "":
+                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
+            return response
+        finally:
+            formatter.resume_live_updates()
+
+    @staticmethod
+    async def _prompt_input_async(crew: Crew | None) -> str:
+        """Show rich panel and prompt for input without blocking the event loop.
+
+        Args:
+            crew: The crew instance for context.
+
+        Returns:
+            User input string from terminal.
+        """
+        from rich.panel import Panel
+        from rich.text import Text
+
+        from crewai.events.event_listener import event_listener
+
+        formatter = event_listener.formatter
+        formatter.pause_live_updates()
+
+        try:
+            if crew and getattr(crew, "_train", False):
+                prompt_text = (
+                    "TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
+                    "This will be used to train better versions of the agent.\n"
+                    "Please provide detailed feedback about the result quality and reasoning process."
+                )
+                title = "🎓 Training Feedback Required"
+            else:
+                prompt_text = (
+                    "Provide feedback on the Final Result above.\n\n"
+                    "• If you are happy with the result, simply hit Enter without typing anything.\n"
+                    "• Otherwise, provide specific improvement requests.\n"
+                    "• You can provide multiple rounds of feedback until satisfied."
+                )
+                title = "💬 Human Feedback Required"
+
+            content = Text()
+            content.append(prompt_text, style="yellow")
+
+            prompt_panel = Panel(
+                content,
+                title=title,
+                border_style="yellow",
+                padding=(1, 2),
+            )
+            formatter.console.print(prompt_panel)
+
+            response = await _async_readline()
+            if response.strip() != "":
+                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
+            return response
+        finally:
+            formatter.resume_live_updates()
+
+
+async def _async_readline() -> str:
+    """Read a line from stdin using the event loop's native I/O.
+
+    Falls back to asyncio.to_thread on platforms where piping stdin
+    is unsupported.
+
+    Returns:
+        The line read from stdin, with trailing newline stripped.
+    """
+    loop = asyncio.get_running_loop()
+    try:
+        reader = asyncio.StreamReader()
+        protocol = asyncio.StreamReaderProtocol(reader)
+        await loop.connect_read_pipe(lambda: protocol, sys.stdin)
+        raw = await reader.readline()
+        return raw.decode().rstrip("\n")
+    except (OSError, NotImplementedError, ValueError):
+        return await asyncio.to_thread(input)
+
+
+_provider: ContextVar[HumanInputProvider | None] = ContextVar(
+    "human_input_provider",
+    default=None,
+)
+
+
+def get_provider() -> HumanInputProvider:
+    """Get the current human input provider.
+
+    Returns:
+        The current provider, or a new SyncHumanInputProvider if none set.
+    """
+    provider = _provider.get()
+    if provider is None:
+        initialized_provider = SyncHumanInputProvider()
+        set_provider(initialized_provider)
+        return initialized_provider
+    return provider
+
+
+def set_provider(provider: HumanInputProvider) -> Token[HumanInputProvider | None]:
+    """Set the human input provider for the current context.
+
+    Args:
+        provider: The provider to use.
+
+    Returns:
+        Token that can be used to reset to previous value.
+    """
+    return _provider.set(provider)
+
+
+def reset_provider(token: Token[HumanInputProvider | None]) -> None:
+    """Reset the provider to its previous value.
+
+    Args:
+        token: Token returned from set_provider.
+    """
+    _provider.reset(token)

lib/crewai/src/crewai/crew.py

@@ -751,6 +751,8 @@ class Crew(FlowTrackable, BaseModel):
             for after_callback in self.after_kickoff_callbacks:
                 result = after_callback(result)
 
+            result = self._post_kickoff(result)
+
             self.usage_metrics = self.calculate_usage_metrics()
 
             return result
@@ -764,6 +766,9 @@ class Crew(FlowTrackable, BaseModel):
             clear_files(self.id)
             detach(token)
 
+    def _post_kickoff(self, result: CrewOutput) -> CrewOutput:
+        return result
+
     def kickoff_for_each(
         self,
         inputs: list[dict[str, Any]],
@@ -936,6 +941,8 @@ class Crew(FlowTrackable, BaseModel):
             for after_callback in self.after_kickoff_callbacks:
                 result = after_callback(result)
 
+            result = self._post_kickoff(result)
+
             self.usage_metrics = self.calculate_usage_metrics()
 
             return result
@@ -1181,6 +1188,9 @@ class Crew(FlowTrackable, BaseModel):
             self.manager_agent = manager
         manager.crew = self
 
+    def _get_execution_start_index(self, tasks: list[Task]) -> int | None:
+        return None
+
     def _execute_tasks(
         self,
         tasks: list[Task],
@@ -1197,6 +1207,9 @@ class Crew(FlowTrackable, BaseModel):
         Returns:
             CrewOutput: Final output of the crew
         """
+        custom_start = self._get_execution_start_index(tasks)
+        if custom_start is not None:
+            start_index = custom_start
 
         task_outputs: list[TaskOutput] = []
         futures: list[tuple[Task, Future[TaskOutput], int]] = []
@@ -1305,8 +1318,10 @@ class Crew(FlowTrackable, BaseModel):
         if files:
             supported_types: list[str] = []
             if agent and agent.llm and agent.llm.supports_multimodal():
-                provider = getattr(agent.llm, "provider", None) or getattr(
-                    agent.llm, "model", "openai"
+                provider = (
+                    getattr(agent.llm, "provider", None)
+                    or getattr(agent.llm, "model", None)
+                    or "openai"
                 )
                 api = getattr(agent.llm, "api", None)
                 supported_types = get_supported_content_types(provider, api)
@@ -1502,6 +1517,7 @@ class Crew(FlowTrackable, BaseModel):
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         self.token_usage = self.calculate_usage_metrics()
+        crewai_event_bus.flush()
         crewai_event_bus.emit(
             self,
             CrewKickoffCompletedEvent(
@@ -2011,7 +2027,13 @@ class Crew(FlowTrackable, BaseModel):
     @staticmethod
     def _show_tracing_disabled_message() -> None:
         """Show a message when tracing is disabled."""
-        from crewai.events.listeners.tracing.utils import has_user_declined_tracing
+        from crewai.events.listeners.tracing.utils import (
+            has_user_declined_tracing,
+            should_suppress_tracing_messages,
+        )
+
+        if should_suppress_tracing_messages():
+            return
 
         console = Console()
 

lib/crewai/src/crewai/events/__init__.py

@@ -195,6 +195,7 @@ __all__ = [
     "ToolUsageFinishedEvent",
     "ToolUsageStartedEvent",
     "ToolValidateInputErrorEvent",
+    "_extension_exports",
     "crewai_event_bus",
 ]
 
@@ -210,14 +211,29 @@ _AGENT_EVENT_MAPPING = {
     "LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
 }
 
+_extension_exports: dict[str, Any] = {}
+
 
 def __getattr__(name: str) -> Any:
-    """Lazy import for agent events to avoid circular imports."""
+    """Lazy import for agent events and registered extensions."""
     if name in _AGENT_EVENT_MAPPING:
         import importlib
 
         module_path = _AGENT_EVENT_MAPPING[name]
         module = importlib.import_module(module_path)
         return getattr(module, name)
+
+    if name in _extension_exports:
+        import importlib
+
+        value = _extension_exports[name]
+        if isinstance(value, str):
+            module_path, _, attr_name = value.rpartition(".")
+            if module_path:
+                module = importlib.import_module(module_path)
+                return getattr(module, attr_name)
+            return importlib.import_module(value)
+        return value
+
     msg = f"module {__name__!r} has no attribute {name!r}"
     raise AttributeError(msg)

lib/crewai/src/crewai/events/base_events.py

@@ -63,6 +63,7 @@ class BaseEvent(BaseModel):
     parent_event_id: str | None = None
     previous_event_id: str | None = None
     triggered_by_event_id: str | None = None
+    started_event_id: str | None = None
     emission_sequence: int | None = None
 
     def to_json(self, exclude: set[str] | None = None) -> Serializable:

lib/crewai/src/crewai/events/event_bus.py

@@ -227,6 +227,39 @@ class CrewAIEventsBus:
 
         return decorator
 
+    def off(
+        self,
+        event_type: type[BaseEvent],
+        handler: Callable[..., Any],
+    ) -> None:
+        """Unregister an event handler for a specific event type.
+
+        Args:
+            event_type: The event class to stop listening for
+            handler: The handler function to unregister
+        """
+        with self._rwlock.w_locked():
+            if event_type in self._sync_handlers:
+                existing_sync = self._sync_handlers[event_type]
+                if handler in existing_sync:
+                    self._sync_handlers[event_type] = existing_sync - {handler}
+                    if not self._sync_handlers[event_type]:
+                        del self._sync_handlers[event_type]
+
+            if event_type in self._async_handlers:
+                existing_async = self._async_handlers[event_type]
+                if handler in existing_async:
+                    self._async_handlers[event_type] = existing_async - {handler}
+                    if not self._async_handlers[event_type]:
+                        del self._async_handlers[event_type]
+
+            if event_type in self._handler_dependencies:
+                self._handler_dependencies[event_type].pop(handler, None)
+                if not self._handler_dependencies[event_type]:
+                    del self._handler_dependencies[event_type]
+
+            self._execution_plan_cache.pop(event_type, None)
+
     def _call_handlers(
         self,
         source: Any,
@@ -374,7 +407,8 @@ class CrewAIEventsBus:
                 if popped is None:
                     handle_empty_pop(event_type_name)
                 else:
-                    _, popped_type = popped
+                    popped_event_id, popped_type = popped
+                    event.started_event_id = popped_event_id
                     expected_start = VALID_EVENT_PAIRS.get(event_type_name)
                     if expected_start and popped_type and popped_type != expected_start:
                         handle_mismatch(event_type_name, popped_type, expected_start)
@@ -536,24 +570,52 @@ class CrewAIEventsBus:
             ...     # Do stuff...
             ... # Handlers are cleared after the context
         """
-        with self._rwlock.w_locked():
-            prev_sync = self._sync_handlers
-            prev_async = self._async_handlers
-            prev_deps = self._handler_dependencies
-            prev_cache = self._execution_plan_cache
-            self._sync_handlers = {}
-            self._async_handlers = {}
-            self._handler_dependencies = {}
-            self._execution_plan_cache = {}
+        with self._rwlock.r_locked():
+            saved_sync: dict[type[BaseEvent], frozenset[SyncHandler]] = dict(
+                self._sync_handlers
+            )
+            saved_async: dict[type[BaseEvent], frozenset[AsyncHandler]] = dict(
+                self._async_handlers
+            )
+            saved_deps: dict[type[BaseEvent], dict[Handler, list[Depends[Any]]]] = {
+                event_type: dict(handlers)
+                for event_type, handlers in self._handler_dependencies.items()
+            }
+
+        for event_type, sync_handlers in saved_sync.items():
+            for sync_handler in sync_handlers:
+                self.off(event_type, sync_handler)
+
+        for event_type, async_handlers in saved_async.items():
+            for async_handler in async_handlers:
+                self.off(event_type, async_handler)
 
         try:
             yield
         finally:
-            with self._rwlock.w_locked():
-                self._sync_handlers = prev_sync
-                self._async_handlers = prev_async
-                self._handler_dependencies = prev_deps
-                self._execution_plan_cache = prev_cache
+            with self._rwlock.r_locked():
+                current_sync = dict(self._sync_handlers)
+                current_async = dict(self._async_handlers)
+
+            for event_type, cur_sync in current_sync.items():
+                orig_sync = saved_sync.get(event_type, frozenset())
+                for new_handler in cur_sync - orig_sync:
+                    self.off(event_type, new_handler)
+
+            for event_type, cur_async in current_async.items():
+                orig_async = saved_async.get(event_type, frozenset())
+                for new_async_handler in cur_async - orig_async:
+                    self.off(event_type, new_async_handler)
+
+            for event_type, sync_handlers in saved_sync.items():
+                for sync_handler in sync_handlers:
+                    deps = saved_deps.get(event_type, {}).get(sync_handler)
+                    self._register_handler(event_type, sync_handler, deps)
+
+            for event_type, async_handlers in saved_async.items():
+                for async_handler in async_handlers:
+                    deps = saved_deps.get(event_type, {}).get(async_handler)
+                    self._register_handler(event_type, async_handler, deps)
 
     def shutdown(self, wait: bool = True) -> None:
         """Gracefully shutdown the event loop and wait for all tasks to finish.

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -797,7 +797,13 @@ class TraceCollectionListener(BaseEventListener):
         from rich.console import Console
         from rich.panel import Panel
 
-        from crewai.events.listeners.tracing.utils import has_user_declined_tracing
+        from crewai.events.listeners.tracing.utils import (
+            has_user_declined_tracing,
+            should_suppress_tracing_messages,
+        )
+
+        if should_suppress_tracing_messages():
+            return
 
         console = Console()
 

lib/crewai/src/crewai/events/listeners/tracing/utils.py

@@ -1,3 +1,4 @@
+from collections.abc import Callable
 from contextvars import ContextVar, Token
 from datetime import datetime
 import getpass
@@ -26,6 +27,35 @@ logger = logging.getLogger(__name__)
 
 _tracing_enabled: ContextVar[bool | None] = ContextVar("_tracing_enabled", default=None)
 
+_first_time_trace_hook: ContextVar[Callable[[], bool] | None] = ContextVar(
+    "_first_time_trace_hook", default=None
+)
+
+_suppress_tracing_messages: ContextVar[bool] = ContextVar(
+    "_suppress_tracing_messages", default=False
+)
+
+
+def set_suppress_tracing_messages(suppress: bool) -> object:
+    """Set whether to suppress tracing-related console messages.
+
+    Args:
+        suppress: True to suppress messages, False to show them.
+
+    Returns:
+        A token that can be used to restore the previous value.
+    """
+    return _suppress_tracing_messages.set(suppress)
+
+
+def should_suppress_tracing_messages() -> bool:
+    """Check if tracing messages should be suppressed.
+
+    Returns:
+        True if messages should be suppressed, False otherwise.
+    """
+    return _suppress_tracing_messages.get()
+
 
 def should_enable_tracing(*, override: bool | None = None) -> bool:
     """Determine if tracing should be enabled.
@@ -407,10 +437,13 @@ def truncate_messages(
 def should_auto_collect_first_time_traces() -> bool:
     """True if we should auto-collect traces for first-time user.
 
-
     Returns:
         True if first-time user AND telemetry not disabled AND tracing not explicitly enabled, False otherwise.
     """
+    hook = _first_time_trace_hook.get()
+    if hook is not None:
+        return hook()
+
     if _is_test_environment():
         return False
 
@@ -432,6 +465,9 @@ def prompt_user_for_trace_viewing(timeout_seconds: int = 20) -> bool:
     if _is_test_environment():
         return False
 
+    if should_suppress_tracing_messages():
+        return False
+
     try:
         import threading
 

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

@@ -16,7 +16,7 @@ class ToolUsageEvent(BaseEvent):
     tool_name: str
     tool_args: dict[str, Any] | str
     tool_class: str | None = None
-    run_attempts: int | None = None
+    run_attempts: int = 0
     delegations: int | None = None
     agent: Any | None = None
     task_name: str | None = None
@@ -26,7 +26,7 @@ class ToolUsageEvent(BaseEvent):
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
-    def __init__(self, **data):
+    def __init__(self, **data: Any) -> None:
         if data.get("from_task"):
             task = data["from_task"]
             data["task_id"] = str(task.id)
@@ -96,10 +96,10 @@ class ToolExecutionErrorEvent(BaseEvent):
     type: str = "tool_execution_error"
     tool_name: str
     tool_args: dict[str, Any]
-    tool_class: Callable
+    tool_class: Callable[..., Any]
     agent: Any | None = None
 
-    def __init__(self, **data):
+    def __init__(self, **data: Any) -> None:
         super().__init__(**data)
         # Set fingerprint data from the agent
         if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:

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

@@ -1,3 +1,4 @@
+from contextvars import ContextVar
 import os
 import threading
 from typing import Any, ClassVar, cast
@@ -10,6 +11,36 @@ from rich.text import Text
 from crewai.cli.version import is_newer_version_available
 
 
+_disable_version_check: ContextVar[bool] = ContextVar(
+    "_disable_version_check", default=False
+)
+
+_suppress_console_output: ContextVar[bool] = ContextVar(
+    "_suppress_console_output", default=False
+)
+
+
+def set_suppress_console_output(suppress: bool) -> object:
+    """Set whether to suppress all console output.
+
+    Args:
+        suppress: True to suppress output, False to show it.
+
+    Returns:
+        A token that can be used to restore the previous value.
+    """
+    return _suppress_console_output.set(suppress)
+
+
+def should_suppress_console_output() -> bool:
+    """Check if console output should be suppressed.
+
+    Returns:
+        True if output should be suppressed, False otherwise.
+    """
+    return _suppress_console_output.get()
+
+
 class ConsoleFormatter:
     tool_usage_counts: ClassVar[dict[str, int]] = {}
 
@@ -46,9 +77,15 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
+        if _disable_version_check.get():
+            return
+
         if os.getenv("CI", "").lower() in ("true", "1"):
             return
 
+        if os.getenv("CREWAI_DISABLE_VERSION_CHECK", "").lower() in ("true", "1"):
+            return
+
         try:
             is_newer, current, latest = is_newer_version_available()
             if is_newer and latest:
@@ -76,8 +113,12 @@ To update, run: uv sync --upgrade-package crewai"""
         from crewai.events.listeners.tracing.utils import (
             has_user_declined_tracing,
             is_tracing_enabled_in_context,
+            should_suppress_tracing_messages,
         )
 
+        if should_suppress_tracing_messages():
+            return
+
         if not is_tracing_enabled_in_context():
             if has_user_declined_tracing():
                 message = """Info: Tracing is disabled.
@@ -129,6 +170,8 @@ To enable tracing, do any one of these:
 
     def print(self, *args: Any, **kwargs: Any) -> None:
         """Print to console. Simplified to only handle panel-based output."""
+        if should_suppress_console_output():
+            return
         # Skip blank lines during streaming
         if len(args) == 0 and self._is_streaming:
             return
@@ -485,6 +528,9 @@ To enable tracing, do any one of these:
         if not self.verbose:
             return
 
+        if should_suppress_console_output():
+            return
+
         self._is_streaming = True
         self._last_stream_call_type = call_type
 

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -18,6 +18,7 @@ from crewai.agents.parser import (
     AgentFinish,
     OutputParserError,
 )
+from crewai.core.providers.human_input import get_provider
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.listeners.tracing.utils import (
     is_tracing_enabled_in_context,
@@ -31,7 +32,8 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from crewai.flow.flow import Flow, listen, or_, router, start
+from crewai.flow.flow import Flow, StateProxy, listen, or_, router, start
+from crewai.flow.types import FlowMethodName
 from crewai.hooks.llm_hooks import (
     get_after_llm_call_hooks,
     get_before_llm_call_hooks,
@@ -41,7 +43,12 @@ from crewai.hooks.tool_hooks import (
     get_after_tool_call_hooks,
     get_before_tool_call_hooks,
 )
-from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
+from crewai.hooks.types import (
+    AfterLLMCallHookCallable,
+    AfterLLMCallHookType,
+    BeforeLLMCallHookCallable,
+    BeforeLLMCallHookType,
+)
 from crewai.utilities.agent_utils import (
     convert_tools_to_openai_schema,
     enforce_rpm_limit,
@@ -191,8 +198,12 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         self._instance_id = str(uuid4())[:8]
 
-        self.before_llm_call_hooks: list[BeforeLLMCallHookType] = []
-        self.after_llm_call_hooks: list[AfterLLMCallHookType] = []
+        self.before_llm_call_hooks: list[
+            BeforeLLMCallHookType | BeforeLLMCallHookCallable
+        ] = []
+        self.after_llm_call_hooks: list[
+            AfterLLMCallHookType | AfterLLMCallHookCallable
+        ] = []
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
 
@@ -207,6 +218,71 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             )
         self._state = AgentReActState()
 
+    @property
+    def messages(self) -> list[LLMMessage]:
+        """Delegate to state for ExecutorContext conformance."""
+        return self._state.messages
+
+    @messages.setter
+    def messages(self, value: list[LLMMessage]) -> None:
+        """Delegate to state for ExecutorContext conformance."""
+        if self._flow_initialized and hasattr(self, "_state_lock"):
+            with self._state_lock:
+                self._state.messages = value
+        else:
+            self._state.messages = value
+
+    @property
+    def ask_for_human_input(self) -> bool:
+        """Delegate to state for ExecutorContext conformance."""
+        return self._state.ask_for_human_input
+
+    @ask_for_human_input.setter
+    def ask_for_human_input(self, value: bool) -> None:
+        """Delegate to state for ExecutorContext conformance."""
+        self._state.ask_for_human_input = value
+
+    def _invoke_loop(self) -> AgentFinish:
+        """Invoke the agent loop and return the result.
+
+        Required by ExecutorContext protocol.
+        """
+        self._state.iterations = 0
+        self._state.is_finished = False
+        self._state.current_answer = None
+
+        self.kickoff()
+
+        answer = self._state.current_answer
+        if not isinstance(answer, AgentFinish):
+            raise RuntimeError("Agent loop did not produce a final answer")
+        return answer
+
+    async def _ainvoke_loop(self) -> AgentFinish:
+        """Invoke the agent loop asynchronously and return the result.
+
+        Required by AsyncExecutorContext protocol.
+        """
+        self._state.iterations = 0
+        self._state.is_finished = False
+        self._state.current_answer = None
+
+        await self.akickoff()
+
+        answer = self._state.current_answer
+        if not isinstance(answer, AgentFinish):
+            raise RuntimeError("Agent loop did not produce a final answer")
+        return answer
+
+    def _format_feedback_message(self, feedback: str) -> LLMMessage:
+        """Format feedback as a message for the LLM.
+
+        Required by ExecutorContext protocol.
+        """
+        return format_message_for_llm(
+            self._i18n.slice("feedback_instructions").format(feedback=feedback)
+        )
+
     def _ensure_flow_initialized(self) -> None:
         """Ensure Flow.__init__() has been called.
 
@@ -298,18 +374,10 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         Flow initialization is deferred to prevent event emission during agent setup.
         Returns the temporary state until invoke() is called.
         """
+        if self._flow_initialized and hasattr(self, "_state_lock"):
+            return StateProxy(self._state, self._state_lock)  # type: ignore[return-value]
         return self._state
 
-    @property
-    def messages(self) -> list[LLMMessage]:
-        """Compatibility property for mixin - returns state messages."""
-        return self._state.messages
-
-    @messages.setter
-    def messages(self, value: list[LLMMessage]) -> None:
-        """Set state messages."""
-        self._state.messages = value
-
     @property
     def iterations(self) -> int:
         """Compatibility property for mixin - returns state iterations."""
@@ -416,15 +484,14 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             raise
 
     @listen("continue_reasoning_native")
-    def call_llm_native_tools(
-        self,
-    ) -> Literal["native_tool_calls", "native_finished", "context_error"]:
+    def call_llm_native_tools(self) -> None:
         """Execute LLM call with native function calling.
 
         Always calls the LLM so it can read reflection prompts and decide
         whether to provide a final answer or request more tools.
 
-        Returns routing decision based on whether tool calls or final answer.
+        Note: This is a listener, not a router. The route_native_tool_result
+        router fires after this to determine the next step based on state.
         """
         try:
             # Clear pending tools - LLM will decide what to do next after reading
@@ -454,8 +521,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             if isinstance(answer, list) and answer and self._is_tool_call_list(answer):
                 # Store tool calls for sequential processing
                 self.state.pending_tool_calls = list(answer)
-
-                return "native_tool_calls"
+                return  # Router will check pending_tool_calls
 
             if isinstance(answer, BaseModel):
                 self.state.current_answer = AgentFinish(
@@ -465,7 +531,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
                 self._invoke_step_callback(self.state.current_answer)
                 self._append_message_to_state(answer.model_dump_json())
-                return "native_finished"
+                return  # Router will check current_answer
 
             # Text response - this is the final answer
             if isinstance(answer, str):
@@ -476,8 +542,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
                 self._invoke_step_callback(self.state.current_answer)
                 self._append_message_to_state(answer)
-
-                return "native_finished"
+                return  # Router will check current_answer
 
             # Unexpected response type, treat as final answer
             self.state.current_answer = AgentFinish(
@@ -487,13 +552,12 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             )
             self._invoke_step_callback(self.state.current_answer)
             self._append_message_to_state(str(answer))
-
-            return "native_finished"
+            # Router will check current_answer
 
         except Exception as e:
             if is_context_length_exceeded(e):
                 self._last_context_error = e
-                return "context_error"
+                return  # Router will check _last_context_error
             if e.__class__.__module__.startswith("litellm"):
                 raise e
             handle_unknown_error(self._printer, e, verbose=self.agent.verbose)
@@ -506,6 +570,22 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             return "execute_tool"
         return "agent_finished"
 
+    @router(call_llm_native_tools)
+    def route_native_tool_result(
+        self,
+    ) -> Literal["native_tool_calls", "native_finished", "context_error"]:
+        """Route based on LLM response for native tool calling.
+
+        Checks state set by call_llm_native_tools to determine next step.
+        This router is needed because only router return values trigger
+        downstream listeners.
+        """
+        if self._last_context_error is not None:
+            return "context_error"
+        if self.state.pending_tool_calls:
+            return "native_tool_calls"
+        return "native_finished"
+
     @listen("execute_tool")
     def execute_tool_action(self) -> Literal["tool_completed", "tool_result_is_final"]:
         """Execute the tool action and handle the result."""
@@ -689,6 +769,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                     agent_key=agent_key,
                 ),
             )
+            error_event_emitted = False
 
             track_delegation_if_needed(func_name, args_dict, self.task)
 
@@ -764,6 +845,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                                 error=e,
                             ),
                         )
+                        error_event_emitted = True
             elif max_usage_reached and original_tool:
                 # Return error message when max usage limit is reached
                 result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
@@ -792,20 +874,20 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                         color="red",
                     )
 
-            # Emit tool usage finished event
-            crewai_event_bus.emit(
-                self,
-                event=ToolUsageFinishedEvent(
-                    output=result,
-                    tool_name=func_name,
-                    tool_args=args_dict,
-                    from_agent=self.agent,
-                    from_task=self.task,
-                    agent_key=agent_key,
-                    started_at=started_at,
-                    finished_at=datetime.now(),
-                ),
-            )
+            if not error_event_emitted:
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageFinishedEvent(
+                        output=result,
+                        tool_name=func_name,
+                        tool_args=args_dict,
+                        from_agent=self.agent,
+                        from_task=self.task,
+                        agent_key=agent_key,
+                        started_at=started_at,
+                        finished_at=datetime.now(),
+                    ),
+                )
 
             # Append tool result message
             tool_message: LLMMessage = {
@@ -861,9 +943,11 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         self.state.iterations += 1
         return "initialized"
 
-    @listen("initialized")
+    @listen(or_("initialized", "tool_completed", "native_tool_completed"))
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""
+        if self._flow_initialized:
+            self._discard_or_listener(FlowMethodName("continue_iteration"))
         return "check_iteration"
 
     @router(or_(initialize_reasoning, continue_iteration))
@@ -1105,7 +1189,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 )
 
             if self.state.ask_for_human_input:
-                formatted_answer = self._handle_human_feedback(formatted_answer)
+                formatted_answer = await self._ahandle_human_feedback(formatted_answer)
 
             self._create_short_term_memory(formatted_answer)
             self._create_long_term_memory(formatted_answer)
@@ -1319,17 +1403,22 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         Returns:
             Final answer after feedback.
         """
-        output_str = (
-            str(formatted_answer.output)
-            if isinstance(formatted_answer.output, BaseModel)
-            else formatted_answer.output
-        )
-        human_feedback = self._ask_human_input(output_str)
+        provider = get_provider()
+        return provider.handle_feedback(formatted_answer, self)
 
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
+    async def _ahandle_human_feedback(
+        self, formatted_answer: AgentFinish
+    ) -> AgentFinish:
+        """Process human feedback asynchronously and refine answer.
 
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
+        Args:
+            formatted_answer: Initial agent result.
+
+        Returns:
+            Final answer after feedback.
+        """
+        provider = get_provider()
+        return await provider.handle_feedback_async(formatted_answer, self)
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -1339,101 +1428,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         """
         return bool(self.crew and self.crew._train)
 
-    def _handle_training_feedback(
-        self, initial_answer: AgentFinish, feedback: str
-    ) -> AgentFinish:
-        """Process training feedback and generate improved answer.
-
-        Args:
-            initial_answer: Initial agent output.
-            feedback: Training feedback.
-
-        Returns:
-            Improved answer.
-        """
-        self._handle_crew_training_output(initial_answer, feedback)
-        self.state.messages.append(
-            format_message_for_llm(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-
-        # Re-run flow for improved answer
-        self.state.iterations = 0
-        self.state.is_finished = False
-        self.state.current_answer = None
-
-        self.kickoff()
-
-        # Get improved answer from state
-        improved_answer = self.state.current_answer
-        if not isinstance(improved_answer, AgentFinish):
-            raise RuntimeError(
-                "Training feedback iteration did not produce final answer"
-            )
-
-        self._handle_crew_training_output(improved_answer)
-        self.state.ask_for_human_input = False
-        return improved_answer
-
-    def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
-    ) -> AgentFinish:
-        """Process regular feedback iteratively until user is satisfied.
-
-        Args:
-            current_answer: Current agent output.
-            initial_feedback: Initial user feedback.
-
-        Returns:
-            Final answer after iterations.
-        """
-        feedback = initial_feedback
-        answer = current_answer
-
-        while self.state.ask_for_human_input:
-            if feedback.strip() == "":
-                self.state.ask_for_human_input = False
-            else:
-                answer = self._process_feedback_iteration(feedback)
-                output_str = (
-                    str(answer.output)
-                    if isinstance(answer.output, BaseModel)
-                    else answer.output
-                )
-                feedback = self._ask_human_input(output_str)
-
-        return answer
-
-    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process a single feedback iteration and generate updated response.
-
-        Args:
-            feedback: User feedback.
-
-        Returns:
-            Updated agent response.
-        """
-        self.state.messages.append(
-            format_message_for_llm(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-
-        # Re-run flow
-        self.state.iterations = 0
-        self.state.is_finished = False
-        self.state.current_answer = None
-
-        self.kickoff()
-
-        # Get answer from state
-        answer = self.state.current_answer
-        if not isinstance(answer, AgentFinish):
-            raise RuntimeError("Feedback iteration did not produce final answer")
-
-        return answer
-
     @classmethod
     def __get_pydantic_core_schema__(
         cls, _source_type: Any, _handler: GetCoreSchemaHandler

lib/crewai/src/crewai/flow/async_feedback/__init__.py

@@ -28,6 +28,8 @@ Example:
     ```
 """
 
+from typing import Any
+
 from crewai.flow.async_feedback.providers import ConsoleProvider
 from crewai.flow.async_feedback.types import (
     HumanFeedbackPending,
@@ -41,4 +43,15 @@ __all__ = [
     "HumanFeedbackPending",
     "HumanFeedbackProvider",
     "PendingFeedbackContext",
+    "_extension_exports",
 ]
+
+_extension_exports: dict[str, Any] = {}
+
+
+def __getattr__(name: str) -> Any:
+    """Support extensions via dynamic attribute lookup."""
+    if name in _extension_exports:
+        return _extension_exports[name]
+    msg = f"module {__name__!r} has no attribute {name!r}"
+    raise AttributeError(msg)

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

@@ -7,7 +7,14 @@ for building event-driven workflows with conditional execution and routing.
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Sequence
+from collections.abc import (
+    Callable,
+    ItemsView,
+    Iterator,
+    KeysView,
+    Sequence,
+    ValuesView,
+)
 from concurrent.futures import Future
 import copy
 import inspect
@@ -45,6 +52,7 @@ from crewai.events.listeners.tracing.utils import (
     has_user_declined_tracing,
     set_tracing_enabled,
     should_enable_tracing,
+    should_suppress_tracing_messages,
 )
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
@@ -408,6 +416,132 @@ def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition
     return {"type": AND_CONDITION, "conditions": processed_conditions}
 
 
+class LockedListProxy(Generic[T]):
+    """Thread-safe proxy for list operations.
+
+    Wraps a list and uses a lock for all mutating operations.
+    """
+
+    def __init__(self, lst: list[T], lock: threading.Lock) -> None:
+        self._list = lst
+        self._lock = lock
+
+    def append(self, item: T) -> None:
+        with self._lock:
+            self._list.append(item)
+
+    def extend(self, items: list[T]) -> None:
+        with self._lock:
+            self._list.extend(items)
+
+    def insert(self, index: int, item: T) -> None:
+        with self._lock:
+            self._list.insert(index, item)
+
+    def remove(self, item: T) -> None:
+        with self._lock:
+            self._list.remove(item)
+
+    def pop(self, index: int = -1) -> T:
+        with self._lock:
+            return self._list.pop(index)
+
+    def clear(self) -> None:
+        with self._lock:
+            self._list.clear()
+
+    def __setitem__(self, index: int, value: T) -> None:
+        with self._lock:
+            self._list[index] = value
+
+    def __delitem__(self, index: int) -> None:
+        with self._lock:
+            del self._list[index]
+
+    def __getitem__(self, index: int) -> T:
+        return self._list[index]
+
+    def __len__(self) -> int:
+        return len(self._list)
+
+    def __iter__(self) -> Iterator[T]:
+        return iter(self._list)
+
+    def __contains__(self, item: object) -> bool:
+        return item in self._list
+
+    def __repr__(self) -> str:
+        return repr(self._list)
+
+    def __bool__(self) -> bool:
+        return bool(self._list)
+
+
+class LockedDictProxy(Generic[T]):
+    """Thread-safe proxy for dict operations.
+
+    Wraps a dict and uses a lock for all mutating operations.
+    """
+
+    def __init__(self, d: dict[str, T], lock: threading.Lock) -> None:
+        self._dict = d
+        self._lock = lock
+
+    def __setitem__(self, key: str, value: T) -> None:
+        with self._lock:
+            self._dict[key] = value
+
+    def __delitem__(self, key: str) -> None:
+        with self._lock:
+            del self._dict[key]
+
+    def pop(self, key: str, *default: T) -> T:
+        with self._lock:
+            return self._dict.pop(key, *default)
+
+    def update(self, other: dict[str, T]) -> None:
+        with self._lock:
+            self._dict.update(other)
+
+    def clear(self) -> None:
+        with self._lock:
+            self._dict.clear()
+
+    def setdefault(self, key: str, default: T) -> T:
+        with self._lock:
+            return self._dict.setdefault(key, default)
+
+    def __getitem__(self, key: str) -> T:
+        return self._dict[key]
+
+    def __len__(self) -> int:
+        return len(self._dict)
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self._dict)
+
+    def __contains__(self, key: object) -> bool:
+        return key in self._dict
+
+    def keys(self) -> KeysView[str]:
+        return self._dict.keys()
+
+    def values(self) -> ValuesView[T]:
+        return self._dict.values()
+
+    def items(self) -> ItemsView[str, T]:
+        return self._dict.items()
+
+    def get(self, key: str, default: T | None = None) -> T | None:
+        return self._dict.get(key, default)
+
+    def __repr__(self) -> str:
+        return repr(self._dict)
+
+    def __bool__(self) -> bool:
+        return bool(self._dict)
+
+
 class StateProxy(Generic[T]):
     """Proxy that provides thread-safe access to flow state.
 
@@ -422,7 +556,13 @@ class StateProxy(Generic[T]):
         object.__setattr__(self, "_proxy_lock", lock)
 
     def __getattr__(self, name: str) -> Any:
-        return getattr(object.__getattribute__(self, "_proxy_state"), name)
+        value = getattr(object.__getattribute__(self, "_proxy_state"), name)
+        lock = object.__getattribute__(self, "_proxy_lock")
+        if isinstance(value, list):
+            return LockedListProxy(value, lock)
+        if isinstance(value, dict):
+            return LockedDictProxy(value, lock)
+        return value
 
     def __setattr__(self, name: str, value: Any) -> None:
         if name in ("_proxy_state", "_proxy_lock"):
@@ -1592,7 +1732,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 reset_emission_counter()
                 reset_last_event_id()
 
-            # Emit FlowStartedEvent and log the start of the flow.
             if not self.suppress_flow_events:
                 future = crewai_event_bus.emit(
                     self,
@@ -1603,7 +1742,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ),
                 )
                 if future:
-                    self._event_futures.append(future)
+                    try:
+                        await asyncio.wrap_future(future)
+                    except Exception:
+                        logger.warning("FlowStartedEvent handler failed", exc_info=True)
                 self._log_flow_event(
                     f"Flow started with ID: {self.flow_id}", color="bold magenta"
                 )
@@ -1695,6 +1837,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             final_output = self._method_outputs[-1] if self._method_outputs else None
 
+            if self._event_futures:
+                await asyncio.gather(
+                    *[asyncio.wrap_future(f) for f in self._event_futures]
+                )
+                self._event_futures.clear()
+
             if not self.suppress_flow_events:
                 future = crewai_event_bus.emit(
                     self,
@@ -1706,13 +1854,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     ),
                 )
                 if future:
-                    self._event_futures.append(future)
-
-            if self._event_futures:
-                await asyncio.gather(
-                    *[asyncio.wrap_future(f) for f in self._event_futures]
-                )
-                self._event_futures.clear()
+                    try:
+                        await asyncio.wrap_future(future)
+                    except Exception:
+                        logger.warning(
+                            "FlowFinishedEvent handler failed", exc_info=True
+                        )
 
             if not self.suppress_flow_events:
                 trace_listener = TraceCollectionListener()
@@ -1787,40 +1934,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
             await self._execute_listeners(start_method_name, result, finished_event_id)
             # Then execute listeners for the router result (e.g., "approved")
             router_result_trigger = FlowMethodName(str(result))
-            listeners_for_result = self._find_triggered_methods(
-                router_result_trigger, router_only=False
+            listener_result = (
+                self.last_human_feedback
+                if self.last_human_feedback is not None
+                else result
+            )
+            await self._execute_listeners(
+                router_result_trigger, listener_result, finished_event_id
             )
-            if listeners_for_result:
-                # Pass the HumanFeedbackResult if available
-                listener_result = (
-                    self.last_human_feedback
-                    if self.last_human_feedback is not None
-                    else result
-                )
-                racing_group = self._get_racing_group_for_listeners(
-                    listeners_for_result
-                )
-                if racing_group:
-                    racing_members, _ = racing_group
-                    other_listeners = [
-                        name
-                        for name in listeners_for_result
-                        if name not in racing_members
-                    ]
-                    await self._execute_racing_listeners(
-                        racing_members,
-                        other_listeners,
-                        listener_result,
-                        finished_event_id,
-                    )
-                else:
-                    tasks = [
-                        self._execute_single_listener(
-                            listener_name, listener_result, finished_event_id
-                        )
-                        for listener_name in listeners_for_result
-                    ]
-                    await asyncio.gather(*tasks)
         else:
             await self._execute_listeners(start_method_name, result, finished_event_id)
 
@@ -2026,15 +2147,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 router_input = router_result_to_feedback.get(
                     str(current_trigger), current_result
                 )
-                current_triggering_event_id = await self._execute_single_listener(
+                (
+                    router_result,
+                    current_triggering_event_id,
+                ) = await self._execute_single_listener(
                     router_name, router_input, current_triggering_event_id
                 )
-                # After executing router, the router's result is the path
-                router_result = (
-                    self._method_outputs[-1] if self._method_outputs else None
-                )
                 if router_result:  # Only add non-None results
-                    router_results.append(router_result)
+                    router_results.append(FlowMethodName(str(router_result)))
                     # If this was a human_feedback router, map the outcome to the feedback
                     if self.last_human_feedback is not None:
                         router_result_to_feedback[str(router_result)] = (
@@ -2074,12 +2194,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
                             racing_members,
                             other_listeners,
                             listener_result,
-                            triggering_event_id,
+                            current_triggering_event_id,
                         )
                     else:
                         tasks = [
                             self._execute_single_listener(
-                                listener_name, listener_result, triggering_event_id
+                                listener_name,
+                                listener_result,
+                                current_triggering_event_id,
                             )
                             for listener_name in listeners_triggered
                         ]
@@ -2262,7 +2384,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         listener_name: FlowMethodName,
         result: Any,
         triggering_event_id: str | None = None,
-    ) -> str | None:
+    ) -> tuple[Any, str | None]:
         """Executes a single listener method with proper event handling.
 
         This internal method manages the execution of an individual listener,
@@ -2275,8 +2397,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 used for causal chain tracking.
 
         Returns:
-            The event_id of the MethodExecutionFinishedEvent emitted by this listener,
-            or None if events are suppressed.
+            A tuple of (listener_result, event_id) where listener_result is the return
+            value of the listener method and event_id is the MethodExecutionFinishedEvent
+            id, or (None, None) if skipped during resumption.
 
         Note:
             - Inspects method signature to determine if it accepts the trigger result
@@ -2302,7 +2425,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                         ):
                             # This conditional start was executed, continue its chain
                             await self._execute_start_method(start_method_name)
-                return None
+                return (None, None)
             # For cyclic flows, clear from completed to allow re-execution
             self._completed_methods.discard(listener_name)
             # Also clear from fired OR listeners for cyclic flows
@@ -2340,46 +2463,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 listener_name, listener_result, finished_event_id
             )
 
-            # If this listener is also a router (e.g., has @human_feedback with emit),
-            # we need to trigger listeners for the router result as well
-            if listener_name in self._routers and listener_result is not None:
-                router_result_trigger = FlowMethodName(str(listener_result))
-                listeners_for_result = self._find_triggered_methods(
-                    router_result_trigger, router_only=False
-                )
-                if listeners_for_result:
-                    # Pass the HumanFeedbackResult if available
-                    feedback_result = (
-                        self.last_human_feedback
-                        if self.last_human_feedback is not None
-                        else listener_result
-                    )
-                    racing_group = self._get_racing_group_for_listeners(
-                        listeners_for_result
-                    )
-                    if racing_group:
-                        racing_members, _ = racing_group
-                        other_listeners = [
-                            name
-                            for name in listeners_for_result
-                            if name not in racing_members
-                        ]
-                        await self._execute_racing_listeners(
-                            racing_members,
-                            other_listeners,
-                            feedback_result,
-                            finished_event_id,
-                        )
-                    else:
-                        tasks = [
-                            self._execute_single_listener(
-                                name, feedback_result, finished_event_id
-                            )
-                            for name in listeners_for_result
-                        ]
-                        await asyncio.gather(*tasks)
-
-            return finished_event_id
+            return (listener_result, finished_event_id)
 
         except Exception as e:
             # Don't log HumanFeedbackPending as an error - it's expected control flow
@@ -2626,6 +2710,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
     @staticmethod
     def _show_tracing_disabled_message() -> None:
         """Show a message when tracing is disabled."""
+        if should_suppress_tracing_messages():
+            return
 
         console = Console()
 

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

@@ -3,7 +3,12 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, cast
 
 from crewai.events.event_listener import event_listener
-from crewai.hooks.types import AfterLLMCallHookType, BeforeLLMCallHookType
+from crewai.hooks.types import (
+    AfterLLMCallHookCallable,
+    AfterLLMCallHookType,
+    BeforeLLMCallHookCallable,
+    BeforeLLMCallHookType,
+)
 from crewai.utilities.printer import Printer
 
 
@@ -149,12 +154,12 @@ class LLMCallHookContext:
             event_listener.formatter.resume_live_updates()
 
 
-_before_llm_call_hooks: list[BeforeLLMCallHookType] = []
-_after_llm_call_hooks: list[AfterLLMCallHookType] = []
+_before_llm_call_hooks: list[BeforeLLMCallHookType | BeforeLLMCallHookCallable] = []
+_after_llm_call_hooks: list[AfterLLMCallHookType | AfterLLMCallHookCallable] = []
 
 
 def register_before_llm_call_hook(
-    hook: BeforeLLMCallHookType,
+    hook: BeforeLLMCallHookType | BeforeLLMCallHookCallable,
 ) -> None:
     """Register a global before_llm_call hook.
 
@@ -190,7 +195,7 @@ def register_before_llm_call_hook(
 
 
 def register_after_llm_call_hook(
-    hook: AfterLLMCallHookType,
+    hook: AfterLLMCallHookType | AfterLLMCallHookCallable,
 ) -> None:
     """Register a global after_llm_call hook.
 
@@ -217,7 +222,9 @@ def register_after_llm_call_hook(
     _after_llm_call_hooks.append(hook)
 
 
-def get_before_llm_call_hooks() -> list[BeforeLLMCallHookType]:
+def get_before_llm_call_hooks() -> list[
+    BeforeLLMCallHookType | BeforeLLMCallHookCallable
+]:
     """Get all registered global before_llm_call hooks.
 
     Returns:
@@ -226,7 +233,7 @@ def get_before_llm_call_hooks() -> list[BeforeLLMCallHookType]:
     return _before_llm_call_hooks.copy()
 
 
-def get_after_llm_call_hooks() -> list[AfterLLMCallHookType]:
+def get_after_llm_call_hooks() -> list[AfterLLMCallHookType | AfterLLMCallHookCallable]:
     """Get all registered global after_llm_call hooks.
 
     Returns:
@@ -236,7 +243,7 @@ def get_after_llm_call_hooks() -> list[AfterLLMCallHookType]:
 
 
 def unregister_before_llm_call_hook(
-    hook: BeforeLLMCallHookType,
+    hook: BeforeLLMCallHookType | BeforeLLMCallHookCallable,
 ) -> bool:
     """Unregister a specific global before_llm_call hook.
 
@@ -262,7 +269,7 @@ def unregister_before_llm_call_hook(
 
 
 def unregister_after_llm_call_hook(
-    hook: AfterLLMCallHookType,
+    hook: AfterLLMCallHookType | AfterLLMCallHookCallable,
 ) -> bool:
     """Unregister a specific global after_llm_call hook.
 

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

@@ -3,7 +3,12 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any
 
 from crewai.events.event_listener import event_listener
-from crewai.hooks.types import AfterToolCallHookType, BeforeToolCallHookType
+from crewai.hooks.types import (
+    AfterToolCallHookCallable,
+    AfterToolCallHookType,
+    BeforeToolCallHookCallable,
+    BeforeToolCallHookType,
+)
 from crewai.utilities.printer import Printer
 
 
@@ -112,12 +117,12 @@ class ToolCallHookContext:
 
 
 # Global hook registries
-_before_tool_call_hooks: list[BeforeToolCallHookType] = []
-_after_tool_call_hooks: list[AfterToolCallHookType] = []
+_before_tool_call_hooks: list[BeforeToolCallHookType | BeforeToolCallHookCallable] = []
+_after_tool_call_hooks: list[AfterToolCallHookType | AfterToolCallHookCallable] = []
 
 
 def register_before_tool_call_hook(
-    hook: BeforeToolCallHookType,
+    hook: BeforeToolCallHookType | BeforeToolCallHookCallable,
 ) -> None:
     """Register a global before_tool_call hook.
 
@@ -154,7 +159,7 @@ def register_before_tool_call_hook(
 
 
 def register_after_tool_call_hook(
-    hook: AfterToolCallHookType,
+    hook: AfterToolCallHookType | AfterToolCallHookCallable,
 ) -> None:
     """Register a global after_tool_call hook.
 
@@ -184,7 +189,9 @@ def register_after_tool_call_hook(
     _after_tool_call_hooks.append(hook)
 
 
-def get_before_tool_call_hooks() -> list[BeforeToolCallHookType]:
+def get_before_tool_call_hooks() -> list[
+    BeforeToolCallHookType | BeforeToolCallHookCallable
+]:
     """Get all registered global before_tool_call hooks.
 
     Returns:
@@ -193,7 +200,9 @@ def get_before_tool_call_hooks() -> list[BeforeToolCallHookType]:
     return _before_tool_call_hooks.copy()
 
 
-def get_after_tool_call_hooks() -> list[AfterToolCallHookType]:
+def get_after_tool_call_hooks() -> list[
+    AfterToolCallHookType | AfterToolCallHookCallable
+]:
     """Get all registered global after_tool_call hooks.
 
     Returns:
@@ -203,7 +212,7 @@ def get_after_tool_call_hooks() -> list[AfterToolCallHookType]:
 
 
 def unregister_before_tool_call_hook(
-    hook: BeforeToolCallHookType,
+    hook: BeforeToolCallHookType | BeforeToolCallHookCallable,
 ) -> bool:
     """Unregister a specific global before_tool_call hook.
 
@@ -229,7 +238,7 @@ def unregister_before_tool_call_hook(
 
 
 def unregister_after_tool_call_hook(
-    hook: AfterToolCallHookType,
+    hook: AfterToolCallHookType | AfterToolCallHookCallable,
 ) -> bool:
     """Unregister a specific global after_tool_call hook.
 

lib/crewai/src/crewai/knowledge/source/utils/__init__.py

@@ -0,0 +1 @@
+"""Knowledge source utilities."""

lib/crewai/src/crewai/knowledge/source/utils/source_helper.py

@@ -0,0 +1,70 @@
+"""Helper utilities for knowledge sources."""
+
+from typing import Any, ClassVar
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
+from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
+from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
+from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
+from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
+
+
+class SourceHelper:
+    """Helper class for creating and managing knowledge sources."""
+
+    SUPPORTED_FILE_TYPES: ClassVar[list[str]] = [
+        ".csv",
+        ".pdf",
+        ".json",
+        ".txt",
+        ".xlsx",
+        ".xls",
+    ]
+
+    _FILE_TYPE_MAP: ClassVar[dict[str, type[BaseKnowledgeSource]]] = {
+        ".csv": CSVKnowledgeSource,
+        ".pdf": PDFKnowledgeSource,
+        ".json": JSONKnowledgeSource,
+        ".txt": TextFileKnowledgeSource,
+        ".xlsx": ExcelKnowledgeSource,
+        ".xls": ExcelKnowledgeSource,
+    }
+
+    @classmethod
+    def is_supported_file(cls, file_path: str) -> bool:
+        """Check if a file type is supported.
+
+        Args:
+            file_path: Path to the file.
+
+        Returns:
+            True if the file type is supported.
+        """
+        return file_path.lower().endswith(tuple(cls.SUPPORTED_FILE_TYPES))
+
+    @classmethod
+    def get_source(
+        cls, file_path: str, metadata: dict[str, Any] | None = None
+    ) -> BaseKnowledgeSource:
+        """Create appropriate KnowledgeSource based on file extension.
+
+        Args:
+            file_path: Path to the file.
+            metadata: Optional metadata to attach to the source.
+
+        Returns:
+            The appropriate KnowledgeSource instance.
+
+        Raises:
+            ValueError: If the file type is not supported.
+        """
+        if not cls.is_supported_file(file_path):
+            raise ValueError(f"Unsupported file type: {file_path}")
+
+        lower_path = file_path.lower()
+        for ext, source_cls in cls._FILE_TYPE_MAP.items():
+            if lower_path.endswith(ext):
+                return source_cls(file_path=[file_path], metadata=metadata)
+
+        raise ValueError(f"Unsupported file type: {file_path}")

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

@@ -290,7 +290,7 @@ class AnthropicCompletion(BaseLLM):
 
                 # Prepare completion parameters
                 completion_params = self._prepare_completion_params(
-                    formatted_messages, system_message, tools
+                    formatted_messages, system_message, tools, available_functions
                 )
 
                 effective_response_model = response_model or self.response_format
@@ -361,7 +361,7 @@ class AnthropicCompletion(BaseLLM):
                 )
 
                 completion_params = self._prepare_completion_params(
-                    formatted_messages, system_message, tools
+                    formatted_messages, system_message, tools, available_functions
                 )
 
                 effective_response_model = response_model or self.response_format
@@ -396,6 +396,7 @@ class AnthropicCompletion(BaseLLM):
         messages: list[LLMMessage],
         system_message: str | None = None,
         tools: list[dict[str, Any]] | None = None,
+        available_functions: dict[str, Any] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Anthropic messages API.
 
@@ -403,6 +404,8 @@ class AnthropicCompletion(BaseLLM):
             messages: Formatted messages for Anthropic
             system_message: Extracted system message
             tools: Tool definitions
+            available_functions: Available functions for tool calling. When provided
+                with a single tool, tool_choice is automatically set to force tool use.
 
         Returns:
             Parameters dictionary for Anthropic API
@@ -428,7 +431,13 @@ class AnthropicCompletion(BaseLLM):
 
         # Handle tools for Claude 3+
         if tools and self.supports_tools:
-            params["tools"] = self._convert_tools_for_interference(tools)
+            converted_tools = self._convert_tools_for_interference(tools)
+            params["tools"] = converted_tools
+
+            if available_functions and len(converted_tools) == 1:
+                tool_name = converted_tools[0].get("name")
+                if tool_name and tool_name in available_functions:
+                    params["tool_choice"] = {"type": "tool", "name": tool_name}
 
         if self.thinking:
             if isinstance(self.thinking, AnthropicThinkingConfig):
@@ -730,15 +739,11 @@ class AnthropicCompletion(BaseLLM):
                     )
                     return list(tool_uses)
 
-                # Handle tool use conversation flow internally
-                return self._handle_tool_use_conversation(
-                    response,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         content = ""
         thinking_blocks: list[ThinkingBlock] = []
@@ -939,14 +944,12 @@ class AnthropicCompletion(BaseLLM):
                 if not available_functions:
                     return list(tool_uses)
 
-                return self._handle_tool_use_conversation(
-                    final_message,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                # Execute first tool and return result directly
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         full_response = self._apply_stop_words(full_response)
 
@@ -1005,6 +1008,41 @@ class AnthropicCompletion(BaseLLM):
 
         return tool_results
 
+    def _execute_first_tool(
+        self,
+        tool_uses: list[ToolUseBlock | BetaToolUseBlock],
+        available_functions: dict[str, Any],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> Any | None:
+        """Execute the first tool from the tool_uses list and return its result.
+
+        This is used when available_functions is provided, to directly execute
+        the tool and return its result (matching OpenAI behavior for use cases
+        like reasoning_handler).
+
+        Args:
+            tool_uses: List of tool use blocks from Claude's response
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            The result of the first tool execution, or None if execution failed
+        """
+        tool_use = tool_uses[0]
+        function_name = tool_use.name
+        function_args = cast(dict[str, Any], tool_use.input)
+
+        return self._handle_tool_execution(
+            function_name=function_name,
+            function_args=function_args,
+            available_functions=available_functions,
+            from_task=from_task,
+            from_agent=from_agent,
+        )
+
+    # TODO: we drop this
     def _handle_tool_use_conversation(
         self,
         initial_response: Message | BetaMessage,
@@ -1220,14 +1258,11 @@ class AnthropicCompletion(BaseLLM):
                     )
                     return list(tool_uses)
 
-                return await self._ahandle_tool_use_conversation(
-                    response,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         content = ""
         if response.content:
@@ -1408,14 +1443,11 @@ class AnthropicCompletion(BaseLLM):
                 if not available_functions:
                     return list(tool_uses)
 
-                return await self._ahandle_tool_use_conversation(
-                    final_message,
-                    tool_uses,
-                    params,
-                    available_functions,
-                    from_task,
-                    from_agent,
+                result = self._execute_first_tool(
+                    tool_uses, available_functions, from_task, from_agent
                 )
+                if result is not None:
+                    return result
 
         full_response = self._apply_stop_words(full_response)
 

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

@@ -27,6 +27,8 @@ if TYPE_CHECKING:
     from crewai import Agent, Task
     from crewai.agents.cache.cache_handler import CacheHandler
     from crewai.crews.crew_output import CrewOutput
+    from crewai.hooks.llm_hooks import LLMCallHookContext
+    from crewai.hooks.tool_hooks import ToolCallHookContext
     from crewai.project.wrappers import (
         CrewInstance,
         OutputJsonClass,
@@ -34,6 +36,8 @@ if TYPE_CHECKING:
     )
     from crewai.tasks.task_output import TaskOutput
 
+_post_initialize_crew_hooks: list[Callable[[Any], None]] = []
+
 
 class AgentConfig(TypedDict, total=False):
     """Type definition for agent configuration dictionary.
@@ -266,6 +270,9 @@ class CrewBaseMeta(type):
         instance.map_all_agent_variables()
         instance.map_all_task_variables()
 
+        for hook in _post_initialize_crew_hooks:
+            hook(instance)
+
         original_methods = {
             name: method
             for name, method in cls.__dict__.items()
@@ -485,47 +492,61 @@ def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
             if has_agent_filter:
                 agents_filter = hook_method._filter_agents
 
-                def make_filtered_before_llm(bound_fn, agents_list):
-                    def filtered(context):
+                def make_filtered_before_llm(
+                    bound_fn: Callable[[LLMCallHookContext], bool | None],
+                    agents_list: list[str],
+                ) -> Callable[[LLMCallHookContext], bool | None]:
+                    def filtered(context: LLMCallHookContext) -> bool | None:
                         if context.agent and context.agent.role not in agents_list:
                             return None
                         return bound_fn(context)
 
                     return filtered
 
-                final_hook = make_filtered_before_llm(bound_hook, agents_filter)
+                before_llm_hook = make_filtered_before_llm(bound_hook, agents_filter)
             else:
-                final_hook = bound_hook
+                before_llm_hook = bound_hook
 
-            register_before_llm_call_hook(final_hook)
-            instance._registered_hook_functions.append(("before_llm_call", final_hook))
+            register_before_llm_call_hook(before_llm_hook)
+            instance._registered_hook_functions.append(
+                ("before_llm_call", before_llm_hook)
+            )
 
         if hasattr(hook_method, "is_after_llm_call_hook"):
             if has_agent_filter:
                 agents_filter = hook_method._filter_agents
 
-                def make_filtered_after_llm(bound_fn, agents_list):
-                    def filtered(context):
+                def make_filtered_after_llm(
+                    bound_fn: Callable[[LLMCallHookContext], str | None],
+                    agents_list: list[str],
+                ) -> Callable[[LLMCallHookContext], str | None]:
+                    def filtered(context: LLMCallHookContext) -> str | None:
                         if context.agent and context.agent.role not in agents_list:
                             return None
                         return bound_fn(context)
 
                     return filtered
 
-                final_hook = make_filtered_after_llm(bound_hook, agents_filter)
+                after_llm_hook = make_filtered_after_llm(bound_hook, agents_filter)
             else:
-                final_hook = bound_hook
+                after_llm_hook = bound_hook
 
-            register_after_llm_call_hook(final_hook)
-            instance._registered_hook_functions.append(("after_llm_call", final_hook))
+            register_after_llm_call_hook(after_llm_hook)
+            instance._registered_hook_functions.append(
+                ("after_llm_call", after_llm_hook)
+            )
 
         if hasattr(hook_method, "is_before_tool_call_hook"):
             if has_tool_filter or has_agent_filter:
                 tools_filter = getattr(hook_method, "_filter_tools", None)
                 agents_filter = getattr(hook_method, "_filter_agents", None)
 
-                def make_filtered_before_tool(bound_fn, tools_list, agents_list):
-                    def filtered(context):
+                def make_filtered_before_tool(
+                    bound_fn: Callable[[ToolCallHookContext], bool | None],
+                    tools_list: list[str] | None,
+                    agents_list: list[str] | None,
+                ) -> Callable[[ToolCallHookContext], bool | None]:
+                    def filtered(context: ToolCallHookContext) -> bool | None:
                         if tools_list and context.tool_name not in tools_list:
                             return None
                         if (
@@ -538,22 +559,28 @@ def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
 
                     return filtered
 
-                final_hook = make_filtered_before_tool(
+                before_tool_hook = make_filtered_before_tool(
                     bound_hook, tools_filter, agents_filter
                 )
             else:
-                final_hook = bound_hook
+                before_tool_hook = bound_hook
 
-            register_before_tool_call_hook(final_hook)
-            instance._registered_hook_functions.append(("before_tool_call", final_hook))
+            register_before_tool_call_hook(before_tool_hook)
+            instance._registered_hook_functions.append(
+                ("before_tool_call", before_tool_hook)
+            )
 
         if hasattr(hook_method, "is_after_tool_call_hook"):
             if has_tool_filter or has_agent_filter:
                 tools_filter = getattr(hook_method, "_filter_tools", None)
                 agents_filter = getattr(hook_method, "_filter_agents", None)
 
-                def make_filtered_after_tool(bound_fn, tools_list, agents_list):
-                    def filtered(context):
+                def make_filtered_after_tool(
+                    bound_fn: Callable[[ToolCallHookContext], str | None],
+                    tools_list: list[str] | None,
+                    agents_list: list[str] | None,
+                ) -> Callable[[ToolCallHookContext], str | None]:
+                    def filtered(context: ToolCallHookContext) -> str | None:
                         if tools_list and context.tool_name not in tools_list:
                             return None
                         if (
@@ -566,14 +593,16 @@ def _register_crew_hooks(instance: CrewInstance, cls: type) -> None:
 
                     return filtered
 
-                final_hook = make_filtered_after_tool(
+                after_tool_hook = make_filtered_after_tool(
                     bound_hook, tools_filter, agents_filter
                 )
             else:
-                final_hook = bound_hook
+                after_tool_hook = bound_hook
 
-            register_after_tool_call_hook(final_hook)
-            instance._registered_hook_functions.append(("after_tool_call", final_hook))
+            register_after_tool_call_hook(after_tool_hook)
+            instance._registered_hook_functions.append(
+                ("after_tool_call", after_tool_hook)
+            )
 
     instance._hooks_being_registered = False
 

lib/crewai/src/crewai/project/wrappers.py

@@ -72,6 +72,8 @@ class CrewInstance(Protocol):
     __crew_metadata__: CrewMetadata
     _mcp_server_adapter: Any
     _all_methods: dict[str, Callable[..., Any]]
+    _registered_hook_functions: list[tuple[str, Callable[..., Any]]]
+    _hooks_being_registered: bool
     agents: list[Agent]
     tasks: list[Task]
     base_directory: Path

lib/crewai/src/crewai/task.py

@@ -31,6 +31,8 @@ from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.context import reset_current_task_id, set_current_task_id
+from crewai.core.providers.content_processor import process_content
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.task_events import (
     TaskCompletedEvent,
@@ -496,6 +498,7 @@ class Task(BaseModel):
         tools: list[BaseTool] | None = None,
     ) -> TaskOutput:
         """Execute the task synchronously."""
+        self.start_time = datetime.datetime.now()
         return self._execute_core(agent, context, tools)
 
     @property
@@ -536,6 +539,7 @@ class Task(BaseModel):
     ) -> None:
         """Execute the task asynchronously with context handling."""
         try:
+            self.start_time = datetime.datetime.now()
             result = self._execute_core(agent, context, tools)
             future.set_result(result)
         except Exception as e:
@@ -548,6 +552,7 @@ class Task(BaseModel):
         tools: list[BaseTool] | None = None,
     ) -> TaskOutput:
         """Execute the task asynchronously using native async/await."""
+        self.start_time = datetime.datetime.now()
         return await self._aexecute_core(agent, context, tools)
 
     async def _aexecute_core(
@@ -557,6 +562,7 @@ class Task(BaseModel):
         tools: list[Any] | None,
     ) -> TaskOutput:
         """Run the core execution logic of the task asynchronously."""
+        task_id_token = set_current_task_id(str(self.id))
         self._store_input_files()
         try:
             agent = agent or self.agent
@@ -566,8 +572,6 @@ class Task(BaseModel):
                     f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
                 )
 
-            self.start_time = datetime.datetime.now()
-
             self.prompt_context = context
             tools = tools or self.tools or []
 
@@ -579,6 +583,8 @@ class Task(BaseModel):
                 tools=tools,
             )
 
+            self._post_agent_execution(agent)
+
             if not self._guardrails and not self._guardrail:
                 pydantic_output, json_output = self._export_output(result)
             else:
@@ -644,6 +650,7 @@ class Task(BaseModel):
             raise e  # Re-raise the exception after emitting the event
         finally:
             clear_task_files(self.id)
+            reset_current_task_id(task_id_token)
 
     def _execute_core(
         self,
@@ -652,6 +659,7 @@ class Task(BaseModel):
         tools: list[Any] | None,
     ) -> TaskOutput:
         """Run the core execution logic of the task."""
+        task_id_token = set_current_task_id(str(self.id))
         self._store_input_files()
         try:
             agent = agent or self.agent
@@ -661,8 +669,6 @@ class Task(BaseModel):
                     f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
                 )
 
-            self.start_time = datetime.datetime.now()
-
             self.prompt_context = context
             tools = tools or self.tools or []
 
@@ -674,6 +680,8 @@ class Task(BaseModel):
                 tools=tools,
             )
 
+            self._post_agent_execution(agent)
+
             if not self._guardrails and not self._guardrail:
                 pydantic_output, json_output = self._export_output(result)
             else:
@@ -740,6 +748,10 @@ class Task(BaseModel):
             raise e  # Re-raise the exception after emitting the event
         finally:
             clear_task_files(self.id)
+            reset_current_task_id(task_id_token)
+
+    def _post_agent_execution(self, agent: BaseAgent) -> None:
+        pass
 
     def prompt(self) -> str:
         """Generates the task prompt with optional markdown formatting.
@@ -863,6 +875,11 @@ Follow these guidelines:
         except ValueError as e:
             raise ValueError(f"Error interpolating description: {e!s}") from e
 
+        self.description = process_content(self.description, {"task": self})
+        self._original_expected_output = process_content(
+            self._original_expected_output, {"task": self}
+        )
+
         try:
             self.expected_output = interpolate_only(
                 input_string=self._original_expected_output, inputs=inputs

lib/crewai/src/crewai/tasks/hallucination_guardrail.py

@@ -6,6 +6,7 @@ Classes:
     HallucinationGuardrail: Placeholder guardrail that validates task outputs.
 """
 
+from collections.abc import Callable
 from typing import Any
 
 from crewai.llm import LLM
@@ -13,32 +14,36 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.logger import Logger
 
 
+_validate_output_hook: Callable[..., tuple[bool, Any]] | None = None
+
+
 class HallucinationGuardrail:
     """Placeholder for the HallucinationGuardrail feature.
 
     Attributes:
-        context: The reference context that outputs would be checked against.
+        context: Optional reference context that outputs would be checked against.
         llm: The language model that would be used for evaluation.
         threshold: Optional minimum faithfulness score that would be required to pass.
         tool_response: Optional tool response information that would be used in evaluation.
 
     Examples:
-        >>> # Basic usage with default verdict logic
+        >>> # Basic usage without context (uses task expected_output as context)
+        >>> guardrail = HallucinationGuardrail(llm=agent.llm)
+
+        >>> # With context for reference
         >>> guardrail = HallucinationGuardrail(
-        ...     context="AI helps with various tasks including analysis and generation.",
         ...     llm=agent.llm,
+        ...     context="AI helps with various tasks including analysis and generation.",
         ... )
 
         >>> # With custom threshold for stricter validation
         >>> strict_guardrail = HallucinationGuardrail(
-        ...     context="Quantum computing uses qubits in superposition.",
         ...     llm=agent.llm,
-        ...     threshold=8.0,  # Would require score >= 8 to pass in enterprise version
+        ...     threshold=8.0,  # Require score >= 8 to pass
         ... )
 
         >>> # With tool response for additional context
         >>> guardrail_with_tools = HallucinationGuardrail(
-        ...     context="The current weather data",
         ...     llm=agent.llm,
         ...     tool_response="Weather API returned: Temperature 22°C, Humidity 65%",
         ... )
@@ -46,16 +51,17 @@ class HallucinationGuardrail:
 
     def __init__(
         self,
-        context: str,
         llm: LLM,
+        context: str | None = None,
         threshold: float | None = None,
         tool_response: str = "",
     ):
         """Initialize the HallucinationGuardrail placeholder.
 
         Args:
-            context: The reference context that outputs would be checked against.
             llm: The language model that would be used for evaluation.
+            context: Optional reference context that outputs would be checked against.
+                If not provided, the task's expected_output will be used as context.
             threshold: Optional minimum faithfulness score that would be required to pass.
             tool_response: Optional tool response information that would be used in evaluation.
         """
@@ -78,16 +84,17 @@ class HallucinationGuardrail:
     def __call__(self, task_output: TaskOutput) -> tuple[bool, Any]:
         """Validate a task output against hallucination criteria.
 
-        In the open source, this method always returns that the output is valid.
-
         Args:
             task_output: The output to be validated.
 
         Returns:
             A tuple containing:
-                - True
-                - The raw task output
+                - True if validation passed, False otherwise
+                - The raw task output if valid, or error feedback if invalid
         """
+        if callable(_validate_output_hook):
+            return _validate_output_hook(self, task_output)
+
         self._logger.log(
             "warning",
             "Premium hallucination detection skipped (use for free at https://app.crewai.com)\n",

lib/crewai/src/crewai/tasks/llm_guardrail.py

@@ -1,6 +1,10 @@
+import asyncio
+from collections.abc import Coroutine
+import inspect
 from typing import Any
 
 from pydantic import BaseModel, Field
+from typing_extensions import TypeIs
 
 from crewai.agent import Agent
 from crewai.lite_agent_output import LiteAgentOutput
@@ -8,6 +12,13 @@ from crewai.llms.base_llm import BaseLLM
 from crewai.tasks.task_output import TaskOutput
 
 
+def _is_coroutine(
+    obj: LiteAgentOutput | Coroutine[Any, Any, LiteAgentOutput],
+) -> TypeIs[Coroutine[Any, Any, LiteAgentOutput]]:
+    """Check if obj is a coroutine for type narrowing."""
+    return inspect.iscoroutine(obj)
+
+
 class LLMGuardrailResult(BaseModel):
     valid: bool = Field(
         description="Whether the task output complies with the guardrail"
@@ -62,7 +73,10 @@ class LLMGuardrail:
         - If the Task result complies with the guardrail, saying that is valid
         """
 
-        return agent.kickoff(query, response_format=LLMGuardrailResult)
+        kickoff_result = agent.kickoff(query, response_format=LLMGuardrailResult)
+        if _is_coroutine(kickoff_result):
+            return asyncio.run(kickoff_result)
+        return kickoff_result
 
     def __call__(self, task_output: TaskOutput) -> tuple[bool, Any]:
         """Validates the output of a task based on specified criteria.

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -903,7 +903,7 @@ class Telemetry:
                         {
                             "id": str(task.id),
                             "description": task.description,
-                            "output": task.output.raw_output,
+                            "output": task.output.raw if task.output else "",
                         }
                         for task in crew.tasks
                     ]
@@ -923,6 +923,9 @@ class Telemetry:
             value: The attribute value.
         """
 
+        if span is None:
+            return
+
         def _operation() -> None:
             return span.set_attribute(key, value)
 

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

@@ -270,6 +270,7 @@ class ToolUsage:
         result = None  # type: ignore
         should_retry = False
         available_tool = None
+        error_event_emitted = False
 
         try:
             if self.tools_handler and self.tools_handler.cache:
@@ -408,6 +409,7 @@ class ToolUsage:
 
                 except Exception as e:
                     self.on_tool_error(tool=tool, tool_calling=calling, e=e)
+                    error_event_emitted = True
                     self._run_attempts += 1
                     if self._run_attempts > self._max_parsing_attempts:
                         self._telemetry.tool_usage_error(llm=self.function_calling_llm)
@@ -435,7 +437,7 @@ class ToolUsage:
                 result = self._format_result(result=result)
 
         finally:
-            if started_event_emitted:
+            if started_event_emitted and not error_event_emitted:
                 self.on_tool_use_finished(
                     tool=tool,
                     tool_calling=calling,
@@ -500,6 +502,7 @@ class ToolUsage:
         result = None  # type: ignore
         should_retry = False
         available_tool = None
+        error_event_emitted = False
 
         try:
             if self.tools_handler and self.tools_handler.cache:
@@ -638,6 +641,7 @@ class ToolUsage:
 
                 except Exception as e:
                     self.on_tool_error(tool=tool, tool_calling=calling, e=e)
+                    error_event_emitted = True
                     self._run_attempts += 1
                     if self._run_attempts > self._max_parsing_attempts:
                         self._telemetry.tool_usage_error(llm=self.function_calling_llm)
@@ -665,7 +669,7 @@ class ToolUsage:
                 result = self._format_result(result=result)
 
         finally:
-            if started_event_emitted:
+            if started_event_emitted and not error_event_emitted:
                 self.on_tool_use_finished(
                     tool=tool,
                     tool_calling=calling,

lib/crewai/src/crewai/types/hitl.py

@@ -1,37 +0,0 @@
-"""Human-in-the-loop (HITL) type definitions.
-
-This module provides type definitions for human-in-the-loop interactions
-in crew executions.
-"""
-
-from typing import TypedDict
-
-
-class HITLResumeInfo(TypedDict, total=False):
-    """HITL resume information passed from flow to crew.
-
-    Attributes:
-        task_id: Unique identifier for the task.
-        crew_execution_id: Unique identifier for the crew execution.
-        task_key: Key identifying the specific task.
-        task_output: Output from the task before human intervention.
-        human_feedback: Feedback provided by the human.
-        previous_messages: History of messages in the conversation.
-    """
-
-    task_id: str
-    crew_execution_id: str
-    task_key: str
-    task_output: str
-    human_feedback: str
-    previous_messages: list[dict[str, str]]
-
-
-class CrewInputsWithHITL(TypedDict, total=False):
-    """Crew inputs that may contain HITL resume information.
-
-    Attributes:
-        _hitl_resume: Optional HITL resume information for continuing execution.
-    """
-
-    _hitl_resume: HITLResumeInfo

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

@@ -42,6 +42,8 @@ if TYPE_CHECKING:
     from crewai.llm import LLM
     from crewai.task import Task
 
+_create_plus_client_hook: Callable[[], Any] | None = None
+
 
 class SummaryContent(TypedDict):
     """Structure for summary content entries.
@@ -91,7 +93,11 @@ def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
 
     for tool in tools:
         if isinstance(tool, CrewAITool):
-            tools_list.append(tool.to_structured_tool())
+            structured_tool = tool.to_structured_tool()
+            structured_tool.current_usage_count = 0
+            if structured_tool._original_tool:
+                structured_tool._original_tool.current_usage_count = 0
+            tools_list.append(structured_tool)
         else:
             raise ValueError("Tool is not a CrewStructuredTool or BaseTool")
 
@@ -818,12 +824,15 @@ def load_agent_from_repository(from_repository: str) -> dict[str, Any]:
     if from_repository:
         import importlib
 
-        from crewai.cli.authentication.token import get_auth_token
-        from crewai.cli.plus_api import PlusAPI
+        if callable(_create_plus_client_hook):
+            client = _create_plus_client_hook()
+        else:
+            from crewai.cli.authentication.token import get_auth_token
+            from crewai.cli.plus_api import PlusAPI
 
-        client = PlusAPI(api_key=get_auth_token())
+            client = PlusAPI(api_key=get_auth_token())
         _print_current_organization()
-        response = client.get_agent(from_repository)
+        response = asyncio.run(client.get_agent(from_repository))
         if response.status_code == 404:
             raise AgentRepositoryError(
                 f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization."

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

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from pydantic import BaseModel, Field, InstanceOf
 from rich.box import HEAVY_EDGE
@@ -36,7 +36,13 @@ class CrewEvaluator:
         iteration: The current iteration of the evaluation.
     """
 
-    def __init__(self, crew: Crew, eval_llm: InstanceOf[BaseLLM]) -> None:
+    def __init__(
+        self,
+        crew: Crew,
+        eval_llm: InstanceOf[BaseLLM] | str | None = None,
+        openai_model_name: str | None = None,
+        llm: InstanceOf[BaseLLM] | str | None = None,
+    ) -> None:
         self.crew = crew
         self.llm = eval_llm
         self.tasks_scores: defaultdict[int, list[float]] = defaultdict(list)
@@ -86,7 +92,9 @@ class CrewEvaluator:
         """
         self.iteration = iteration
 
-    def print_crew_evaluation_result(self) -> None:
+    def print_crew_evaluation_result(
+        self, token_usage: list[dict[str, Any]] | None = None
+    ) -> None:
         """
         Prints the evaluation result of the crew in a table.
         A Crew with 2 tasks using the command crewai test -n 3
@@ -204,7 +212,7 @@ class CrewEvaluator:
                 CrewTestResultEvent(
                     quality=quality_score,
                     execution_duration=current_task.execution_duration,
-                    model=self.llm.model,
+                    model=getattr(self.llm, "model", str(self.llm)),
                     crew_name=self.crew.name,
                     crew=self.crew,
                 ),

lib/crewai/src/crewai/utilities/printer.py

@@ -4,6 +4,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Final, Literal, NamedTuple
 
+from crewai.events.utils.console_formatter import should_suppress_console_output
+
 
 if TYPE_CHECKING:
     from _typeshed import SupportsWrite
@@ -77,6 +79,8 @@ class Printer:
             file: A file-like object (stream); defaults to the current sys.stdout.
             flush: Whether to forcibly flush the stream.
         """
+        if should_suppress_console_output():
+            return
         if isinstance(content, str):
             content = [ColoredText(content, color)]
         print(

lib/crewai/src/crewai/utilities/pydantic_schema_utils.py

@@ -19,9 +19,10 @@ from collections.abc import Callable
 from copy import deepcopy
 import datetime
 import logging
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Union
+from typing import TYPE_CHECKING, Annotated, Any, Final, Literal, TypedDict, Union
 import uuid
 
+import jsonref  # type: ignore[import-untyped]
 from pydantic import (
     UUID1,
     UUID3,
@@ -69,6 +70,21 @@ else:
         EmailStr = str
 
 
+class JsonSchemaInfo(TypedDict):
+    """Inner structure for JSON schema metadata."""
+
+    name: str
+    strict: Literal[True]
+    schema: dict[str, Any]
+
+
+class ModelDescription(TypedDict):
+    """Return type for generate_model_description."""
+
+    type: Literal["json_schema"]
+    json_schema: JsonSchemaInfo
+
+
 def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
     """Recursively resolve all local $refs in the given JSON Schema using $defs as the source.
 
@@ -157,6 +173,72 @@ def force_additional_properties_false(d: Any) -> Any:
     return d
 
 
+OPENAI_SUPPORTED_FORMATS: Final[
+    set[Literal["date-time", "date", "time", "duration"]]
+] = {
+    "date-time",
+    "date",
+    "time",
+    "duration",
+}
+
+
+def strip_unsupported_formats(d: Any) -> Any:
+    """Remove format annotations that OpenAI strict mode doesn't support.
+
+    OpenAI only supports: date-time, date, time, duration.
+    Other formats like uri, email, uuid etc. cause validation errors.
+
+    Args:
+        d: The dictionary/list to modify.
+
+    Returns:
+        The modified dictionary/list.
+    """
+    if isinstance(d, dict):
+        format_value = d.get("format")
+        if (
+            isinstance(format_value, str)
+            and format_value not in OPENAI_SUPPORTED_FORMATS
+        ):
+            del d["format"]
+        for v in d.values():
+            strip_unsupported_formats(v)
+    elif isinstance(d, list):
+        for i in d:
+            strip_unsupported_formats(i)
+    return d
+
+
+def ensure_type_in_schemas(d: Any) -> Any:
+    """Ensure all schema objects in anyOf/oneOf have a 'type' key.
+
+    OpenAI strict mode requires every schema to have a 'type' key.
+    Empty schemas {} in anyOf/oneOf are converted to {"type": "object"}.
+
+    Args:
+        d: The dictionary/list to modify.
+
+    Returns:
+        The modified dictionary/list.
+    """
+    if isinstance(d, dict):
+        for key in ("anyOf", "oneOf"):
+            if key in d:
+                schema_list = d[key]
+                for i, schema in enumerate(schema_list):
+                    if isinstance(schema, dict) and schema == {}:
+                        schema_list[i] = {"type": "object"}
+                    else:
+                        ensure_type_in_schemas(schema)
+        for v in d.values():
+            ensure_type_in_schemas(v)
+    elif isinstance(d, list):
+        for item in d:
+            ensure_type_in_schemas(item)
+    return d
+
+
 def fix_discriminator_mappings(schema: dict[str, Any]) -> dict[str, Any]:
     """Replace '#/$defs/...' references in discriminator.mapping with just the model name.
 
@@ -293,7 +375,49 @@ def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
     return schema
 
 
-def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
+def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
+    """Remove null type from anyOf/type arrays.
+
+    Pydantic generates `T | None` for optional fields, which creates schemas with
+    null in the type. However, for MCP tools, optional fields should be omitted
+    entirely rather than sent as null. This function strips null from types.
+
+    Args:
+        schema: JSON schema dictionary.
+
+    Returns:
+        Modified schema with null types removed.
+    """
+    if isinstance(schema, dict):
+        if "anyOf" in schema:
+            any_of = schema["anyOf"]
+            non_null = [opt for opt in any_of if opt.get("type") != "null"]
+            if len(non_null) == 1:
+                schema.pop("anyOf")
+                schema.update(non_null[0])
+            elif len(non_null) > 1:
+                schema["anyOf"] = non_null
+
+        type_value = schema.get("type")
+        if isinstance(type_value, list) and "null" in type_value:
+            non_null_types = [t for t in type_value if t != "null"]
+            if len(non_null_types) == 1:
+                schema["type"] = non_null_types[0]
+            elif len(non_null_types) > 1:
+                schema["type"] = non_null_types
+
+        for value in schema.values():
+            if isinstance(value, dict):
+                strip_null_from_types(value)
+            elif isinstance(value, list):
+                for item in value:
+                    if isinstance(item, dict):
+                        strip_null_from_types(item)
+
+    return schema
+
+
+def generate_model_description(model: type[BaseModel]) -> ModelDescription:
     """Generate JSON schema description of a Pydantic model.
 
     This function takes a Pydantic model class and returns its JSON schema,
@@ -304,11 +428,13 @@ def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
         model: A Pydantic model class.
 
     Returns:
-        A JSON schema dictionary representation of the model.
+        A ModelDescription with JSON schema representation of the model.
     """
     json_schema = model.model_json_schema(ref_template="#/$defs/{model}")
 
     json_schema = force_additional_properties_false(json_schema)
+    json_schema = strip_unsupported_formats(json_schema)
+    json_schema = ensure_type_in_schemas(json_schema)
 
     json_schema = resolve_refs(json_schema)
 
@@ -316,6 +442,7 @@ def generate_model_description(model: type[BaseModel]) -> dict[str, Any]:
     json_schema = fix_discriminator_mappings(json_schema)
     json_schema = convert_oneof_to_anyof(json_schema)
     json_schema = ensure_all_properties_required(json_schema)
+    json_schema = strip_null_from_types(json_schema)
 
     return {
         "type": "json_schema",
@@ -400,6 +527,8 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         >>> person.name
         'John'
     """
+    json_schema = dict(jsonref.replace_refs(json_schema, proxies=False))
+
     effective_root = root_schema or json_schema
 
     json_schema = force_additional_properties_false(json_schema)
@@ -410,7 +539,7 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         if "title" not in json_schema and "title" in (root_schema or {}):
             json_schema["title"] = (root_schema or {}).get("title")
 
-    model_name = json_schema.get("title", "DynamicModel")
+    model_name = json_schema.get("title") or "DynamicModel"
     field_definitions = {
         name: _json_schema_to_pydantic_field(
             name, prop, json_schema.get("required", []), effective_root
@@ -418,9 +547,11 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         for name, prop in (json_schema.get("properties", {}) or {}).items()
     }
 
+    effective_config = __config__ or ConfigDict(extra="forbid")
+
     return create_model_base(
         model_name,
-        __config__=__config__,
+        __config__=effective_config,
         __base__=__base__,
         __module__=__module__,
         __validators__=__validators__,
@@ -599,8 +730,10 @@ def _json_schema_to_pydantic_type(
         any_of_schemas = json_schema.get("anyOf", []) + json_schema.get("oneOf", [])
     if any_of_schemas:
         any_of_types = [
-            _json_schema_to_pydantic_type(schema, root_schema)
-            for schema in any_of_schemas
+            _json_schema_to_pydantic_type(
+                schema, root_schema, name_=f"{name_ or 'Union'}Option{i}"
+            )
+            for i, schema in enumerate(any_of_schemas)
         ]
         return Union[tuple(any_of_types)]  # noqa: UP007
 
@@ -636,7 +769,7 @@ def _json_schema_to_pydantic_type(
         if properties:
             json_schema_ = json_schema.copy()
             if json_schema_.get("title") is None:
-                json_schema_["title"] = name_
+                json_schema_["title"] = name_ or "DynamicModel"
             return create_model_from_schema(json_schema_, root_schema=root_schema)
         return dict
     if type_ == "null":

lib/crewai/src/crewai/utilities/serialization.py

@@ -19,6 +19,7 @@ def to_serializable(
     exclude: set[str] | None = None,
     max_depth: int = 5,
     _current_depth: int = 0,
+    _ancestors: set[int] | None = None,
 ) -> Serializable:
     """Converts a Python object into a JSON-compatible representation.
 
@@ -31,6 +32,7 @@ def to_serializable(
         exclude: Set of keys to exclude from the result.
         max_depth: Maximum recursion depth. Defaults to 5.
         _current_depth: Current recursion depth (for internal use).
+        _ancestors: Set of ancestor object ids for cycle detection (for internal use).
 
     Returns:
         Serializable: A JSON-compatible structure.
@@ -41,16 +43,29 @@ def to_serializable(
     if exclude is None:
         exclude = set()
 
+    if _ancestors is None:
+        _ancestors = set()
+
     if isinstance(obj, (str, int, float, bool, type(None))):
         return obj
     if isinstance(obj, uuid.UUID):
         return str(obj)
     if isinstance(obj, (date, datetime)):
         return obj.isoformat()
+
+    object_id = id(obj)
+    if object_id in _ancestors:
+        return f"<circular_ref:{type(obj).__name__}>"
+    new_ancestors = _ancestors | {object_id}
+
     if isinstance(obj, (list, tuple, set)):
         return [
             to_serializable(
-                item, max_depth=max_depth, _current_depth=_current_depth + 1
+                item,
+                exclude=exclude,
+                max_depth=max_depth,
+                _current_depth=_current_depth + 1,
+                _ancestors=new_ancestors,
             )
             for item in obj
         ]
@@ -61,6 +76,7 @@ def to_serializable(
                 exclude=exclude,
                 max_depth=max_depth,
                 _current_depth=_current_depth + 1,
+                _ancestors=new_ancestors,
             )
             for key, value in obj.items()
             if key not in exclude
@@ -71,12 +87,16 @@ def to_serializable(
                 obj=obj.model_dump(exclude=exclude),
                 max_depth=max_depth,
                 _current_depth=_current_depth + 1,
+                _ancestors=new_ancestors,
             )
         except Exception:
             try:
                 return {
                     _to_serializable_key(k): to_serializable(
-                        v, max_depth=max_depth, _current_depth=_current_depth + 1
+                        v,
+                        max_depth=max_depth,
+                        _current_depth=_current_depth + 1,
+                        _ancestors=new_ancestors,
                     )
                     for k, v in obj.__dict__.items()
                     if k not in (exclude or set())

lib/crewai/tests/agents/agent_adapters/test_base_agent_adapter.py

@@ -51,6 +51,10 @@ class ConcreteAgentAdapter(BaseAgentAdapter):
         # Dummy implementation for MCP tools
         return []
 
+    def configure_structured_output(self, task: Any) -> None:
+        # Dummy implementation for structured output
+        pass
+
     async def aexecute_task(
         self,
         task: Any,

lib/crewai/tests/agents/test_agent.py

@@ -703,6 +703,8 @@ def test_agent_definition_based_on_dict():
 # test for human input
 @pytest.mark.vcr()
 def test_agent_human_input():
+    from crewai.core.providers.human_input import SyncHumanInputProvider
+
     # Agent configuration
     config = {
         "role": "test role",
@@ -720,7 +722,7 @@ def test_agent_human_input():
         human_input=True,
     )
 
-    # Side effect function for _ask_human_input to simulate multiple feedback iterations
+    # Side effect function for _prompt_input to simulate multiple feedback iterations
     feedback_responses = iter(
         [
             "Don't say hi, say Hello instead!",  # First feedback: instruct change
@@ -728,16 +730,16 @@ def test_agent_human_input():
         ]
     )
 
-    def ask_human_input_side_effect(*args, **kwargs):
+    def prompt_input_side_effect(*args, **kwargs):
         return next(feedback_responses)
 
-    # Patch both _ask_human_input and _invoke_loop to avoid real API/network calls.
+    # Patch both _prompt_input on provider and _invoke_loop to avoid real API/network calls.
     with (
         patch.object(
-            CrewAgentExecutor,
-            "_ask_human_input",
-            side_effect=ask_human_input_side_effect,
-        ) as mock_human_input,
+            SyncHumanInputProvider,
+            "_prompt_input",
+            side_effect=prompt_input_side_effect,
+        ) as mock_prompt_input,
         patch.object(
             CrewAgentExecutor,
             "_invoke_loop",
@@ -749,7 +751,7 @@ def test_agent_human_input():
 
         # Assertions to ensure the agent behaves correctly.
         # It should have requested feedback twice.
-        assert mock_human_input.call_count == 2
+        assert mock_prompt_input.call_count == 2
         # The final result should be processed to "Hello"
         assert output.strip().lower() == "hello"
 

lib/crewai/tests/agents/test_lite_agent.py

@@ -606,9 +606,10 @@ def test_lite_agent_with_invalid_llm():
 
 
 @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
+@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool.requests.post")
 @patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get")
 @pytest.mark.vcr()
-def test_agent_kickoff_with_platform_tools(mock_get):
+def test_agent_kickoff_with_platform_tools(mock_get, mock_post):
     """Test that Agent.kickoff() properly integrates platform tools with LiteAgent"""
     mock_response = Mock()
     mock_response.raise_for_status.return_value = None
@@ -632,6 +633,15 @@ def test_agent_kickoff_with_platform_tools(mock_get):
     }
     mock_get.return_value = mock_response
 
+    # Mock the platform tool execution
+    mock_post_response = Mock()
+    mock_post_response.ok = True
+    mock_post_response.json.return_value = {
+        "success": True,
+        "issue_url": "https://github.com/test/repo/issues/1"
+    }
+    mock_post.return_value = mock_post_response
+
     agent = Agent(
         role="Test Agent",
         goal="Test goal",

lib/crewai/tests/cassettes/agents/test_agent_kickoff_with_platform_tools.yaml

@@ -1,98 +1,227 @@
 interactions:
 - request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour personal goal is: Test goal\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: create_issue\nTool Arguments: {''title'': {''description'': ''Issue title'', ''type'': ''str''}, ''body'': {''description'': ''Issue body'', ''type'': ''Union[str, NoneType]''}}\nTool Description: Create a GitHub issue\nDetailed Parameter Structure:\nObject with properties:\n  - title: Issue title (required)\n  - body: Issue body (optional)\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 [create_issue], 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```"}, {"role": "user", "content": "Create a GitHub issue"}], "model": "gpt-3.5-turbo", "stream": false}'
+    body: '{"messages":[{"role":"system","content":"You are Test Agent. Test backstory\nYour
+      personal goal is: Test goal"},{"role":"user","content":"\nCurrent Task: Create
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-        141,\n    \"completion_tokens\": 8,\n    \"total_tokens\": 149,\n    \"prompt_tokens_details\":
-        {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+        \"assistant\",\n        \"content\": \"The attempt to use the failing tool
+        resulted in an error, as expected since it is designed to always fail. If
+        there's anything else you would like to calculate or explore, please let me
+        know!\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n
+        \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
+        \ \"usage\": {\n    \"prompt_tokens\": 93,\n    \"completion_tokens\": 40,\n
+        \   \"total_tokens\": 133,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+        0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
         {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
         0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-        \"default\",\n  \"system_fingerprint\": \"fp_c4585b5b9c\"\n}\n"
+        \"default\",\n  \"system_fingerprint\": \"fp_6c0d1490cb\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -182,7 +179,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Thu, 22 Jan 2026 20:40:55 GMT
+      - Fri, 06 Feb 2026 18:41:26 GMT
       Server:
       - cloudflare
       Strict-Transport-Security:
@@ -200,13 +197,11 @@ interactions:
       openai-organization:
       - OPENAI-ORG-XXX
       openai-processing-ms:
-      - '420'
+      - '776'
       openai-project:
       - OPENAI-PROJECT-XXX
       openai-version:
       - '2020-10-01'
-      x-envoy-upstream-service-time:
-      - '436'
       x-openai-proxy-wasm:
       - v0.1
       x-ratelimit-limit-requests:

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_agent_kickoff_structured_output_with_tools.yaml

@@ -43,15 +43,15 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.13.3
+      - 3.13.5
       x-stainless-timeout:
       - NOT_GIVEN
     method: POST
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_0149zKBgM47utdBdrfJjM6YZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011jnBYLgtzXqdmSi7JDyQHj","name":"structured_output","input":{"operation":"Addition","result":42,"explanation":"Adding
-        15 and 27 together results in 42"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":573,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":79,"service_tier":"standard"}}'
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01A41GpDoJbZLUhR8dQzUcUX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UNPdzpayoWyqDYVE7fR5oA","name":"structured_output","input":{"operation":"Addition","result":42,"explanation":"Added
+        15 and 27 together"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":573,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":75,"service_tier":"standard","inference_geo":"not_available"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -62,7 +62,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Fri, 30 Jan 2026 18:56:15 GMT
+      - Fri, 06 Feb 2026 18:41:25 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -88,7 +88,7 @@ interactions:
       anthropic-ratelimit-requests-remaining:
       - '3999'
       anthropic-ratelimit-requests-reset:
-      - '2026-01-30T18:56:14Z'
+      - '2026-02-06T18:41:24Z'
       anthropic-ratelimit-tokens-limit:
       - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
       anthropic-ratelimit-tokens-remaining:
@@ -102,7 +102,7 @@ interactions:
       strict-transport-security:
       - STS-XXX
       x-envoy-upstream-service-time:
-      - '1473'
+      - '1247'
     status:
       code: 200
       message: OK

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_agent_kickoff_structured_output_without_tools.yaml

@@ -44,21 +44,20 @@ interactions:
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.13.3
+      - 3.13.5
       x-stainless-timeout:
       - NOT_GIVEN
     method: POST
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_013iHkpmto99iyH5kDvn8uER","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Kpda2DzHBqWq9a2FS2Bdw6","name":"structured_output","input":{"topic":"Benefits
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_016wrV83wm3FLYD4JoTy2Piw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V6Pzr7eGfuG4Q3mc25ZXwN","name":"structured_output","input":{"topic":"Benefits
         of Remote Work","summary":"Remote work offers significant advantages for both
-        employees and employers, transforming traditional work paradigms by providing
-        flexibility, increased productivity, and cost savings.","key_points":["Increased
-        employee flexibility and work-life balance","Reduced commuting time and associated
-        stress","Cost savings for companies on office infrastructure","Access to a
-        global talent pool","Higher employee productivity and job satisfaction","Lower
-        carbon footprint due to reduced travel"]}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":589,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":153,"service_tier":"standard"}}'
+        employees and employers, transforming traditional workplace dynamics.","key_points":["Increased
+        flexibility in work schedule","Reduced commute time and transportation costs","Improved
+        work-life balance","Higher productivity for many employees","Cost savings
+        for companies on office infrastructure","Expanded talent pool for hiring","Enhanced
+        employee job satisfaction"]}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":589,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":142,"service_tier":"standard","inference_geo":"not_available"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -69,7 +68,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Fri, 30 Jan 2026 18:56:19 GMT
+      - Fri, 06 Feb 2026 18:41:28 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -95,7 +94,7 @@ interactions:
       anthropic-ratelimit-requests-remaining:
       - '3999'
       anthropic-ratelimit-requests-reset:
-      - '2026-01-30T18:56:16Z'
+      - '2026-02-06T18:41:26Z'
       anthropic-ratelimit-tokens-limit:
       - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
       anthropic-ratelimit-tokens-remaining:
@@ -109,7 +108,7 @@ interactions:
       strict-transport-security:
       - STS-XXX
       x-envoy-upstream-service-time:
-      - '3107'
+      - '2650'
     status:
       code: 200
       message: OK

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_tool_execution_returns_tool_result_directly.yaml

@@ -0,0 +1,102 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Calculate 5 +
+      3 using the simple_calculator tool with operation ''add''."}],"model":"claude-3-5-haiku-20241022","stream":false,"tool_choice":{"type":"tool","name":"simple_calculator"},"tools":[{"name":"simple_calculator","description":"Perform
+      simple math operations","input_schema":{"type":"object","properties":{"operation":{"type":"string","enum":["add","multiply"],"description":"The
+      operation to perform"},"a":{"type":"integer","description":"First number"},"b":{"type":"integer","description":"Second
+      number"}},"required":["operation","a","b"]}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '608'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - X-USER-AGENT-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.73.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.3
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01Q2F83aAeqqTCxsd8WpZjK7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BW4XkHnhRVM5JZsvoaQKw5","name":"simple_calculator","input":{"operation":"add","a":5,"b":3}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":498,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":67,"service_tier":"standard"}}'
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Security-Policy:
+      - CSP-FILTERED
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 03 Feb 2026 23:26:35 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2026-02-03T23:26:34Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '1228'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_tool_execution_with_available_functions.yaml

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Create a simple
+      plan to say hello. Use the create_reasoning_plan tool."}],"model":"claude-3-5-haiku-20241022","stream":false,"tool_choice":{"type":"tool","name":"create_reasoning_plan"},"tools":[{"name":"create_reasoning_plan","description":"Create
+      a structured reasoning plan for completing a task","input_schema":{"type":"object","properties":{"plan":{"type":"string","description":"High-level
+      plan description"},"steps":{"type":"array","items":{"type":"object"},"description":"List
+      of steps to execute"},"ready":{"type":"boolean","description":"Whether the plan
+      is ready to execute"}},"required":["plan","steps","ready"]}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '684'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - X-USER-AGENT-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.73.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.3
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: '{"model":"claude-3-5-haiku-20241022","id":"msg_01HLuGgGRFseMdhTYAhkKtfz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GQAUFHffGzMd3ufA6YRMZF","name":"create_reasoning_plan","input":{"plan":"Say
+        hello in a friendly and straightforward manner","steps":[{"description":"Take
+        a deep breath","action":"Pause and relax"},{"description":"Smile","action":"Prepare
+        a warm facial expression"},{"description":"Greet the person","action":"Say
+        ''Hello!''"},{"description":"Wait for response","action":"Listen and be ready
+        to continue conversation"}],"ready":true}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":513,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":162,"service_tier":"standard"}}'
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Security-Policy:
+      - CSP-FILTERED
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 03 Feb 2026 23:26:38 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2026-02-03T23:26:35Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '2994'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cli/test_plus_api.py

@@ -1,6 +1,8 @@
 import os
 import unittest
-from unittest.mock import ANY, MagicMock, patch
+from unittest.mock import ANY, AsyncMock, MagicMock, patch
+
+import pytest
 
 from crewai.cli.plus_api import PlusAPI
 
@@ -68,37 +70,6 @@ class TestPlusAPI(unittest.TestCase):
         )
         self.assertEqual(response, mock_response)
 
-    @patch("crewai.cli.plus_api.PlusAPI._make_request")
-    def test_get_agent(self, mock_make_request):
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-
-        response = self.api.get_agent("test_agent_handle")
-        mock_make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai.cli.plus_api.Settings")
-    @patch("requests.Session.request")
-    def test_get_agent_with_org_uuid(self, mock_make_request, mock_settings_class):
-        mock_settings = MagicMock()
-        mock_settings.org_uuid = self.org_uuid
-        mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL')
-        mock_settings_class.return_value = mock_settings
-        # re-initialize Client
-        self.api = PlusAPI(self.api_key)
-
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-
-        response = self.api.get_agent("test_agent_handle")
-
-        self.assert_request_with_org_id(
-            mock_make_request, "GET", "/crewai_plus/api/v1/agents/test_agent_handle"
-        )
-        self.assertEqual(response, mock_response)
-
     @patch("crewai.cli.plus_api.PlusAPI._make_request")
     def test_get_tool(self, mock_make_request):
         mock_response = MagicMock()
@@ -338,3 +309,49 @@ class TestPlusAPI(unittest.TestCase):
             custom_api.base_url,
             "https://custom-url-from-env.com",
         )
+
+
+@pytest.mark.asyncio
+@patch("httpx.AsyncClient")
+async def test_get_agent(mock_async_client_class):
+    api = PlusAPI("test_api_key")
+    mock_response = MagicMock()
+    mock_client_instance = AsyncMock()
+    mock_client_instance.get.return_value = mock_response
+    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
+
+    response = await api.get_agent("test_agent_handle")
+
+    mock_client_instance.get.assert_called_once_with(
+        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
+        headers=api.headers,
+    )
+    assert response == mock_response
+
+
+@pytest.mark.asyncio
+@patch("httpx.AsyncClient")
+@patch("crewai.cli.plus_api.Settings")
+async def test_get_agent_with_org_uuid(mock_settings_class, mock_async_client_class):
+    org_uuid = "test-org-uuid"
+    mock_settings = MagicMock()
+    mock_settings.org_uuid = org_uuid
+    mock_settings.enterprise_base_url = os.getenv("CREWAI_PLUS_URL")
+    mock_settings_class.return_value = mock_settings
+
+    api = PlusAPI("test_api_key")
+
+    mock_response = MagicMock()
+    mock_client_instance = AsyncMock()
+    mock_client_instance.get.return_value = mock_response
+    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
+
+    response = await api.get_agent("test_agent_handle")
+
+    mock_client_instance.get.assert_called_once_with(
+        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
+        headers=api.headers,
+    )
+    assert "X-Crewai-Organization-Id" in api.headers
+    assert api.headers["X-Crewai-Organization-Id"] == org_uuid
+    assert response == mock_response

lib/crewai/tests/events/test_event_context.py

@@ -177,4 +177,40 @@ class TestTriggeredByScope:
                 raise ValueError("test error")
         except ValueError:
             pass
-        assert get_triggering_event_id() is None
+        assert get_triggering_event_id() is None
+
+
+def test_agent_scope_preserved_after_tool_error_event() -> None:
+    from crewai.events import crewai_event_bus
+    from crewai.events.types.tool_usage_events import (
+        ToolUsageErrorEvent,
+        ToolUsageStartedEvent,
+    )
+
+    push_event_scope("crew-1", "crew_kickoff_started")
+    push_event_scope("task-1", "task_started")
+    push_event_scope("agent-1", "agent_execution_started")
+
+    crewai_event_bus.emit(
+        None,
+        ToolUsageStartedEvent(
+            tool_name="test_tool",
+            tool_args={},
+            agent_key="test_agent",
+        )
+    )
+
+    crewai_event_bus.emit(
+        None,
+        ToolUsageErrorEvent(
+            tool_name="test_tool",
+            tool_args={},
+            agent_key="test_agent",
+            error=ValueError("test error"),
+        )
+    )
+
+    crewai_event_bus.flush()
+
+    assert get_current_parent_id() == "agent-1"
+

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

@@ -45,85 +45,6 @@ def test_anthropic_completion_is_used_when_claude_provider():
 
 
 
-def test_anthropic_tool_use_conversation_flow():
-    """
-    Test that the Anthropic completion properly handles tool use conversation flow
-    """
-    from unittest.mock import Mock, patch
-    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
-    from anthropic.types.tool_use_block import ToolUseBlock
-
-    # Create AnthropicCompletion instance
-    completion = AnthropicCompletion(model="claude-3-5-sonnet-20241022")
-
-    # Mock tool function
-    def mock_weather_tool(location: str) -> str:
-        return f"The weather in {location} is sunny and 75°F"
-
-    available_functions = {"get_weather": mock_weather_tool}
-
-    # Mock the Anthropic client responses
-    with patch.object(completion.client.messages, 'create') as mock_create:
-        # Mock initial response with tool use - need to properly mock ToolUseBlock
-        mock_tool_use = Mock(spec=ToolUseBlock)
-        mock_tool_use.type = "tool_use"
-        mock_tool_use.id = "tool_123"
-        mock_tool_use.name = "get_weather"
-        mock_tool_use.input = {"location": "San Francisco"}
-
-        mock_initial_response = Mock()
-        mock_initial_response.content = [mock_tool_use]
-        mock_initial_response.usage = Mock()
-        mock_initial_response.usage.input_tokens = 100
-        mock_initial_response.usage.output_tokens = 50
-
-        # Mock final response after tool result - properly mock text content
-        mock_text_block = Mock()
-        mock_text_block.type = "text"
-        # Set the text attribute as a string, not another Mock
-        mock_text_block.configure_mock(text="Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature.")
-
-        mock_final_response = Mock()
-        mock_final_response.content = [mock_text_block]
-        mock_final_response.usage = Mock()
-        mock_final_response.usage.input_tokens = 150
-        mock_final_response.usage.output_tokens = 75
-
-        # Configure mock to return different responses on successive calls
-        mock_create.side_effect = [mock_initial_response, mock_final_response]
-
-        # Test the call
-        messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
-        result = completion.call(
-            messages=messages,
-            available_functions=available_functions
-        )
-
-        # Verify the result contains the final response
-        assert "beautiful day in San Francisco" in result
-        assert "sunny skies" in result
-        assert "75°F" in result
-
-        # Verify that two API calls were made (initial + follow-up)
-        assert mock_create.call_count == 2
-
-        # Verify the second call includes tool results
-        second_call_args = mock_create.call_args_list[1][1]  # kwargs of second call
-        messages_in_second_call = second_call_args["messages"]
-
-        # Should have original user message + assistant tool use + user tool result
-        assert len(messages_in_second_call) == 3
-        assert messages_in_second_call[0]["role"] == "user"
-        assert messages_in_second_call[1]["role"] == "assistant"
-        assert messages_in_second_call[2]["role"] == "user"
-
-        # Verify tool result format
-        tool_result = messages_in_second_call[2]["content"][0]
-        assert tool_result["type"] == "tool_result"
-        assert tool_result["tool_use_id"] == "tool_123"
-        assert "sunny and 75°F" in tool_result["content"]
-
-
 def test_anthropic_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Anthropic provider
@@ -874,6 +795,125 @@ def test_anthropic_function_calling():
 # =============================================================================
 
 
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_tool_execution_with_available_functions():
+    """
+    Test that Anthropic provider correctly executes tools when available_functions is provided.
+
+    This specifically tests the fix for double llm_call_completed emission - when
+    available_functions is provided, _handle_tool_execution is called which already
+    emits llm_call_completed, so the caller should not emit it again.
+
+    The test verifies:
+    1. The tool is called with correct arguments
+    2. The tool result is returned directly (not wrapped in conversation)
+    3. The result is valid JSON matching the tool output format
+    """
+    import json
+
+    llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
+
+    # Simple tool that returns a formatted string
+    def create_reasoning_plan(plan: str, steps: list, ready: bool) -> str:
+        """Create a reasoning plan with steps."""
+        return json.dumps({"plan": plan, "steps": steps, "ready": ready})
+
+    tools = [
+        {
+            "name": "create_reasoning_plan",
+            "description": "Create a structured reasoning plan for completing a task",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "plan": {
+                        "type": "string",
+                        "description": "High-level plan description"
+                    },
+                    "steps": {
+                        "type": "array",
+                        "items": {"type": "object"},
+                        "description": "List of steps to execute"
+                    },
+                    "ready": {
+                        "type": "boolean",
+                        "description": "Whether the plan is ready to execute"
+                    }
+                },
+                "required": ["plan", "steps", "ready"]
+            }
+        }
+    ]
+
+    result = llm.call(
+        messages=[{"role": "user", "content": "Create a simple plan to say hello. Use the create_reasoning_plan tool."}],
+        tools=tools,
+        available_functions={"create_reasoning_plan": create_reasoning_plan}
+    )
+
+    # Verify result is valid JSON from the tool
+    assert result is not None
+    assert isinstance(result, str)
+
+    # Parse the result to verify it's valid JSON
+    parsed_result = json.loads(result)
+    assert "plan" in parsed_result
+    assert "steps" in parsed_result
+    assert "ready" in parsed_result
+
+
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_tool_execution_returns_tool_result_directly():
+    """
+    Test that when available_functions is provided, the tool result is returned directly
+    without additional LLM conversation (matching OpenAI behavior for reasoning_handler).
+    """
+    llm = LLM(model="anthropic/claude-3-5-haiku-20241022")
+
+    call_count = 0
+
+    def simple_calculator(operation: str, a: int, b: int) -> str:
+        """Perform a simple calculation."""
+        nonlocal call_count
+        call_count += 1
+        if operation == "add":
+            return str(a + b)
+        elif operation == "multiply":
+            return str(a * b)
+        return "Unknown operation"
+
+    tools = [
+        {
+            "name": "simple_calculator",
+            "description": "Perform simple math operations",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "operation": {
+                        "type": "string",
+                        "enum": ["add", "multiply"],
+                        "description": "The operation to perform"
+                    },
+                    "a": {"type": "integer", "description": "First number"},
+                    "b": {"type": "integer", "description": "Second number"}
+                },
+                "required": ["operation", "a", "b"]
+            }
+        }
+    ]
+
+    result = llm.call(
+        messages=[{"role": "user", "content": "Calculate 5 + 3 using the simple_calculator tool with operation 'add'."}],
+        tools=tools,
+        available_functions={"simple_calculator": simple_calculator}
+    )
+
+    # Tool should have been called exactly once
+    assert call_count == 1, f"Expected tool to be called once, got {call_count}"
+
+    # Result should be the direct tool output
+    assert result == "8", f"Expected '8' but got '{result}'"
+
+
 @pytest.mark.vcr()
 def test_anthropic_agent_kickoff_structured_output_without_tools():
     """

lib/crewai/tests/memory/test_external_memory.py

@@ -308,6 +308,7 @@ def test_external_memory_search_events(
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "limit": 3,
@@ -330,6 +331,7 @@ def test_external_memory_search_events(
         "parent_event_id": ANY,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "results": [],
@@ -390,6 +392,7 @@ def test_external_memory_save_events(
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "saving value",
         "metadata": {"task": "test_task"},
@@ -411,6 +414,7 @@ def test_external_memory_save_events(
         "parent_event_id": ANY,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "saving value",
         "metadata": {"task": "test_task"},

lib/crewai/tests/memory/test_long_term_memory.py

@@ -74,6 +74,7 @@ def test_long_term_memory_save_events(long_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test_task",
         "metadata": {"task": "test_task", "quality": 0.5},
@@ -94,6 +95,7 @@ def test_long_term_memory_save_events(long_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test_task",
         "metadata": {
@@ -153,6 +155,7 @@ def test_long_term_memory_search_events(long_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test query",
         "limit": 5,
@@ -175,6 +178,7 @@ def test_long_term_memory_search_events(long_term_memory):
         "parent_event_id": ANY,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test query",
         "results": None,

lib/crewai/tests/memory/test_short_term_memory.py

@@ -85,6 +85,7 @@ def test_short_term_memory_search_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "limit": 3,
@@ -107,6 +108,7 @@ def test_short_term_memory_search_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "query": "test value",
         "results": [],
@@ -164,6 +166,7 @@ def test_short_term_memory_save_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test value",
         "metadata": {"task": "test_task"},
@@ -185,6 +188,7 @@ def test_short_term_memory_save_events(short_term_memory):
         "parent_event_id": None,
         "previous_event_id": ANY,
         "triggered_by_event_id": None,
+        "started_event_id": ANY,
         "emission_sequence": ANY,
         "value": "test value",
         "metadata": {"task": "test_task"},

lib/crewai/tests/test_flow_human_input_integration.py

@@ -2,6 +2,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 from crewai.events.event_listener import event_listener
+from crewai.core.providers.human_input import SyncHumanInputProvider
 
 
 class TestFlowHumanInputIntegration:
@@ -24,14 +25,9 @@ class TestFlowHumanInputIntegration:
     @patch("builtins.input", return_value="")
     def test_human_input_pauses_flow_updates(self, mock_input):
         """Test that human input pauses Flow status updates."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = False
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = False
 
         formatter = event_listener.formatter
 
@@ -39,7 +35,7 @@ class TestFlowHumanInputIntegration:
             patch.object(formatter, "pause_live_updates") as mock_pause,
             patch.object(formatter, "resume_live_updates") as mock_resume,
         ):
-            result = executor._ask_human_input("Test result")
+            result = provider._prompt_input(crew)
 
             mock_pause.assert_called_once()
             mock_resume.assert_called_once()
@@ -49,14 +45,9 @@ class TestFlowHumanInputIntegration:
     @patch("builtins.input", side_effect=["feedback", ""])
     def test_multiple_human_input_rounds(self, mock_input):
         """Test multiple rounds of human input with Flow status management."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = False
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = False
 
         formatter = event_listener.formatter
 
@@ -75,10 +66,10 @@ class TestFlowHumanInputIntegration:
                 formatter, "resume_live_updates", side_effect=track_resume
             ),
         ):
-            result1 = executor._ask_human_input("Test result 1")
+            result1 = provider._prompt_input(crew)
             assert result1 == "feedback"
 
-            result2 = executor._ask_human_input("Test result 2")
+            result2 = provider._prompt_input(crew)
             assert result2 == ""
 
             assert len(pause_calls) == 2
@@ -103,14 +94,9 @@ class TestFlowHumanInputIntegration:
 
     def test_pause_resume_exception_handling(self):
         """Test that resume is called even if exception occurs during human input."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = False
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = False
 
         formatter = event_listener.formatter
 
@@ -122,21 +108,16 @@ class TestFlowHumanInputIntegration:
             ),
         ):
             with pytest.raises(KeyboardInterrupt):
-                executor._ask_human_input("Test result")
+                provider._prompt_input(crew)
 
             mock_pause.assert_called_once()
             mock_resume.assert_called_once()
 
     def test_training_mode_human_input(self):
         """Test human input in training mode."""
-        from crewai.agents.agent_builder.base_agent_executor_mixin import (
-            CrewAgentExecutorMixin,
-        )
-
-        executor = CrewAgentExecutorMixin()
-        executor.crew = MagicMock()
-        executor.crew._train = True
-        executor._printer = MagicMock()
+        provider = SyncHumanInputProvider()
+        crew = MagicMock()
+        crew._train = True
 
         formatter = event_listener.formatter
 
@@ -146,7 +127,7 @@ class TestFlowHumanInputIntegration:
             patch.object(formatter.console, "print") as mock_console_print,
             patch("builtins.input", return_value="training feedback"),
         ):
-            result = executor._ask_human_input("Test result")
+            result = provider._prompt_input(crew)
 
             mock_pause.assert_called_once()
             mock_resume.assert_called_once()
@@ -161,4 +142,4 @@ class TestFlowHumanInputIntegration:
                 for call in call_args
                 if call[0]
             )
-            assert training_panel_found
+            assert training_panel_found

lib/crewai/tests/test_human_feedback_integration.py

@@ -157,6 +157,176 @@ class TestMultiStepFlows:
 
         assert execution_order == ["generate", "review", "finalize"]
 
+    def test_chained_router_feedback_steps(self):
+        """Test that a router outcome can trigger another router method.
+
+        Regression test: @listen("outcome") combined with @human_feedback(emit=...)
+        creates a method that is both a listener and a router. The flow must find
+        and execute it when the upstream router emits the matching outcome.
+        """
+        execution_order: list[str] = []
+
+        class ChainedRouterFlow(Flow):
+            @start()
+            @human_feedback(
+                message="First review:",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            def draft(self):
+                execution_order.append("draft")
+                return "draft content"
+
+            @listen("approved")
+            @human_feedback(
+                message="Final review:",
+                emit=["publish", "revise"],
+                llm="gpt-4o-mini",
+            )
+            def final_review(self, prev: HumanFeedbackResult):
+                execution_order.append("final_review")
+                return "final content"
+
+            @listen("rejected")
+            def on_rejected(self, prev: HumanFeedbackResult):
+                execution_order.append("on_rejected")
+                return "rejected"
+
+            @listen("publish")
+            def on_publish(self, prev: HumanFeedbackResult):
+                execution_order.append("on_publish")
+                return "published"
+
+            @listen("revise")
+            def on_revise(self, prev: HumanFeedbackResult):
+                execution_order.append("on_revise")
+                return "revised"
+
+        flow = ChainedRouterFlow()
+
+        with (
+            patch.object(
+                flow,
+                "_request_human_feedback",
+                side_effect=["looks good", "ship it"],
+            ),
+            patch.object(
+                flow,
+                "_collapse_to_outcome",
+                side_effect=["approved", "publish"],
+            ),
+        ):
+            result = flow.kickoff()
+
+        assert execution_order == ["draft", "final_review", "on_publish"]
+        assert result == "published"
+        assert len(flow.human_feedback_history) == 2
+        assert flow.human_feedback_history[0].outcome == "approved"
+        assert flow.human_feedback_history[1].outcome == "publish"
+
+    def test_chained_router_rejected_path(self):
+        """Test that a start-router outcome routes to a non-router listener."""
+        execution_order: list[str] = []
+
+        class ChainedRouterFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            def draft(self):
+                execution_order.append("draft")
+                return "draft"
+
+            @listen("approved")
+            @human_feedback(
+                message="Final:",
+                emit=["publish", "revise"],
+                llm="gpt-4o-mini",
+            )
+            def final_review(self, prev: HumanFeedbackResult):
+                execution_order.append("final_review")
+                return "final"
+
+            @listen("rejected")
+            def on_rejected(self, prev: HumanFeedbackResult):
+                execution_order.append("on_rejected")
+                return "rejected"
+
+        flow = ChainedRouterFlow()
+
+        with (
+            patch.object(
+                flow, "_request_human_feedback", return_value="bad"
+            ),
+            patch.object(
+                flow, "_collapse_to_outcome", return_value="rejected"
+            ),
+        ):
+            result = flow.kickoff()
+
+        assert execution_order == ["draft", "on_rejected"]
+        assert result == "rejected"
+        assert len(flow.human_feedback_history) == 1
+        assert flow.human_feedback_history[0].outcome == "rejected"
+
+    def test_router_and_non_router_listeners_for_same_outcome(self):
+        """Test that both router and non-router listeners fire for the same outcome."""
+        execution_order: list[str] = []
+
+        class MixedListenerFlow(Flow):
+            @start()
+            @human_feedback(
+                message="Review:",
+                emit=["approved", "rejected"],
+                llm="gpt-4o-mini",
+            )
+            def draft(self):
+                execution_order.append("draft")
+                return "draft"
+
+            @listen("approved")
+            @human_feedback(
+                message="Final:",
+                emit=["publish", "revise"],
+                llm="gpt-4o-mini",
+            )
+            def router_listener(self, prev: HumanFeedbackResult):
+                execution_order.append("router_listener")
+                return "final"
+
+            @listen("approved")
+            def plain_listener(self, prev: HumanFeedbackResult):
+                execution_order.append("plain_listener")
+                return "logged"
+
+            @listen("publish")
+            def on_publish(self, prev: HumanFeedbackResult):
+                execution_order.append("on_publish")
+                return "published"
+
+        flow = MixedListenerFlow()
+
+        with (
+            patch.object(
+                flow,
+                "_request_human_feedback",
+                side_effect=["approve it", "publish it"],
+            ),
+            patch.object(
+                flow,
+                "_collapse_to_outcome",
+                side_effect=["approved", "publish"],
+            ),
+        ):
+            flow.kickoff()
+
+        assert "draft" in execution_order
+        assert "router_listener" in execution_order
+        assert "plain_listener" in execution_order
+        assert "on_publish" in execution_order
+
 
 class TestStateManagement:
     """Tests for state management with human feedback."""

lib/crewai/tests/tools/test_tool_usage.py

@@ -10,7 +10,9 @@ from crewai import Agent, Task
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import (
     ToolSelectionErrorEvent,
+    ToolUsageErrorEvent,
     ToolUsageFinishedEvent,
+    ToolUsageStartedEvent,
     ToolValidateInputErrorEvent,
 )
 from crewai.tools import BaseTool
@@ -744,3 +746,78 @@ def test_tool_usage_finished_event_with_cached_result():
     assert isinstance(event.started_at, datetime.datetime)
     assert isinstance(event.finished_at, datetime.datetime)
     assert event.type == "tool_usage_finished"
+
+
+def test_tool_error_does_not_emit_finished_event():
+    from crewai.tools.tool_calling import ToolCalling
+
+    class FailingTool(BaseTool):
+        name: str = "Failing Tool"
+        description: str = "A tool that always fails"
+
+        def _run(self, **kwargs) -> str:
+            raise ValueError("Intentional failure")
+
+    failing_tool = FailingTool().to_structured_tool()
+
+    mock_agent = MagicMock()
+    mock_agent.key = "test_agent_key"
+    mock_agent.role = "test_agent_role"
+    mock_agent._original_role = "test_agent_role"
+    mock_agent.verbose = False
+    mock_agent.fingerprint = None
+    mock_agent.i18n.tools.return_value = {"name": "Add Image"}
+    mock_agent.i18n.errors.return_value = "Error: {error}"
+    mock_agent.i18n.slice.return_value = "Available tools: {tool_names}"
+
+    mock_task = MagicMock()
+    mock_task.delegations = 0
+    mock_task.name = "Test Task"
+    mock_task.description = "A test task"
+    mock_task.id = "test-task-id"
+
+    mock_action = MagicMock()
+    mock_action.tool = "failing_tool"
+    mock_action.tool_input = "{}"
+
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(cache=None, last_used_tool=None),
+        tools=[failing_tool],
+        task=mock_task,
+        function_calling_llm=None,
+        agent=mock_agent,
+        action=mock_action,
+    )
+
+    started_events = []
+    error_events = []
+    finished_events = []
+    error_received = threading.Event()
+
+    @crewai_event_bus.on(ToolUsageStartedEvent)
+    def on_started(source, event):
+        if event.tool_name == "failing_tool":
+            started_events.append(event)
+
+    @crewai_event_bus.on(ToolUsageErrorEvent)
+    def on_error(source, event):
+        if event.tool_name == "failing_tool":
+            error_events.append(event)
+            error_received.set()
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def on_finished(source, event):
+        if event.tool_name == "failing_tool":
+            finished_events.append(event)
+
+    tool_calling = ToolCalling(tool_name="failing_tool", arguments={})
+    tool_usage.use(calling=tool_calling, tool_string="Action: failing_tool")
+
+    assert error_received.wait(timeout=5), "Timeout waiting for error event"
+    crewai_event_bus.flush()
+
+    assert len(started_events) >= 1, "Expected at least one ToolUsageStartedEvent"
+    assert len(error_events) >= 1, "Expected at least one ToolUsageErrorEvent"
+    assert len(finished_events) == 0, (
+        "ToolUsageFinishedEvent should NOT be emitted after ToolUsageErrorEvent"
+    )

pyproject.toml

@@ -10,7 +10,7 @@ authors = [
 dev = [
     "ruff==0.14.7",
     "mypy==1.19.0",
-    "pre-commit==4.5.0",
+    "pre-commit==4.5.1",
     "bandit==1.9.2",
     "pytest==8.4.2",
     "pytest-asyncio==1.3.0",

uv.lock

@@ -41,7 +41,7 @@ dev = [
     { name = "bandit", specifier = "==1.9.2" },
     { name = "boto3-stubs", extras = ["bedrock-runtime"], specifier = "==1.40.54" },
     { name = "mypy", specifier = "==1.19.0" },
-    { name = "pre-commit", specifier = "==4.5.0" },
+    { name = "pre-commit", specifier = "==4.5.1" },
     { name = "pytest", specifier = "==8.4.2" },
     { name = "pytest-asyncio", specifier = "==1.3.0" },
     { name = "pytest-randomly", specifier = "==4.0.1" },
@@ -5346,7 +5346,7 @@ wheels = [
 
 [[package]]
 name = "pre-commit"
-version = "4.5.0"
+version = "4.5.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cfgv" },
@@ -5355,9 +5355,9 @@ dependencies = [
     { name = "pyyaml" },
     { name = "virtualenv" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/40/f1/6d86a29246dfd2e9b6237f0b5823717f60cad94d47ddc26afa916d21f525/pre_commit-4.5.1.tar.gz", hash = "sha256:eb545fcff725875197837263e977ea257a402056661f09dae08e4b149b030a61", size = 198232, upload-time = "2025-12-16T21:14:33.552Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/5d/19/fd3ef348460c80af7bb4669ea7926651d1f95c23ff2df18b9d24bab4f3fa/pre_commit-4.5.1-py2.py3-none-any.whl", hash = "sha256:3b3afd891e97337708c1674210f8eba659b52a38ea5f822ff142d10786221f77", size = 226437, upload-time = "2025-12-16T21:14:32.409Z" },
 ]
 
 [[package]]