chore: add tracing message suppression via ContextVar

Refactor agent executor to delegate human interactions to a provider: add messages and ask_for_human_input properties, implement _invoke_loop and _format_feedback_message, and replace the internal iterative/training feedback logic with a call to get_provider().handle_feedback.

Make LLMGuardrail kickoff coroutine-aware by detecting coroutines and running them via asyncio.run so both sync and async agents are supported.

Make telemetry more robust by safely handling missing task.output (use empty string) and returning early if span is None before setting attributes.

Improve serialization to detect circular references via an _ancestors set, propagate it through recursive calls, and pass exclude/max_depth/_current_depth consistently to prevent infinite recursion and produce stable serializable output.

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)
 
@@ -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 +1505,8 @@ 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)
-
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
-
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
+        provider = get_provider()
+        return provider.handle_feedback(formatted_answer, self)
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -1519,74 +1516,18 @@ 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/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 +1,304 @@
+"""Human input provider for HITL (Human-in-the-Loop) flows."""
+
+from __future__ import annotations
+
+from contextvars import ContextVar, Token
+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."""
+        ...
+
+
+@runtime_checkable
+class HumanInputProvider(Protocol):
+    """Protocol for human input handling.
+
+    Implementations handle the full feedback flow:
+    - Sync: prompt user, loop until satisfied
+    - Async: raise exception for external handling
+    """
+
+    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.
+
+        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.
+        """
+        ...
+
+    @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 synchronous human input via terminal."""
+
+    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)
+
+    @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
+
+    @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()
+
+
+_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)
@@ -2011,7 +2026,13 @@ 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/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,

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,
@@ -41,7 +42,12 @@ 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 +197,12 @@ 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 +217,51 @@ 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."""
+        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
+
+    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.
 
@@ -300,16 +355,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         """
         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."""
@@ -689,6 +734,7 @@ 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 +810,7 @@ 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 +839,20 @@ 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 = {
@@ -1319,17 +1366,8 @@ 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)
-
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
-
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
+        provider = get_provider()
+        return provider.handle_feedback(formatted_answer, self)
 
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
@@ -1339,101 +1377,6 @@ 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

@@ -45,6 +45,7 @@ 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,
@@ -2074,12 +2075,14 @@ 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
                         ]
@@ -2626,6 +2629,8 @@ 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 +31,7 @@ from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+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 +497,7 @@ 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 +538,7 @@ 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 +551,7 @@ 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(
@@ -566,8 +570,6 @@ 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 +581,8 @@ 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:
@@ -661,8 +665,6 @@ 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 +676,8 @@ 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:
@@ -741,6 +745,9 @@ class Task(BaseModel):
         finally:
             clear_task_files(self.id)
 
+    def _post_agent_execution(self, agent: BaseAgent) -> None:
+        pass
+
     def prompt(self) -> str:
         """Generates the task prompt with optional markdown formatting.
 
@@ -863,6 +870,11 @@ 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/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")
 
@@ -818,10 +824,13 @@ 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)
         if response.status_code == 404:

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/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/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/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/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"
+    )