Merge branch 'main' into codex/fix-codeql-alert-28

* fix(security): bump regex from 2024.9.11 to 2026.1.15

Address security vulnerability flagged in regex==2024.9.11

* bump mcp from 1.23.1 to 1.26.0

Address security vulnerability flagged in mcp==1.16.0 (resolved to 1.23.3)

lib/crewai/pyproject.toml

@@ -14,7 +14,7 @@ dependencies = [
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
-    "regex~=2024.9.11",
+    "regex~=2026.1.15",
     # Telemetry and Monitoring
     "opentelemetry-api~=1.34.0",
     "opentelemetry-sdk~=1.34.0",
@@ -36,7 +36,7 @@ dependencies = [
     "json5~=0.10.0",
     "portalocker~=2.7.0",
     "pydantic-settings~=2.10.1",
-    "mcp~=1.23.1",
+    "mcp~=1.26.0",
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
 ]

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

@@ -24,7 +24,6 @@ from pydantic import (
 )
 from typing_extensions import Self
 
-from crewai.agent.json_schema_converter import JSONSchemaConverter
 from crewai.agent.utils import (
     ahandle_knowledge_retrieval,
     apply_training_data,
@@ -1179,7 +1178,6 @@ class Agent(BaseAgent):
             tools = []
             for tool_def in tools_list:
                 tool_name = tool_def.get("name", "")
-                original_tool_name = tool_def.get("original_name", tool_name)
                 if not tool_name:
                     continue
 
@@ -1201,7 +1199,6 @@ class Agent(BaseAgent):
                         tool_name=tool_name,
                         tool_schema=tool_schema,
                         server_name=server_name,
-                        original_tool_name=original_tool_name,
                     )
                     tools.append(native_tool)
                 except Exception as e:
@@ -1216,63 +1213,26 @@ class Agent(BaseAgent):
             raise RuntimeError(f"Failed to get native MCP tools: {e}") from e
 
     def _get_amp_mcp_tools(self, amp_ref: str) -> list[BaseTool]:
-        """Get tools from CrewAI AMP MCP via crewai-oauth service.
-
-        Fetches MCP server configuration with tokens injected from crewai-oauth,
-        then uses _get_native_mcp_tools to connect and discover tools.
-        """
-        # Parse: "crewai-amp:mcp-slug" or "crewai-amp:mcp-slug#tool_name"
+        """Get tools from CrewAI AMP MCP marketplace."""
+        # Parse: "crewai-amp:mcp-name" or "crewai-amp:mcp-name#tool_name"
         amp_part = amp_ref.replace("crewai-amp:", "")
         if "#" in amp_part:
-            mcp_slug, specific_tool = amp_part.split("#", 1)
+            mcp_name, specific_tool = amp_part.split("#", 1)
         else:
-            mcp_slug, specific_tool = amp_part, None
+            mcp_name, specific_tool = amp_part, None
 
-        # Fetch MCP config from crewai-oauth (with tokens injected)
-        mcp_config_dict = self._fetch_amp_mcp_config(mcp_slug)
+        # Call AMP API to get MCP server URLs
+        mcp_servers = self._fetch_amp_mcp_servers(mcp_name)
 
-        if not mcp_config_dict:
-            self._logger.log(
-                "warning", f"Failed to fetch MCP config for '{mcp_slug}' from crewai-oauth"
-            )
-            return []
+        tools = []
+        for server_config in mcp_servers:
+            server_ref = server_config["url"]
+            if specific_tool:
+                server_ref += f"#{specific_tool}"
+            server_tools = self._get_external_mcp_tools(server_ref)
+            tools.extend(server_tools)
 
-        # Convert dict to MCPServerConfig (MCPServerHTTP or MCPServerSSE)
-        config_type = mcp_config_dict.get("type", "http")
-
-        if config_type == "sse":
-            mcp_config = MCPServerSSE(
-                url=mcp_config_dict["url"],
-                headers=mcp_config_dict.get("headers"),
-                cache_tools_list=mcp_config_dict.get("cache_tools_list", False),
-            )
-        else:
-            mcp_config = MCPServerHTTP(
-                url=mcp_config_dict["url"],
-                headers=mcp_config_dict.get("headers"),
-                streamable=mcp_config_dict.get("streamable", True),
-                cache_tools_list=mcp_config_dict.get("cache_tools_list", False),
-            )
-
-        # Apply tool filter if specific tool requested
-        if specific_tool:
-            from crewai.mcp.filters import create_static_tool_filter
-
-            mcp_config.tool_filter = create_static_tool_filter(
-                allowed_tool_names=[specific_tool]
-            )
-
-        # Use native MCP tools to connect and discover tools
-        try:
-            tools, client = self._get_native_mcp_tools(mcp_config)
-            if client:
-                self._mcp_clients.append(client)
-            return tools
-        except Exception as e:
-            self._logger.log(
-                "warning", f"Failed to get MCP tools from '{mcp_slug}': {e}"
-            )
-            return []
+        return tools
 
     @staticmethod
     def _extract_server_name(server_url: str) -> str:
@@ -1429,9 +1389,6 @@ class Agent(BaseAgent):
                     }
                 return schemas
 
-    # Shared JSON Schema converter instance
-    _schema_converter: JSONSchemaConverter = JSONSchemaConverter()
-
     def _json_schema_to_pydantic(
         self, tool_name: str, json_schema: dict[str, Any]
     ) -> type:
@@ -1444,62 +1401,77 @@ class Agent(BaseAgent):
         Returns:
             Pydantic BaseModel class
         """
-        return self._schema_converter.json_schema_to_pydantic(tool_name, json_schema)
+        from pydantic import Field, create_model
 
-    def _fetch_amp_mcp_config(self, mcp_slug: str) -> dict[str, Any] | None:
-        """Fetch MCP server configuration from crewai-oauth service.
+        properties = json_schema.get("properties", {})
+        required_fields = json_schema.get("required", [])
 
-        Returns MCPServerConfig dict with tokens injected, ready for use with
-        _get_native_mcp_tools.
+        field_definitions: dict[str, Any] = {}
 
-        Environment variables:
-            CREWAI_OAUTH_URL: Base URL of crewai-oauth service
-            CREWAI_OAUTH_API_KEY: API key for authenticating with crewai-oauth
+        for field_name, field_schema in properties.items():
+            field_type = self._json_type_to_python(field_schema)
+            field_description = field_schema.get("description", "")
+
+            is_required = field_name in required_fields
+
+            if is_required:
+                field_definitions[field_name] = (
+                    field_type,
+                    Field(..., description=field_description),
+                )
+            else:
+                field_definitions[field_name] = (
+                    field_type | None,
+                    Field(default=None, description=field_description),
+                )
+
+        model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
+        return create_model(model_name, **field_definitions)  # type: ignore[no-any-return]
+
+    def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
+        """Convert JSON Schema type to Python type.
 
         Args:
-            mcp_slug: The MCP server slug (e.g., "notion-mcp-abc123")
+            field_schema: JSON Schema field definition
 
         Returns:
-            Dict with type, url, headers, streamable, cache_tools_list, or None if failed.
+            Python type
         """
-        import os
 
-        import requests
+        json_type = field_schema.get("type")
 
-        try:
-            endpoint = f"http://localhost:8787/mcps/{mcp_slug}/config"
-            response = requests.get(
-                endpoint,
-                headers={"Authorization": "Bearer 6b327f9ebe62726590f8de8f624cf018ad4765fecb7373f9db475a940ad546d0"},
-                timeout=30,
-            )
+        if "anyOf" in field_schema:
+            types: list[type] = []
+            for option in field_schema["anyOf"]:
+                if "const" in option:
+                    types.append(str)
+                else:
+                    types.append(self._json_type_to_python(option))
+            unique_types = list(set(types))
+            if len(unique_types) > 1:
+                result: Any = unique_types[0]
+                for t in unique_types[1:]:
+                    result = result | t
+                return result  # type: ignore[no-any-return]
+            return unique_types[0]
 
-            if response.status_code == 200:
-                return response.json()
-            elif response.status_code == 400:
-                error_data = response.json()
-                self._logger.log(
-                    "warning",
-                    f"MCP '{mcp_slug}' is not connected: {error_data.get('error_description', 'Unknown error')}",
-                )
-                return None
-            elif response.status_code == 404:
-                self._logger.log(
-                    "warning", f"MCP server '{mcp_slug}' not found in crewai-oauth"
-                )
-                return None
-            else:
-                self._logger.log(
-                    "warning",
-                    f"Failed to fetch MCP config from crewai-oauth: HTTP {response.status_code}",
-                )
-                return None
+        type_mapping: dict[str | None, type] = {
+            "string": str,
+            "number": float,
+            "integer": int,
+            "boolean": bool,
+            "array": list,
+            "object": dict,
+        }
 
-        except requests.exceptions.RequestException as e:
-            self._logger.log(
-                "warning", f"Failed to connect to crewai-oauth: {e}"
-            )
-            return None
+        return type_mapping.get(json_type, Any)
+
+    @staticmethod
+    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]:
+        """Fetch MCP server configurations from CrewAI AMP API."""
+        # TODO: Implement AMP API call to "integrations/mcps" endpoint
+        # Should return list of server configs with URLs
+        return []
 
     @staticmethod
     def get_multimodal_tools() -> Sequence[BaseTool]:

lib/crewai/src/crewai/agent/json_schema_converter.py

@@ -1,399 +0,0 @@
-from typing import Any, Literal, Type, Union, get_args
-from pydantic import Field, create_model
-from pydantic.fields import FieldInfo
-import datetime
-import uuid
-
-
-class JSONSchemaConverter:
-    """Converts JSON Schema definitions to Python/Pydantic types."""
-
-    def json_schema_to_pydantic(
-        self, tool_name: str, json_schema: dict[str, Any]
-    ) -> Type[Any]:
-        """Convert JSON Schema to Pydantic model for tool arguments.
-
-        Args:
-            tool_name: Name of the tool (used for model naming)
-            json_schema: JSON Schema dict with 'properties', 'required', etc.
-
-        Returns:
-            Pydantic BaseModel class
-        """
-        properties = json_schema.get("properties", {})
-        required_fields = json_schema.get("required", [])
-
-        model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
-        return self._create_pydantic_model(model_name, properties, required_fields)
-
-    def _json_type_to_python(
-        self, field_schema: dict[str, Any], field_name: str = "Field"
-    ) -> Type[Any]:
-        """Convert JSON Schema type to Python type, handling nested structures.
-
-        Args:
-            field_schema: JSON Schema field definition
-            field_name: Name of the field (used for nested model naming)
-
-        Returns:
-            Python type (may be a dynamically created Pydantic model for objects/arrays)
-        """
-        if not field_schema:
-            return Any
-
-        # Handle $ref if needed
-        if "$ref" in field_schema:
-            # You might want to implement reference resolution here
-            return Any
-
-        # Handle enum constraint - create Literal type
-        if "enum" in field_schema:
-            return self._handle_enum(field_schema)
-
-        # Handle different schema constructs in order of precedence
-        if "allOf" in field_schema:
-            return self._handle_allof(field_schema, field_name)
-
-        if "anyOf" in field_schema or "oneOf" in field_schema:
-            return self._handle_union_schemas(field_schema, field_name)
-
-        json_type = field_schema.get("type")
-
-        if isinstance(json_type, list):
-            return self._handle_type_union(json_type)
-
-        if json_type == "array":
-            return self._handle_array_type(field_schema, field_name)
-
-        if json_type == "object":
-            return self._handle_object_type(field_schema, field_name)
-
-        # Handle format for string types
-        if json_type == "string" and "format" in field_schema:
-            return self._get_formatted_type(field_schema["format"])
-
-        return self._get_simple_type(json_type)
-
-    def _get_formatted_type(self, format_type: str) -> Type[Any]:
-        """Get Python type for JSON Schema format constraint.
-
-        Args:
-            format_type: JSON Schema format string (date, date-time, email, etc.)
-
-        Returns:
-            Appropriate Python type for the format
-        """
-        format_mapping: dict[str, Type[Any]] = {
-            "date": datetime.date,
-            "date-time": datetime.datetime,
-            "time": datetime.time,
-            "email": str,  # Could use EmailStr from pydantic
-            "uri": str,
-            "uuid": str,  # Could use UUID
-            "hostname": str,
-            "ipv4": str,
-            "ipv6": str,
-        }
-        return format_mapping.get(format_type, str)
-
-    def _handle_enum(self, field_schema: dict[str, Any]) -> Type[Any]:
-        """Handle enum constraint by creating a Literal type.
-
-        Args:
-            field_schema: Schema containing enum values
-
-        Returns:
-            Literal type with enum values
-        """
-        enum_values = field_schema.get("enum", [])
-
-        if not enum_values:
-            return str
-
-        # Filter out None values for the Literal type
-        non_null_values = [v for v in enum_values if v is not None]
-
-        if not non_null_values:
-            return type(None)
-
-        # Create Literal type with enum values
-        # For strings, create Literal["value1", "value2", ...]
-        if all(isinstance(v, str) for v in non_null_values):
-            literal_type = Literal[tuple(non_null_values)]  # type: ignore[valid-type]
-            # If null is in enum, make it optional
-            if None in enum_values:
-                return literal_type | None  # type: ignore[return-value]
-            return literal_type  # type: ignore[return-value]
-
-        # For mixed types or non-strings, fall back to the base type
-        json_type = field_schema.get("type", "string")
-        return self._get_simple_type(json_type)
-
-    def _handle_allof(
-        self, field_schema: dict[str, Any], field_name: str
-    ) -> Type[Any]:
-        """Handle allOf schema composition by merging all schemas.
-
-        Args:
-            field_schema: Schema containing allOf
-            field_name: Name for the generated model
-
-        Returns:
-            Merged Pydantic model or basic type
-        """
-        merged_properties: dict[str, Any] = {}
-        merged_required: list[str] = []
-        found_type: str | None = None
-
-        for sub_schema in field_schema["allOf"]:
-            # Collect type information
-            if sub_schema.get("type"):
-                found_type = sub_schema.get("type")
-
-            # Merge properties
-            if sub_schema.get("properties"):
-                merged_properties.update(sub_schema["properties"])
-
-            # Merge required fields
-            if sub_schema.get("required"):
-                merged_required.extend(sub_schema["required"])
-
-            # Handle nested anyOf/oneOf - merge properties from all variants
-            for union_key in ("anyOf", "oneOf"):
-                if union_key in sub_schema:
-                    for variant in sub_schema[union_key]:
-                        if variant.get("properties"):
-                            # Merge variant properties (will be optional)
-                            for prop_name, prop_schema in variant["properties"].items():
-                                if prop_name not in merged_properties:
-                                    merged_properties[prop_name] = prop_schema
-
-        # If we found properties, create a merged object model
-        if merged_properties:
-            return self._create_pydantic_model(
-                field_name, merged_properties, merged_required
-            )
-
-        # Fallback: return the found type or dict
-        if found_type == "object":
-            return dict
-        elif found_type == "array":
-            return list
-        return dict  # Default for complex allOf
-
-    def _handle_union_schemas(
-        self, field_schema: dict[str, Any], field_name: str
-    ) -> Type[Any]:
-        """Handle anyOf/oneOf union schemas.
-
-        Args:
-            field_schema: Schema containing anyOf or oneOf
-            field_name: Name for nested types
-
-        Returns:
-            Union type combining all options
-        """
-        key = "anyOf" if "anyOf" in field_schema else "oneOf"
-        types: list[Type[Any]] = []
-
-        for option in field_schema[key]:
-            if "const" in option:
-                # For const values, use string type
-                # Could use Literal[option["const"]] for more precision
-                types.append(str)
-            else:
-                types.append(self._json_type_to_python(option, field_name))
-
-        return self._build_union_type(types)
-
-    def _handle_type_union(self, json_types: list[str]) -> Type[Any]:
-        """Handle union types from type arrays.
-
-        Args:
-            json_types: List of JSON Schema type strings
-
-        Returns:
-            Union of corresponding Python types
-        """
-        type_mapping: dict[str, Type[Any]] = {
-            "string": str,
-            "number": float,
-            "integer": int,
-            "boolean": bool,
-            "null": type(None),
-            "array": list,
-            "object": dict,
-        }
-
-        types = [type_mapping.get(t, Any) for t in json_types]
-        return self._build_union_type(types)
-
-    def _handle_array_type(
-        self, field_schema: dict[str, Any], field_name: str
-    ) -> Type[Any]:
-        """Handle array type with typed items.
-
-        Args:
-            field_schema: Schema with type="array"
-            field_name: Name for item types
-
-        Returns:
-            list or list[ItemType]
-        """
-        items_schema = field_schema.get("items")
-        if items_schema:
-            item_type = self._json_type_to_python(items_schema, f"{field_name}Item")
-            return list[item_type]  # type: ignore[valid-type]
-        return list
-
-    def _handle_object_type(
-        self, field_schema: dict[str, Any], field_name: str
-    ) -> Type[Any]:
-        """Handle object type with properties.
-
-        Args:
-            field_schema: Schema with type="object"
-            field_name: Name for the generated model
-
-        Returns:
-            Pydantic model or dict
-        """
-        properties = field_schema.get("properties")
-        if properties:
-            required_fields = field_schema.get("required", [])
-            return self._create_pydantic_model(field_name, properties, required_fields)
-
-        # Object without properties (e.g., additionalProperties only)
-        return dict
-
-    def _create_pydantic_model(
-        self,
-        field_name: str,
-        properties: dict[str, Any],
-        required_fields: list[str],
-    ) -> Type[Any]:
-        """Create a Pydantic model from properties.
-
-        Args:
-            field_name: Base name for the model
-            properties: Property schemas
-            required_fields: List of required property names
-
-        Returns:
-            Dynamically created Pydantic model
-        """
-        model_name = f"Generated_{field_name}_{uuid.uuid4().hex[:8]}"
-        field_definitions: dict[str, Any] = {}
-
-        for prop_name, prop_schema in properties.items():
-            prop_type = self._json_type_to_python(prop_schema, prop_name.title())
-            prop_description = self._build_field_description(prop_schema)
-            is_required = prop_name in required_fields
-
-            if is_required:
-                field_definitions[prop_name] = (
-                    prop_type,
-                    Field(..., description=prop_description),
-                )
-            else:
-                field_definitions[prop_name] = (
-                    prop_type | None,
-                    Field(default=None, description=prop_description),
-                )
-
-        return create_model(model_name, **field_definitions)  # type: ignore[return-value]
-
-    def _build_field_description(self, prop_schema: dict[str, Any]) -> str:
-        """Build a comprehensive field description including constraints.
-
-        Args:
-            prop_schema: Property schema with description and constraints
-
-        Returns:
-            Enhanced description with format, enum, and other constraints
-        """
-        parts: list[str] = []
-
-        # Start with the original description
-        description = prop_schema.get("description", "")
-        if description:
-            parts.append(description)
-
-        # Add format constraint
-        format_type = prop_schema.get("format")
-        if format_type:
-            parts.append(f"Format: {format_type}")
-
-        # Add enum constraint (if not already handled by Literal type)
-        enum_values = prop_schema.get("enum")
-        if enum_values:
-            enum_str = ", ".join(repr(v) for v in enum_values)
-            parts.append(f"Allowed values: [{enum_str}]")
-
-        # Add pattern constraint
-        pattern = prop_schema.get("pattern")
-        if pattern:
-            parts.append(f"Pattern: {pattern}")
-
-        # Add min/max constraints
-        minimum = prop_schema.get("minimum")
-        maximum = prop_schema.get("maximum")
-        if minimum is not None:
-            parts.append(f"Minimum: {minimum}")
-        if maximum is not None:
-            parts.append(f"Maximum: {maximum}")
-
-        min_length = prop_schema.get("minLength")
-        max_length = prop_schema.get("maxLength")
-        if min_length is not None:
-            parts.append(f"Min length: {min_length}")
-        if max_length is not None:
-            parts.append(f"Max length: {max_length}")
-
-        # Add examples if available
-        examples = prop_schema.get("examples")
-        if examples:
-            examples_str = ", ".join(repr(e) for e in examples[:3])  # Limit to 3
-            parts.append(f"Examples: {examples_str}")
-
-        return ". ".join(parts) if parts else ""
-
-    def _get_simple_type(self, json_type: str | None) -> Type[Any]:
-        """Map simple JSON Schema types to Python types.
-
-        Args:
-            json_type: JSON Schema type string
-
-        Returns:
-            Corresponding Python type
-        """
-        simple_type_mapping: dict[str | None, Type[Any]] = {
-            "string": str,
-            "number": float,
-            "integer": int,
-            "boolean": bool,
-            "null": type(None),
-        }
-
-        return simple_type_mapping.get(json_type, Any)
-
-    def _build_union_type(self, types: list[Type[Any]]) -> Type[Any]:
-        """Build a union type from a list of types.
-
-        Args:
-            types: List of Python types to combine
-
-        Returns:
-            Union type or single type if only one unique type
-        """
-        # Remove duplicates while preserving order
-        unique_types = list(dict.fromkeys(types))
-
-        if len(unique_types) == 1:
-            return unique_types[0]
-
-        # Build union using | operator
-        result = unique_types[0]
-        for t in unique_types[1:]:
-            result = result | t
-        return result  # type: ignore[no-any-return]

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/crew_agent_executor.py

@@ -814,6 +814,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 agent_key=agent_key,
             ),
         )
+        error_event_emitted = False
 
         track_delegation_if_needed(func_name, args_dict, self.task)
 
@@ -896,6 +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."
@@ -923,20 +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 = {
@@ -1007,7 +1009,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             raise
 
         if self.ask_for_human_input:
-            formatted_answer = self._handle_human_feedback(formatted_answer)
+            formatted_answer = await self._ahandle_human_feedback(formatted_answer)
 
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
@@ -1506,6 +1508,20 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         provider = get_provider()
         return provider.handle_feedback(formatted_answer, self)
 
+    async def _ahandle_human_feedback(
+        self, formatted_answer: AgentFinish
+    ) -> AgentFinish:
+        """Process human feedback asynchronously via the configured provider.
+
+        Args:
+            formatted_answer: Initial agent result.
+
+        Returns:
+            Final answer after feedback.
+        """
+        provider = get_provider()
+        return await provider.handle_feedback_async(formatted_answer, self)
+
     def _is_training_mode(self) -> bool:
         """Check if training mode is active.
 

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

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

lib/crewai/src/crewai/context.py

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

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

@@ -2,7 +2,9 @@
 
 from __future__ import annotations
 
+import asyncio
 from contextvars import ContextVar, Token
+import sys
 from typing import TYPE_CHECKING, Protocol, runtime_checkable
 
 
@@ -46,13 +48,21 @@ class ExecutorContext(Protocol):
         ...
 
 
+class AsyncExecutorContext(ExecutorContext, Protocol):
+    """Extended context for executors that support async invocation."""
+
+    async def _ainvoke_loop(self) -> AgentFinish:
+        """Invoke the agent loop asynchronously and return the result."""
+        ...
+
+
 @runtime_checkable
 class HumanInputProvider(Protocol):
     """Protocol for human input handling.
 
     Implementations handle the full feedback flow:
     - Sync: prompt user, loop until satisfied
-    - Async: raise exception for external handling
+    - Async: use non-blocking I/O and async invoke loop
     """
 
     def setup_messages(self, context: ExecutorContext) -> bool:
@@ -86,7 +96,7 @@ class HumanInputProvider(Protocol):
         formatted_answer: AgentFinish,
         context: ExecutorContext,
     ) -> AgentFinish:
-        """Handle the full human feedback flow.
+        """Handle the full human feedback flow synchronously.
 
         Args:
             formatted_answer: The agent's current answer.
@@ -100,6 +110,25 @@ class HumanInputProvider(Protocol):
         """
         ...
 
+    async def handle_feedback_async(
+        self,
+        formatted_answer: AgentFinish,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Handle the full human feedback flow asynchronously.
+
+        Uses non-blocking I/O for user prompts and async invoke loop
+        for agent re-execution.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Async executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+        """
+        ...
+
     @staticmethod
     def _get_output_string(answer: AgentFinish) -> str:
         """Extract output string from answer.
@@ -116,7 +145,7 @@ class HumanInputProvider(Protocol):
 
 
 class SyncHumanInputProvider(HumanInputProvider):
-    """Default synchronous human input via terminal."""
+    """Default human input provider with sync and async support."""
 
     def setup_messages(self, context: ExecutorContext) -> bool:
         """Use standard message setup.
@@ -157,6 +186,33 @@ class SyncHumanInputProvider(HumanInputProvider):
 
         return self._handle_regular_feedback(formatted_answer, feedback, context)
 
+    async def handle_feedback_async(
+        self,
+        formatted_answer: AgentFinish,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Handle feedback asynchronously without blocking the event loop.
+
+        Args:
+            formatted_answer: The agent's current answer.
+            context: Async executor context for callbacks.
+
+        Returns:
+            The final answer after feedback processing.
+        """
+        feedback = await self._prompt_input_async(context.crew)
+
+        if context._is_training_mode():
+            return await self._handle_training_feedback_async(
+                formatted_answer, feedback, context
+            )
+
+        return await self._handle_regular_feedback_async(
+            formatted_answer, feedback, context
+        )
+
+    # ── Sync helpers ──────────────────────────────────────────────────
+
     @staticmethod
     def _handle_training_feedback(
         initial_answer: AgentFinish,
@@ -209,6 +265,62 @@ class SyncHumanInputProvider(HumanInputProvider):
 
         return answer
 
+    # ── Async helpers ─────────────────────────────────────────────────
+
+    @staticmethod
+    async def _handle_training_feedback_async(
+        initial_answer: AgentFinish,
+        feedback: str,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Process training feedback asynchronously (single iteration).
+
+        Args:
+            initial_answer: The agent's initial answer.
+            feedback: Human feedback string.
+            context: Async executor context for callbacks.
+
+        Returns:
+            Improved answer after processing feedback.
+        """
+        context._handle_crew_training_output(initial_answer, feedback)
+        context.messages.append(context._format_feedback_message(feedback))
+        improved_answer = await context._ainvoke_loop()
+        context._handle_crew_training_output(improved_answer)
+        context.ask_for_human_input = False
+        return improved_answer
+
+    async def _handle_regular_feedback_async(
+        self,
+        current_answer: AgentFinish,
+        initial_feedback: str,
+        context: AsyncExecutorContext,
+    ) -> AgentFinish:
+        """Process regular feedback with async iteration loop.
+
+        Args:
+            current_answer: The agent's current answer.
+            initial_feedback: Initial human feedback string.
+            context: Async executor context for callbacks.
+
+        Returns:
+            Final answer after all feedback iterations.
+        """
+        feedback = initial_feedback
+        answer = current_answer
+
+        while context.ask_for_human_input:
+            if feedback.strip() == "":
+                context.ask_for_human_input = False
+            else:
+                context.messages.append(context._format_feedback_message(feedback))
+                answer = await context._ainvoke_loop()
+                feedback = await self._prompt_input_async(context.crew)
+
+        return answer
+
+    # ── I/O ───────────────────────────────────────────────────────────
+
     @staticmethod
     def _prompt_input(crew: Crew | None) -> str:
         """Show rich panel and prompt for input.
@@ -262,6 +374,79 @@ class SyncHumanInputProvider(HumanInputProvider):
         finally:
             formatter.resume_live_updates()
 
+    @staticmethod
+    async def _prompt_input_async(crew: Crew | None) -> str:
+        """Show rich panel and prompt for input without blocking the event loop.
+
+        Args:
+            crew: The crew instance for context.
+
+        Returns:
+            User input string from terminal.
+        """
+        from rich.panel import Panel
+        from rich.text import Text
+
+        from crewai.events.event_listener import event_listener
+
+        formatter = event_listener.formatter
+        formatter.pause_live_updates()
+
+        try:
+            if crew and getattr(crew, "_train", False):
+                prompt_text = (
+                    "TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
+                    "This will be used to train better versions of the agent.\n"
+                    "Please provide detailed feedback about the result quality and reasoning process."
+                )
+                title = "🎓 Training Feedback Required"
+            else:
+                prompt_text = (
+                    "Provide feedback on the Final Result above.\n\n"
+                    "• If you are happy with the result, simply hit Enter without typing anything.\n"
+                    "• Otherwise, provide specific improvement requests.\n"
+                    "• You can provide multiple rounds of feedback until satisfied."
+                )
+                title = "💬 Human Feedback Required"
+
+            content = Text()
+            content.append(prompt_text, style="yellow")
+
+            prompt_panel = Panel(
+                content,
+                title=title,
+                border_style="yellow",
+                padding=(1, 2),
+            )
+            formatter.console.print(prompt_panel)
+
+            response = await _async_readline()
+            if response.strip() != "":
+                formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
+            return response
+        finally:
+            formatter.resume_live_updates()
+
+
+async def _async_readline() -> str:
+    """Read a line from stdin using the event loop's native I/O.
+
+    Falls back to asyncio.to_thread on platforms where piping stdin
+    is unsupported.
+
+    Returns:
+        The line read from stdin, with trailing newline stripped.
+    """
+    loop = asyncio.get_running_loop()
+    try:
+        reader = asyncio.StreamReader()
+        protocol = asyncio.StreamReaderProtocol(reader)
+        await loop.connect_read_pipe(lambda: protocol, sys.stdin)
+        raw = await reader.readline()
+        return raw.decode().rstrip("\n")
+    except (OSError, NotImplementedError, ValueError):
+        return await asyncio.to_thread(input)
+
 
 _provider: ContextVar[HumanInputProvider | None] = ContextVar(
     "human_input_provider",

lib/crewai/src/crewai/crew.py

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -4,6 +4,7 @@ import json
 import logging
 import os
 from typing import TYPE_CHECKING, Any, TypedDict
+from urllib.parse import urlparse
 
 from pydantic import BaseModel
 from typing_extensions import Self
@@ -175,11 +176,51 @@ class AzureCompletion(BaseLLM):
             prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
         )
 
-        self.is_azure_openai_endpoint = (
-            "openai.azure.com" in self.endpoint
-            and "/openai/deployments/" in self.endpoint
+        self.is_azure_openai_endpoint = self._is_azure_openai_deployment_endpoint(
+            self.endpoint
         )
 
+    @staticmethod
+    def _parse_endpoint_url(endpoint: str):
+        parsed_endpoint = urlparse(endpoint)
+        if parsed_endpoint.hostname:
+            return parsed_endpoint
+
+        # Support endpoint values without a URL scheme.
+        return urlparse(f"https://{endpoint}")
+
+    @staticmethod
+    def _is_azure_openai_hostname(endpoint: str) -> bool:
+        parsed_endpoint = AzureCompletion._parse_endpoint_url(endpoint)
+        hostname = parsed_endpoint.hostname or ""
+        labels = [label for label in hostname.lower().split(".") if label]
+
+        return len(labels) >= 3 and labels[-3:] == ["openai", "azure", "com"]
+
+    @staticmethod
+    def _get_endpoint_path_segments(endpoint: str) -> list[str]:
+        parsed_endpoint = AzureCompletion._parse_endpoint_url(endpoint)
+        return [segment for segment in parsed_endpoint.path.split("/") if segment]
+
+    @staticmethod
+    def _is_azure_openai_deployment_endpoint(endpoint: str) -> bool:
+        if not AzureCompletion._is_azure_openai_hostname(endpoint):
+            return False
+
+        path_segments = AzureCompletion._get_endpoint_path_segments(endpoint)
+        return len(path_segments) >= 3 and path_segments[:2] == [
+            "openai",
+            "deployments",
+        ]
+
+    @staticmethod
+    def _is_azure_openai_deployments_collection(endpoint: str) -> bool:
+        if not AzureCompletion._is_azure_openai_hostname(endpoint):
+            return False
+
+        path_segments = AzureCompletion._get_endpoint_path_segments(endpoint)
+        return path_segments == ["openai", "deployments"]
+
     @staticmethod
     def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
         """Validate and fix Azure endpoint URL format.
@@ -194,10 +235,12 @@ class AzureCompletion(BaseLLM):
         Returns:
             Validated and potentially corrected endpoint URL
         """
-        if "openai.azure.com" in endpoint and "/openai/deployments/" not in endpoint:
+        if AzureCompletion._is_azure_openai_hostname(
+            endpoint
+        ) and not AzureCompletion._is_azure_openai_deployment_endpoint(endpoint):
             endpoint = endpoint.rstrip("/")
 
-            if not endpoint.endswith("/openai/deployments"):
+            if not AzureCompletion._is_azure_openai_deployments_collection(endpoint):
                 deployment_name = model.replace("azure/", "")
                 endpoint = f"{endpoint}/openai/deployments/{deployment_name}"
                 logging.info(f"Constructed Azure OpenAI endpoint URL: {endpoint}")

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

@@ -420,7 +420,6 @@ class MCPClient:
         return [
             {
                 "name": sanitize_tool_name(tool.name),
-                "original_name": tool.name,
                 "description": getattr(tool, "description", ""),
                 "inputSchema": getattr(tool, "inputSchema", {}),
             }

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.context import reset_current_task_id, set_current_task_id
 from crewai.core.providers.content_processor import process_content
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.task_events import (
@@ -561,6 +562,7 @@ class Task(BaseModel):
         tools: list[Any] | None,
     ) -> TaskOutput:
         """Run the core execution logic of the task asynchronously."""
+        task_id_token = set_current_task_id(str(self.id))
         self._store_input_files()
         try:
             agent = agent or self.agent
@@ -648,6 +650,7 @@ class Task(BaseModel):
             raise e  # Re-raise the exception after emitting the event
         finally:
             clear_task_files(self.id)
+            reset_current_task_id(task_id_token)
 
     def _execute_core(
         self,
@@ -656,6 +659,7 @@ class Task(BaseModel):
         tools: list[Any] | None,
     ) -> TaskOutput:
         """Run the core execution logic of the task."""
+        task_id_token = set_current_task_id(str(self.id))
         self._store_input_files()
         try:
             agent = agent or self.agent
@@ -744,6 +748,7 @@ class Task(BaseModel):
             raise e  # Re-raise the exception after emitting the event
         finally:
             clear_task_files(self.id)
+            reset_current_task_id(task_id_token)
 
     def _post_agent_execution(self, agent: BaseAgent) -> None:
         pass

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/mcp_native_tool.py

@@ -27,16 +27,14 @@ class MCPNativeTool(BaseTool):
         tool_name: str,
         tool_schema: dict[str, Any],
         server_name: str,
-        original_tool_name: str | None = None,
     ) -> None:
         """Initialize native MCP tool.
 
         Args:
             mcp_client: MCPClient instance with active session.
-            tool_name: Name of the tool (may be prefixed).
+            tool_name: Original name of the tool on the MCP server.
             tool_schema: Schema information for the tool.
             server_name: Name of the MCP server for prefixing.
-            original_tool_name: Original name of the tool on the MCP server.
         """
         # Create tool name with server prefix to avoid conflicts
         prefixed_name = f"{server_name}_{tool_name}"
@@ -59,7 +57,7 @@ class MCPNativeTool(BaseTool):
 
         # Set instance attributes after super().__init__
         self._mcp_client = mcp_client
-        self._original_tool_name = original_tool_name or tool_name
+        self._original_tool_name = tool_name
         self._server_name = server_name
         # self._logger = logging.getLogger(__name__)
 

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

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

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

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

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

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

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

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

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

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

lib/crewai/src/crewai/utilities/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_lite_agent.py

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

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

@@ -1,98 +1,227 @@
 interactions:
 - request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour personal goal is: Test goal\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: create_issue\nTool Arguments: {''title'': {''description'': ''Issue title'', ''type'': ''str''}, ''body'': {''description'': ''Issue body'', ''type'': ''Union[str, NoneType]''}}\nTool Description: Create a GitHub issue\nDetailed Parameter Structure:\nObject with properties:\n  - title: Issue title (required)\n  - body: Issue body (optional)\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [create_issue], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information
-      is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```"}, {"role": "user", "content": "Create a GitHub issue"}], "model": "gpt-3.5-turbo", "stream": false}'
+    body: '{"messages":[{"role":"system","content":"You are Test Agent. Test backstory\nYour
+      personal goal is: Test goal"},{"role":"user","content":"\nCurrent Task: Create
+      a GitHub issue"}],"model":"gpt-3.5-turbo","tool_choice":"auto","tools":[{"type":"function","function":{"name":"create_issue","description":"Create
+      a GitHub issue","strict":true,"parameters":{"additionalProperties":false,"properties":{"title":{"description":"Issue
+      title","title":"Title","type":"string"},"body":{"default":null,"description":"Issue
+      body","title":"Body","type":"string"}},"required":["title","body"],"type":"object"}}}]}'
     headers:
+      User-Agent:
+      - X-USER-AGENT-XXX
       accept:
       - application/json
       accept-encoding:
-      - gzip, deflate
+      - ACCEPT-ENCODING-XXX
+      authorization:
+      - AUTHORIZATION-XXX
       connection:
       - keep-alive
       content-length:
-      - '1233'
+      - '596'
       content-type:
       - application/json
       host:
       - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.109.1
       x-stainless-arch:
-      - arm64
+      - X-STAINLESS-ARCH-XXX
       x-stainless-async:
       - 'false'
       x-stainless-lang:
       - python
       x-stainless-os:
-      - MacOS
+      - X-STAINLESS-OS-XXX
       x-stainless-package-version:
-      - 1.109.1
+      - 1.83.0
       x-stainless-read-timeout:
-      - '600'
+      - X-STAINLESS-READ-TIMEOUT-XXX
       x-stainless-retry-count:
       - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
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+        \"assistant\",\n        \"content\": \"The attempt to use the failing tool
+        resulted in an error, as expected since it is designed to always fail. If
+        there's anything else you would like to calculate or explore, please let me
+        know!\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n
+        \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
+        \ \"usage\": {\n    \"prompt_tokens\": 93,\n    \"completion_tokens\": 40,\n
+        \   \"total_tokens\": 133,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+        0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
         {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
         0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-        \"default\",\n  \"system_fingerprint\": \"fp_c4585b5b9c\"\n}\n"
+        \"default\",\n  \"system_fingerprint\": \"fp_6c0d1490cb\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -182,7 +179,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Thu, 22 Jan 2026 20:40:55 GMT
+      - Fri, 06 Feb 2026 18:41:26 GMT
       Server:
       - cloudflare
       Strict-Transport-Security:
@@ -200,13 +197,11 @@ interactions:
       openai-organization:
       - OPENAI-ORG-XXX
       openai-processing-ms:
-      - '420'
+      - '776'
       openai-project:
       - OPENAI-PROJECT-XXX
       openai-version:
       - '2020-10-01'
-      x-envoy-upstream-service-time:
-      - '436'
       x-openai-proxy-wasm:
       - v0.1
       x-ratelimit-limit-requests:

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

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

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

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

lib/crewai/tests/cli/test_plus_api.py

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

lib/crewai/tests/events/test_event_context.py

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

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

@@ -958,6 +958,34 @@ def test_azure_endpoint_detection_flags():
         assert llm_other.is_azure_openai_endpoint == False
 
 
+def test_azure_endpoint_detection_ignores_spoofed_urls():
+    """
+    Test that endpoint detection does not trust spoofed host/path substrings
+    """
+    with patch.dict(os.environ, {
+        "AZURE_API_KEY": "test-key",
+        "AZURE_ENDPOINT": (
+            "https://evil.example.com/?redirect="
+            "https://test.openai.azure.com/openai/deployments/gpt-4"
+        ),
+    }):
+        llm_query_spoof = LLM(model="azure/gpt-4")
+        assert llm_query_spoof.is_azure_openai_endpoint == False
+        assert "model" in llm_query_spoof._prepare_completion_params(
+            messages=[{"role": "user", "content": "test"}]
+        )
+
+    with patch.dict(os.environ, {
+        "AZURE_API_KEY": "test-key",
+        "AZURE_ENDPOINT": "https://test.openai.azure.com.evil/openai/deployments/gpt-4",
+    }):
+        llm_host_spoof = LLM(model="azure/gpt-4")
+        assert llm_host_spoof.is_azure_openai_endpoint == False
+        assert "model" in llm_host_spoof._prepare_completion_params(
+            messages=[{"role": "user", "content": "test"}]
+        )
+
+
 def test_azure_improved_error_messages():
     """
     Test that improved error messages are provided for common HTTP errors

lib/crewai/tests/memory/test_external_memory.py

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

lib/crewai/tests/memory/test_long_term_memory.py

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

lib/crewai/tests/memory/test_short_term_memory.py

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

lib/crewai/tests/test_human_feedback_integration.py

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

lib/crewai/tests/tools/test_tool_usage.py

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

uv.lock

@@ -1295,7 +1295,7 @@ requires-dist = [
     { name = "json5", specifier = "~=0.10.0" },
     { name = "jsonref", specifier = "~=1.1.0" },
     { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.74.9,<3" },
-    { name = "mcp", specifier = "~=1.23.1" },
+    { name = "mcp", specifier = "~=1.26.0" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = "~=0.1.94" },
     { name = "openai", specifier = ">=1.83.0,<3" },
     { name = "openpyxl", specifier = "~=3.1.5" },
@@ -1311,7 +1311,7 @@ requires-dist = [
     { name = "pyjwt", specifier = ">=2.9.0,<3" },
     { name = "python-dotenv", specifier = "~=1.1.1" },
     { name = "qdrant-client", extras = ["fastembed"], marker = "extra == 'qdrant'", specifier = "~=1.14.3" },
-    { name = "regex", specifier = "~=2024.9.11" },
+    { name = "regex", specifier = "~=2026.1.15" },
     { name = "tiktoken", marker = "extra == 'embeddings'", specifier = "~=0.8.0" },
     { name = "tokenizers", specifier = "~=0.20.3" },
     { name = "tomli", specifier = "~=2.0.2" },
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-version = "1.23.3"
+version = "1.26.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -3795,9 +3795,9 @@ dependencies = [
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