Merge branch 'main' into fix/oss-9-extra-forbidden-security-context

Changes the default Pydantic config in create_model_from_schema() from
extra='forbid' to extra='ignore'. This fixes OSS-9 where the framework
injects security_context metadata into tool call arguments, but MCP tools
and integration tools (created via create_model_from_schema) reject any
extra fields with Pydantic's extra_forbidden error.

Affected tools: all MCP tools (MCPServerAdapter, MCPToolResolver) and
all platform integration tools (CrewAIPlatformActionTool) — these all
use create_model_from_schema() without a custom __config__, so they
inherited the extra='forbid' default.

Regular user-defined tools (subclassing BaseModel) were not affected
because BaseModel defaults to extra='ignore'.

The fix is backward-compatible:
- Required fields are still enforced
- Type validation is still enforced
- Callers can still opt into extra='forbid' via __config__ parameter
- Tools that define security_context in their schema still receive it

Fixes: OSS-9
Related: #4796, #4841

docs/ar/changelog.mdx

@@ -4,6 +4,28 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2 أبريل 2026">
+  ## v1.13.0a7
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة امتداد A2UI مع دعم v0.8/v0.9، والمخططات، والوثائق
+
+  ### إصلاحات الأخطاء
+  - إصلاح بادئات الرؤية متعددة الأنماط عن طريق إضافة GPT-5 وسلسلة o
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.13.0a6
+
+  ## المساهمون
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
 <Update label="1 أبريل 2026">
   ## v1.13.0a6
 

docs/en/changelog.mdx

@@ -4,6 +4,28 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Apr 02, 2026">
+  ## v1.13.0a7
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## What's Changed
+
+  ### Features
+  - Add A2UI extension with v0.8/v0.9 support, schemas, and docs
+
+  ### Bug Fixes
+  - Fix multimodal vision prefixes by adding GPT-5 and o-series
+
+  ### Documentation
+  - Update changelog and version for v1.13.0a6
+
+  ## Contributors
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
 <Update label="Apr 01, 2026">
   ## v1.13.0a6
 

docs/ko/changelog.mdx

@@ -4,6 +4,28 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 4월 2일">
+  ## v1.13.0a7
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## 변경 사항
+
+  ### 기능
+  - v0.8/v0.9 지원, 스키마 및 문서가 포함된 A2UI 확장 추가
+
+  ### 버그 수정
+  - GPT-5 및 o-series를 추가하여 다중 모드 비전 접두사 수정
+
+  ### 문서
+  - v1.13.0a6에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
 <Update label="2026년 4월 1일">
   ## v1.13.0a6
 

docs/pt-BR/changelog.mdx

@@ -4,6 +4,28 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="02 abr 2026">
+  ## v1.13.0a7
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar a extensão A2UI com suporte a v0.8/v0.9, esquemas e documentação
+
+  ### Correções de Bugs
+  - Corrigir prefixos de visão multimodal adicionando GPT-5 e o-series
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.13.0a6
+
+  ## Contribuidores
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
 <Update label="01 abr 2026">
   ## v1.13.0a6
 

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.13.0a6"
+__version__ = "1.13.0a7"

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.13.0a6",
+    "crewai==1.13.0a7",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

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

@@ -309,4 +309,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.13.0a6"
+__version__ = "1.13.0a7"

lib/crewai/pyproject.toml

@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.13.0a6",
+    "crewai-tools==1.13.0a7",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -10,6 +10,7 @@ from crewai.agent.core import Agent
 from crewai.agent.planning_config import PlanningConfig
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
+from crewai.execution_context import ExecutionContext
 from crewai.flow.flow import Flow
 from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
@@ -44,7 +45,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.13.0a6"
+__version__ = "1.13.0a7"
 _telemetry_submitted = False
 
 
@@ -96,6 +97,10 @@ def __getattr__(name: str) -> Any:
 
 
 try:
+    from crewai.agents.agent_builder.base_agent import BaseAgent as _BaseAgent
+    from crewai.agents.agent_builder.base_agent_executor_mixin import (
+        CrewAgentExecutorMixin as _CrewAgentExecutorMixin,
+    )
     from crewai.agents.tools_handler import ToolsHandler as _ToolsHandler
     from crewai.experimental.agent_executor import AgentExecutor as _AgentExecutor
     from crewai.hooks.llm_hooks import LLMCallHookContext as _LLMCallHookContext
@@ -105,25 +110,66 @@ try:
         SystemPromptResult as _SystemPromptResult,
     )
 
-    _AgentExecutor.model_rebuild(
-        force=True,
-        _types_namespace={
-            "Agent": Agent,
-            "ToolsHandler": _ToolsHandler,
-            "Crew": Crew,
-            "BaseLLM": BaseLLM,
-            "Task": Task,
-            "StandardPromptResult": _StandardPromptResult,
-            "SystemPromptResult": _SystemPromptResult,
-            "LLMCallHookContext": _LLMCallHookContext,
-            "ToolResult": _ToolResult,
-        },
-    )
+    _base_namespace: dict[str, type] = {
+        "Agent": Agent,
+        "Crew": Crew,
+        "BaseLLM": BaseLLM,
+        "Task": Task,
+        "CrewAgentExecutorMixin": _CrewAgentExecutorMixin,
+    }
+
+    try:
+        from crewai.a2a.config import (
+            A2AClientConfig as _A2AClientConfig,
+            A2AConfig as _A2AConfig,
+            A2AServerConfig as _A2AServerConfig,
+        )
+
+        _base_namespace.update(
+            {
+                "A2AConfig": _A2AConfig,
+                "A2AClientConfig": _A2AClientConfig,
+                "A2AServerConfig": _A2AServerConfig,
+            }
+        )
+    except ImportError:
+        pass
+
+    import sys
+
+    _full_namespace = {
+        **_base_namespace,
+        "ToolsHandler": _ToolsHandler,
+        "StandardPromptResult": _StandardPromptResult,
+        "SystemPromptResult": _SystemPromptResult,
+        "LLMCallHookContext": _LLMCallHookContext,
+        "ToolResult": _ToolResult,
+    }
+
+    _resolve_namespace = {
+        **_full_namespace,
+        **sys.modules[_BaseAgent.__module__].__dict__,
+    }
+
+    for _mod_name in (
+        _BaseAgent.__module__,
+        Agent.__module__,
+        _AgentExecutor.__module__,
+    ):
+        sys.modules[_mod_name].__dict__.update(_resolve_namespace)
+
+    _BaseAgent.model_rebuild(force=True, _types_namespace=_full_namespace)
+    _AgentExecutor.model_rebuild(force=True, _types_namespace=_full_namespace)
+
+    try:
+        Agent.model_rebuild(force=True, _types_namespace=_full_namespace)
+    except PydanticUserError:
+        pass
 except (ImportError, PydanticUserError):
     import logging as _logging
 
     _logging.getLogger(__name__).warning(
-        "AgentExecutor.model_rebuild() failed; forward refs may be unresolved.",
+        "model_rebuild() failed; forward refs may be unresolved.",
         exc_info=True,
     )
 
@@ -133,6 +179,7 @@ __all__ = [
     "BaseLLM",
     "Crew",
     "CrewOutput",
+    "ExecutionContext",
     "Flow",
     "Knowledge",
     "LLMGuardrail",

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

@@ -25,6 +25,7 @@ from pydantic import (
     BaseModel,
     ConfigDict,
     Field,
+    InstanceOf,
     PrivateAttr,
     model_validator,
 )
@@ -267,6 +268,9 @@ class Agent(BaseAgent):
         Can be a single A2AConfig/A2AClientConfig/A2AServerConfig, or a list of any number of A2AConfig/A2AClientConfig with a single A2AServerConfig.
         """,
     )
+    agent_executor: InstanceOf[CrewAgentExecutor] | InstanceOf[AgentExecutor] | None = (
+        Field(default=None, description="An instance of the CrewAgentExecutor class.")
+    )
     executor_class: type[CrewAgentExecutor] | type[AgentExecutor] = Field(
         default=CrewAgentExecutor,
         description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use AgentExecutor.",
@@ -690,7 +694,9 @@ class Agent(BaseAgent):
             task_prompt,
             knowledge_config,
             self.knowledge.query if self.knowledge else lambda *a, **k: None,
-            self.crew.query_knowledge if self.crew else lambda *a, **k: None,
+            self.crew.query_knowledge
+            if self.crew and not isinstance(self.crew, str)
+            else lambda *a, **k: None,
         )
 
         task_prompt = self._finalize_task_prompt(task_prompt, tools, task)
@@ -777,14 +783,18 @@ class Agent(BaseAgent):
         if not self.agent_executor:
             raise RuntimeError("Agent executor is not initialized.")
 
-        return self.agent_executor.invoke(
-            {
-                "input": task_prompt,
-                "tool_names": self.agent_executor.tools_names,
-                "tools": self.agent_executor.tools_description,
-                "ask_for_human_input": task.human_input,
-            }
-        )["output"]
+        result = cast(
+            dict[str, Any],
+            self.agent_executor.invoke(
+                {
+                    "input": task_prompt,
+                    "tool_names": self.agent_executor.tools_names,
+                    "tools": self.agent_executor.tools_description,
+                    "ask_for_human_input": task.human_input,
+                }
+            ),
+        )
+        return result["output"]
 
     async def aexecute_task(
         self,
@@ -955,19 +965,23 @@ class Agent(BaseAgent):
         if self.agent_executor is not None:
             self._update_executor_parameters(
                 task=task,
-                tools=parsed_tools,  # type: ignore[arg-type]
+                tools=parsed_tools,
                 raw_tools=raw_tools,
                 prompt=prompt,
                 stop_words=stop_words,
                 rpm_limit_fn=rpm_limit_fn,
             )
         else:
+            if not isinstance(self.llm, BaseLLM):
+                raise RuntimeError(
+                    "LLM must be resolved before creating agent executor."
+                )
             self.agent_executor = self.executor_class(
-                llm=cast(BaseLLM, self.llm),
+                llm=self.llm,
                 task=task,  # type: ignore[arg-type]
                 i18n=self.i18n,
                 agent=self,
-                crew=self.crew,
+                crew=self.crew,  # type: ignore[arg-type]
                 tools=parsed_tools,
                 prompt=prompt,
                 original_tools=raw_tools,
@@ -991,7 +1005,7 @@ class Agent(BaseAgent):
     def _update_executor_parameters(
         self,
         task: Task | None,
-        tools: list[BaseTool],
+        tools: list[CrewStructuredTool],
         raw_tools: list[BaseTool],
         prompt: SystemPromptResult | StandardPromptResult,
         stop_words: list[str],
@@ -1007,11 +1021,17 @@ class Agent(BaseAgent):
             stop_words: Stop words list.
             rpm_limit_fn: RPM limit callback function.
         """
+        if self.agent_executor is None:
+            raise RuntimeError("Agent executor is not initialized.")
+
         self.agent_executor.task = task
         self.agent_executor.tools = tools
         self.agent_executor.original_tools = raw_tools
         self.agent_executor.prompt = prompt
-        self.agent_executor.stop_words = stop_words
+        if isinstance(self.agent_executor, AgentExecutor):
+            self.agent_executor.stop_words = stop_words
+        else:
+            self.agent_executor.stop = stop_words
         self.agent_executor.tools_names = get_tool_names(tools)
         self.agent_executor.tools_description = render_text_description_and_args(tools)
         self.agent_executor.response_model = (
@@ -1787,21 +1807,3 @@ class Agent(BaseAgent):
             LiteAgentOutput: The result of the agent execution.
         """
         return await self.kickoff_async(messages, response_format, input_files)
-
-
-try:
-    from crewai.a2a.config import (
-        A2AClientConfig as _A2AClientConfig,
-        A2AConfig as _A2AConfig,
-        A2AServerConfig as _A2AServerConfig,
-    )
-
-    Agent.model_rebuild(
-        _types_namespace={
-            "A2AConfig": _A2AConfig,
-            "A2AClientConfig": _A2AClientConfig,
-            "A2AServerConfig": _A2AServerConfig,
-        }
-    )
-except ImportError:
-    pass

lib/crewai/src/crewai/agent/utils.py

@@ -137,7 +137,8 @@ def handle_knowledge_retrieval(
     Returns:
         The task prompt potentially augmented with knowledge context.
     """
-    if not (agent.knowledge or (agent.crew and agent.crew.knowledge)):
+    _crew = agent.crew if not isinstance(agent.crew, str) else None
+    if not (agent.knowledge or (_crew and _crew.knowledge)):
         return task_prompt
 
     crewai_event_bus.emit(
@@ -244,7 +245,7 @@ def apply_training_data(agent: Agent, task_prompt: str) -> str:
     Returns:
         The task prompt with training data applied.
     """
-    if agent.crew and agent.crew._train:
+    if agent.crew and not isinstance(agent.crew, str) and agent.crew._train:
         return agent._training_handler(task_prompt=task_prompt)
     return agent._use_trained_data(task_prompt=task_prompt)
 
@@ -355,7 +356,8 @@ async def ahandle_knowledge_retrieval(
     Returns:
         The task prompt potentially augmented with knowledge context.
     """
-    if not (agent.knowledge or (agent.crew and agent.crew.knowledge)):
+    _crew = agent.crew if not isinstance(agent.crew, str) else None
+    if not (agent.knowledge or (_crew and _crew.knowledge)):
         return task_prompt
 
     crewai_event_bus.emit(
@@ -381,15 +383,16 @@ async def ahandle_knowledge_retrieval(
                     if agent.agent_knowledge_context:
                         task_prompt += agent.agent_knowledge_context
 
-            knowledge_snippets = await agent.crew.aquery_knowledge(
-                [agent.knowledge_search_query], **knowledge_config
-            )
-            if knowledge_snippets:
-                agent.crew_knowledge_context = extract_knowledge_context(
-                    knowledge_snippets
+            if _crew:
+                knowledge_snippets = await _crew.aquery_knowledge(
+                    [agent.knowledge_search_query], **knowledge_config
                 )
-                if agent.crew_knowledge_context:
-                    task_prompt += agent.crew_knowledge_context
+                if knowledge_snippets:
+                    agent.crew_knowledge_context = extract_knowledge_context(
+                        knowledge_snippets
+                    )
+                    if agent.crew_knowledge_context:
+                        task_prompt += agent.crew_knowledge_context
 
             crewai_event_bus.emit(
                 agent,

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -188,14 +188,14 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         self._openai_agent = OpenAIAgent(
             name=self.role,
             instructions=instructions,
-            model=self.llm,
+            model=str(self.llm),
             **self._agent_config or {},
         )
 
         if all_tools:
             self.configure_tools(all_tools)
 
-        self.agent_executor = Runner
+        self.agent_executor = Runner  # type: ignore[assignment]
 
     def configure_tools(self, tools: list[BaseTool] | None = None) -> None:
         """Configure tools for the OpenAI Assistant.

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

@@ -5,21 +5,25 @@ from copy import copy as shallow_copy
 from hashlib import md5
 from pathlib import Path
 import re
-from typing import Any, Final, Literal
+from typing import TYPE_CHECKING, Annotated, Any, Final, Literal
 import uuid
 
 from pydantic import (
     UUID4,
     BaseModel,
+    BeforeValidator,
     Field,
+    InstanceOf,
     PrivateAttr,
     field_validator,
     model_validator,
 )
+from pydantic.functional_serializers import PlainSerializer
 from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
 from crewai.agent.internal.meta import AgentMeta
+from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
@@ -27,6 +31,7 @@ from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
+from crewai.llms.base_llm import BaseLLM
 from crewai.mcp.config import MCPServerConfig
 from crewai.memory.memory_scope import MemoryScope, MemorySlice
 from crewai.memory.unified_memory import Memory
@@ -42,6 +47,20 @@ from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.string_utils import interpolate_only
 
 
+if TYPE_CHECKING:
+    from crewai.crew import Crew
+
+
+def _validate_crew_ref(value: Any) -> Any:
+    return value
+
+
+def _serialize_crew_ref(value: Any) -> str | None:
+    if value is None:
+        return None
+    return str(value.id) if hasattr(value, "id") else str(value)
+
+
 _SLUG_RE: Final[re.Pattern[str]] = re.compile(
     r"^(?:crewai-amp:)?[a-zA-Z0-9][a-zA-Z0-9_-]*(?:#[\w-]+)?$"
 )
@@ -122,7 +141,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     __hash__ = object.__hash__
     _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
     _rpm_controller: RPMController | None = PrivateAttr(default=None)
-    _request_within_rpm_limit: Any = PrivateAttr(default=None)
+    _request_within_rpm_limit: SerializableCallable | None = PrivateAttr(default=None)
     _original_role: str | None = PrivateAttr(default=None)
     _original_goal: str | None = PrivateAttr(default=None)
     _original_backstory: str | None = PrivateAttr(default=None)
@@ -154,13 +173,19 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     max_iter: int = Field(
         default=25, description="Maximum iterations for an agent to execute a task"
     )
-    agent_executor: Any = Field(
+    agent_executor: InstanceOf[CrewAgentExecutorMixin] | None = Field(
         default=None, description="An instance of the CrewAgentExecutor class."
     )
-    llm: Any = Field(
+    llm: str | BaseLLM | None = Field(
         default=None, description="Language model that will run the agent."
     )
-    crew: Any = Field(default=None, description="Crew to which the agent belongs.")
+    crew: Annotated[
+        Crew | str | None,
+        BeforeValidator(_validate_crew_ref),
+        PlainSerializer(
+            _serialize_crew_ref, return_type=str | None, when_used="always"
+        ),
+    ] = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(
         default_factory=get_i18n, description="Internationalization settings."
     )

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a6"
+    "crewai[tools]==1.13.0a7"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a6"
+    "crewai[tools]==1.13.0a7"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a6"
+    "crewai[tools]==1.13.0a7"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/crew.py

@@ -266,7 +266,7 @@ class Crew(FlowTrackable, BaseModel):
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: str | BaseLLM | Any | None = Field(
+    planning_llm: str | BaseLLM | None = Field(
         default=None,
         description=(
             "Language model that will run the AgentPlanner if planning is True."
@@ -287,7 +287,7 @@ class Crew(FlowTrackable, BaseModel):
             "knowledge object."
         ),
     )
-    chat_llm: str | BaseLLM | Any | None = Field(
+    chat_llm: str | BaseLLM | None = Field(
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
@@ -1311,7 +1311,7 @@ class Crew(FlowTrackable, BaseModel):
             and hasattr(agent, "multimodal")
             and getattr(agent, "multimodal", False)
         ):
-            if not (agent.llm and agent.llm.supports_multimodal()):
+            if not (isinstance(agent.llm, BaseLLM) and agent.llm.supports_multimodal()):
                 tools = self._add_multimodal_tools(agent, tools)
 
         if agent and (hasattr(agent, "apps") and getattr(agent, "apps", None)):
@@ -1328,7 +1328,11 @@ class Crew(FlowTrackable, BaseModel):
         files = get_all_files(self.id, task.id)
         if files:
             supported_types: list[str] = []
-            if agent and agent.llm and agent.llm.supports_multimodal():
+            if (
+                agent
+                and isinstance(agent.llm, BaseLLM)
+                and agent.llm.supports_multimodal()
+            ):
                 provider = (
                     getattr(agent.llm, "provider", None)
                     or getattr(agent.llm, "model", None)
@@ -1781,17 +1785,10 @@ class Crew(FlowTrackable, BaseModel):
             token_sum = self.manager_agent._token_process.get_summary()
             total_usage_metrics.add_usage_metrics(token_sum)
 
-        if (
-            self.manager_agent
-            and hasattr(self.manager_agent, "llm")
-            and hasattr(self.manager_agent.llm, "get_token_usage_summary")
-        ):
+        if self.manager_agent:
             if isinstance(self.manager_agent.llm, BaseLLM):
                 llm_usage = self.manager_agent.llm.get_token_usage_summary()
-            else:
-                llm_usage = self.manager_agent.llm._token_process.get_summary()
-
-            total_usage_metrics.add_usage_metrics(llm_usage)
+                total_usage_metrics.add_usage_metrics(llm_usage)
 
         self.usage_metrics = total_usage_metrics
         return total_usage_metrics

lib/crewai/src/crewai/crews/utils.py

@@ -11,6 +11,7 @@ from opentelemetry import baggage
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.crews.crew_output import CrewOutput
+from crewai.llms.base_llm import BaseLLM
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.skills.loader import activate_skill, discover_skills
 from crewai.skills.models import INSTRUCTIONS, Skill as SkillModel
@@ -50,7 +51,7 @@ def enable_agent_streaming(agents: Iterable[BaseAgent]) -> None:
         agents: Iterable of agents to enable streaming on.
     """
     for agent in agents:
-        if agent.llm is not None:
+        if isinstance(agent.llm, BaseLLM):
             agent.llm.stream = True
 
 

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

@@ -25,13 +25,25 @@ def _get_or_create_counter() -> Iterator[int]:
         return counter
 
 
+_last_emitted: contextvars.ContextVar[int] = contextvars.ContextVar(
+    "_last_emitted", default=0
+)
+
+
 def get_next_emission_sequence() -> int:
     """Get the next emission sequence number.
 
     Returns:
         The next sequence number.
     """
-    return next(_get_or_create_counter())
+    seq = next(_get_or_create_counter())
+    _last_emitted.set(seq)
+    return seq
+
+
+def get_emission_sequence() -> int:
+    """Get the current emission sequence value without incrementing."""
+    return _last_emitted.get()
 
 
 def reset_emission_counter() -> None:
@@ -41,6 +53,14 @@ def reset_emission_counter() -> None:
     """
     counter: Iterator[int] = itertools.count(start=1)
     _emission_counter.set(counter)
+    _last_emitted.set(0)
+
+
+def set_emission_counter(start: int) -> None:
+    """Set the emission counter to resume from a given value."""
+    counter: Iterator[int] = itertools.count(start=start + 1)
+    _emission_counter.set(counter)
+    _last_emitted.set(start)
 
 
 class BaseEvent(BaseModel):

lib/crewai/src/crewai/execution_context.py

@@ -0,0 +1,80 @@
+"""Checkpointable execution context for the crewAI runtime.
+
+Captures the ContextVar state needed to resume execution from a checkpoint.
+Used by the RootModel (step 5) to include execution context in snapshots.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from crewai.context import (
+    _current_task_id,
+    _platform_integration_token,
+)
+from crewai.events.base_events import (
+    get_emission_sequence,
+    set_emission_counter,
+)
+from crewai.events.event_context import (
+    _event_id_stack,
+    _last_event_id,
+    _triggering_event_id,
+)
+from crewai.flow.flow_context import (
+    current_flow_id,
+    current_flow_method_name,
+    current_flow_request_id,
+)
+
+
+class ExecutionContext(BaseModel):
+    """Snapshot of ContextVar state required for checkpoint/resume."""
+
+    current_task_id: str | None = Field(default=None)
+    flow_request_id: str | None = Field(default=None)
+    flow_id: str | None = Field(default=None)
+    flow_method_name: str = Field(default="unknown")
+
+    event_id_stack: tuple[tuple[str, str], ...] = Field(default=())
+    last_event_id: str | None = Field(default=None)
+    triggering_event_id: str | None = Field(default=None)
+    emission_sequence: int = Field(default=0)
+
+    feedback_callback_info: dict[str, Any] | None = Field(default=None)
+    platform_token: str | None = Field(default=None)
+
+
+def capture_execution_context(
+    feedback_callback_info: dict[str, Any] | None = None,
+) -> ExecutionContext:
+    """Read all checkpoint-required ContextVars into an ExecutionContext."""
+    return ExecutionContext(
+        current_task_id=_current_task_id.get(),
+        flow_request_id=current_flow_request_id.get(),
+        flow_id=current_flow_id.get(),
+        flow_method_name=current_flow_method_name.get(),
+        event_id_stack=_event_id_stack.get(),
+        last_event_id=_last_event_id.get(),
+        triggering_event_id=_triggering_event_id.get(),
+        emission_sequence=get_emission_sequence(),
+        feedback_callback_info=feedback_callback_info,
+        platform_token=_platform_integration_token.get(),
+    )
+
+
+def apply_execution_context(ctx: ExecutionContext) -> None:
+    """Write an ExecutionContext back into the ContextVars."""
+    _current_task_id.set(ctx.current_task_id)
+    current_flow_request_id.set(ctx.flow_request_id)
+    current_flow_id.set(ctx.flow_id)
+    current_flow_method_name.set(ctx.flow_method_name)
+
+    _event_id_stack.set(ctx.event_id_stack)
+    _last_event_id.set(ctx.last_event_id)
+    _triggering_event_id.set(ctx.triggering_event_id)
+    set_emission_counter(ctx.emission_sequence)
+
+    _platform_integration_token.set(ctx.platform_token)

lib/crewai/src/crewai/task.py

@@ -41,13 +41,14 @@ from crewai.events.types.task_events import (
     TaskFailedEvent,
     TaskStartedEvent,
 )
+from crewai.llms.base_llm import BaseLLM
 from crewai.security import Fingerprint, SecurityConfig
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.constants import NOT_SPECIFIED, _NotSpecified
-from crewai.utilities.converter import Converter, aconvert_to_model, convert_to_model
+from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.file_store import (
     clear_task_files,
     get_all_files,
@@ -316,6 +317,10 @@ class Task(BaseModel):
             if self.agent is None:
                 raise ValueError("Agent is required to use LLMGuardrail")
 
+            if not isinstance(self.agent.llm, BaseLLM):
+                raise ValueError(
+                    "Agent must have a BaseLLM instance to use LLMGuardrail"
+                )
             self._guardrail = cast(
                 GuardrailCallable,
                 LLMGuardrail(description=self.guardrail, llm=self.agent.llm),
@@ -339,6 +344,10 @@ class Task(BaseModel):
                                 )
                             from crewai.tasks.llm_guardrail import LLMGuardrail
 
+                            if not isinstance(self.agent.llm, BaseLLM):
+                                raise ValueError(
+                                    "Agent must have a BaseLLM instance to use LLMGuardrail"
+                                )
                             guardrails.append(
                                 cast(
                                     GuardrailCallable,
@@ -359,6 +368,10 @@ class Task(BaseModel):
                         )
                     from crewai.tasks.llm_guardrail import LLMGuardrail
 
+                    if not isinstance(self.agent.llm, BaseLLM):
+                        raise ValueError(
+                            "Agent must have a BaseLLM instance to use LLMGuardrail"
+                        )
                     guardrails.append(
                         cast(
                             GuardrailCallable,
@@ -602,7 +615,7 @@ class Task(BaseModel):
                     json_output = None
             elif not self._guardrails and not self._guardrail:
                 raw = result
-                pydantic_output, json_output = await self._aexport_output(result)
+                pydantic_output, json_output = self._export_output(result)
             else:
                 raw = result
                 pydantic_output, json_output = None, None
@@ -646,7 +659,12 @@ class Task(BaseModel):
                     await cb_result
 
             crew = self.agent.crew  # type: ignore[union-attr]
-            if crew and crew.task_callback and crew.task_callback != self.callback:
+            if (
+                crew
+                and not isinstance(crew, str)
+                and crew.task_callback
+                and crew.task_callback != self.callback
+            ):
                 cb_result = crew.task_callback(self.output)
                 if inspect.isawaitable(cb_result):
                     await cb_result
@@ -761,7 +779,12 @@ class Task(BaseModel):
                     asyncio.run(cb_result)
 
             crew = self.agent.crew  # type: ignore[union-attr]
-            if crew and crew.task_callback and crew.task_callback != self.callback:
+            if (
+                crew
+                and not isinstance(crew, str)
+                and crew.task_callback
+                and crew.task_callback != self.callback
+            ):
                 cb_result = crew.task_callback(self.output)
                 if inspect.iscoroutine(cb_result):
                     asyncio.run(cb_result)
@@ -812,11 +835,14 @@ class Task(BaseModel):
                 if trigger_payload is not None:
                     description += f"\n\nTrigger Payload: {trigger_payload}"
 
-        if self.agent and self.agent.crew:
+        if self.agent and self.agent.crew and not isinstance(self.agent.crew, str):
             files = get_all_files(self.agent.crew.id, self.id)
             if files:
                 supported_types: list[str] = []
-                if self.agent.llm and self.agent.llm.supports_multimodal():
+                if (
+                    isinstance(self.agent.llm, BaseLLM)
+                    and self.agent.llm.supports_multimodal()
+                ):
                     provider: str = str(
                         getattr(self.agent.llm, "provider", None)
                         or getattr(self.agent.llm, "model", "openai")
@@ -1040,34 +1066,6 @@ Follow these guidelines:
 
         return pydantic_output, json_output
 
-    async def _aexport_output(
-        self, result: str
-    ) -> tuple[BaseModel | None, dict[str, Any] | None]:
-        """Async version of _export_output that uses async LLM calls."""
-        pydantic_output: BaseModel | None = None
-        json_output: dict[str, Any] | None = None
-
-        if self.output_pydantic or self.output_json:
-            model_output = await aconvert_to_model(
-                result,
-                self.output_pydantic,
-                self.output_json,
-                self.agent,
-                self.converter_cls,
-            )
-
-            if isinstance(model_output, BaseModel):
-                pydantic_output = model_output
-            elif isinstance(model_output, dict):
-                json_output = model_output
-            elif isinstance(model_output, str):
-                try:
-                    json_output = json.loads(model_output)
-                except json.JSONDecodeError:
-                    json_output = None
-
-        return pydantic_output, json_output
-
     def _get_output_format(self) -> OutputFormat:
         if self.output_json:
             return OutputFormat.JSON
@@ -1283,7 +1281,7 @@ Follow these guidelines:
 
                 if isinstance(guardrail_result.result, str):
                     task_output.raw = guardrail_result.result
-                    pydantic_output, json_output = await self._aexport_output(
+                    pydantic_output, json_output = self._export_output(
                         guardrail_result.result
                     )
                     task_output.pydantic = pydantic_output
@@ -1328,7 +1326,7 @@ Follow these guidelines:
                 tools=tools,
             )
 
-            pydantic_output, json_output = await self._aexport_output(result)
+            pydantic_output, json_output = self._export_output(result)
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,

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

@@ -41,6 +41,7 @@ from crewai.events.types.system_events import (
     SigTStpEvent,
     SigTermEvent,
 )
+from crewai.llms.base_llm import BaseLLM
 from crewai.telemetry.constants import (
     CREWAI_TELEMETRY_BASE_URL,
     CREWAI_TELEMETRY_SERVICE_NAME,
@@ -323,7 +324,9 @@ class Telemetry:
                                     if getattr(agent, "function_calling_llm", None)
                                     else ""
                                 ),
-                                "llm": agent.llm.model,
+                                "llm": agent.llm.model
+                                if isinstance(agent.llm, BaseLLM)
+                                else str(agent.llm),
                                 "delegation_enabled?": agent.allow_delegation,
                                 "allow_code_execution?": getattr(
                                     agent, "allow_code_execution", False
@@ -427,7 +430,9 @@ class Telemetry:
                                     if getattr(agent, "function_calling_llm", None)
                                     else ""
                                 ),
-                                "llm": agent.llm.model,
+                                "llm": agent.llm.model
+                                if isinstance(agent.llm, BaseLLM)
+                                else str(agent.llm),
                                 "delegation_enabled?": agent.allow_delegation,
                                 "allow_code_execution?": getattr(
                                     agent, "allow_code_execution", False
@@ -840,7 +845,9 @@ class Telemetry:
                             "max_iter": agent.max_iter,
                             "max_rpm": agent.max_rpm,
                             "i18n": agent.i18n.prompt_file,
-                            "llm": agent.llm.model,
+                            "llm": agent.llm.model
+                            if isinstance(agent.llm, BaseLLM)
+                            else str(agent.llm),
                             "delegation_enabled?": agent.allow_delegation,
                             "tools_names": [
                                 sanitize_tool_name(tool.name)

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

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import asyncio
 import json
 import re
 from typing import TYPE_CHECKING, Any, Final, TypedDict
@@ -143,103 +142,6 @@ class Converter(OutputConverter):
                 return self.to_json(current_attempt + 1)
             return ConverterError(f"Failed to convert text into JSON, error: {e}.")
 
-    async def ato_pydantic(self, current_attempt: int = 1) -> BaseModel:
-        """Async version of to_pydantic. Convert text to pydantic without blocking the event loop.
-
-        Args:
-            current_attempt: The current attempt number for conversion retries.
-
-        Returns:
-            A Pydantic BaseModel instance.
-
-        Raises:
-            ConverterError: If conversion fails after maximum attempts.
-        """
-        try:
-            if self.llm.supports_function_calling():
-                response = await self.llm.acall(
-                    messages=[
-                        {"role": "system", "content": self.instructions},
-                        {"role": "user", "content": self.text},
-                    ],
-                    response_model=self.model,
-                )
-                if isinstance(response, BaseModel):
-                    result = response
-                else:
-                    result = self.model.model_validate_json(response)
-            else:
-                response = await self.llm.acall(
-                    [
-                        {"role": "system", "content": self.instructions},
-                        {"role": "user", "content": self.text},
-                    ]
-                )
-                try:
-                    result = self.model.model_validate_json(response)
-                except ValidationError:
-                    result = handle_partial_json(  # type: ignore[assignment]
-                        result=response,
-                        model=self.model,
-                        is_json_output=False,
-                        agent=None,
-                    )
-                    if not isinstance(result, BaseModel):
-                        if isinstance(result, dict):
-                            result = self.model.model_validate(result)
-                        elif isinstance(result, str):
-                            try:
-                                result = self.model.model_validate_json(result)
-                            except Exception as parse_err:
-                                raise ConverterError(
-                                    f"Failed to convert partial JSON result into Pydantic: {parse_err}"
-                                ) from parse_err
-                        else:
-                            raise ConverterError(
-                                "handle_partial_json returned an unexpected type."
-                            ) from None
-            return result
-        except ValidationError as e:
-            if current_attempt < self.max_attempts:
-                return await self.ato_pydantic(current_attempt + 1)
-            raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to validation error: {e}"
-            ) from e
-        except Exception as e:
-            if current_attempt < self.max_attempts:
-                return await self.ato_pydantic(current_attempt + 1)
-            raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to error: {e}"
-            ) from e
-
-    async def ato_json(self, current_attempt: int = 1) -> str | ConverterError | Any:
-        """Async version of to_json. Convert text to json without blocking the event loop.
-
-        Args:
-            current_attempt: The current attempt number for conversion retries.
-
-        Returns:
-            A JSON string or ConverterError if conversion fails.
-
-        Raises:
-            ConverterError: If conversion fails after maximum attempts.
-        """
-        try:
-            if self.llm.supports_function_calling():
-                return await asyncio.to_thread(self._create_instructor().to_json)
-            return json.dumps(
-                await self.llm.acall(
-                    [
-                        {"role": "system", "content": self.instructions},
-                        {"role": "user", "content": self.text},
-                    ]
-                )
-            )
-        except Exception as e:
-            if current_attempt < self.max_attempts:
-                return await self.ato_json(current_attempt + 1)
-            return ConverterError(f"Failed to convert text into JSON, error: {e}.")
-
     def _create_instructor(self) -> InternalInstructor[Any]:
         """Create an instructor."""
 
@@ -436,176 +338,6 @@ def convert_with_instructions(
     return exported_result
 
 
-async def aconvert_to_model(
-    result: str,
-    output_pydantic: type[BaseModel] | None,
-    output_json: type[BaseModel] | None,
-    agent: Agent | BaseAgent | None = None,
-    converter_cls: type[Converter] | None = None,
-) -> dict[str, Any] | BaseModel | str:
-    """Async version of convert_to_model. Convert a result string to a Pydantic model or JSON.
-
-    Uses async LLM calls to avoid blocking the event loop.
-
-    Args:
-        result: The result string to convert.
-        output_pydantic: The Pydantic model class to convert to.
-        output_json: The Pydantic model class to convert to JSON.
-        agent: The agent instance.
-        converter_cls: The converter class to use.
-
-    Returns:
-        The converted result as a dict, BaseModel, or original string.
-    """
-    model = output_pydantic or output_json
-    if model is None:
-        return result
-
-    if converter_cls:
-        return await aconvert_with_instructions(
-            result=result,
-            model=model,
-            is_json_output=bool(output_json),
-            agent=agent,
-            converter_cls=converter_cls,
-        )
-
-    try:
-        escaped_result = json.dumps(json.loads(result, strict=False))
-        return validate_model(
-            result=escaped_result, model=model, is_json_output=bool(output_json)
-        )
-    except json.JSONDecodeError:
-        return await ahandle_partial_json(
-            result=result,
-            model=model,
-            is_json_output=bool(output_json),
-            agent=agent,
-            converter_cls=converter_cls,
-        )
-
-    except ValidationError:
-        return await ahandle_partial_json(
-            result=result,
-            model=model,
-            is_json_output=bool(output_json),
-            agent=agent,
-            converter_cls=converter_cls,
-        )
-
-    except Exception as e:
-        if agent and getattr(agent, "verbose", True):
-            Printer().print(
-                content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
-                color="red",
-            )
-        return result
-
-
-async def ahandle_partial_json(
-    result: str,
-    model: type[BaseModel],
-    is_json_output: bool,
-    agent: Agent | BaseAgent | None,
-    converter_cls: type[Converter] | None = None,
-) -> dict[str, Any] | BaseModel | str:
-    """Async version of handle_partial_json.
-
-    Args:
-        result: The result string to process.
-        model: The Pydantic model class to convert to.
-        is_json_output: Whether to return a dict (True) or Pydantic model (False).
-        agent: The agent instance.
-        converter_cls: The converter class to use.
-
-    Returns:
-        The converted result as a dict, BaseModel, or original string.
-    """
-    match = _JSON_PATTERN.search(result)
-    if match:
-        try:
-            exported_result = model.model_validate_json(match.group())
-            if is_json_output:
-                return exported_result.model_dump()
-            return exported_result
-        except json.JSONDecodeError:
-            pass
-        except ValidationError:
-            raise
-        except Exception as e:
-            if agent and getattr(agent, "verbose", True):
-                Printer().print(
-                    content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
-                    color="red",
-                )
-
-    return await aconvert_with_instructions(
-        result=result,
-        model=model,
-        is_json_output=is_json_output,
-        agent=agent,
-        converter_cls=converter_cls,
-    )
-
-
-async def aconvert_with_instructions(
-    result: str,
-    model: type[BaseModel],
-    is_json_output: bool,
-    agent: Agent | BaseAgent | None,
-    converter_cls: type[Converter] | None = None,
-) -> dict[str, Any] | BaseModel | str:
-    """Async version of convert_with_instructions.
-
-    Uses async LLM calls to avoid blocking the event loop.
-
-    Args:
-        result: The result string to convert.
-        model: The Pydantic model class to convert to.
-        is_json_output: Whether to return a dict (True) or Pydantic model (False).
-        agent: The agent instance.
-        converter_cls: The converter class to use.
-
-    Returns:
-        The converted result as a dict, BaseModel, or original string.
-
-    Raises:
-        TypeError: If neither agent nor converter_cls is provided.
-    """
-    if agent is None:
-        raise TypeError("Agent must be provided if converter_cls is not specified.")
-
-    llm = getattr(agent, "function_calling_llm", None) or agent.llm
-
-    if llm is None:
-        raise ValueError("Agent must have a valid LLM instance for conversion")
-
-    instructions = get_conversion_instructions(model=model, llm=llm)
-    converter = create_converter(
-        agent=agent,
-        converter_cls=converter_cls,
-        llm=llm,
-        text=result,
-        model=model,
-        instructions=instructions,
-    )
-    exported_result = (
-        await converter.ato_pydantic()
-        if not is_json_output
-        else await converter.ato_json()
-    )
-
-    if isinstance(exported_result, ConverterError):
-        if agent and getattr(agent, "verbose", True):
-            Printer().print(
-                content=f"Failed to convert result to model: {exported_result}",
-                color="red",
-            )
-        return result
-
-    return exported_result
-
-
 def get_conversion_instructions(
     model: type[BaseModel], llm: BaseLLM | LLM | str | Any
 ) -> str:

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

@@ -623,7 +623,7 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         for name, prop in (json_schema.get("properties", {}) or {}).items()
     }
 
-    effective_config = __config__ or ConfigDict(extra="forbid")
+    effective_config = __config__ or ConfigDict(extra="ignore")
 
     return create_model_base(
         effective_name,

lib/crewai/tests/utilities/test_converter.py

@@ -8,9 +8,6 @@ from crewai.llm import LLM
 from crewai.utilities.converter import (
     Converter,
     ConverterError,
-    aconvert_to_model,
-    aconvert_with_instructions,
-    ahandle_partial_json,
     convert_to_model,
     convert_with_instructions,
     create_converter,
@@ -955,351 +952,3 @@ def test_internal_instructor_real_unsupported_provider() -> None:
 
     # Verify it's a configuration error about unsupported provider
     assert "Unsupported provider" in str(exc_info.value) or "unsupported" in str(exc_info.value).lower()
-
-
-# ============================================================
-# Async converter tests (issue #5230)
-# ============================================================
-
-@pytest.mark.asyncio
-async def test_ato_pydantic_with_function_calling() -> None:
-    """Test that ato_pydantic uses llm.acall instead of llm.call."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = True
-    llm.acall = AsyncMock(return_value='{"name": "Eve", "age": 35}')
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Eve, Age: 35",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-
-    output = await converter.ato_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Eve"
-    assert output.age == 35
-
-    # Verify acall was used, not call
-    llm.acall.assert_called_once()
-    assert not hasattr(llm, "call") or not llm.call.called
-
-
-@pytest.mark.asyncio
-async def test_ato_pydantic_without_function_calling() -> None:
-    """Test that ato_pydantic uses llm.acall for non-function-calling LLMs."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.acall = AsyncMock(return_value='{"name": "Alice", "age": 30}')
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Alice, Age: 30",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-
-    output = await converter.ato_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Alice"
-    assert output.age == 30
-    llm.acall.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_ato_json_without_function_calling() -> None:
-    """Test that ato_json uses llm.acall instead of llm.call."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.acall = AsyncMock(return_value='{"name": "Bob", "age": 40}')
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Bob, Age: 40",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-
-    output = await converter.ato_json()
-
-    assert isinstance(output, str)
-    llm.acall.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_ato_pydantic_retry_logic() -> None:
-    """Test that ato_pydantic retries on failure using acall."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.acall = AsyncMock(
-        side_effect=[
-            "Invalid JSON",
-            "Still invalid",
-            '{"name": "Retry Alice", "age": 30}',
-        ]
-    )
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Retry Alice, Age: 30",
-        model=SimpleModel,
-        instructions="Convert this text.",
-        max_attempts=3,
-    )
-
-    output = await converter.ato_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Retry Alice"
-    assert output.age == 30
-    assert llm.acall.call_count == 3
-
-
-@pytest.mark.asyncio
-async def test_ato_pydantic_error_after_max_attempts() -> None:
-    """Test that ato_pydantic raises ConverterError after max attempts."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.acall = AsyncMock(return_value="Invalid JSON")
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Alice, Age: 30",
-        model=SimpleModel,
-        instructions="Convert this text.",
-        max_attempts=3,
-    )
-
-    with pytest.raises(ConverterError) as exc_info:
-        await converter.ato_pydantic()
-
-    assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
-
-
-@pytest.mark.asyncio
-async def test_ato_json_retry_logic() -> None:
-    """Test that ato_json retries on failure using acall."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.acall = AsyncMock(
-        side_effect=[
-            Exception("LLM error"),
-            Exception("LLM error again"),
-            '{"name": "Bob", "age": 40}',
-        ]
-    )
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Bob, Age: 40",
-        model=SimpleModel,
-        instructions="Convert this text.",
-        max_attempts=3,
-    )
-
-    output = await converter.ato_json()
-
-    assert isinstance(output, str)
-    assert llm.acall.call_count == 3
-
-
-@pytest.mark.asyncio
-async def test_aconvert_to_model_with_valid_json() -> None:
-    """Test aconvert_to_model with valid JSON (no LLM call needed)."""
-    result = '{"name": "John", "age": 30}'
-    output = await aconvert_to_model(result, SimpleModel, None, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "John"
-    assert output.age == 30
-
-
-@pytest.mark.asyncio
-async def test_aconvert_to_model_with_no_model() -> None:
-    """Test aconvert_to_model returns plain text when no model specified."""
-    result = "Plain text"
-    output = await aconvert_to_model(result, None, None, None)
-    assert output == "Plain text"
-
-
-@pytest.mark.asyncio
-async def test_aconvert_to_model_with_json_output() -> None:
-    """Test aconvert_to_model returns dict when output_json is specified."""
-    result = '{"name": "John", "age": 30}'
-    output = await aconvert_to_model(result, None, SimpleModel, None)
-    assert isinstance(output, dict)
-    assert output == {"name": "John", "age": 30}
-
-
-@pytest.mark.asyncio
-async def test_aconvert_with_instructions_success() -> None:
-    """Test aconvert_with_instructions uses async converter methods."""
-    from unittest.mock import AsyncMock
-
-    mock_agent = Mock()
-    mock_agent.function_calling_llm = None
-    mock_agent.llm = Mock()
-
-    with patch("crewai.utilities.converter.create_converter") as mock_create_converter, \
-         patch("crewai.utilities.converter.get_conversion_instructions") as mock_get_instructions:
-        mock_get_instructions.return_value = "Instructions"
-        mock_converter = Mock()
-        mock_converter.ato_pydantic = AsyncMock(
-            return_value=SimpleModel(name="David", age=50)
-        )
-        mock_create_converter.return_value = mock_converter
-
-        result = "Some text to convert"
-        output = await aconvert_with_instructions(result, SimpleModel, False, mock_agent)
-
-        assert isinstance(output, SimpleModel)
-        assert output.name == "David"
-        assert output.age == 50
-        mock_converter.ato_pydantic.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_aconvert_with_instructions_json_output() -> None:
-    """Test aconvert_with_instructions uses ato_json for JSON output."""
-    from unittest.mock import AsyncMock
-
-    mock_agent = Mock()
-    mock_agent.function_calling_llm = None
-    mock_agent.llm = Mock()
-
-    with patch("crewai.utilities.converter.create_converter") as mock_create_converter, \
-         patch("crewai.utilities.converter.get_conversion_instructions") as mock_get_instructions:
-        mock_get_instructions.return_value = "Instructions"
-        mock_converter = Mock()
-        mock_converter.ato_json = AsyncMock(
-            return_value='{"name": "David", "age": 50}'
-        )
-        mock_create_converter.return_value = mock_converter
-
-        result = "Some text to convert"
-        output = await aconvert_with_instructions(result, SimpleModel, True, mock_agent)
-
-        assert output == '{"name": "David", "age": 50}'
-        mock_converter.ato_json.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_aconvert_with_instructions_failure() -> None:
-    """Test aconvert_with_instructions returns original result on ConverterError."""
-    from unittest.mock import AsyncMock
-
-    mock_agent = Mock()
-    mock_agent.function_calling_llm = None
-    mock_agent.llm = Mock()
-    mock_agent.verbose = True
-
-    with patch("crewai.utilities.converter.create_converter") as mock_create_converter, \
-         patch("crewai.utilities.converter.get_conversion_instructions") as mock_get_instructions:
-        mock_get_instructions.return_value = "Instructions"
-        mock_converter = Mock()
-        mock_converter.ato_pydantic = AsyncMock(
-            return_value=ConverterError("Conversion failed")
-        )
-        mock_create_converter.return_value = mock_converter
-
-        result = "Some text to convert"
-        with patch("crewai.utilities.converter.Printer") as mock_printer:
-            output = await aconvert_with_instructions(result, SimpleModel, False, mock_agent)
-            assert output == result
-            mock_printer.return_value.print.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_aconvert_with_instructions_no_agent() -> None:
-    """Test aconvert_with_instructions raises TypeError without agent."""
-    with pytest.raises(TypeError, match="Agent must be provided"):
-        await aconvert_with_instructions("text", SimpleModel, False, None)
-
-
-@pytest.mark.asyncio
-async def test_ahandle_partial_json_with_valid_partial() -> None:
-    """Test ahandle_partial_json with valid embedded JSON."""
-    result = 'Some text {"name": "Charlie", "age": 35} more text'
-    output = await ahandle_partial_json(result, SimpleModel, False, None)
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Charlie"
-    assert output.age == 35
-
-
-@pytest.mark.asyncio
-async def test_ahandle_partial_json_delegates_to_aconvert_with_instructions() -> None:
-    """Test ahandle_partial_json delegates to aconvert_with_instructions for invalid JSON."""
-    from unittest.mock import AsyncMock
-
-    mock_agent = Mock()
-    mock_agent.function_calling_llm = None
-    mock_agent.llm = Mock()
-
-    result = "No valid JSON here"
-    with patch("crewai.utilities.converter.aconvert_with_instructions", new_callable=AsyncMock) as mock_aconvert:
-        mock_aconvert.return_value = "Converted result"
-        output = await ahandle_partial_json(result, SimpleModel, False, mock_agent)
-        assert output == "Converted result"
-        mock_aconvert.assert_called_once()
-
-
-@pytest.mark.asyncio
-async def test_async_converter_does_not_call_sync_llm_call() -> None:
-    """Core test for issue #5230: verify that async converter path never uses sync llm.call()."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = True
-    llm.acall = AsyncMock(return_value='{"name": "Test", "age": 25}')
-    llm.call = Mock(side_effect=AssertionError("sync llm.call() should not be called in async path"))
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Test, Age: 25",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-
-    # ato_pydantic should use acall, never call
-    output = await converter.ato_pydantic()
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Test"
-    assert output.age == 25
-    llm.acall.assert_called_once()
-    llm.call.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_async_converter_json_does_not_call_sync_llm_call() -> None:
-    """Verify ato_json for non-function-calling LLMs never uses sync llm.call()."""
-    from unittest.mock import AsyncMock
-
-    llm = Mock(spec=LLM)
-    llm.supports_function_calling.return_value = False
-    llm.acall = AsyncMock(return_value='{"name": "Test", "age": 25}')
-    llm.call = Mock(side_effect=AssertionError("sync llm.call() should not be called in async path"))
-
-    converter = Converter(
-        llm=llm,
-        text="Name: Test, Age: 25",
-        model=SimpleModel,
-        instructions="Convert this text.",
-    )
-
-    output = await converter.ato_json()
-    assert isinstance(output, str)
-    llm.acall.assert_called_once()
-    llm.call.assert_not_called()

lib/crewai/tests/utilities/test_pydantic_schema_utils.py

@@ -882,3 +882,129 @@ class TestEndToEndMCPSchema:
         )
         assert obj.filters.date_from == datetime.date(2025, 1, 1)
         assert obj.filters.categories == ["news", "tech"]
+
+
+class TestExtraFieldsIgnored:
+    """Regression tests for OSS-9: security_context injection causing
+    extra_forbidden errors on MCP and integration tool schemas.
+
+    When the framework injects metadata like security_context into tool call
+    arguments, dynamically-created Pydantic models must ignore (not reject)
+    extra fields so that tool execution is not blocked.
+    """
+
+    SIMPLE_TOOL_SCHEMA: dict[str, Any] = {
+        "type": "object",
+        "title": "ExecuteSqlSchema",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "The SQL query to execute.",
+            },
+        },
+        "required": ["query"],
+    }
+
+    OUTLOOK_TOOL_SCHEMA: dict[str, Any] = {
+        "type": "object",
+        "title": "MicrosoftOutlookSendEmailSchema",
+        "properties": {
+            "to_recipients": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "Array of recipient email addresses.",
+            },
+            "subject": {
+                "type": "string",
+                "description": "Email subject line.",
+            },
+            "body": {
+                "type": "string",
+                "description": "Email body content.",
+            },
+        },
+        "required": ["to_recipients", "subject", "body"],
+    }
+
+    SECURITY_CONTEXT_PAYLOAD: dict[str, Any] = {
+        "agent_fingerprint": {
+            "user_id": "test-user-123",
+            "session_id": "test-session-456",
+            "metadata": {},
+        },
+    }
+
+    def test_mcp_tool_schema_ignores_security_context(self) -> None:
+        """Reproduces OSS-9 Case 1: Databricks MCP execute_sql fails when
+        security_context is injected into tool args."""
+        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
+        # This previously raised: Extra inputs are not permitted
+        # [type=extra_forbidden, input_value={'agent_fingerprint': ...}]
+        obj = Model.model_validate(
+            {
+                "query": "SELECT * FROM my_table",
+                "security_context": self.SECURITY_CONTEXT_PAYLOAD,
+            }
+        )
+        assert obj.query == "SELECT * FROM my_table"
+        # security_context should be silently dropped, not present on the model
+        assert not hasattr(obj, "security_context")
+
+    def test_integration_tool_schema_ignores_security_context(self) -> None:
+        """Reproduces OSS-9 Case 2: Microsoft Outlook send_email fails when
+        security_context is injected into tool args."""
+        Model = create_model_from_schema(self.OUTLOOK_TOOL_SCHEMA)
+        obj = Model.model_validate(
+            {
+                "to_recipients": ["user@example.com"],
+                "subject": "Test",
+                "body": "Hello",
+                "security_context": self.SECURITY_CONTEXT_PAYLOAD,
+            }
+        )
+        assert obj.to_recipients == ["user@example.com"]
+        assert obj.subject == "Test"
+        assert not hasattr(obj, "security_context")
+
+    def test_arbitrary_extra_fields_ignored(self) -> None:
+        """Any unexpected extra field should be silently ignored, not just
+        security_context."""
+        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
+        obj = Model.model_validate(
+            {
+                "query": "SELECT 1",
+                "some_unknown_field": "should be dropped",
+                "another_extra": 42,
+            }
+        )
+        assert obj.query == "SELECT 1"
+        assert not hasattr(obj, "some_unknown_field")
+        assert not hasattr(obj, "another_extra")
+
+    def test_required_fields_still_enforced(self) -> None:
+        """Changing to extra=ignore must NOT weaken required field validation."""
+        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
+        with pytest.raises(Exception):
+            Model.model_validate({"security_context": self.SECURITY_CONTEXT_PAYLOAD})
+
+    def test_type_validation_still_enforced(self) -> None:
+        """Changing to extra=ignore must NOT weaken type validation."""
+        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
+        with pytest.raises(Exception):
+            Model.model_validate({"query": 12345})  # should be string
+
+    def test_explicit_extra_forbid_still_works(self) -> None:
+        """Callers can still opt into extra=forbid via __config__."""
+        from pydantic import ConfigDict
+
+        Model = create_model_from_schema(
+            self.SIMPLE_TOOL_SCHEMA,
+            __config__=ConfigDict(extra="forbid"),
+        )
+        with pytest.raises(Exception):
+            Model.model_validate(
+                {
+                    "query": "SELECT 1",
+                    "security_context": self.SECURITY_CONTEXT_PAYLOAD,
+                }
+            )

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.13.0a6"
+__version__ = "1.13.0a7"