Fix tool output token overflow issue

- Add max_tool_output_tokens parameter to Agent (default: 4096)
- Implement token counting and truncation utilities in agent_utils
- Truncate large tool outputs before appending to messages
- Update CONTEXT_LIMIT_ERRORS to recognize negative max_tokens error
- Add comprehensive tests for tool output truncation

Fixes #3843

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

.pre-commit-config.yaml

@@ -19,6 +19,7 @@ repos:
         language: system
         pass_filenames: true
         types: [python]
+        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/)
   - repo: https://github.com/astral-sh/uv-pre-commit
     rev: 0.9.3
     hooks:

docs/en/concepts/llms.mdx

@@ -1200,6 +1200,52 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+
+  <Accordion title="Transport Interceptors">
+    CrewAI provides message interceptors for several providers, allowing you to hook into request/response cycles at the transport layer.
+
+    **Supported Providers:**
+    - ✅ OpenAI
+    - ✅ Anthropic
+
+    **Basic Usage:**
+    ```python
+import httpx
+from crewai import LLM
+from crewai.llms.hooks import BaseInterceptor
+
+class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Custom interceptor to modify requests and responses."""
+
+    def on_outbound(self, request: httpx.Request) -> httpx.Request:
+        """Print request before sending to the LLM provider."""
+        print(request)
+        return request
+
+    def on_inbound(self, response: httpx.Response) -> httpx.Response:
+        """Process response after receiving from the LLM provider."""
+        print(f"Status: {response.status_code}")
+        print(f"Response time: {response.elapsed}")
+        return response
+
+# Use the interceptor with an LLM
+llm = LLM(
+    model="openai/gpt-4o",
+    interceptor=CustomInterceptor()
+)
+    ```
+
+    **Important Notes:**
+    - Both methods must return the received object or type of object.
+    - Modifying received objects may result in unexpected behavior or application crashes.
+    - Not all providers support interceptors - check the supported providers list above
+
+    <Info>
+      Interceptors operate at the transport layer. This is particularly useful for:
+      - Message transformation and filtering
+      - Debugging API interactions
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

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

@@ -229,6 +229,7 @@ class CrewAIRagAdapter(Adapter):
                             continue
             else:
                 metadata: dict[str, Any] = base_metadata.copy()
+                source_content = SourceContent(source_ref)
 
                 if data_type in [
                     DataType.PDF_FILE,
@@ -239,13 +240,12 @@ class CrewAIRagAdapter(Adapter):
                     DataType.XML,
                     DataType.MDX,
                 ]:
-                    if not os.path.isfile(source_ref):
+                    if not source_content.is_url() and not source_content.path_exists():
                         raise FileNotFoundError(f"File does not exist: {source_ref}")
 
                 loader = data_type.get_loader()
                 chunker = data_type.get_chunker()
 
-                source_content = SourceContent(source_ref)
                 loader_result: LoaderResult = loader.load(source_content)
 
                 chunks = chunker.chunk(loader_result.content)

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

@@ -142,6 +142,10 @@ class Agent(BaseAgent):
         default=True,
         description="Keep messages under the context window size by summarizing content.",
     )
+    max_tool_output_tokens: int = Field(
+        default=4096,
+        description="Maximum number of tokens allowed in tool outputs before truncation. Prevents context window overflow from large tool results.",
+    )
     max_retry_limit: int = Field(
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",

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

@@ -7,7 +7,7 @@ output conversion for OpenAI agents, supporting JSON and Pydantic model formats.
 from typing import Any
 
 from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
 
 
 class OpenAIConverterAdapter(BaseConverterAdapter):
@@ -59,7 +59,7 @@ class OpenAIConverterAdapter(BaseConverterAdapter):
             return base_prompt
 
         output_schema: str = (
-            I18N()
+            get_i18n()
             .slice("formatted_task_instructions")
             .format(output_format=self._schema)
         )

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

@@ -29,7 +29,7 @@ from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities.config import process_config
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.logger import Logger
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.string_utils import interpolate_only
@@ -107,7 +107,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             Set private attributes.
     """
 
-    __hash__ = object.__hash__  # type: ignore
+    __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)
@@ -150,7 +150,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(
-        default_factory=I18N, description="Internationalization settings."
+        default_factory=get_i18n, description="Internationalization settings."
     )
     cache_handler: CacheHandler | None = Field(
         default=None, description="An instance of the CacheHandler class."
@@ -180,8 +180,8 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default_factory=SecurityConfig,
         description="Security configuration for the agent, including fingerprinting.",
     )
-    callbacks: list[Callable] = Field(
-        default=[], description="Callbacks to be used for the agent"
+    callbacks: list[Callable[[Any], Any]] = Field(
+        default_factory=list, description="Callbacks to be used for the agent"
     )
     adapted_agent: bool = Field(
         default=False, description="Whether the agent is adapted"
@@ -201,7 +201,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
 
     @model_validator(mode="before")
     @classmethod
-    def process_model_config(cls, values):
+    def process_model_config(cls, values: Any) -> dict[str, Any]:
         return process_config(values, cls)
 
     @field_validator("tools")
@@ -269,7 +269,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         return list(set(validated_mcps))
 
     @model_validator(mode="after")
-    def validate_and_set_attributes(self):
+    def validate_and_set_attributes(self) -> Self:
         # Validate required fields
         for field in ["role", "goal", "backstory"]:
             if getattr(self, field) is None:
@@ -301,7 +301,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             )
 
     @model_validator(mode="after")
-    def set_private_attrs(self):
+    def set_private_attrs(self) -> Self:
         """Set private attributes."""
         self._logger = Logger(verbose=self.verbose)
         if self.max_rpm and not self._rpm_controller:
@@ -313,7 +313,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         return self
 
     @property
-    def key(self):
+    def key(self) -> str:
         source = [
             self._original_role or self.role,
             self._original_goal or self.goal,
@@ -331,7 +331,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         pass
 
     @abstractmethod
-    def create_agent_executor(self, tools=None) -> None:
+    def create_agent_executor(self, tools: list[BaseTool] | None = None) -> None:
         pass
 
     @abstractmethod
@@ -443,5 +443,5 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
 
-    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None):
+    def set_knowledge(self, crew_embedder: EmbedderConfig | None = None) -> None:
         pass

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

@@ -37,7 +37,7 @@ from crewai.utilities.agent_utils import (
     process_llm_response,
 )
 from crewai.utilities.constants import TRAINING_DATA_FILE
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -65,7 +65,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def __init__(
         self,
-        llm: BaseLLM | Any | None,
+        llm: BaseLLM,
         task: Task,
         crew: Crew,
         agent: Agent,
@@ -106,7 +106,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             callbacks: Optional callbacks list.
             response_model: Optional Pydantic model for structured outputs.
         """
-        self._i18n: I18N = I18N()
+        self._i18n: I18N = get_i18n()
         self.llm = llm
         self.task = task
         self.agent = agent
@@ -323,12 +323,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             self.messages.append({"role": "assistant", "content": tool_result.result})
             return formatted_answer
 
+        max_tool_output_tokens = (
+            self.agent.max_tool_output_tokens if self.agent else 4096
+        )
         return handle_agent_action_core(
             formatted_answer=formatted_answer,
             tool_result=tool_result,
             messages=self.messages,
             step_callback=self.step_callback,
             show_logs=self._show_logs,
+            max_tool_output_tokens=max_tool_output_tokens,
         )
 
     def _invoke_step_callback(

lib/crewai/src/crewai/agents/parser.py

@@ -18,10 +18,10 @@ from crewai.agents.constants import (
     MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
     UNABLE_TO_REPAIR_JSON_RESULTS,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
 
 
-_I18N = I18N()
+_I18N = get_i18n()
 
 
 @dataclass

lib/crewai/src/crewai/crew.py

@@ -27,6 +27,7 @@ from pydantic import (
     model_validator,
 )
 from pydantic_core import PydanticCustomError
+from typing_extensions import Self
 
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -70,7 +71,7 @@ from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import BaseTool, Tool
+from crewai.tools.base_tool import BaseTool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
 from crewai.utilities.crew.models import CrewContext
@@ -81,7 +82,7 @@ from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.logger import Logger
 from crewai.utilities.planning_handler import CrewPlanner
@@ -195,7 +196,7 @@ class Crew(FlowTrackable, BaseModel):
     function_calling_llm: str | InstanceOf[LLM] | Any | None = Field(
         description="Language model that will run the agent.", default=None
     )
-    config: Json | dict[str, Any] | None = Field(default=None)
+    config: Json[dict[str, Any]] | dict[str, Any] | None = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     share_crew: bool | None = Field(default=False)
     step_callback: Any | None = Field(
@@ -294,7 +295,9 @@ class Crew(FlowTrackable, BaseModel):
 
     @field_validator("config", mode="before")
     @classmethod
-    def check_config_type(cls, v: Json | dict[str, Any]) -> Json | dict[str, Any]:
+    def check_config_type(
+        cls, v: Json[dict[str, Any]] | dict[str, Any]
+    ) -> dict[str, Any]:
         """Validates that the config is a valid type.
         Args:
             v: The config to be validated.
@@ -310,7 +313,7 @@ class Crew(FlowTrackable, BaseModel):
         """set private attributes."""
 
         self._cache_handler = CacheHandler()
-        event_listener = EventListener()
+        event_listener = EventListener()  # type: ignore[no-untyped-call]
 
         if (
             is_tracing_enabled()
@@ -330,13 +333,13 @@ class Crew(FlowTrackable, BaseModel):
 
         return self
 
-    def _initialize_default_memories(self):
-        self._long_term_memory = self._long_term_memory or LongTermMemory()
-        self._short_term_memory = self._short_term_memory or ShortTermMemory(
+    def _initialize_default_memories(self) -> None:
+        self._long_term_memory = self._long_term_memory or LongTermMemory()  # type: ignore[no-untyped-call]
+        self._short_term_memory = self._short_term_memory or ShortTermMemory(  # type: ignore[no-untyped-call]
             crew=self,
             embedder_config=self.embedder,
         )
-        self._entity_memory = self.entity_memory or EntityMemory(
+        self._entity_memory = self.entity_memory or EntityMemory(  # type: ignore[no-untyped-call]
             crew=self, embedder_config=self.embedder
         )
 
@@ -380,7 +383,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_manager_llm(self):
+    def check_manager_llm(self) -> Self:
         """Validates that the language model is set when using hierarchical process."""
         if self.process == Process.hierarchical:
             if not self.manager_llm and not self.manager_agent:
@@ -405,7 +408,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_config(self):
+    def check_config(self) -> Self:
         """Validates that the crew is properly configured with agents and tasks."""
         if not self.config and not self.tasks and not self.agents:
             raise PydanticCustomError(
@@ -426,23 +429,20 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_tasks(self):
+    def validate_tasks(self) -> Self:
         if self.process == Process.sequential:
             for task in self.tasks:
                 if task.agent is None:
                     raise PydanticCustomError(
                         "missing_agent_in_task",
-                        (
-                            f"Sequential process error: Agent is missing in the task "
-                            f"with the following description: {task.description}"
-                        ),  # type: ignore # Dynamic string in error message
-                        {},
+                        "Sequential process error: Agent is missing in the task with the following description: {description}",
+                        {"description": task.description},
                     )
 
         return self
 
     @model_validator(mode="after")
-    def validate_end_with_at_most_one_async_task(self):
+    def validate_end_with_at_most_one_async_task(self) -> Self:
         """Validates that the crew ends with at most one asynchronous task."""
         final_async_task_count = 0
 
@@ -505,7 +505,9 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
+    def validate_async_task_cannot_include_sequential_async_tasks_in_context(
+        self,
+    ) -> Self:
         """
         Validates that if a task is set to be executed asynchronously,
         it cannot include other asynchronous tasks in its context unless
@@ -527,7 +529,7 @@ class Crew(FlowTrackable, BaseModel):
         return self
 
     @model_validator(mode="after")
-    def validate_context_no_future_tasks(self):
+    def validate_context_no_future_tasks(self) -> Self:
         """Validates that a task's context does not include future tasks."""
         task_indices = {id(task): i for i, task in enumerate(self.tasks)}
 
@@ -561,7 +563,7 @@ class Crew(FlowTrackable, BaseModel):
         """
         return self.security_config.fingerprint
 
-    def _setup_from_config(self):
+    def _setup_from_config(self) -> None:
         """Initializes agents and tasks from the provided config."""
         if self.config is None:
             raise ValueError("Config should not be None.")
@@ -628,12 +630,12 @@ class Crew(FlowTrackable, BaseModel):
 
             for agent in train_crew.agents:
                 if training_data.get(str(agent.id)):
-                    result = TaskEvaluator(agent).evaluate_training_data(
+                    result = TaskEvaluator(agent).evaluate_training_data(  # type: ignore[arg-type]
                         training_data=training_data, agent_id=str(agent.id)
                     )
                     CrewTrainingHandler(filename).save_trained_data(
                         agent_id=str(agent.role),
-                        trained_data=result.model_dump(),  # type: ignore[arg-type]
+                        trained_data=result.model_dump(),
                     )
 
             crewai_event_bus.emit(
@@ -684,12 +686,8 @@ class Crew(FlowTrackable, BaseModel):
             self._set_tasks_callbacks()
             self._set_allow_crewai_trigger_context_for_first_task()
 
-            i18n = I18N(prompt_file=self.prompt_file)
-
             for agent in self.agents:
-                agent.i18n = i18n
-                # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
-                agent.crew = self  # type: ignore[attr-defined]
+                agent.crew = self
                 agent.set_knowledge(crew_embedder=self.embedder)
                 # TODO: Create an AgentFunctionCalling protocol for future refactoring
                 if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
@@ -753,10 +751,12 @@ class Crew(FlowTrackable, BaseModel):
         inputs = inputs or {}
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: list[dict]) -> list[CrewOutput]:
+    async def kickoff_for_each_async(
+        self, inputs: list[dict[str, Any]]
+    ) -> list[CrewOutput]:
         crew_copies = [self.copy() for _ in inputs]
 
-        async def run_crew(crew, input_data):
+        async def run_crew(crew: Self, input_data: Any) -> CrewOutput:
             return await crew.kickoff_async(inputs=input_data)
 
         tasks = [
@@ -775,7 +775,7 @@ class Crew(FlowTrackable, BaseModel):
         self._task_output_handler.reset()
         return results
 
-    def _handle_crew_planning(self):
+    def _handle_crew_planning(self) -> None:
         """Handles the Crew planning."""
         self._logger.log("info", "Planning the crew execution")
         result = CrewPlanner(
@@ -793,7 +793,7 @@ class Crew(FlowTrackable, BaseModel):
         output: TaskOutput,
         task_index: int,
         was_replayed: bool = False,
-    ):
+    ) -> None:
         if self._inputs:
             inputs = self._inputs
         else:
@@ -825,19 +825,21 @@ class Crew(FlowTrackable, BaseModel):
         self._create_manager_agent()
         return self._execute_tasks(self.tasks)
 
-    def _create_manager_agent(self):
-        i18n = I18N(prompt_file=self.prompt_file)
+    def _create_manager_agent(self) -> None:
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
             manager = self.manager_agent
             if manager.tools is not None and len(manager.tools) > 0:
                 self._logger.log(
-                    "warning", "Manager agent should not have tools", color="orange"
+                    "warning",
+                    "Manager agent should not have tools",
+                    color="bold_yellow",
                 )
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
         else:
             self.manager_llm = create_llm(self.manager_llm)
+            i18n = get_i18n(prompt_file=self.prompt_file)
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -895,7 +897,7 @@ class Crew(FlowTrackable, BaseModel):
             tools_for_task = self._prepare_tools(
                 agent_to_use,
                 task,
-                cast(list[Tool] | list[BaseTool], tools_for_task),
+                tools_for_task,
             )
 
             self._log_task_start(task, agent_to_use.role)
@@ -915,7 +917,7 @@ class Crew(FlowTrackable, BaseModel):
                 future = task.execute_async(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(list[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 futures.append((task, future, task_index))
             else:
@@ -927,7 +929,7 @@ class Crew(FlowTrackable, BaseModel):
                 task_output = task.execute_sync(
                     agent=agent_to_use,
                     context=context,
-                    tools=cast(list[BaseTool], tools_for_task),
+                    tools=tools_for_task,
                 )
                 task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
@@ -965,7 +967,7 @@ class Crew(FlowTrackable, BaseModel):
         return None
 
     def _prepare_tools(
-        self, agent: BaseAgent, task: Task, tools: list[Tool] | list[BaseTool]
+        self, agent: BaseAgent, task: Task, tools: list[BaseTool]
     ) -> list[BaseTool]:
         # Add delegation tools if agent allows delegation
         if hasattr(agent, "allow_delegation") and getattr(
@@ -1002,21 +1004,21 @@ class Crew(FlowTrackable, BaseModel):
             tools = self._add_mcp_tools(task, tools)
 
         # Return a list[BaseTool] compatible with Task.execute_sync and execute_async
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _get_agent_to_use(self, task: Task) -> BaseAgent | None:
         if self.process == Process.hierarchical:
             return self.manager_agent
         return task.agent
 
+    @staticmethod
     def _merge_tools(
-        self,
-        existing_tools: list[Tool] | list[BaseTool],
-        new_tools: list[Tool] | list[BaseTool],
+        existing_tools: list[BaseTool],
+        new_tools: list[BaseTool],
     ) -> list[BaseTool]:
         """Merge new tools into existing tools list, avoiding duplicates."""
         if not new_tools:
-            return cast(list[BaseTool], existing_tools)
+            return existing_tools
 
         # Create mapping of tool names to new tools
         new_tool_map = {tool.name: tool for tool in new_tools}
@@ -1027,63 +1029,62 @@ class Crew(FlowTrackable, BaseModel):
         # Add all new tools
         tools.extend(new_tools)
 
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _inject_delegation_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: list[BaseTool],
         task_agent: BaseAgent,
         agents: list[BaseAgent],
     ) -> list[BaseTool]:
         if hasattr(task_agent, "get_delegation_tools"):
             delegation_tools = task_agent.get_delegation_tools(agents)
             # Cast delegation_tools to the expected type for _merge_tools
-            return self._merge_tools(tools, cast(list[BaseTool], delegation_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, delegation_tools)
+        return tools
 
     def _inject_platform_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: list[BaseTool],
         task_agent: BaseAgent,
     ) -> list[BaseTool]:
         apps = getattr(task_agent, "apps", None) or []
 
         if hasattr(task_agent, "get_platform_tools") and apps:
             platform_tools = task_agent.get_platform_tools(apps=apps)
-            return self._merge_tools(tools, cast(list[BaseTool], platform_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, platform_tools)
+        return tools
 
     def _inject_mcp_tools(
         self,
-        tools: list[Tool] | list[BaseTool],
+        tools: list[BaseTool],
         task_agent: BaseAgent,
     ) -> list[BaseTool]:
         mcps = getattr(task_agent, "mcps", None) or []
         if hasattr(task_agent, "get_mcp_tools") and mcps:
             mcp_tools = task_agent.get_mcp_tools(mcps=mcps)
-            return self._merge_tools(tools, cast(list[BaseTool], mcp_tools))
-        return cast(list[BaseTool], tools)
+            return self._merge_tools(tools, mcp_tools)
+        return tools
 
     def _add_multimodal_tools(
-        self, agent: BaseAgent, tools: list[Tool] | list[BaseTool]
+        self, agent: BaseAgent, tools: list[BaseTool]
     ) -> list[BaseTool]:
         if hasattr(agent, "get_multimodal_tools"):
             multimodal_tools = agent.get_multimodal_tools()
-            # Cast multimodal_tools to the expected type for _merge_tools
             return self._merge_tools(tools, cast(list[BaseTool], multimodal_tools))
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _add_code_execution_tools(
-        self, agent: BaseAgent, tools: list[Tool] | list[BaseTool]
+        self, agent: BaseAgent, tools: list[BaseTool]
     ) -> list[BaseTool]:
         if hasattr(agent, "get_code_execution_tools"):
             code_tools = agent.get_code_execution_tools()
             # Cast code_tools to the expected type for _merge_tools
             return self._merge_tools(tools, cast(list[BaseTool], code_tools))
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _add_delegation_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
+        self, task: Task, tools: list[BaseTool]
     ) -> list[BaseTool]:
         agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
@@ -1092,25 +1093,21 @@ class Crew(FlowTrackable, BaseModel):
             tools = self._inject_delegation_tools(
                 tools, task.agent, agents_for_delegation
             )
-        return cast(list[BaseTool], tools)
+        return tools
 
-    def _add_platform_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+    def _add_platform_tools(self, task: Task, tools: list[BaseTool]) -> list[BaseTool]:
         if task.agent:
             tools = self._inject_platform_tools(tools, task.agent)
 
-        return cast(list[BaseTool], tools or [])
+        return tools or []
 
-    def _add_mcp_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
-    ) -> list[BaseTool]:
+    def _add_mcp_tools(self, task: Task, tools: list[BaseTool]) -> list[BaseTool]:
         if task.agent:
             tools = self._inject_mcp_tools(tools, task.agent)
 
-        return cast(list[BaseTool], tools or [])
+        return tools or []
 
-    def _log_task_start(self, task: Task, role: str = "None"):
+    def _log_task_start(self, task: Task, role: str = "None") -> None:
         if self.output_log_file:
             self._file_handler.log(
                 task_name=task.name,  # type: ignore[arg-type]
@@ -1120,7 +1117,7 @@ class Crew(FlowTrackable, BaseModel):
             )
 
     def _update_manager_tools(
-        self, task: Task, tools: list[Tool] | list[BaseTool]
+        self, task: Task, tools: list[BaseTool]
     ) -> list[BaseTool]:
         if self.manager_agent:
             if task.agent:
@@ -1129,7 +1126,7 @@ class Crew(FlowTrackable, BaseModel):
                 tools = self._inject_delegation_tools(
                     tools, self.manager_agent, self.agents
                 )
-        return cast(list[BaseTool], tools)
+        return tools
 
     def _get_context(self, task: Task, task_outputs: list[TaskOutput]) -> str:
         if not task.context:
@@ -1280,7 +1277,7 @@ class Crew(FlowTrackable, BaseModel):
 
         return required_inputs
 
-    def copy(self):
+    def copy(self) -> Crew:  # type: ignore[override]
         """
         Creates a deep copy of the Crew instance.
 
@@ -1311,7 +1308,7 @@ class Crew(FlowTrackable, BaseModel):
         manager_agent = self.manager_agent.copy() if self.manager_agent else None
         manager_llm = shallow_copy(self.manager_llm) if self.manager_llm else None
 
-        task_mapping = {}
+        task_mapping: dict[str, Any] = {}
 
         cloned_tasks = []
         existing_knowledge_sources = shallow_copy(self.knowledge_sources)
@@ -1373,7 +1370,6 @@ class Crew(FlowTrackable, BaseModel):
             )
             for task in self.tasks
         ]
-        # type: ignore # "interpolate_inputs" of "Agent" does not return a value (it only ever returns None)
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
@@ -1463,7 +1459,7 @@ class Crew(FlowTrackable, BaseModel):
             )
             raise
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return (
             f"Crew(id={self.id}, process={self.process}, "
             f"number_of_agents={len(self.agents)}, "
@@ -1520,7 +1516,9 @@ class Crew(FlowTrackable, BaseModel):
             if (system := config.get("system")) is not None:
                 name = config.get("name")
                 try:
-                    reset_fn: Callable = cast(Callable, config.get("reset"))
+                    reset_fn: Callable[[Any], Any] = cast(
+                        Callable[[Any], Any], config.get("reset")
+                    )
                     reset_fn(system)
                     self._logger.log(
                         "info",
@@ -1551,7 +1549,9 @@ class Crew(FlowTrackable, BaseModel):
             raise RuntimeError(f"{name} memory system is not initialized")
 
         try:
-            reset_fn: Callable = cast(Callable, config.get("reset"))
+            reset_fn: Callable[[Any], Any] = cast(
+                Callable[[Any], Any], config.get("reset")
+            )
             reset_fn(system)
             self._logger.log(
                 "info",
@@ -1564,7 +1564,7 @@ class Crew(FlowTrackable, BaseModel):
                 f"Failed to reset {name} memory: {e!s}"
             ) from e
 
-    def _get_memory_systems(self):
+    def _get_memory_systems(self) -> dict[str, Any]:
         """Get all available memory systems with their configuration.
 
         Returns:
@@ -1572,10 +1572,10 @@ class Crew(FlowTrackable, BaseModel):
             display names.
         """
 
-        def default_reset(memory):
+        def default_reset(memory: Any) -> Any:
             return memory.reset()
 
-        def knowledge_reset(memory):
+        def knowledge_reset(memory: Any) -> Any:
             return self.reset_knowledge(memory)
 
         # Get knowledge for agents
@@ -1635,7 +1635,7 @@ class Crew(FlowTrackable, BaseModel):
         for ks in knowledges:
             ks.reset()
 
-    def _set_allow_crewai_trigger_context_for_first_task(self):
+    def _set_allow_crewai_trigger_context_for_first_task(self) -> None:
         crewai_trigger_payload = self._inputs and self._inputs.get(
             "crewai_trigger_payload"
         )

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

@@ -1,12 +1,10 @@
+from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.persistence import persist
 from crewai.flow.visualization import (
     FlowStructure,
     build_flow_structure,
-    print_structure_summary,
-    structure_to_dict,
     visualize_flow_structure,
 )
-from crewai.flow.flow import Flow, and_, listen, or_, router, start
-from crewai.flow.persistence import persist
 
 
 __all__ = [
@@ -17,9 +15,7 @@ __all__ = [
     "listen",
     "or_",
     "persist",
-    "print_structure_summary",
     "router",
     "start",
-    "structure_to_dict",
     "visualize_flow_structure",
 ]

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

@@ -15,7 +15,6 @@ import logging
 from typing import (
     Any,
     ClassVar,
-    Final,
     Generic,
     Literal,
     ParamSpec,
@@ -45,7 +44,7 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
-from crewai.flow.visualization import build_flow_structure, render_interactive
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
 from crewai.flow.flow_wrappers import (
     FlowCondition,
     FlowConditions,
@@ -58,18 +57,16 @@ from crewai.flow.flow_wrappers import (
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey
 from crewai.flow.utils import (
+    _extract_all_methods,
+    _normalize_condition,
     get_possible_return_constants,
     is_flow_condition_dict,
-    is_flow_condition_list,
     is_flow_method,
     is_flow_method_callable,
     is_flow_method_name,
     is_simple_flow_condition,
-    _extract_all_methods,
-    _extract_all_methods_recursive,
-    _normalize_condition,
 )
-from crewai.flow.constants import AND_CONDITION, OR_CONDITION
+from crewai.flow.visualization import build_flow_structure, render_interactive
 from crewai.utilities.printer import Printer, PrinterColor
 
 
@@ -495,7 +492,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             or should_auto_collect_first_time_traces()
         ):
             trace_listener = TraceCollectionListener()
-            trace_listener.setup_listeners(crewai_event_bus)  # type: ignore[no-untyped-call]
+            trace_listener.setup_listeners(crewai_event_bus)
         # Apply any additional kwargs
         if kwargs:
             self._initialize_state(kwargs)
@@ -601,7 +598,26 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
 
     def _copy_state(self) -> T:
-        return copy.deepcopy(self._state)
+        """Create a copy of the current state.
+
+        Returns:
+            A copy of the current state
+        """
+        if isinstance(self._state, BaseModel):
+            try:
+                return self._state.model_copy(deep=True)
+            except (TypeError, AttributeError):
+                try:
+                    state_dict = self._state.model_dump()
+                    model_class = type(self._state)
+                    return model_class(**state_dict)
+                except Exception:
+                    return self._state.model_copy(deep=False)
+        else:
+            try:
+                return copy.deepcopy(self._state)
+            except (TypeError, AttributeError):
+                return cast(T, self._state.copy())
 
     @property
     def state(self) -> T:
@@ -926,8 +942,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 trace_listener = TraceCollectionListener()
                 if trace_listener.batch_manager.batch_owner_type == "flow":
                     if trace_listener.first_time_handler.is_first_time:
-                        trace_listener.first_time_handler.mark_events_collected()  # type: ignore[no-untyped-call]
-                        trace_listener.first_time_handler.handle_execution_completion()  # type: ignore[no-untyped-call]
+                        trace_listener.first_time_handler.mark_events_collected()
+                        trace_listener.first_time_handler.handle_execution_completion()
                     else:
                         trace_listener.batch_manager.finalize_batch()
 

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

@@ -3,8 +3,6 @@
 from crewai.flow.visualization.builder import (
     build_flow_structure,
     calculate_execution_paths,
-    print_structure_summary,
-    structure_to_dict,
 )
 from crewai.flow.visualization.renderers import render_interactive
 from crewai.flow.visualization.types import FlowStructure, NodeMetadata, StructureEdge
@@ -18,8 +16,6 @@ __all__ = [
     "StructureEdge",
     "build_flow_structure",
     "calculate_execution_paths",
-    "print_structure_summary",
     "render_interactive",
-    "structure_to_dict",
     "visualize_flow_structure",
 ]

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

@@ -66,19 +66,19 @@
             <div class="legend-title">Edge Types</div>
             <div class="legend-item">
                 <svg width="24" height="12" style="margin-right: 12px;">
-                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2"/>
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2" stroke-dasharray="5,5"/>
                 </svg>
                 <span>Router Paths</span>
             </div>
             <div class="legend-item">
-                <svg width="24" height="12" style="margin-right: 12px;">
-                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ GRAY }}'" stroke-width="2"/>
+                <svg width="24" height="12" style="margin-right: 12px;" class="legend-or-line">
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="var(--edge-or-color)" stroke-width="2"/>
                 </svg>
                 <span>OR Conditions</span>
             </div>
             <div class="legend-item">
                 <svg width="24" height="12" style="margin-right: 12px;">
-                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2" stroke-dasharray="5,5"/>
+                    <line x1="0" y1="6" x2="24" y2="6" stroke="'{{ CREWAI_ORANGE }}'" stroke-width="2"/>
                 </svg>
                 <span>AND Conditions</span>
             </div>

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

@@ -12,6 +12,7 @@
     --shadow-strong: rgba(0, 0, 0, 0.15);
     --edge-label-text: '{{ GRAY }}';
     --edge-label-bg: rgba(255, 255, 255, 0.8);
+    --edge-or-color: #000000;
 }
 
 [data-theme="dark"] {
@@ -28,6 +29,7 @@
     --shadow-strong: rgba(0, 0, 0, 0.5);
     --edge-label-text: #c9d1d9;
     --edge-label-bg: rgba(22, 27, 34, 0.9);
+    --edge-or-color: #ffffff;
 }
 
 @keyframes dash {

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

@@ -1,14 +1,15 @@
 """Flow structure builder for analyzing Flow execution."""
 
 from __future__ import annotations
+
 from collections import defaultdict
 import inspect
 from typing import TYPE_CHECKING, Any
 
-from crewai.flow.constants import OR_CONDITION
+from crewai.flow.constants import AND_CONDITION, OR_CONDITION
+from crewai.flow.flow_wrappers import FlowCondition
 from crewai.flow.types import FlowMethodName
 from crewai.flow.utils import (
-    _extract_all_methods_recursive,
     is_flow_condition_dict,
     is_simple_flow_condition,
 )
@@ -21,7 +22,7 @@ if TYPE_CHECKING:
 
 
 def _extract_direct_or_triggers(
-    condition: str | dict[str, Any] | list[Any],
+    condition: str | dict[str, Any] | list[Any] | FlowCondition,
 ) -> list[str]:
     """Extract direct OR-level trigger strings from a condition.
 
@@ -43,16 +44,15 @@ def _extract_direct_or_triggers(
     if isinstance(condition, str):
         return [condition]
     if isinstance(condition, dict):
-        cond_type = condition.get("type", "OR")
+        cond_type = condition.get("type", OR_CONDITION)
         conditions_list = condition.get("conditions", [])
 
-        if cond_type == "OR":
+        if cond_type == OR_CONDITION:
             strings = []
             for sub_cond in conditions_list:
                 strings.extend(_extract_direct_or_triggers(sub_cond))
             return strings
-        else:
-            return []
+        return []
     if isinstance(condition, list):
         strings = []
         for item in condition:
@@ -64,7 +64,7 @@ def _extract_direct_or_triggers(
 
 
 def _extract_all_trigger_names(
-    condition: str | dict[str, Any] | list[Any],
+    condition: str | dict[str, Any] | list[Any] | FlowCondition,
 ) -> list[str]:
     """Extract ALL trigger names from a condition for display purposes.
 
@@ -101,6 +101,76 @@ def _extract_all_trigger_names(
     return []
 
 
+def _create_edges_from_condition(
+    condition: str | dict[str, Any] | list[Any] | FlowCondition,
+    target: str,
+    nodes: dict[str, NodeMetadata],
+) -> list[StructureEdge]:
+    """Create edges from a condition tree, preserving AND/OR semantics.
+
+    This function recursively processes the condition tree and creates edges
+    with the appropriate condition_type for each trigger.
+
+    For AND conditions, all triggers get edges with condition_type="AND".
+    For OR conditions, triggers get edges with condition_type="OR".
+
+    Args:
+        condition: The condition tree (string, dict, or list).
+        target: The target node name.
+        nodes: Dictionary of all nodes for validation.
+
+    Returns:
+        List of StructureEdge objects representing the condition.
+    """
+    edges: list[StructureEdge] = []
+
+    if isinstance(condition, str):
+        if condition in nodes:
+            edges.append(
+                StructureEdge(
+                    source=condition,
+                    target=target,
+                    condition_type=OR_CONDITION,
+                    is_router_path=False,
+                )
+            )
+    elif callable(condition) and hasattr(condition, "__name__"):
+        method_name = condition.__name__
+        if method_name in nodes:
+            edges.append(
+                StructureEdge(
+                    source=method_name,
+                    target=target,
+                    condition_type=OR_CONDITION,
+                    is_router_path=False,
+                )
+            )
+    elif isinstance(condition, dict):
+        cond_type = condition.get("type", OR_CONDITION)
+        conditions_list = condition.get("conditions", [])
+
+        if cond_type == AND_CONDITION:
+            triggers = _extract_all_trigger_names(condition)
+            edges.extend(
+                StructureEdge(
+                    source=trigger,
+                    target=target,
+                    condition_type=AND_CONDITION,
+                    is_router_path=False,
+                )
+                for trigger in triggers
+                if trigger in nodes
+            )
+        else:
+            for sub_cond in conditions_list:
+                edges.extend(_create_edges_from_condition(sub_cond, target, nodes))
+    elif isinstance(condition, list):
+        for item in condition:
+            edges.extend(_create_edges_from_condition(item, target, nodes))
+
+    return edges
+
+
 def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
     """Build a structure representation of a Flow's execution.
 
@@ -228,28 +298,22 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
         nodes[method_name] = node_metadata
 
     for listener_name, condition_data in flow._listeners.items():
-        condition_type: str | None = None
-        trigger_methods_list: list[str] = []
-
         if is_simple_flow_condition(condition_data):
             cond_type, methods = condition_data
-            condition_type = cond_type
-            trigger_methods_list = [str(m) for m in methods]
-        elif is_flow_condition_dict(condition_data):
-            condition_type = condition_data.get("type", OR_CONDITION)
-            methods_recursive = _extract_all_methods_recursive(condition_data, flow)
-            trigger_methods_list = [str(m) for m in methods_recursive]
-
-        edges.extend(
-            StructureEdge(
-                source=str(trigger_method),
-                target=str(listener_name),
-                condition_type=condition_type,
-                is_router_path=False,
+            edges.extend(
+                StructureEdge(
+                    source=str(trigger_method),
+                    target=str(listener_name),
+                    condition_type=cond_type,
+                    is_router_path=False,
+                )
+                for trigger_method in methods
+                if str(trigger_method) in nodes
+            )
+        elif is_flow_condition_dict(condition_data):
+            edges.extend(
+                _create_edges_from_condition(condition_data, str(listener_name), nodes)
             )
-            for trigger_method in trigger_methods_list
-            if trigger_method in nodes
-        )
 
     for router_method_name in router_methods:
         if router_method_name not in flow._router_paths:
@@ -299,76 +363,6 @@ def build_flow_structure(flow: Flow[Any]) -> FlowStructure:
     )
 
 
-def structure_to_dict(structure: FlowStructure) -> dict[str, Any]:
-    """Convert FlowStructure to plain dictionary for serialization.
-
-    Args:
-        structure: FlowStructure to convert.
-
-    Returns:
-        Plain dictionary representation.
-    """
-    return {
-        "nodes": dict(structure["nodes"]),
-        "edges": list(structure["edges"]),
-        "start_methods": list(structure["start_methods"]),
-        "router_methods": list(structure["router_methods"]),
-    }
-
-
-def print_structure_summary(structure: FlowStructure) -> str:
-    """Generate human-readable summary of Flow structure.
-
-    Args:
-        structure: FlowStructure to summarize.
-
-    Returns:
-        Formatted string summary.
-    """
-    lines: list[str] = []
-    lines.append("Flow Execution Structure")
-    lines.append("=" * 50)
-    lines.append(f"Total nodes: {len(structure['nodes'])}")
-    lines.append(f"Total edges: {len(structure['edges'])}")
-    lines.append(f"Start methods: {len(structure['start_methods'])}")
-    lines.append(f"Router methods: {len(structure['router_methods'])}")
-    lines.append("")
-
-    if structure["start_methods"]:
-        lines.append("Start Methods:")
-        for method_name in structure["start_methods"]:
-            node = structure["nodes"][method_name]
-            lines.append(f"  - {method_name}")
-            if node.get("condition_type"):
-                lines.append(f"    Condition: {node['condition_type']}")
-            if node.get("trigger_methods"):
-                lines.append(f"    Triggers on: {', '.join(node['trigger_methods'])}")
-        lines.append("")
-
-    if structure["router_methods"]:
-        lines.append("Router Methods:")
-        for method_name in structure["router_methods"]:
-            node = structure["nodes"][method_name]
-            lines.append(f"  - {method_name}")
-            if node.get("router_paths"):
-                lines.append(f"    Paths: {', '.join(node['router_paths'])}")
-        lines.append("")
-
-    if structure["edges"]:
-        lines.append("Connections:")
-        for edge in structure["edges"]:
-            edge_type = ""
-            if edge["is_router_path"]:
-                edge_type = " [Router Path]"
-            elif edge["condition_type"]:
-                edge_type = f" [{edge['condition_type']}]"
-
-            lines.append(f"  {edge['source']} -> {edge['target']}{edge_type}")
-        lines.append("")
-
-    return "\n".join(lines)
-
-
 def calculate_execution_paths(structure: FlowStructure) -> int:
     """Calculate number of possible execution paths through the flow.
 
@@ -396,6 +390,15 @@ def calculate_execution_paths(structure: FlowStructure) -> int:
         return 0
 
     def count_paths_from(node: str, visited: set[str]) -> int:
+        """Recursively count execution paths from a given node.
+
+        Args:
+            node: Node name to start counting from.
+            visited: Set of already visited nodes to prevent cycles.
+
+        Returns:
+            Number of execution paths from this node to terminal nodes.
+        """
         if node in terminal_nodes:
             return 1
 

lib/crewai/src/crewai/lite_agent.py

@@ -1,6 +1,7 @@
 import asyncio
 from collections.abc import Callable
 import inspect
+import json
 from typing import (
     Any,
     Literal,
@@ -58,10 +59,14 @@ from crewai.utilities.agent_utils import (
     process_llm_response,
     render_text_description_and_args,
 )
-from crewai.utilities.converter import generate_model_description
+from crewai.utilities.converter import (
+    Converter,
+    ConverterError,
+    generate_model_description,
+)
 from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -90,8 +95,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     """
 
     model_config = {"arbitrary_types_allowed": True}
-
-    # Core Agent Properties
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Goal of the agent")
@@ -102,8 +105,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     tools: list[BaseTool] = Field(
         default_factory=list, description="Tools at agent's disposal"
     )
-
-    # Execution Control Properties
     max_iterations: int = Field(
         default=15, description="Maximum number of iterations for tool usage"
     )
@@ -120,24 +121,17 @@ class LiteAgent(FlowTrackable, BaseModel):
     )
     request_within_rpm_limit: Callable[[], bool] | None = Field(
         default=None,
-        description="Callback to check if the request is within the RPM limit",
+        description="Callback to check if the request is within the RPM8 limit",
     )
     i18n: I18N = Field(
-        default_factory=I18N, description="Internationalization settings."
+        default_factory=get_i18n, description="Internationalization settings."
     )
-
-    # Output and Formatting Properties
     response_format: type[BaseModel] | None = Field(
         default=None, description="Pydantic model for structured output"
     )
     verbose: bool = Field(
         default=False, description="Whether to print execution details"
     )
-    callbacks: list[Callable] = Field(
-        default_factory=list, description="Callbacks to be used for the agent"
-    )
-
-    # Guardrail Properties
     guardrail: GuardrailType | None = Field(
         default=None,
         description="Function or string description of a guardrail to validate agent output",
@@ -145,17 +139,12 @@ class LiteAgent(FlowTrackable, BaseModel):
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
     )
-
-    # State and Results
     tools_results: list[dict[str, Any]] = Field(
         default_factory=list, description="Results of the tools used by the agent."
     )
-
-    # Reference of Agent
     original_agent: BaseAgent | None = Field(
         default=None, description="Reference to the agent that created this LiteAgent"
     )
-    # Private Attributes
     _parsed_tools: list[CrewStructuredTool] = PrivateAttr(default_factory=list)
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     _cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
@@ -165,6 +154,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     _printer: Printer = PrivateAttr(default_factory=Printer)
     _guardrail: GuardrailCallable | None = PrivateAttr(default=None)
     _guardrail_retry_count: int = PrivateAttr(default=0)
+    _callbacks: list[TokenCalcHandler] = PrivateAttr(default_factory=list)
 
     @model_validator(mode="after")
     def setup_llm(self) -> Self:
@@ -174,15 +164,13 @@ class LiteAgent(FlowTrackable, BaseModel):
             raise ValueError(
                 f"Expected LLM instance of type BaseLLM, got {type(self.llm).__name__}"
             )
-
-        # Initialize callbacks
         token_callback = TokenCalcHandler(token_cost_process=self._token_process)
         self._callbacks = [token_callback]
 
         return self
 
     @model_validator(mode="after")
-    def parse_tools(self):
+    def parse_tools(self) -> Self:
         """Parse the tools and convert them to CrewStructuredTool instances."""
         self._parsed_tools = parse_tools(self.tools)
 
@@ -201,7 +189,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                 )
             self._guardrail = cast(
                 GuardrailCallable,
-                LLMGuardrail(description=self.guardrail, llm=self.llm),
+                cast(object, LLMGuardrail(description=self.guardrail, llm=self.llm)),
             )
 
         return self
@@ -209,8 +197,8 @@ class LiteAgent(FlowTrackable, BaseModel):
     @field_validator("guardrail", mode="before")
     @classmethod
     def validate_guardrail_function(
-        cls, v: Callable | str | None
-    ) -> Callable | str | None:
+        cls, v: GuardrailCallable | str | None
+    ) -> GuardrailCallable | str | None:
         """Validate that the guardrail function has the correct signature.
 
         If v is a callable, validate that it has the correct signature.
@@ -258,7 +246,11 @@ class LiteAgent(FlowTrackable, BaseModel):
         """Return the original role for compatibility with tool interfaces."""
         return self.role
 
-    def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
+    def kickoff(
+        self,
+        messages: str | list[LLMMessage],
+        response_format: type[BaseModel] | None = None,
+    ) -> LiteAgentOutput:
         """
         Execute the agent with the given messages.
 
@@ -266,6 +258,8 @@ class LiteAgent(FlowTrackable, BaseModel):
             messages: Either a string query or a list of message dictionaries.
                      If a string is provided, it will be converted to a user message.
                      If a list is provided, each dict should have 'role' and 'content' keys.
+            response_format: Optional Pydantic model for structured output. If provided,
+                           overrides self.response_format for this execution.
 
         Returns:
             LiteAgentOutput: The result of the agent execution.
@@ -286,9 +280,13 @@ class LiteAgent(FlowTrackable, BaseModel):
             self.tools_results = []
 
             # Format messages for the LLM
-            self._messages = self._format_messages(messages)
+            self._messages = self._format_messages(
+                messages, response_format=response_format
+            )
 
-            return self._execute_core(agent_info=agent_info)
+            return self._execute_core(
+                agent_info=agent_info, response_format=response_format
+            )
 
         except Exception as e:
             self._printer.print(
@@ -306,7 +304,9 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
             raise e
 
-    def _execute_core(self, agent_info: dict[str, Any]) -> LiteAgentOutput:
+    def _execute_core(
+        self, agent_info: dict[str, Any], response_format: type[BaseModel] | None = None
+    ) -> LiteAgentOutput:
         # Emit event for agent execution start
         crewai_event_bus.emit(
             self,
@@ -320,15 +320,29 @@ class LiteAgent(FlowTrackable, BaseModel):
         # Execute the agent using invoke loop
         agent_finish = self._invoke_loop()
         formatted_result: BaseModel | None = None
-        if self.response_format:
+
+        active_response_format = response_format or self.response_format
+        if active_response_format:
             try:
-                # Cast to BaseModel to ensure type safety
-                result = self.response_format.model_validate_json(agent_finish.output)
+                model_schema = generate_model_description(active_response_format)
+                schema = json.dumps(model_schema, indent=2)
+                instructions = self.i18n.slice("formatted_task_instructions").format(
+                    output_format=schema
+                )
+
+                converter = Converter(
+                    llm=self.llm,
+                    text=agent_finish.output,
+                    model=active_response_format,
+                    instructions=instructions,
+                )
+
+                result = converter.to_pydantic()
                 if isinstance(result, BaseModel):
                     formatted_result = result
-            except Exception as e:
+            except ConverterError as e:
                 self._printer.print(
-                    content=f"Failed to parse output into response format: {e!s}",
+                    content=f"Failed to parse output into response format after retries: {e.message}",
                     color="yellow",
                 )
 
@@ -417,8 +431,14 @@ class LiteAgent(FlowTrackable, BaseModel):
         """
         return await asyncio.to_thread(self.kickoff, messages)
 
-    def _get_default_system_prompt(self) -> str:
-        """Get the default system prompt for the agent."""
+    def _get_default_system_prompt(
+        self, response_format: type[BaseModel] | None = None
+    ) -> str:
+        """Get the default system prompt for the agent.
+
+        Args:
+            response_format: Optional response format to use instead of self.response_format
+        """
         base_prompt = ""
         if self._parsed_tools:
             # Use the prompt template for agents with tools
@@ -439,21 +459,31 @@ class LiteAgent(FlowTrackable, BaseModel):
                 goal=self.goal,
             )
 
-        # Add response format instructions if specified
-        if self.response_format:
-            schema = generate_model_description(self.response_format)
+        active_response_format = response_format or self.response_format
+        if active_response_format:
+            model_description = generate_model_description(active_response_format)
+            schema_json = json.dumps(model_description, indent=2)
             base_prompt += self.i18n.slice("lite_agent_response_format").format(
-                response_format=schema
+                response_format=schema_json
             )
 
         return base_prompt
 
-    def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
-        """Format messages for the LLM."""
+    def _format_messages(
+        self,
+        messages: str | list[LLMMessage],
+        response_format: type[BaseModel] | None = None,
+    ) -> list[LLMMessage]:
+        """Format messages for the LLM.
+
+        Args:
+            messages: Input messages to format
+            response_format: Optional response format to use instead of self.response_format
+        """
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
-        system_prompt = self._get_default_system_prompt()
+        system_prompt = self._get_default_system_prompt(response_format=response_format)
 
         # Add system message at the beginning
         formatted_messages: list[LLMMessage] = [
@@ -523,6 +553,10 @@ class LiteAgent(FlowTrackable, BaseModel):
 
                 self._append_message(formatted_answer.text, role="assistant")
             except OutputParserError as e:  # noqa: PERF203
+                self._printer.print(
+                    content="Failed to parse LLM output. Retrying...",
+                    color="yellow",
+                )
                 formatted_answer = handle_output_parser_exception(
                     e=e,
                     messages=self._messages,
@@ -559,7 +593,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         self._show_logs(formatted_answer)
         return formatted_answer
 
-    def _show_logs(self, formatted_answer: AgentAction | AgentFinish):
+    def _show_logs(self, formatted_answer: AgentAction | AgentFinish) -> None:
         """Show logs for the agent's execution."""
         crewai_event_bus.emit(
             self,
@@ -574,4 +608,4 @@ class LiteAgent(FlowTrackable, BaseModel):
         self, text: str, role: Literal["user", "assistant", "system"] = "assistant"
     ) -> None:
         """Append a message to the message list with the given role."""
-        self._messages.append(cast(LLMMessage, format_message_for_llm(text, role=role)))
+        self._messages.append(format_message_for_llm(text, role=role))

lib/crewai/src/crewai/llm.py

@@ -20,6 +20,7 @@ from typing import (
 )
 
 from dotenv import load_dotenv
+import httpx
 from pydantic import BaseModel, Field
 from typing_extensions import Self
 
@@ -53,6 +54,7 @@ if TYPE_CHECKING:
     from litellm.utils import supports_response_schema
 
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -334,6 +336,8 @@ class LLM(BaseLLM):
                 return cast(
                     Self, native_class(model=model_string, provider=provider, **kwargs)
                 )
+            except NotImplementedError:
+                raise
             except Exception as e:
                 raise ImportError(f"Error importing native provider: {e}") from e
 
@@ -403,6 +407,7 @@ class LLM(BaseLLM):
         callbacks: list[Any] | None = None,
         reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
         stream: bool = False,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize LLM instance.
@@ -442,6 +447,7 @@ class LLM(BaseLLM):
         self.additional_params = kwargs
         self.is_anthropic = self._is_anthropic_model(model)
         self.stream = stream
+        self.interceptor = interceptor
 
         litellm.drop_params = True
 

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

@@ -0,0 +1,6 @@
+"""Interceptor contracts for crewai"""
+
+from crewai.llms.hooks.base import BaseInterceptor
+
+
+__all__ = ["BaseInterceptor"]

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

@@ -0,0 +1,82 @@
+"""Base classes for LLM transport interceptors.
+
+This module provides abstract base classes for intercepting and modifying
+outbound and inbound messages at the transport level.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Generic, TypeVar
+
+
+T = TypeVar("T")
+U = TypeVar("U")
+
+
+class BaseInterceptor(ABC, Generic[T, U]):
+    """Abstract base class for intercepting transport-level messages.
+
+    Provides hooks to intercept and modify outbound and inbound messages
+    at the transport layer.
+
+    Type parameters:
+        T: Outbound message type (e.g., httpx.Request)
+        U: Inbound message type (e.g., httpx.Response)
+
+    Example:
+        >>> class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+        ...     def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        ...         message.headers["X-Custom-Header"] = "value"
+        ...         return message
+        ...
+        ...     def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        ...         print(f"Status: {message.status_code}")
+        ...         return message
+    """
+
+    @abstractmethod
+    def on_outbound(self, message: T) -> T:
+        """Intercept outbound message before sending.
+
+        Args:
+            message: Outbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        ...
+
+    @abstractmethod
+    def on_inbound(self, message: U) -> U:
+        """Intercept inbound message after receiving.
+
+        Args:
+            message: Inbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        ...
+
+    async def aon_outbound(self, message: T) -> T:
+        """Async version of on_outbound.
+
+        Args:
+            message: Outbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        raise NotImplementedError
+
+    async def aon_inbound(self, message: U) -> U:
+        """Async version of on_inbound.
+
+        Args:
+            message: Inbound message object.
+
+        Returns:
+            Modified message object.
+        """
+        raise NotImplementedError

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

@@ -0,0 +1,87 @@
+"""HTTP transport implementations for LLM request/response interception.
+
+This module provides internal transport classes that integrate with BaseInterceptor
+to enable request/response modification at the transport level.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import httpx
+
+
+if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
+
+
+class HTTPTransport(httpx.HTTPTransport):
+    """HTTP transport that uses an interceptor for request/response modification.
+
+    This transport is used internally when a user provides a BaseInterceptor.
+    Users should not instantiate this class directly - instead, pass an interceptor
+    to the LLM client and this transport will be created automatically.
+    """
+
+    def __init__(
+        self,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response],
+        **kwargs: Any,
+    ) -> None:
+        """Initialize transport with interceptor.
+
+        Args:
+            interceptor: HTTP interceptor for modifying raw request/response objects.
+            **kwargs: Additional arguments passed to httpx.HTTPTransport.
+        """
+        super().__init__(**kwargs)
+        self.interceptor = interceptor
+
+    def handle_request(self, request: httpx.Request) -> httpx.Response:
+        """Handle request with interception.
+
+        Args:
+            request: The HTTP request to handle.
+
+        Returns:
+            The HTTP response.
+        """
+        request = self.interceptor.on_outbound(request)
+        response = super().handle_request(request)
+        return self.interceptor.on_inbound(response)
+
+
+class AsyncHTTPransport(httpx.AsyncHTTPTransport):
+    """Async HTTP transport that uses an interceptor for request/response modification.
+
+    This transport is used internally when a user provides a BaseInterceptor.
+    Users should not instantiate this class directly - instead, pass an interceptor
+    to the LLM client and this transport will be created automatically.
+    """
+
+    def __init__(
+        self,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response],
+        **kwargs: Any,
+    ) -> None:
+        """Initialize async transport with interceptor.
+
+        Args:
+            interceptor: HTTP interceptor for modifying raw request/response objects.
+            **kwargs: Additional arguments passed to httpx.AsyncHTTPTransport.
+        """
+        super().__init__(**kwargs)
+        self.interceptor = interceptor
+
+    async def handle_async_request(self, request: httpx.Request) -> httpx.Response:
+        """Handle async request with interception.
+
+        Args:
+            request: The HTTP request to handle.
+
+        Returns:
+            The HTTP response.
+        """
+        request = await self.interceptor.aon_outbound(request)
+        response = await super().handle_async_request(request)
+        return await self.interceptor.aon_inbound(response)

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

@@ -1,15 +1,15 @@
 from __future__ import annotations
 
-
 import json
 import logging
 import os
-from typing import Any, cast
+from typing import TYPE_CHECKING, Any, cast
 
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -17,10 +17,14 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 from crewai.utilities.types import LLMMessage
 
 
+if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
+
 try:
     from anthropic import Anthropic
     from anthropic.types import Message
     from anthropic.types.tool_use_block import ToolUseBlock
+    import httpx
 except ImportError:
     raise ImportError(
         'Anthropic native provider not available, to install: uv add "crewai[anthropic]"'
@@ -47,7 +51,8 @@ class AnthropicCompletion(BaseLLM):
         stop_sequences: list[str] | None = None,
         stream: bool = False,
         client_params: dict[str, Any] | None = None,
-        **kwargs,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        **kwargs: Any,
     ):
         """Initialize Anthropic chat completion client.
 
@@ -63,6 +68,7 @@ class AnthropicCompletion(BaseLLM):
             stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop)
             stream: Enable streaming responses
             client_params: Additional parameters for the Anthropic client
+            interceptor: HTTP interceptor for modifying requests/responses at transport level.
             **kwargs: Additional parameters
         """
         super().__init__(
@@ -70,6 +76,7 @@ class AnthropicCompletion(BaseLLM):
         )
 
         # Client params
+        self.interceptor = interceptor
         self.client_params = client_params
         self.base_url = base_url
         self.timeout = timeout
@@ -102,6 +109,11 @@ class AnthropicCompletion(BaseLLM):
             "max_retries": self.max_retries,
         }
 
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            client_params["http_client"] = http_client  # type: ignore[assignment]
+
         if self.client_params:
             client_params.update(self.client_params)
 
@@ -110,7 +122,7 @@ class AnthropicCompletion(BaseLLM):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -133,7 +145,7 @@ class AnthropicCompletion(BaseLLM):
         try:
             # Emit call started event
             self._emit_call_started_event(
-                messages=messages,  # type: ignore[arg-type]
+                messages=messages,
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
@@ -143,7 +155,7 @@ class AnthropicCompletion(BaseLLM):
 
             # Format messages for Anthropic
             formatted_messages, system_message = self._format_messages_for_anthropic(
-                messages  # type: ignore[arg-type]
+                messages
             )
 
             # Prepare completion parameters
@@ -181,7 +193,7 @@ class AnthropicCompletion(BaseLLM):
         self,
         messages: list[LLMMessage],
         system_message: str | None = None,
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Anthropic messages API.
 
@@ -218,7 +230,9 @@ class AnthropicCompletion(BaseLLM):
 
         return params
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+    def _convert_tools_for_interference(
+        self, tools: list[dict[str, Any]]
+    ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to Anthropic tool use format."""
         anthropic_tools = []
 

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

@@ -3,7 +3,7 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import Any, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from pydantic import BaseModel
 
@@ -13,23 +13,25 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.types import LLMMessage
 
+
 if TYPE_CHECKING:
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.tools.base_tool import BaseTool
 
 
 try:
-    from azure.ai.inference import (  # type: ignore[import-not-found]
+    from azure.ai.inference import (
         ChatCompletionsClient,
     )
-    from azure.ai.inference.models import (  # type: ignore[import-not-found]
+    from azure.ai.inference.models import (
         ChatCompletions,
         ChatCompletionsToolCall,
         StreamingChatCompletionsUpdate,
     )
-    from azure.core.credentials import (  # type: ignore[import-not-found]
+    from azure.core.credentials import (
         AzureKeyCredential,
     )
-    from azure.core.exceptions import (  # type: ignore[import-not-found]
+    from azure.core.exceptions import (
         HttpResponseError,
     )
 
@@ -64,7 +66,8 @@ class AzureCompletion(BaseLLM):
         max_tokens: int | None = None,
         stop: list[str] | None = None,
         stream: bool = False,
-        **kwargs,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
     ):
         """Initialize Azure AI Inference chat completion client.
 
@@ -82,8 +85,15 @@ class AzureCompletion(BaseLLM):
             max_tokens: Maximum tokens in response
             stop: Stop sequences
             stream: Enable streaming responses
+            interceptor: HTTP interceptor (not yet supported for Azure).
             **kwargs: Additional parameters
         """
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Azure AI Inference provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         super().__init__(
             model=model, temperature=temperature, stop=stop or [], **kwargs
         )
@@ -121,7 +131,7 @@ class AzureCompletion(BaseLLM):
         if self.api_version:
             client_kwargs["api_version"] = self.api_version
 
-        self.client = ChatCompletionsClient(**client_kwargs)
+        self.client = ChatCompletionsClient(**client_kwargs)  # type: ignore[arg-type]
 
         self.top_p = top_p
         self.frequency_penalty = frequency_penalty
@@ -249,7 +259,7 @@ class AzureCompletion(BaseLLM):
     def _prepare_completion_params(
         self,
         messages: list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> dict[str, Any]:
         """Prepare parameters for Azure AI Inference chat completion.
@@ -302,7 +312,9 @@ class AzureCompletion(BaseLLM):
 
         return params
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+    def _convert_tools_for_interference(
+        self, tools: list[dict[str, Any]]
+    ) -> list[dict[str, Any]]:
         """Convert CrewAI tool format to Azure OpenAI function calling format."""
 
         from crewai.llms.providers.utils.common import safe_tool_conversion

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

@@ -30,6 +30,8 @@ if TYPE_CHECKING:
         ToolTypeDef,
     )
 
+    from crewai.llms.hooks.base import BaseInterceptor
+
 
 try:
     from boto3.session import Session
@@ -157,8 +159,9 @@ class BedrockCompletion(BaseLLM):
         guardrail_config: dict[str, Any] | None = None,
         additional_model_request_fields: dict[str, Any] | None = None,
         additional_model_response_field_paths: list[str] | None = None,
-        **kwargs,
-    ):
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
+    ) -> None:
         """Initialize AWS Bedrock completion client.
 
         Args:
@@ -176,8 +179,15 @@ class BedrockCompletion(BaseLLM):
             guardrail_config: Guardrail configuration for content filtering
             additional_model_request_fields: Model-specific request parameters
             additional_model_response_field_paths: Custom response field paths
+            interceptor: HTTP interceptor (not yet supported for Bedrock).
             **kwargs: Additional parameters
         """
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for AWS Bedrock provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         # Extract provider from kwargs to avoid duplicate argument
         kwargs.pop("provider", None)
 
@@ -247,7 +257,7 @@ class BedrockCompletion(BaseLLM):
         try:
             # Emit call started event
             self._emit_call_started_event(
-                messages=messages,  # type: ignore[arg-type]
+                messages=messages,
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
@@ -740,7 +750,9 @@ class BedrockCompletion(BaseLLM):
         return converse_messages, system_message
 
     @staticmethod
-    def _format_tools_for_converse(tools: list[dict]) -> list[ConverseToolTypeDef]:
+    def _format_tools_for_converse(
+        tools: list[dict[str, Any]],
+    ) -> list[ConverseToolTypeDef]:
         """Convert CrewAI tools to Converse API format following AWS specification."""
         from crewai.llms.providers.utils.common import safe_tool_conversion
 

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

@@ -1,4 +1,3 @@
-import json
 import logging
 import os
 from typing import Any, cast
@@ -7,6 +6,7 @@ from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -45,7 +45,8 @@ class GeminiCompletion(BaseLLM):
         stream: bool = False,
         safety_settings: dict[str, Any] | None = None,
         client_params: dict[str, Any] | None = None,
-        **kwargs,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
     ):
         """Initialize Google Gemini chat completion client.
 
@@ -63,8 +64,15 @@ class GeminiCompletion(BaseLLM):
             safety_settings: Safety filter settings
             client_params: Additional parameters to pass to the Google Gen AI Client constructor.
                           Supports parameters like http_options, credentials, debug_config, etc.
+            interceptor: HTTP interceptor (not yet supported for Gemini).
             **kwargs: Additional parameters
         """
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Google Gemini provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
+
         super().__init__(
             model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
         )
@@ -96,7 +104,7 @@ class GeminiCompletion(BaseLLM):
         self.is_gemini_1_5 = "gemini-1.5" in model.lower()
         self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
 
-    def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:
+    def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:  # type: ignore[no-any-unimported]
         """Initialize the Google Gen AI client with proper parameter handling.
 
         Args:
@@ -171,7 +179,7 @@ class GeminiCompletion(BaseLLM):
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -193,7 +201,7 @@ class GeminiCompletion(BaseLLM):
         """
         try:
             self._emit_call_started_event(
-                messages=messages,  # type: ignore[arg-type]
+                messages=messages,
                 tools=tools,
                 callbacks=callbacks,
                 available_functions=available_functions,
@@ -203,7 +211,7 @@ class GeminiCompletion(BaseLLM):
             self.tools = tools
 
             formatted_content, system_instruction = self._format_messages_for_gemini(
-                messages  # type: ignore[arg-type]
+                messages
             )
 
             config = self._prepare_generation_config(
@@ -245,10 +253,10 @@ class GeminiCompletion(BaseLLM):
             )
             raise
 
-    def _prepare_generation_config(
+    def _prepare_generation_config(  # type: ignore[no-any-unimported]
         self,
         system_instruction: str | None = None,
-        tools: list[dict] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> types.GenerateContentConfig:
         """Prepare generation config for Google Gemini API.
@@ -297,7 +305,9 @@ class GeminiCompletion(BaseLLM):
 
         return types.GenerateContentConfig(**config_params)
 
-    def _convert_tools_for_interference(self, tools: list[dict]) -> list[types.Tool]:
+    def _convert_tools_for_interference(  # type: ignore[no-any-unimported]
+        self, tools: list[dict[str, Any]]
+    ) -> list[types.Tool]:
         """Convert CrewAI tool format to Gemini function declaration format."""
         gemini_tools = []
 
@@ -320,7 +330,7 @@ class GeminiCompletion(BaseLLM):
 
         return gemini_tools
 
-    def _format_messages_for_gemini(
+    def _format_messages_for_gemini(  # type: ignore[no-any-unimported]
         self, messages: str | list[LLMMessage]
     ) -> tuple[list[types.Content], str | None]:
         """Format messages for Gemini API.
@@ -364,7 +374,7 @@ class GeminiCompletion(BaseLLM):
 
         return contents, system_instruction
 
-    def _handle_completion(
+    def _handle_completion(  # type: ignore[no-any-unimported]
         self,
         contents: list[types.Content],
         system_instruction: str | None,
@@ -431,7 +441,7 @@ class GeminiCompletion(BaseLLM):
 
         return content
 
-    def _handle_streaming_completion(
+    def _handle_streaming_completion(  # type: ignore[no-any-unimported]
         self,
         contents: list[types.Content],
         config: types.GenerateContentConfig,
@@ -560,8 +570,9 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
-    def _convert_contents_to_dict(
-        self, contents: list[types.Content]
+    def _convert_contents_to_dict(  # type: ignore[no-any-unimported]
+        self,
+        contents: list[types.Content],
     ) -> list[dict[str, str]]:
         """Convert contents to dict format."""
         return [

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

@@ -6,6 +6,7 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any
 
+import httpx
 from openai import APIConnectionError, NotFoundError, OpenAI
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion import Choice
@@ -14,6 +15,7 @@ from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -23,6 +25,7 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
 
@@ -59,6 +62,7 @@ class OpenAICompletion(BaseLLM):
         top_logprobs: int | None = None,
         reasoning_effort: str | None = None,
         provider: str | None = None,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize OpenAI chat completion client."""
@@ -66,6 +70,7 @@ class OpenAICompletion(BaseLLM):
         if provider is None:
             provider = kwargs.pop("provider", "openai")
 
+        self.interceptor = interceptor
         # Client configuration attributes
         self.organization = organization
         self.project = project
@@ -88,6 +93,11 @@ class OpenAICompletion(BaseLLM):
         )
 
         client_config = self._get_client_params()
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            client_config["http_client"] = http_client
+
         self.client = OpenAI(**client_config)
 
         # Completion parameters

lib/crewai/src/crewai/project/utils.py

@@ -1,21 +1,75 @@
 """Utility functions for the crewai project module."""
 
 from collections.abc import Callable
-from functools import lru_cache
-from typing import ParamSpec, TypeVar, cast
+from functools import wraps
+from typing import Any, ParamSpec, TypeVar, cast
+
+from pydantic import BaseModel
+
+from crewai.agents.cache.cache_handler import CacheHandler
 
 
 P = ParamSpec("P")
 R = TypeVar("R")
+cache = CacheHandler()
+
+
+def _make_hashable(arg: Any) -> Any:
+    """Convert argument to hashable form for caching.
+
+    Args:
+        arg: The argument to convert.
+
+    Returns:
+        Hashable representation of the argument.
+    """
+    if isinstance(arg, BaseModel):
+        return arg.model_dump_json()
+    if isinstance(arg, dict):
+        return tuple(sorted((k, _make_hashable(v)) for k, v in arg.items()))
+    if isinstance(arg, list):
+        return tuple(_make_hashable(item) for item in arg)
+    if hasattr(arg, "__dict__"):
+        return ("__instance__", id(arg))
+    return arg
 
 
 def memoize(meth: Callable[P, R]) -> Callable[P, R]:
     """Memoize a method by caching its results based on arguments.
 
+    Handles Pydantic BaseModel instances by converting them to JSON strings
+    before hashing for cache lookup.
+
     Args:
         meth: The method to memoize.
 
     Returns:
         A memoized version of the method that caches results.
     """
-    return cast(Callable[P, R], lru_cache(typed=True)(meth))
+
+    @wraps(meth)
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+        """Wrapper that converts arguments to hashable form before caching.
+
+        Args:
+            *args: Positional arguments to the memoized method.
+            **kwargs: Keyword arguments to the memoized method.
+
+        Returns:
+            The result of the memoized method call.
+        """
+        hashable_args = tuple(_make_hashable(arg) for arg in args)
+        hashable_kwargs = tuple(
+            sorted((k, _make_hashable(v)) for k, v in kwargs.items())
+        )
+        cache_key = str((hashable_args, hashable_kwargs))
+
+        cached_result: R | None = cache.read(tool=meth.__name__, input=cache_key)
+        if cached_result is not None:
+            return cached_result
+
+        result = meth(*args, **kwargs)
+        cache.add(tool=meth.__name__, input=cache_key, output=result)
+        return result
+
+    return cast(Callable[P, R], wrapper)

lib/crewai/src/crewai/task.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from collections.abc import Callable
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 import datetime
@@ -29,6 +28,7 @@ from pydantic import (
     model_validator,
 )
 from pydantic_core import PydanticCustomError
+from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.events.event_bus import crewai_event_bus
@@ -52,7 +52,7 @@ from crewai.utilities.guardrail_types import (
     GuardrailType,
     GuardrailsType,
 )
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
 from crewai.utilities.string_utils import interpolate_only
 
@@ -90,7 +90,7 @@ class Task(BaseModel):
     used_tools: int = 0
     tools_errors: int = 0
     delegations: int = 0
-    i18n: I18N = Field(default_factory=I18N)
+    i18n: I18N = Field(default_factory=get_i18n)
     name: str | None = Field(default=None)
     prompt_context: str | None = None
     description: str = Field(description="Description of the actual task.")
@@ -207,8 +207,8 @@ class Task(BaseModel):
     @field_validator("guardrail")
     @classmethod
     def validate_guardrail_function(
-        cls, v: str | Callable | None
-    ) -> str | Callable | None:
+        cls, v: str | GuardrailCallable | None
+    ) -> str | GuardrailCallable | None:
         """
         If v is a callable, validate that the guardrail function has the correct signature and behavior.
         If v is a string, return it as is.
@@ -265,11 +265,11 @@ class Task(BaseModel):
 
     @model_validator(mode="before")
     @classmethod
-    def process_model_config(cls, values):
+    def process_model_config(cls, values: dict[str, Any]) -> dict[str, Any]:
         return process_config(values, cls)
 
     @model_validator(mode="after")
-    def validate_required_fields(self):
+    def validate_required_fields(self) -> Self:
         required_fields = ["description", "expected_output"]
         for field in required_fields:
             if getattr(self, field) is None:
@@ -418,14 +418,14 @@ class Task(BaseModel):
         return self
 
     @model_validator(mode="after")
-    def check_tools(self):
+    def check_tools(self) -> Self:
         """Check if the tools are set."""
         if not self.tools and self.agent and self.agent.tools:
-            self.tools.extend(self.agent.tools)
+            self.tools = self.agent.tools
         return self
 
     @model_validator(mode="after")
-    def check_output(self):
+    def check_output(self) -> Self:
         """Check if an output type is set."""
         output_types = [self.output_json, self.output_pydantic]
         if len([type for type in output_types if type]) > 1:
@@ -437,7 +437,7 @@ class Task(BaseModel):
         return self
 
     @model_validator(mode="after")
-    def handle_max_retries_deprecation(self):
+    def handle_max_retries_deprecation(self) -> Self:
         if self.max_retries is not None:
             warnings.warn(
                 "The 'max_retries' parameter is deprecated and will be removed in CrewAI v1.0.0. "
@@ -518,14 +518,18 @@ class Task(BaseModel):
             tools = tools or self.tools or []
 
             self.processed_by_agents.add(agent.role)
-            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))
+            crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))  # type: ignore[no-untyped-call]
             result = agent.execute_task(
                 task=self,
                 context=context,
                 tools=tools,
             )
 
-            pydantic_output, json_output = self._export_output(result)
+            if not self._guardrails and not self._guardrail:
+                pydantic_output, json_output = self._export_output(result)
+            else:
+                pydantic_output, json_output = None, None
+
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,
@@ -576,12 +580,13 @@ class Task(BaseModel):
                 )
                 self._save_file(content)
             crewai_event_bus.emit(
-                self, TaskCompletedEvent(output=task_output, task=self)
+                self,
+                TaskCompletedEvent(output=task_output, task=self),  # type: ignore[no-untyped-call]
             )
             return task_output
         except Exception as e:
             self.end_time = datetime.datetime.now()
-            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))
+            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))  # type: ignore[no-untyped-call]
             raise e  # Re-raise the exception after emitting the event
 
     def prompt(self) -> str:
@@ -786,7 +791,7 @@ Follow these guidelines:
             return OutputFormat.PYDANTIC
         return OutputFormat.RAW
 
-    def _save_file(self, result: dict | str | Any) -> None:
+    def _save_file(self, result: dict[str, Any] | str | Any) -> None:
         """Save task output to a file.
 
         Note:
@@ -838,7 +843,7 @@ Follow these guidelines:
             ) from e
         return
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return f"Task(description={self.description}, expected_output={self.expected_output})"
 
     @property

lib/crewai/src/crewai/tools/agent_tools/agent_tools.py

@@ -1,8 +1,16 @@
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
 from crewai.tools.agent_tools.ask_question_tool import AskQuestionTool
 from crewai.tools.agent_tools.delegate_work_tool import DelegateWorkTool
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import get_i18n
+
+
+if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.tools.base_tool import BaseTool
+    from crewai.utilities.i18n import I18N
 
 
 class AgentTools:
@@ -10,7 +18,7 @@ class AgentTools:
 
     def __init__(self, agents: list[BaseAgent], i18n: I18N | None = None) -> None:
         self.agents = agents
-        self.i18n = i18n if i18n is not None else I18N()
+        self.i18n = i18n if i18n is not None else get_i18n()
 
     def tools(self) -> list[BaseTool]:
         """Get all available agent tools"""

lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,12 +1,12 @@
 import logging
+from typing import Any
 
 from pydantic import Field
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
 from crewai.tools.base_tool import BaseTool
-from crewai.utilities.i18n import I18N
-
+from crewai.utilities.i18n import I18N, get_i18n
 
 
 logger = logging.getLogger(__name__)
@@ -17,7 +17,7 @@ class BaseAgentTool(BaseTool):
 
     agents: list[BaseAgent] = Field(description="List of available agents")
     i18n: I18N = Field(
-        default_factory=I18N, description="Internationalization settings"
+        default_factory=get_i18n, description="Internationalization settings"
     )
 
     def sanitize_agent_name(self, name: str) -> str:
@@ -40,7 +40,7 @@ class BaseAgentTool(BaseTool):
         return normalized.replace('"', "").casefold()
 
     @staticmethod
-    def _get_coworker(coworker: str | None, **kwargs) -> str | None:
+    def _get_coworker(coworker: str | None, **kwargs: Any) -> str | None:
         coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
         if coworker:
             is_list = coworker.startswith("[") and coworker.endswith("]")
@@ -83,7 +83,7 @@ class BaseAgentTool(BaseTool):
             available_agents = [agent.role for agent in self.agents]
             logger.debug(f"Available agents: {available_agents}")
 
-            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
+            agent = [
                 available_agent
                 for available_agent in self.agents
                 if self.sanitize_agent_name(available_agent.role) == sanitized_name

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

@@ -7,10 +7,10 @@ import json
 from json import JSONDecodeError
 from textwrap import dedent
 import time
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
 import json5
-from json_repair import repair_json  # type: ignore[import-untyped,import-error]
+from json_repair import repair_json  # type: ignore[import-untyped]
 
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import (
@@ -28,7 +28,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
 )
 from crewai.utilities.converter import Converter
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
 
 
@@ -39,7 +39,18 @@ if TYPE_CHECKING:
     from crewai.llm import LLM
     from crewai.task import Task
 
-OPENAI_BIGGER_MODELS = [
+
+OPENAI_BIGGER_MODELS: list[
+    Literal[
+        "gpt-4",
+        "gpt-4o",
+        "o1-preview",
+        "o1-mini",
+        "o1",
+        "o3",
+        "o3-mini",
+    ]
+] = [
     "gpt-4",
     "gpt-4o",
     "o1-preview",
@@ -81,7 +92,7 @@ class ToolUsage:
         action: Any = None,
         fingerprint_context: dict[str, str] | None = None,
     ) -> None:
-        self._i18n: I18N = agent.i18n if agent else I18N()
+        self._i18n: I18N = agent.i18n if agent else get_i18n()
         self._printer: Printer = Printer()
         self._telemetry: Telemetry = Telemetry()
         self._run_attempts: int = 1
@@ -100,12 +111,14 @@ class ToolUsage:
         # Set the maximum parsing attempts for bigger models
         if (
             self.function_calling_llm
-            and self.function_calling_llm in OPENAI_BIGGER_MODELS
+            and self.function_calling_llm.model in OPENAI_BIGGER_MODELS
         ):
             self._max_parsing_attempts = 2
             self._remember_format_after_usages = 4
 
-    def parse_tool_calling(self, tool_string: str):
+    def parse_tool_calling(
+        self, tool_string: str
+    ) -> ToolCalling | InstructorToolCalling | ToolUsageError:
         """Parse the tool string and return the tool calling."""
         return self._tool_calling(tool_string)
 
@@ -153,7 +166,7 @@ class ToolUsage:
         tool: CrewStructuredTool,
         calling: ToolCalling | InstructorToolCalling,
     ) -> str:
-        if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
+        if self._check_tool_repeated_usage(calling=calling):
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
@@ -163,7 +176,7 @@ class ToolUsage:
                     tool_name=tool.name,
                     attempts=self._run_attempts,
                 )
-                return self._format_result(result=result)  # type: ignore #  "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+                return self._format_result(result=result)
 
             except Exception:
                 if self.task:
@@ -241,7 +254,7 @@ class ToolUsage:
                     try:
                         acceptable_args = tool.args_schema.model_json_schema()[
                             "properties"
-                        ].keys()  # type: ignore
+                        ].keys()
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
@@ -276,19 +289,19 @@ class ToolUsage:
                         self._printer.print(
                             content=f"\n\n{error_message}\n", color="red"
                         )
-                    return error  # type: ignore # No return value expected
+                    return error
 
                 if self.task:
                     self.task.increment_tools_errors()
-                return self.use(calling=calling, tool_string=tool_string)  # type: ignore # No return value expected
+                return self.use(calling=calling, tool_string=tool_string)
 
             if self.tools_handler:
                 should_cache = True
                 if (
                     hasattr(available_tool, "cache_function")
-                    and available_tool.cache_function  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+                    and available_tool.cache_function
                 ):
-                    should_cache = available_tool.cache_function(  # type: ignore # Item "None" of "Any | None" has no attribute "cache_function"
+                    should_cache = available_tool.cache_function(
                         calling.arguments, result
                     )
 
@@ -300,7 +313,7 @@ class ToolUsage:
             tool_name=tool.name,
             attempts=self._run_attempts,
         )
-        result = self._format_result(result=result)  # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
+        result = self._format_result(result=result)
         data = {
             "result": result,
             "tool_name": tool.name,
@@ -508,7 +521,7 @@ class ToolUsage:
                     self.task.increment_tools_errors()
                 if self.agent and self.agent.verbose:
                     self._printer.print(content=f"\n\n{e}\n", color="red")
-                return ToolUsageError(  # type: ignore # Incompatible return value type (got "ToolUsageError", expected "ToolCalling | InstructorToolCalling")
+                return ToolUsageError(
                     f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                 )
             return self._tool_calling(tool_string)
@@ -567,7 +580,7 @@ class ToolUsage:
         # If all parsing attempts fail, raise an error
         raise Exception(error_message)
 
-    def _emit_validate_input_error(self, final_error: str):
+    def _emit_validate_input_error(self, final_error: str) -> None:
         tool_selection_data = {
             "agent_key": getattr(self.agent, "key", None) if self.agent else None,
             "agent_role": getattr(self.agent, "role", None) if self.agent else None,
@@ -636,7 +649,7 @@ class ToolUsage:
 
     def _prepare_event_data(
         self, tool: Any, tool_calling: ToolCalling | InstructorToolCalling
-    ) -> dict:
+    ) -> dict[str, Any]:
         event_data = {
             "run_attempts": self._run_attempts,
             "delegations": self.task.delegations if self.task else 0,
@@ -660,7 +673,7 @@ class ToolUsage:
 
         return event_data
 
-    def _add_fingerprint_metadata(self, arguments: dict) -> dict:
+    def _add_fingerprint_metadata(self, arguments: dict[str, Any]) -> dict[str, Any]:
         """Add fingerprint metadata to tool arguments if available.
 
         Args:

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

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

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

@@ -290,12 +290,57 @@ def process_llm_response(
     return format_answer(answer)
 
 
+def estimate_token_count(text: str) -> int:
+    """Estimate the number of tokens in a text string.
+
+    Uses a simple heuristic: ~4 characters per token on average.
+    This is a rough approximation but sufficient for truncation purposes.
+
+    Args:
+        text: The text to estimate tokens for.
+
+    Returns:
+        Estimated number of tokens.
+    """
+    return len(text) // 4
+
+
+def truncate_tool_output(
+    tool_output: str, max_tokens: int, tool_name: str = ""
+) -> str:
+    """Truncate tool output to fit within token limit.
+
+    Args:
+        tool_output: The tool output to truncate.
+        max_tokens: Maximum number of tokens allowed.
+        tool_name: Name of the tool (for the truncation message).
+
+    Returns:
+        Truncated tool output with a clear truncation message.
+    """
+    estimated_tokens = estimate_token_count(tool_output)
+    
+    if estimated_tokens <= max_tokens:
+        return tool_output
+    
+    truncation_msg = f"\n\n[Tool output truncated: showing first {max_tokens} of ~{estimated_tokens} tokens. Please refine your query to get more specific results.]"
+    chars_for_message = len(truncation_msg)
+    max_chars = (max_tokens * 4) - chars_for_message
+    
+    if max_chars <= 0:
+        return truncation_msg
+    
+    truncated_output = tool_output[:max_chars]
+    return truncated_output + truncation_msg
+
+
 def handle_agent_action_core(
     formatted_answer: AgentAction,
     tool_result: ToolResult,
     messages: list[LLMMessage] | None = None,
     step_callback: Callable | None = None,
     show_logs: Callable | None = None,
+    max_tool_output_tokens: int = 4096,
 ) -> AgentAction | AgentFinish:
     """Core logic for handling agent actions and tool results.
 
@@ -305,6 +350,7 @@ def handle_agent_action_core(
         messages: Optional list of messages to append results to
         step_callback: Optional callback to execute after processing
         show_logs: Optional function to show logs
+        max_tool_output_tokens: Maximum tokens allowed in tool output before truncation
 
     Returns:
         Either an AgentAction or AgentFinish
@@ -315,13 +361,18 @@ def handle_agent_action_core(
     if step_callback:
         step_callback(tool_result)
 
-    formatted_answer.text += f"\nObservation: {tool_result.result}"
-    formatted_answer.result = tool_result.result
+    tool_output = str(tool_result.result)
+    truncated_output = truncate_tool_output(
+        tool_output, max_tool_output_tokens, formatted_answer.tool
+    )
+
+    formatted_answer.text += f"\nObservation: {truncated_output}"
+    formatted_answer.result = truncated_output
 
     if tool_result.result_as_answer:
         return AgentFinish(
             thought="",
-            output=tool_result.result,
+            output=truncated_output,
             text=formatted_answer.text,
         )
 

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

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

lib/crewai/src/crewai/utilities/exceptions/context_window_exceeding_exception.py

@@ -10,6 +10,7 @@ CONTEXT_LIMIT_ERRORS: Final[list[str]] = [
     "too many tokens",
     "input is too long",
     "exceeds token limit",
+    "max_tokens must be at least 1",
 ]
 
 

lib/crewai/src/crewai/utilities/i18n.py

@@ -1,5 +1,6 @@
 """Internationalization support for CrewAI prompts and messages."""
 
+from functools import lru_cache
 import json
 import os
 from typing import Literal
@@ -108,3 +109,19 @@ class I18N(BaseModel):
             return self._prompts[kind][key]
         except Exception as e:
             raise Exception(f"Prompt for '{kind}':'{key}'  not found.") from e
+
+
+@lru_cache(maxsize=None)
+def get_i18n(prompt_file: str | None = None) -> I18N:
+    """Get a cached I18N instance.
+
+    This function caches I18N instances to avoid redundant file I/O and JSON parsing.
+    Each unique prompt_file path gets its own cached instance.
+
+    Args:
+        prompt_file: Optional custom prompt file path. Defaults to None (uses built-in prompts).
+
+    Returns:
+        Cached I18N instance.
+    """
+    return I18N(prompt_file=prompt_file)

lib/crewai/src/crewai/utilities/prompts.py

@@ -1,29 +1,38 @@
+"""Prompt generation and management utilities for CrewAI agents."""
+
 from __future__ import annotations
 
-from typing import Any, TypedDict
+from typing import Annotated, Any, Literal, TypedDict
 
 from pydantic import BaseModel, Field
 
-from crewai.utilities.i18n import I18N
+from crewai.utilities.i18n import I18N, get_i18n
 
 
 class StandardPromptResult(TypedDict):
     """Result with only prompt field for standard mode."""
 
-    prompt: str
+    prompt: Annotated[str, "The generated prompt string"]
 
 
 class SystemPromptResult(StandardPromptResult):
     """Result with system, user, and prompt fields for system prompt mode."""
 
-    system: str
-    user: str
+    system: Annotated[str, "The system prompt component"]
+    user: Annotated[str, "The user prompt component"]
+
+
+COMPONENTS = Literal["role_playing", "tools", "no_tools", "task"]
 
 
 class Prompts(BaseModel):
-    """Manages and generates prompts for a generic agent."""
+    """Manages and generates prompts for a generic agent.
 
-    i18n: I18N = Field(default_factory=I18N)
+    Notes:
+        - Need to refactor so that prompt is not tightly coupled to agent.
+    """
+
+    i18n: I18N = Field(default_factory=get_i18n)
     has_tools: bool = Field(
         default=False, description="Indicates if the agent has access to tools"
     )
@@ -36,7 +45,7 @@ class Prompts(BaseModel):
     response_template: str | None = Field(
         default=None, description="Custom response prompt template"
     )
-    use_system_prompt: bool | None = Field(
+    use_system_prompt: bool = Field(
         default=False,
         description="Whether to use the system prompt when no custom templates are provided",
     )
@@ -48,7 +57,7 @@ class Prompts(BaseModel):
         Returns:
             A dictionary containing the constructed prompt(s).
         """
-        slices: list[str] = ["role_playing"]
+        slices: list[COMPONENTS] = ["role_playing"]
         if self.has_tools:
             slices.append("tools")
         else:
@@ -77,7 +86,7 @@ class Prompts(BaseModel):
 
     def _build_prompt(
         self,
-        components: list[str],
+        components: list[COMPONENTS],
         system_template: str | None = None,
         prompt_template: str | None = None,
         response_template: str | None = None,

lib/crewai/src/crewai/utilities/reasoning_handler.py

@@ -13,7 +13,6 @@ from crewai.events.types.reasoning_events import (
 )
 from crewai.llm import LLM
 from crewai.task import Task
-from crewai.utilities.i18n import I18N
 
 
 class ReasoningPlan(BaseModel):
@@ -62,7 +61,6 @@ class AgentReasoning:
         agent: The agent performing the reasoning.
         llm: The language model used for reasoning.
         logger: Logger for logging events and errors.
-        i18n: Internationalization utility for retrieving prompts.
     """
 
     def __init__(self, task: Task, agent: Agent) -> None:
@@ -76,7 +74,6 @@ class AgentReasoning:
         self.agent = agent
         self.llm = cast(LLM, agent.llm)
         self.logger = logging.getLogger(__name__)
-        self.i18n = I18N()
 
     def handle_agent_reasoning(self) -> AgentReasoningOutput:
         """Public method for the reasoning process that creates and refines a plan for the task until the agent is ready to execute it.
@@ -163,8 +160,7 @@ class AgentReasoning:
             llm=self.llm,
             prompt=reasoning_prompt,
             task=self.task,
-            agent=self.agent,
-            i18n=self.i18n,
+            reasoning_agent=self.agent,
             backstory=self.__get_agent_backstory(),
             plan_type="initial_plan",
         )
@@ -208,8 +204,7 @@ class AgentReasoning:
                     llm=self.llm,
                     prompt=refine_prompt,
                     task=self.task,
-                    agent=self.agent,
-                    i18n=self.i18n,
+                    reasoning_agent=self.agent,
                     backstory=self.__get_agent_backstory(),
                     plan_type="refine_plan",
                 )
@@ -238,14 +233,14 @@ class AgentReasoning:
         self.logger.debug(f"Using function calling for {prompt_type} reasoning")
 
         try:
-            system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
+            system_prompt = self.agent.i18n.retrieve("reasoning", prompt_type).format(
                 role=self.agent.role,
                 goal=self.agent.goal,
                 backstory=self.__get_agent_backstory(),
             )
 
             # Prepare a simple callable that just returns the tool arguments as JSON
-            def _create_reasoning_plan(plan: str, ready: bool = True):
+            def _create_reasoning_plan(plan: str, ready: bool = True) -> str:
                 """Return the reasoning plan result in JSON string form."""
                 return json.dumps({"plan": plan, "ready": ready})
 
@@ -281,7 +276,9 @@ class AgentReasoning:
             )
 
             try:
-                system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
+                system_prompt = self.agent.i18n.retrieve(
+                    "reasoning", prompt_type
+                ).format(
                     role=self.agent.role,
                     goal=self.agent.goal,
                     backstory=self.__get_agent_backstory(),
@@ -326,7 +323,7 @@ class AgentReasoning:
         """
         available_tools = self.__format_available_tools()
 
-        return self.i18n.retrieve("reasoning", "create_plan_prompt").format(
+        return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
@@ -357,7 +354,7 @@ class AgentReasoning:
         Returns:
             str: The refine prompt.
         """
-        return self.i18n.retrieve("reasoning", "refine_plan_prompt").format(
+        return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
             role=self.agent.role,
             goal=self.agent.goal,
             backstory=self.__get_agent_backstory(),
@@ -405,8 +402,7 @@ def _call_llm_with_reasoning_prompt(
     llm: LLM,
     prompt: str,
     task: Task,
-    agent: Agent,
-    i18n: I18N,
+    reasoning_agent: Agent,
     backstory: str,
     plan_type: Literal["initial_plan", "refine_plan"],
 ) -> str:
@@ -416,17 +412,16 @@ def _call_llm_with_reasoning_prompt(
         llm: The language model to use.
         prompt: The prompt to send to the LLM.
         task: The task for which the agent is reasoning.
-        agent: The agent performing the reasoning.
-        i18n: Internationalization utility for retrieving prompts.
+        reasoning_agent: The agent performing the reasoning.
         backstory: The agent's backstory.
         plan_type: The type of plan being created ("initial_plan" or "refine_plan").
 
     Returns:
         The LLM response.
     """
-    system_prompt = i18n.retrieve("reasoning", plan_type).format(
-        role=agent.role,
-        goal=agent.goal,
+    system_prompt = reasoning_agent.i18n.retrieve("reasoning", plan_type).format(
+        role=reasoning_agent.role,
+        goal=reasoning_agent.goal,
         backstory=backstory,
     )
 
@@ -436,6 +431,6 @@ def _call_llm_with_reasoning_prompt(
             {"role": "user", "content": prompt},
         ],
         from_task=task,
-        from_agent=agent,
+        from_agent=reasoning_agent,
     )
     return str(response)

lib/crewai/tests/agents/test_lite_agent.py

@@ -382,8 +382,8 @@ def test_guardrail_is_called_using_string():
     assert not guardrail_events["completed"][0].success
     assert guardrail_events["completed"][1].success
     assert (
-        "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
-        in result.raw
+        "top 10 best Brazilian soccer players" in result.raw or
+        "Brazilian players" in result.raw
     )
 
 

lib/crewai/tests/cassettes/TestAnthropicHeaderInterceptor.test_header_interceptor_with_real_call.yaml

@@ -0,0 +1,106 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Reply with just
+      the word: SUCCESS"}],"model":"claude-3-5-haiku-20241022","stream":false}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '145'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - Anthropic/Python 0.71.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 0.71.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAAwAAAP//dJDLasMwEEX/5a5lsF1nUe1KCARCu6gpFEoRgzTEamzJ1qNNCf734tDQ
+        F10N3HNmBu4JgzfcQ0L3lA0XV8Wq6MgeclGXdVOVdQ0BayAxxL0qq9rcXm/vb2g37ehtc+xeHu+m
+        4xYC6X3kxeIYac8QCL5fAorRxkQuQUB7l9glyKfTxU98XMh5SLQP6/WmbTE/C8TkRxWYoneQYGdU
+        ysHhE0SeMjvNkC73vUA+f5UnWDfmpJI/sIuQVSOgSXesdGBK1jv1UygvPDCZ/9hld7nPY8cDB+rV
+        avjrf9Gq+01nAZ/T96gRiBxerWaVLAdILE0ZCgbz/AEAAP//AwA4VVIcmwEAAA==
+    headers:
+      CF-RAY:
+      - 9997ac4cbfb443fa-EWR
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Nov 2025 22:50:55 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - 87faf353-e074-4658-b885-bfac7aa5a7b5
+      anthropic-ratelimit-input-tokens-limit:
+      - '400000'
+      anthropic-ratelimit-input-tokens-remaining:
+      - '400000'
+      anthropic-ratelimit-input-tokens-reset:
+      - '2025-11-04T22:50:55Z'
+      anthropic-ratelimit-output-tokens-limit:
+      - '80000'
+      anthropic-ratelimit-output-tokens-remaining:
+      - '80000'
+      anthropic-ratelimit-output-tokens-reset:
+      - '2025-11-04T22:50:55Z'
+      anthropic-ratelimit-requests-limit:
+      - '4000'
+      anthropic-ratelimit-requests-remaining:
+      - '3999'
+      anthropic-ratelimit-requests-reset:
+      - '2025-11-04T22:50:54Z'
+      anthropic-ratelimit-tokens-limit:
+      - '480000'
+      anthropic-ratelimit-tokens-remaining:
+      - '480000'
+      anthropic-ratelimit-tokens-reset:
+      - '2025-11-04T22:50:55Z'
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - req_011CUofgQ1jTrjQ2sveXU1cC
+      retry-after:
+      - '5'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-envoy-upstream-service-time:
+      - '461'
+    status:
+      code: 200
+      message: OK
+version: 1

lib/crewai/tests/cassettes/TestAnthropicInterceptorIntegration.test_anthropic_call_with_interceptor_tracks_requests.yaml

@@ -0,0 +1,108 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say ''Hello World''
+      and nothing else"}],"model":"claude-3-5-haiku-20241022","stream":false}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, zstd
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '146'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - Anthropic/Python 0.71.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 0.71.0
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.10
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
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lib/crewai/tests/cassettes/test_task_with_max_execution_time_exceeded.yaml

@@ -208,4 +208,100 @@ interactions:
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lib/crewai/tests/experimental/evaluation/test_agent_evaluator.py

@@ -62,18 +62,23 @@ class TestAgentEvaluator:
             agents=mock_crew.agents, evaluators=[GoalAlignmentEvaluator()]
         )
 
-        task_completed_event = threading.Event()
+        task_completed_condition = threading.Condition()
+        task_completed = False
 
         @crewai_event_bus.on(TaskCompletedEvent)
         async def on_task_completed(source, event):
             # TaskCompletedEvent fires AFTER evaluation results are stored
-            task_completed_event.set()
+            nonlocal task_completed
+            with task_completed_condition:
+                task_completed = True
+                task_completed_condition.notify()
 
         mock_crew.kickoff()
 
-        assert task_completed_event.wait(timeout=5), (
-            "Timeout waiting for task completion"
-        )
+        with task_completed_condition:
+            assert task_completed_condition.wait_for(
+                lambda: task_completed, timeout=5
+            ), "Timeout waiting for task completion"
 
         results = agent_evaluator.get_evaluation_results()
 

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

@@ -0,0 +1 @@
+"""Tests for LLM interceptor hooks functionality."""

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

@@ -0,0 +1,311 @@
+"""Tests for Anthropic provider with interceptor integration."""
+
+import os
+
+import httpx
+import pytest
+
+from crewai.llm import LLM
+from crewai.llms.hooks.base import BaseInterceptor
+
+
+@pytest.fixture(autouse=True)
+def setup_anthropic_api_key(monkeypatch):
+    """Set dummy Anthropic API key for tests that don't make real API calls."""
+    if "ANTHROPIC_API_KEY" not in os.environ:
+        monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-ant-test-key-dummy")
+
+
+class AnthropicTestInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Test interceptor for Anthropic provider."""
+
+    def __init__(self) -> None:
+        """Initialize tracking and modification state."""
+        self.outbound_calls: list[httpx.Request] = []
+        self.inbound_calls: list[httpx.Response] = []
+        self.custom_header_value = "anthropic-test-value"
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Track and modify outbound Anthropic requests.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Modified request with custom headers.
+        """
+        self.outbound_calls.append(message)
+        message.headers["X-Anthropic-Interceptor"] = self.custom_header_value
+        message.headers["X-Request-ID"] = "test-request-456"
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Track inbound Anthropic responses.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response with tracking header.
+        """
+        self.inbound_calls.append(message)
+        message.headers["X-Response-Tracked"] = "true"
+        return message
+
+
+class TestAnthropicInterceptorIntegration:
+    """Test suite for Anthropic provider with interceptor."""
+
+    def test_anthropic_llm_accepts_interceptor(self) -> None:
+        """Test that Anthropic LLM accepts interceptor parameter."""
+        interceptor = AnthropicTestInterceptor()
+
+        llm = LLM(model="anthropic/claude-3-5-sonnet-20241022", interceptor=interceptor)
+
+        assert llm.interceptor is interceptor
+
+    @pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+    def test_anthropic_call_with_interceptor_tracks_requests(self) -> None:
+        """Test that interceptor tracks Anthropic API requests."""
+        interceptor = AnthropicTestInterceptor()
+        llm = LLM(model="anthropic/claude-3-5-haiku-20241022", interceptor=interceptor)
+
+        # Make a simple completion call
+        result = llm.call(
+            messages=[{"role": "user", "content": "Say 'Hello World' and nothing else"}]
+        )
+
+        # Verify custom headers were added
+        for request in interceptor.outbound_calls:
+            assert "X-Anthropic-Interceptor" in request.headers
+            assert request.headers["X-Anthropic-Interceptor"] == "anthropic-test-value"
+            assert "X-Request-ID" in request.headers
+            assert request.headers["X-Request-ID"] == "test-request-456"
+
+        # Verify response was tracked
+        for response in interceptor.inbound_calls:
+            assert "X-Response-Tracked" in response.headers
+            assert response.headers["X-Response-Tracked"] == "true"
+
+        # Verify result is valid
+        assert result is not None
+        assert isinstance(result, str)
+        assert len(result) > 0
+
+    def test_anthropic_without_interceptor_works(self) -> None:
+        """Test that Anthropic LLM works without interceptor."""
+        llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
+
+        assert llm.interceptor is None
+
+    def test_multiple_anthropic_llms_different_interceptors(self) -> None:
+        """Test that multiple Anthropic LLMs can have different interceptors."""
+        interceptor1 = AnthropicTestInterceptor()
+        interceptor1.custom_header_value = "claude-opus-value"
+
+        interceptor2 = AnthropicTestInterceptor()
+        interceptor2.custom_header_value = "claude-sonnet-value"
+
+        llm1 = LLM(model="anthropic/claude-3-opus-20240229", interceptor=interceptor1)
+        llm2 = LLM(model="anthropic/claude-3-5-sonnet-20241022", interceptor=interceptor2)
+
+        assert llm1.interceptor is interceptor1
+        assert llm2.interceptor is interceptor2
+        assert llm1.interceptor.custom_header_value == "claude-opus-value"
+        assert llm2.interceptor.custom_header_value == "claude-sonnet-value"
+
+
+class AnthropicLoggingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Interceptor that logs Anthropic request/response details."""
+
+    def __init__(self) -> None:
+        """Initialize logging lists."""
+        self.request_urls: list[str] = []
+        self.request_methods: list[str] = []
+        self.response_status_codes: list[int] = []
+        self.anthropic_version_headers: list[str] = []
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Log outbound request details.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            The request unchanged.
+        """
+        self.request_urls.append(str(message.url))
+        self.request_methods.append(message.method)
+        if "anthropic-version" in message.headers:
+            self.anthropic_version_headers.append(message.headers["anthropic-version"])
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Log inbound response details.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        self.response_status_codes.append(message.status_code)
+        return message
+
+
+class TestAnthropicLoggingInterceptor:
+    """Test suite for logging interceptor with Anthropic."""
+
+    def test_logging_interceptor_instantiation(self) -> None:
+        """Test that logging interceptor can be created with Anthropic LLM."""
+        interceptor = AnthropicLoggingInterceptor()
+        llm = LLM(model="anthropic/claude-3-5-sonnet-20241022", interceptor=interceptor)
+
+        assert llm.interceptor is interceptor
+        assert isinstance(llm.interceptor, AnthropicLoggingInterceptor)
+
+    @pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+    def test_logging_interceptor_tracks_details(self) -> None:
+        """Test that logging interceptor tracks request/response details."""
+        interceptor = AnthropicLoggingInterceptor()
+        llm = LLM(model="anthropic/claude-3-5-haiku-20241022", interceptor=interceptor)
+
+        # Make a completion call
+        result = llm.call(messages=[{"role": "user", "content": "Count from 1 to 3"}])
+
+        # Verify URL points to Anthropic API
+        for url in interceptor.request_urls:
+            assert "anthropic" in url.lower() or "api" in url.lower()
+
+        # Verify methods are POST (messages endpoint uses POST)
+        for method in interceptor.request_methods:
+            assert method == "POST"
+
+        # Verify successful status codes
+        for status_code in interceptor.response_status_codes:
+            assert 200 <= status_code < 300
+
+
+        # Verify result is valid
+        assert result is not None
+
+
+class AnthropicHeaderInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Interceptor that adds Anthropic-specific headers."""
+
+    def __init__(self, workspace_id: str, user_id: str) -> None:
+        """Initialize with Anthropic-specific metadata.
+
+        Args:
+            workspace_id: The workspace ID to inject.
+            user_id: The user ID to inject.
+        """
+        self.workspace_id = workspace_id
+        self.user_id = user_id
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Add custom metadata headers to request.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Request with metadata headers.
+        """
+        message.headers["X-Workspace-ID"] = self.workspace_id
+        message.headers["X-User-ID"] = self.user_id
+        message.headers["X-Custom-Client"] = "crewai-interceptor"
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Pass through inbound response.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        return message
+
+
+class TestAnthropicHeaderInterceptor:
+    """Test suite for header interceptor with Anthropic."""
+
+    def test_header_interceptor_with_anthropic(self) -> None:
+        """Test that header interceptor can be used with Anthropic LLM."""
+        interceptor = AnthropicHeaderInterceptor(
+            workspace_id="ws-789", user_id="user-012"
+        )
+        llm = LLM(model="anthropic/claude-3-5-sonnet-20241022", interceptor=interceptor)
+
+        assert llm.interceptor is interceptor
+        assert llm.interceptor.workspace_id == "ws-789"
+        assert llm.interceptor.user_id == "user-012"
+
+    def test_header_interceptor_adds_headers(self) -> None:
+        """Test that header interceptor adds custom headers to requests."""
+        interceptor = AnthropicHeaderInterceptor(workspace_id="ws-123", user_id="u-456")
+        request = httpx.Request("POST", "https://api.anthropic.com/v1/messages")
+
+        modified_request = interceptor.on_outbound(request)
+
+        assert "X-Workspace-ID" in modified_request.headers
+        assert modified_request.headers["X-Workspace-ID"] == "ws-123"
+        assert "X-User-ID" in modified_request.headers
+        assert modified_request.headers["X-User-ID"] == "u-456"
+        assert "X-Custom-Client" in modified_request.headers
+        assert modified_request.headers["X-Custom-Client"] == "crewai-interceptor"
+
+    @pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+    def test_header_interceptor_with_real_call(self) -> None:
+        """Test that header interceptor works with real Anthropic API call."""
+        interceptor = AnthropicHeaderInterceptor(workspace_id="ws-999", user_id="u-888")
+        llm = LLM(model="anthropic/claude-3-5-haiku-20241022", interceptor=interceptor)
+
+        # Make a simple call
+        result = llm.call(
+            messages=[{"role": "user", "content": "Reply with just the word: SUCCESS"}]
+        )
+
+        # Verify the call succeeded
+        assert result is not None
+        assert len(result) > 0
+
+        # Verify the interceptor was configured
+        assert llm.interceptor is interceptor
+
+
+class TestMixedProviderInterceptors:
+    """Test suite for using interceptors with different providers."""
+
+    def test_openai_and_anthropic_different_interceptors(self) -> None:
+        """Test that OpenAI and Anthropic LLMs can have different interceptors."""
+        openai_interceptor = AnthropicTestInterceptor()
+        openai_interceptor.custom_header_value = "openai-specific"
+
+        anthropic_interceptor = AnthropicTestInterceptor()
+        anthropic_interceptor.custom_header_value = "anthropic-specific"
+
+        openai_llm = LLM(model="gpt-4", interceptor=openai_interceptor)
+        anthropic_llm = LLM(
+            model="anthropic/claude-3-5-sonnet-20241022", interceptor=anthropic_interceptor
+        )
+
+        assert openai_llm.interceptor is openai_interceptor
+        assert anthropic_llm.interceptor is anthropic_interceptor
+        assert openai_llm.interceptor.custom_header_value == "openai-specific"
+        assert anthropic_llm.interceptor.custom_header_value == "anthropic-specific"
+
+    def test_same_interceptor_different_providers(self) -> None:
+        """Test that same interceptor instance can be used with multiple providers."""
+        shared_interceptor = AnthropicTestInterceptor()
+
+        openai_llm = LLM(model="gpt-4", interceptor=shared_interceptor)
+        anthropic_llm = LLM(
+            model="anthropic/claude-3-5-sonnet-20241022", interceptor=shared_interceptor
+        )
+
+        assert openai_llm.interceptor is shared_interceptor
+        assert anthropic_llm.interceptor is shared_interceptor
+        assert openai_llm.interceptor is anthropic_llm.interceptor

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

@@ -0,0 +1,287 @@
+"""Tests for base interceptor functionality."""
+
+import httpx
+import pytest
+
+from crewai.llms.hooks.base import BaseInterceptor
+
+
+class SimpleInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Simple test interceptor implementation."""
+
+    def __init__(self) -> None:
+        """Initialize tracking lists."""
+        self.outbound_calls: list[httpx.Request] = []
+        self.inbound_calls: list[httpx.Response] = []
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Track outbound calls.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            The request unchanged.
+        """
+        self.outbound_calls.append(message)
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Track inbound calls.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        self.inbound_calls.append(message)
+        return message
+
+
+class ModifyingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Interceptor that modifies requests and responses."""
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Add custom header to outbound request.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Modified request with custom header.
+        """
+        message.headers["X-Custom-Header"] = "test-value"
+        message.headers["X-Intercepted"] = "true"
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Add custom header to inbound response.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            Modified response with custom header.
+        """
+        message.headers["X-Response-Intercepted"] = "true"
+        return message
+
+
+class AsyncInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Interceptor with async support."""
+
+    def __init__(self) -> None:
+        """Initialize tracking lists."""
+        self.async_outbound_calls: list[httpx.Request] = []
+        self.async_inbound_calls: list[httpx.Response] = []
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Handle sync outbound.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            The request unchanged.
+        """
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Handle sync inbound.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        return message
+
+    async def aon_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Handle async outbound.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Modified request with async header.
+        """
+        self.async_outbound_calls.append(message)
+        message.headers["X-Async-Outbound"] = "true"
+        return message
+
+    async def aon_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Handle async inbound.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            Modified response with async header.
+        """
+        self.async_inbound_calls.append(message)
+        message.headers["X-Async-Inbound"] = "true"
+        return message
+
+
+class TestBaseInterceptor:
+    """Test suite for BaseInterceptor class."""
+
+    def test_interceptor_instantiation(self) -> None:
+        """Test that interceptor can be instantiated."""
+        interceptor = SimpleInterceptor()
+        assert interceptor is not None
+        assert isinstance(interceptor, BaseInterceptor)
+
+    def test_on_outbound_called(self) -> None:
+        """Test that on_outbound is called and tracks requests."""
+        interceptor = SimpleInterceptor()
+        request = httpx.Request("GET", "https://api.example.com/test")
+
+        result = interceptor.on_outbound(request)
+
+        assert len(interceptor.outbound_calls) == 1
+        assert interceptor.outbound_calls[0] is request
+        assert result is request
+
+    def test_on_inbound_called(self) -> None:
+        """Test that on_inbound is called and tracks responses."""
+        interceptor = SimpleInterceptor()
+        response = httpx.Response(200, json={"status": "ok"})
+
+        result = interceptor.on_inbound(response)
+
+        assert len(interceptor.inbound_calls) == 1
+        assert interceptor.inbound_calls[0] is response
+        assert result is response
+
+    def test_multiple_outbound_calls(self) -> None:
+        """Test that interceptor tracks multiple outbound calls."""
+        interceptor = SimpleInterceptor()
+        requests = [
+            httpx.Request("GET", "https://api.example.com/1"),
+            httpx.Request("POST", "https://api.example.com/2"),
+            httpx.Request("PUT", "https://api.example.com/3"),
+        ]
+
+        for req in requests:
+            interceptor.on_outbound(req)
+
+        assert len(interceptor.outbound_calls) == 3
+        assert interceptor.outbound_calls == requests
+
+    def test_multiple_inbound_calls(self) -> None:
+        """Test that interceptor tracks multiple inbound calls."""
+        interceptor = SimpleInterceptor()
+        responses = [
+            httpx.Response(200, json={"id": 1}),
+            httpx.Response(201, json={"id": 2}),
+            httpx.Response(404, json={"error": "not found"}),
+        ]
+
+        for resp in responses:
+            interceptor.on_inbound(resp)
+
+        assert len(interceptor.inbound_calls) == 3
+        assert interceptor.inbound_calls == responses
+
+
+class TestModifyingInterceptor:
+    """Test suite for interceptor that modifies messages."""
+
+    def test_outbound_header_modification(self) -> None:
+        """Test that interceptor can add headers to outbound requests."""
+        interceptor = ModifyingInterceptor()
+        request = httpx.Request("GET", "https://api.example.com/test")
+
+        result = interceptor.on_outbound(request)
+
+        assert result is request
+        assert "X-Custom-Header" in result.headers
+        assert result.headers["X-Custom-Header"] == "test-value"
+        assert "X-Intercepted" in result.headers
+        assert result.headers["X-Intercepted"] == "true"
+
+    def test_inbound_header_modification(self) -> None:
+        """Test that interceptor can add headers to inbound responses."""
+        interceptor = ModifyingInterceptor()
+        response = httpx.Response(200, json={"status": "ok"})
+
+        result = interceptor.on_inbound(response)
+
+        assert result is response
+        assert "X-Response-Intercepted" in result.headers
+        assert result.headers["X-Response-Intercepted"] == "true"
+
+    def test_preserves_existing_headers(self) -> None:
+        """Test that interceptor preserves existing headers."""
+        interceptor = ModifyingInterceptor()
+        request = httpx.Request(
+            "GET",
+            "https://api.example.com/test",
+            headers={"Authorization": "Bearer token123", "Content-Type": "application/json"},
+        )
+
+        result = interceptor.on_outbound(request)
+
+        assert result.headers["Authorization"] == "Bearer token123"
+        assert result.headers["Content-Type"] == "application/json"
+        assert result.headers["X-Custom-Header"] == "test-value"
+
+
+class TestAsyncInterceptor:
+    """Test suite for async interceptor functionality."""
+
+    def test_sync_methods_work(self) -> None:
+        """Test that sync methods still work on async interceptor."""
+        interceptor = AsyncInterceptor()
+        request = httpx.Request("GET", "https://api.example.com/test")
+        response = httpx.Response(200)
+
+        req_result = interceptor.on_outbound(request)
+        resp_result = interceptor.on_inbound(response)
+
+        assert req_result is request
+        assert resp_result is response
+
+    @pytest.mark.asyncio
+    async def test_async_outbound(self) -> None:
+        """Test async outbound hook."""
+        interceptor = AsyncInterceptor()
+        request = httpx.Request("GET", "https://api.example.com/test")
+
+        result = await interceptor.aon_outbound(request)
+
+        assert result is request
+        assert len(interceptor.async_outbound_calls) == 1
+        assert interceptor.async_outbound_calls[0] is request
+        assert "X-Async-Outbound" in result.headers
+        assert result.headers["X-Async-Outbound"] == "true"
+
+    @pytest.mark.asyncio
+    async def test_async_inbound(self) -> None:
+        """Test async inbound hook."""
+        interceptor = AsyncInterceptor()
+        response = httpx.Response(200, json={"status": "ok"})
+
+        result = await interceptor.aon_inbound(response)
+
+        assert result is response
+        assert len(interceptor.async_inbound_calls) == 1
+        assert interceptor.async_inbound_calls[0] is response
+        assert "X-Async-Inbound" in result.headers
+        assert result.headers["X-Async-Inbound"] == "true"
+
+    @pytest.mark.asyncio
+    async def test_default_async_not_implemented(self) -> None:
+        """Test that default async methods raise NotImplementedError."""
+        interceptor = SimpleInterceptor()
+        request = httpx.Request("GET", "https://api.example.com/test")
+        response = httpx.Response(200)
+
+        with pytest.raises(NotImplementedError):
+            await interceptor.aon_outbound(request)
+
+        with pytest.raises(NotImplementedError):
+            await interceptor.aon_inbound(response)

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

@@ -0,0 +1,262 @@
+"""Tests for OpenAI provider with interceptor integration."""
+
+import httpx
+import pytest
+
+from crewai.llm import LLM
+from crewai.llms.hooks.base import BaseInterceptor
+
+
+class OpenAITestInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Test interceptor for OpenAI provider."""
+
+    def __init__(self) -> None:
+        """Initialize tracking and modification state."""
+        self.outbound_calls: list[httpx.Request] = []
+        self.inbound_calls: list[httpx.Response] = []
+        self.custom_header_value = "openai-test-value"
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Track and modify outbound OpenAI requests.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Modified request with custom headers.
+        """
+        self.outbound_calls.append(message)
+        message.headers["X-OpenAI-Interceptor"] = self.custom_header_value
+        message.headers["X-Request-ID"] = "test-request-123"
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Track inbound OpenAI responses.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response with tracking header.
+        """
+        self.inbound_calls.append(message)
+        message.headers["X-Response-Tracked"] = "true"
+        return message
+
+
+class TestOpenAIInterceptorIntegration:
+    """Test suite for OpenAI provider with interceptor."""
+
+    def test_openai_llm_accepts_interceptor(self) -> None:
+        """Test that OpenAI LLM accepts interceptor parameter."""
+        interceptor = OpenAITestInterceptor()
+
+        llm = LLM(model="gpt-4", interceptor=interceptor)
+
+        assert llm.interceptor is interceptor
+
+    @pytest.mark.vcr(filter_headers=["authorization"])
+    def test_openai_call_with_interceptor_tracks_requests(self) -> None:
+        """Test that interceptor tracks OpenAI API requests."""
+        interceptor = OpenAITestInterceptor()
+        llm = LLM(model="gpt-4o-mini", interceptor=interceptor)
+
+        # Make a simple completion call
+        result = llm.call(
+            messages=[{"role": "user", "content": "Say 'Hello World' and nothing else"}]
+        )
+
+        # Verify custom headers were added
+        for request in interceptor.outbound_calls:
+            assert "X-OpenAI-Interceptor" in request.headers
+            assert request.headers["X-OpenAI-Interceptor"] == "openai-test-value"
+            assert "X-Request-ID" in request.headers
+            assert request.headers["X-Request-ID"] == "test-request-123"
+
+        # Verify response was tracked
+        for response in interceptor.inbound_calls:
+            assert "X-Response-Tracked" in response.headers
+            assert response.headers["X-Response-Tracked"] == "true"
+
+        # Verify result is valid
+        assert result is not None
+        assert isinstance(result, str)
+        assert len(result) > 0
+
+    def test_openai_without_interceptor_works(self) -> None:
+        """Test that OpenAI LLM works without interceptor."""
+        llm = LLM(model="gpt-4")
+
+        assert llm.interceptor is None
+
+    def test_multiple_openai_llms_different_interceptors(self) -> None:
+        """Test that multiple OpenAI LLMs can have different interceptors."""
+        interceptor1 = OpenAITestInterceptor()
+        interceptor1.custom_header_value = "llm1-value"
+
+        interceptor2 = OpenAITestInterceptor()
+        interceptor2.custom_header_value = "llm2-value"
+
+        llm1 = LLM(model="gpt-4", interceptor=interceptor1)
+        llm2 = LLM(model="gpt-3.5-turbo", interceptor=interceptor2)
+
+        assert llm1.interceptor is interceptor1
+        assert llm2.interceptor is interceptor2
+        assert llm1.interceptor.custom_header_value == "llm1-value"
+        assert llm2.interceptor.custom_header_value == "llm2-value"
+
+
+class LoggingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Interceptor that logs request/response details for testing."""
+
+    def __init__(self) -> None:
+        """Initialize logging lists."""
+        self.request_urls: list[str] = []
+        self.request_methods: list[str] = []
+        self.response_status_codes: list[int] = []
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Log outbound request details.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            The request unchanged.
+        """
+        self.request_urls.append(str(message.url))
+        self.request_methods.append(message.method)
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Log inbound response details.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        self.response_status_codes.append(message.status_code)
+        return message
+
+
+class TestOpenAILoggingInterceptor:
+    """Test suite for logging interceptor with OpenAI."""
+
+    def test_logging_interceptor_instantiation(self) -> None:
+        """Test that logging interceptor can be created with OpenAI LLM."""
+        interceptor = LoggingInterceptor()
+        llm = LLM(model="gpt-4", interceptor=interceptor)
+
+        assert llm.interceptor is interceptor
+        assert isinstance(llm.interceptor, LoggingInterceptor)
+
+    @pytest.mark.vcr(filter_headers=["authorization"])
+    def test_logging_interceptor_tracks_details(self) -> None:
+        """Test that logging interceptor tracks request/response details."""
+        interceptor = LoggingInterceptor()
+        llm = LLM(model="gpt-4o-mini", interceptor=interceptor)
+
+        # Make a completion call
+        result = llm.call(
+            messages=[{"role": "user", "content": "Count from 1 to 3"}]
+        )
+
+        # Verify URL points to OpenAI API
+        for url in interceptor.request_urls:
+            assert "openai" in url.lower() or "api" in url.lower()
+
+        # Verify methods are POST (chat completions use POST)
+        for method in interceptor.request_methods:
+            assert method == "POST"
+
+        # Verify successful status codes
+        for status_code in interceptor.response_status_codes:
+            assert 200 <= status_code < 300
+
+        # Verify result is valid
+        assert result is not None
+
+
+class AuthInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Interceptor that adds authentication headers."""
+
+    def __init__(self, api_key: str, org_id: str) -> None:
+        """Initialize with auth credentials.
+
+        Args:
+            api_key: The API key to inject.
+            org_id: The organization ID to inject.
+        """
+        self.api_key = api_key
+        self.org_id = org_id
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Add authentication headers to request.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Request with auth headers.
+        """
+        message.headers["X-Custom-API-Key"] = self.api_key
+        message.headers["X-Organization-ID"] = self.org_id
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Pass through inbound response.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        return message
+
+
+class TestOpenAIAuthInterceptor:
+    """Test suite for authentication interceptor with OpenAI."""
+
+    def test_auth_interceptor_with_openai(self) -> None:
+        """Test that auth interceptor can be used with OpenAI LLM."""
+        interceptor = AuthInterceptor(api_key="custom-key-123", org_id="org-456")
+        llm = LLM(model="gpt-4", interceptor=interceptor)
+
+        assert llm.interceptor is interceptor
+        assert llm.interceptor.api_key == "custom-key-123"
+        assert llm.interceptor.org_id == "org-456"
+
+    def test_auth_interceptor_adds_headers(self) -> None:
+        """Test that auth interceptor adds custom headers to requests."""
+        interceptor = AuthInterceptor(api_key="test-key", org_id="test-org")
+        request = httpx.Request("POST", "https://api.openai.com/v1/chat/completions")
+
+        modified_request = interceptor.on_outbound(request)
+
+        assert "X-Custom-API-Key" in modified_request.headers
+        assert modified_request.headers["X-Custom-API-Key"] == "test-key"
+        assert "X-Organization-ID" in modified_request.headers
+        assert modified_request.headers["X-Organization-ID"] == "test-org"
+
+    @pytest.mark.vcr(filter_headers=["authorization"])
+    def test_auth_interceptor_with_real_call(self) -> None:
+        """Test that auth interceptor works with real OpenAI API call."""
+        interceptor = AuthInterceptor(api_key="custom-123", org_id="org-789")
+        llm = LLM(model="gpt-4o-mini", interceptor=interceptor)
+
+        # Make a simple call
+        result = llm.call(
+            messages=[{"role": "user", "content": "Reply with just the word: SUCCESS"}]
+        )
+
+        # Verify the call succeeded
+        assert result is not None
+        assert len(result) > 0
+
+        # Verify headers were added to outbound requests
+        # (We can't directly inspect the request sent to OpenAI in this test,
+        # but we verify the interceptor was configured and the call succeeded)
+        assert llm.interceptor is interceptor

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

@@ -0,0 +1,248 @@
+"""Tests for transport layer with interceptor integration."""
+
+from unittest.mock import Mock
+
+import httpx
+import pytest
+
+from crewai.llms.hooks.base import BaseInterceptor
+from crewai.llms.hooks.transport import AsyncHTTPransport, HTTPTransport
+
+
+class TrackingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Test interceptor that tracks all calls."""
+
+    def __init__(self) -> None:
+        """Initialize tracking lists."""
+        self.outbound_calls: list[httpx.Request] = []
+        self.inbound_calls: list[httpx.Response] = []
+        self.async_outbound_calls: list[httpx.Request] = []
+        self.async_inbound_calls: list[httpx.Response] = []
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Track outbound calls and add header.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Modified request with tracking header.
+        """
+        self.outbound_calls.append(message)
+        message.headers["X-Intercepted-Sync"] = "true"
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Track inbound calls.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response with tracking header.
+        """
+        self.inbound_calls.append(message)
+        message.headers["X-Response-Intercepted-Sync"] = "true"
+        return message
+
+    async def aon_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Track async outbound calls and add header.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            Modified request with tracking header.
+        """
+        self.async_outbound_calls.append(message)
+        message.headers["X-Intercepted-Async"] = "true"
+        return message
+
+    async def aon_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Track async inbound calls.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response with tracking header.
+        """
+        self.async_inbound_calls.append(message)
+        message.headers["X-Response-Intercepted-Async"] = "true"
+        return message
+
+
+class TestHTTPTransport:
+    """Test suite for sync HTTPTransport with interceptor."""
+
+    def test_transport_instantiation(self) -> None:
+        """Test that transport can be instantiated with interceptor."""
+        interceptor = TrackingInterceptor()
+        transport = HTTPTransport(interceptor=interceptor)
+
+        assert transport.interceptor is interceptor
+
+    def test_transport_requires_interceptor(self) -> None:
+        """Test that transport requires interceptor parameter."""
+        # HTTPTransport requires an interceptor parameter
+        with pytest.raises(TypeError):
+            HTTPTransport()
+
+    def test_interceptor_called_on_request(self) -> None:
+        """Test that interceptor hooks are called during request handling."""
+        interceptor = TrackingInterceptor()
+        transport = HTTPTransport(interceptor=interceptor)
+
+        # Create a mock parent transport that returns a response
+        mock_response = httpx.Response(200, json={"success": True})
+        mock_parent_handle = Mock(return_value=mock_response)
+
+        # Monkey-patch the parent's handle_request
+        original_handle = httpx.HTTPTransport.handle_request
+        httpx.HTTPTransport.handle_request = mock_parent_handle
+
+        try:
+            request = httpx.Request("GET", "https://api.example.com/test")
+            response = transport.handle_request(request)
+
+            # Verify interceptor was called
+            assert len(interceptor.outbound_calls) == 1
+            assert len(interceptor.inbound_calls) == 1
+            assert interceptor.outbound_calls[0] is request
+            assert interceptor.inbound_calls[0] is response
+
+            # Verify headers were added
+            assert "X-Intercepted-Sync" in request.headers
+            assert request.headers["X-Intercepted-Sync"] == "true"
+            assert "X-Response-Intercepted-Sync" in response.headers
+            assert response.headers["X-Response-Intercepted-Sync"] == "true"
+        finally:
+            # Restore original method
+            httpx.HTTPTransport.handle_request = original_handle
+
+
+
+class TestAsyncHTTPTransport:
+    """Test suite for async AsyncHTTPransport with interceptor."""
+
+    def test_async_transport_instantiation(self) -> None:
+        """Test that async transport can be instantiated with interceptor."""
+        interceptor = TrackingInterceptor()
+        transport = AsyncHTTPransport(interceptor=interceptor)
+
+        assert transport.interceptor is interceptor
+
+    def test_async_transport_requires_interceptor(self) -> None:
+        """Test that async transport requires interceptor parameter."""
+        # AsyncHTTPransport requires an interceptor parameter
+        with pytest.raises(TypeError):
+            AsyncHTTPransport()
+
+    @pytest.mark.asyncio
+    async def test_async_interceptor_called_on_request(self) -> None:
+        """Test that async interceptor hooks are called during request handling."""
+        interceptor = TrackingInterceptor()
+        transport = AsyncHTTPransport(interceptor=interceptor)
+
+        # Create a mock parent transport that returns a response
+        mock_response = httpx.Response(200, json={"success": True})
+
+        async def mock_handle(*args, **kwargs):
+            return mock_response
+
+        mock_parent_handle = Mock(side_effect=mock_handle)
+
+        # Monkey-patch the parent's handle_async_request
+        original_handle = httpx.AsyncHTTPTransport.handle_async_request
+        httpx.AsyncHTTPTransport.handle_async_request = mock_parent_handle
+
+        try:
+            request = httpx.Request("GET", "https://api.example.com/test")
+            response = await transport.handle_async_request(request)
+
+            # Verify async interceptor was called
+            assert len(interceptor.async_outbound_calls) == 1
+            assert len(interceptor.async_inbound_calls) == 1
+            assert interceptor.async_outbound_calls[0] is request
+            assert interceptor.async_inbound_calls[0] is response
+
+            # Verify sync interceptor was NOT called
+            assert len(interceptor.outbound_calls) == 0
+            assert len(interceptor.inbound_calls) == 0
+
+            # Verify async headers were added
+            assert "X-Intercepted-Async" in request.headers
+            assert request.headers["X-Intercepted-Async"] == "true"
+            assert "X-Response-Intercepted-Async" in response.headers
+            assert response.headers["X-Response-Intercepted-Async"] == "true"
+        finally:
+            # Restore original method
+            httpx.AsyncHTTPTransport.handle_async_request = original_handle
+
+
+
+class TestTransportIntegration:
+    """Test suite for transport integration scenarios."""
+
+    def test_multiple_requests_same_interceptor(self) -> None:
+        """Test that multiple requests through same interceptor are tracked."""
+        interceptor = TrackingInterceptor()
+        transport = HTTPTransport(interceptor=interceptor)
+
+        mock_response = httpx.Response(200)
+        mock_parent_handle = Mock(return_value=mock_response)
+
+        original_handle = httpx.HTTPTransport.handle_request
+        httpx.HTTPTransport.handle_request = mock_parent_handle
+
+        try:
+            # Make multiple requests
+            requests = [
+                httpx.Request("GET", "https://api.example.com/1"),
+                httpx.Request("POST", "https://api.example.com/2"),
+                httpx.Request("PUT", "https://api.example.com/3"),
+            ]
+
+            for req in requests:
+                transport.handle_request(req)
+
+            # Verify all requests were intercepted
+            assert len(interceptor.outbound_calls) == 3
+            assert len(interceptor.inbound_calls) == 3
+            assert interceptor.outbound_calls == requests
+        finally:
+            httpx.HTTPTransport.handle_request = original_handle
+
+    @pytest.mark.asyncio
+    async def test_multiple_async_requests_same_interceptor(self) -> None:
+        """Test that multiple async requests through same interceptor are tracked."""
+        interceptor = TrackingInterceptor()
+        transport = AsyncHTTPransport(interceptor=interceptor)
+
+        mock_response = httpx.Response(200)
+
+        async def mock_handle(*args, **kwargs):
+            return mock_response
+
+        mock_parent_handle = Mock(side_effect=mock_handle)
+
+        original_handle = httpx.AsyncHTTPTransport.handle_async_request
+        httpx.AsyncHTTPTransport.handle_async_request = mock_parent_handle
+
+        try:
+            # Make multiple async requests
+            requests = [
+                httpx.Request("GET", "https://api.example.com/1"),
+                httpx.Request("POST", "https://api.example.com/2"),
+                httpx.Request("DELETE", "https://api.example.com/3"),
+            ]
+
+            for req in requests:
+                await transport.handle_async_request(req)
+
+            # Verify all requests were intercepted
+            assert len(interceptor.async_outbound_calls) == 3
+            assert len(interceptor.async_inbound_calls) == 3
+            assert interceptor.async_outbound_calls == requests
+        finally:
+            httpx.AsyncHTTPTransport.handle_async_request = original_handle

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

@@ -0,0 +1,319 @@
+"""Tests for interceptor behavior with unsupported providers."""
+
+import os
+
+import httpx
+import pytest
+
+from crewai.llm import LLM
+from crewai.llms.hooks.base import BaseInterceptor
+
+
+@pytest.fixture(autouse=True)
+def setup_provider_api_keys(monkeypatch):
+    """Set dummy API keys for providers that require them."""
+    if "OPENAI_API_KEY" not in os.environ:
+        monkeypatch.setenv("OPENAI_API_KEY", "sk-test-key-dummy")
+    if "ANTHROPIC_API_KEY" not in os.environ:
+        monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-ant-test-key-dummy")
+    if "GOOGLE_API_KEY" not in os.environ:
+        monkeypatch.setenv("GOOGLE_API_KEY", "test-google-key-dummy")
+
+
+class DummyInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Simple dummy interceptor for testing."""
+
+    def on_outbound(self, message: httpx.Request) -> httpx.Request:
+        """Pass through outbound request.
+
+        Args:
+            message: The outbound request.
+
+        Returns:
+            The request unchanged.
+        """
+        message.headers["X-Dummy"] = "true"
+        return message
+
+    def on_inbound(self, message: httpx.Response) -> httpx.Response:
+        """Pass through inbound response.
+
+        Args:
+            message: The inbound response.
+
+        Returns:
+            The response unchanged.
+        """
+        return message
+
+
+class TestAzureProviderInterceptor:
+    """Test suite for Azure provider with interceptor (unsupported)."""
+
+    def test_azure_llm_accepts_interceptor_parameter(self) -> None:
+        """Test that Azure LLM raises NotImplementedError with interceptor."""
+        interceptor = DummyInterceptor()
+
+        # Azure provider should raise NotImplementedError
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="azure/gpt-4",
+                interceptor=interceptor,
+                api_key="test-key",
+                endpoint="https://test.openai.azure.com/openai/deployments/gpt-4",
+            )
+
+        assert "interceptor" in str(exc_info.value).lower()
+
+    def test_azure_raises_not_implemented_on_initialization(self) -> None:
+        """Test that Azure raises NotImplementedError when interceptor is used."""
+        interceptor = DummyInterceptor()
+
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="azure/gpt-4",
+                interceptor=interceptor,
+                api_key="test-key",
+                endpoint="https://test.openai.azure.com/openai/deployments/gpt-4",
+            )
+
+        error_msg = str(exc_info.value).lower()
+        assert "interceptor" in error_msg
+        assert "azure" in error_msg
+
+    def test_azure_without_interceptor_works(self) -> None:
+        """Test that Azure LLM works without interceptor."""
+        llm = LLM(
+            model="azure/gpt-4",
+            api_key="test-key",
+            endpoint="https://test.openai.azure.com/openai/deployments/gpt-4",
+        )
+
+        # Azure provider doesn't have interceptor attribute
+        assert not hasattr(llm, 'interceptor') or llm.interceptor is None
+
+
+class TestBedrockProviderInterceptor:
+    """Test suite for Bedrock provider with interceptor (unsupported)."""
+
+    def test_bedrock_llm_accepts_interceptor_parameter(self) -> None:
+        """Test that Bedrock LLM raises NotImplementedError with interceptor."""
+        interceptor = DummyInterceptor()
+
+        # Bedrock provider should raise NotImplementedError
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+                interceptor=interceptor,
+                aws_access_key_id="test-access-key",
+                aws_secret_access_key="test-secret-key",
+                aws_region_name="us-east-1",
+            )
+
+        error_msg = str(exc_info.value).lower()
+        assert "interceptor" in error_msg
+        assert "bedrock" in error_msg
+
+    def test_bedrock_raises_not_implemented_on_initialization(self) -> None:
+        """Test that Bedrock raises NotImplementedError when interceptor is used."""
+        interceptor = DummyInterceptor()
+
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+                interceptor=interceptor,
+                aws_access_key_id="test-access-key",
+                aws_secret_access_key="test-secret-key",
+                aws_region_name="us-east-1",
+            )
+
+        error_msg = str(exc_info.value).lower()
+        assert "interceptor" in error_msg
+        assert "bedrock" in error_msg
+
+    def test_bedrock_without_interceptor_works(self) -> None:
+        """Test that Bedrock LLM works without interceptor."""
+        llm = LLM(
+            model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+            aws_access_key_id="test-access-key",
+            aws_secret_access_key="test-secret-key",
+            aws_region_name="us-east-1",
+        )
+
+        # Bedrock provider doesn't have interceptor attribute
+        assert not hasattr(llm, 'interceptor') or llm.interceptor is None
+
+
+class TestGeminiProviderInterceptor:
+    """Test suite for Gemini provider with interceptor (unsupported)."""
+
+    def test_gemini_llm_accepts_interceptor_parameter(self) -> None:
+        """Test that Gemini LLM raises NotImplementedError with interceptor."""
+        interceptor = DummyInterceptor()
+
+        # Gemini provider should raise NotImplementedError
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="gemini/gemini-pro",
+                interceptor=interceptor,
+                api_key="test-gemini-key",
+            )
+
+        error_msg = str(exc_info.value).lower()
+        assert "interceptor" in error_msg
+        assert "gemini" in error_msg
+
+    def test_gemini_raises_not_implemented_on_initialization(self) -> None:
+        """Test that Gemini raises NotImplementedError when interceptor is used."""
+        interceptor = DummyInterceptor()
+
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="gemini/gemini-pro",
+                interceptor=interceptor,
+                api_key="test-gemini-key",
+            )
+
+        error_msg = str(exc_info.value).lower()
+        assert "interceptor" in error_msg
+        assert "gemini" in error_msg
+
+    def test_gemini_without_interceptor_works(self) -> None:
+        """Test that Gemini LLM works without interceptor."""
+        llm = LLM(
+            model="gemini/gemini-pro",
+            api_key="test-gemini-key",
+        )
+
+        # Gemini provider doesn't have interceptor attribute
+        assert not hasattr(llm, 'interceptor') or llm.interceptor is None
+
+
+class TestUnsupportedProviderMessages:
+    """Test suite for error messages from unsupported providers."""
+
+    def test_azure_error_message_is_clear(self) -> None:
+        """Test that Azure error message clearly states lack of support."""
+        interceptor = DummyInterceptor()
+
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="azure/gpt-4",
+                interceptor=interceptor,
+                api_key="test-key",
+                endpoint="https://test.openai.azure.com/openai/deployments/gpt-4",
+            )
+
+        error_message = str(exc_info.value).lower()
+        assert "azure" in error_message
+        assert "interceptor" in error_message
+
+    def test_bedrock_error_message_is_clear(self) -> None:
+        """Test that Bedrock error message clearly states lack of support."""
+        interceptor = DummyInterceptor()
+
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+                interceptor=interceptor,
+                aws_access_key_id="test-access-key",
+                aws_secret_access_key="test-secret-key",
+                aws_region_name="us-east-1",
+            )
+
+        error_message = str(exc_info.value).lower()
+        assert "bedrock" in error_message
+        assert "interceptor" in error_message
+
+    def test_gemini_error_message_is_clear(self) -> None:
+        """Test that Gemini error message clearly states lack of support."""
+        interceptor = DummyInterceptor()
+
+        with pytest.raises(NotImplementedError) as exc_info:
+            LLM(
+                model="gemini/gemini-pro",
+                interceptor=interceptor,
+                api_key="test-gemini-key",
+            )
+
+        error_message = str(exc_info.value).lower()
+        assert "gemini" in error_message
+        assert "interceptor" in error_message
+
+
+class TestProviderSupportMatrix:
+    """Test suite to document which providers support interceptors."""
+
+    def test_supported_providers_accept_interceptor(self) -> None:
+        """Test that supported providers accept and use interceptors."""
+        interceptor = DummyInterceptor()
+
+        # OpenAI - SUPPORTED
+        openai_llm = LLM(model="gpt-4", interceptor=interceptor)
+        assert openai_llm.interceptor is interceptor
+
+        # Anthropic - SUPPORTED
+        anthropic_llm = LLM(model="anthropic/claude-3-opus-20240229", interceptor=interceptor)
+        assert anthropic_llm.interceptor is interceptor
+
+    def test_unsupported_providers_raise_error(self) -> None:
+        """Test that unsupported providers raise NotImplementedError."""
+        interceptor = DummyInterceptor()
+
+        # Azure - NOT SUPPORTED
+        with pytest.raises(NotImplementedError):
+            LLM(
+                model="azure/gpt-4",
+                interceptor=interceptor,
+                api_key="test",
+                endpoint="https://test.openai.azure.com/openai/deployments/gpt-4",
+            )
+
+        # Bedrock - NOT SUPPORTED
+        with pytest.raises(NotImplementedError):
+            LLM(
+                model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+                interceptor=interceptor,
+                aws_access_key_id="test",
+                aws_secret_access_key="test",
+                aws_region_name="us-east-1",
+            )
+
+        # Gemini - NOT SUPPORTED
+        with pytest.raises(NotImplementedError):
+            LLM(
+                model="gemini/gemini-pro",
+                interceptor=interceptor,
+                api_key="test",
+            )
+
+    def test_all_providers_work_without_interceptor(self) -> None:
+        """Test that all providers work normally without interceptor."""
+        # OpenAI
+        openai_llm = LLM(model="gpt-4")
+        assert openai_llm.interceptor is None
+
+        # Anthropic
+        anthropic_llm = LLM(model="anthropic/claude-3-opus-20240229")
+        assert anthropic_llm.interceptor is None
+
+        # Azure - doesn't have interceptor attribute
+        azure_llm = LLM(
+            model="azure/gpt-4",
+            api_key="test",
+            endpoint="https://test.openai.azure.com/openai/deployments/gpt-4",
+        )
+        assert not hasattr(azure_llm, 'interceptor') or azure_llm.interceptor is None
+
+        # Bedrock - doesn't have interceptor attribute
+        bedrock_llm = LLM(
+            model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+            aws_access_key_id="test",
+            aws_secret_access_key="test",
+            aws_region_name="us-east-1",
+        )
+        assert not hasattr(bedrock_llm, 'interceptor') or bedrock_llm.interceptor is None
+
+        # Gemini - doesn't have interceptor attribute
+        gemini_llm = LLM(model="gemini/gemini-pro", api_key="test")
+        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None

lib/crewai/tests/project/test_callback_with_taskoutput.py

@@ -0,0 +1,94 @@
+"""Test callback decorator with TaskOutput arguments."""
+
+from unittest.mock import MagicMock, patch
+
+from crewai import Agent, Crew, Task
+from crewai.project import CrewBase, callback, task
+from crewai.tasks.output_format import OutputFormat
+from crewai.tasks.task_output import TaskOutput
+
+
+def test_callback_decorator_with_taskoutput() -> None:
+    """Test that @callback decorator works with TaskOutput arguments."""
+
+    @CrewBase
+    class TestCrew:
+        """Test crew with callback."""
+
+        callback_called = False
+        callback_output = None
+
+        @callback
+        def task_callback(self, output: TaskOutput) -> None:
+            """Test callback that receives TaskOutput."""
+            self.callback_called = True
+            self.callback_output = output
+
+        @task
+        def test_task(self) -> Task:
+            """Test task with callback."""
+            return Task(
+                description="Test task",
+                expected_output="Test output",
+                callback=self.task_callback,
+            )
+
+    test_crew = TestCrew()
+    task_instance = test_crew.test_task()
+
+    test_output = TaskOutput(
+        description="Test task",
+        agent="Test Agent",
+        raw="test result",
+        output_format=OutputFormat.RAW,
+    )
+
+    task_instance.callback(test_output)
+
+    assert test_crew.callback_called
+    assert test_crew.callback_output == test_output
+
+
+def test_callback_decorator_with_taskoutput_integration() -> None:
+    """Integration test for callback with actual task execution."""
+
+    @CrewBase
+    class TestCrew:
+        """Test crew with callback integration."""
+
+        callback_called = False
+        received_output: TaskOutput | None = None
+
+        @callback
+        def task_callback(self, output: TaskOutput) -> None:
+            """Callback executed after task completion."""
+            self.callback_called = True
+            self.received_output = output
+
+        @task
+        def test_task(self) -> Task:
+            """Test task."""
+            return Task(
+                description="Test task",
+                expected_output="Test output",
+                callback=self.task_callback,
+            )
+
+    test_crew = TestCrew()
+
+    agent = Agent(
+        role="Test Agent",
+        goal="Test goal",
+        backstory="Test backstory",
+    )
+
+    task_instance = test_crew.test_task()
+    task_instance.agent = agent
+
+    with patch.object(Agent, "execute_task") as mock_execute:
+        mock_execute.return_value = "test result"
+        task_instance.execute_sync()
+
+        assert test_crew.callback_called
+        assert test_crew.received_output is not None
+        assert test_crew.received_output.raw == "test result"

lib/crewai/tests/test_flow.py

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

lib/crewai/tests/test_flow_visualization.py

@@ -10,8 +10,6 @@ import pytest
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
 from crewai.flow.visualization import (
     build_flow_structure,
-    print_structure_summary,
-    structure_to_dict,
     visualize_flow_structure,
 )
 
@@ -144,65 +142,6 @@ def test_build_flow_structure_with_and_or_conditions():
     assert len(or_edges) == 2
 
 
-def test_structure_to_dict():
-    """Test converting flow structure to dictionary format."""
-    flow = SimpleFlow()
-    structure = build_flow_structure(flow)
-    dag_dict = structure_to_dict(structure)
-
-    assert "nodes" in dag_dict
-    assert "edges" in dag_dict
-    assert "start_methods" in dag_dict
-    assert "router_methods" in dag_dict
-
-    assert "begin" in dag_dict["nodes"]
-    assert "process" in dag_dict["nodes"]
-
-    begin_node = dag_dict["nodes"]["begin"]
-    assert begin_node["type"] == "start"
-    assert "method_signature" in begin_node
-    assert "source_code" in begin_node
-
-    assert len(dag_dict["edges"]) == 1
-    edge = dag_dict["edges"][0]
-    assert "source" in edge
-    assert "target" in edge
-    assert "condition_type" in edge
-    assert "is_router_path" in edge
-
-
-def test_structure_to_dict_with_router():
-    """Test dictionary conversion for flow with router."""
-    flow = RouterFlow()
-    structure = build_flow_structure(flow)
-    dag_dict = structure_to_dict(structure)
-
-    decide_node = dag_dict["nodes"]["decide"]
-    assert decide_node["type"] == "router"
-    assert decide_node["is_router"] is True
-
-    if "router_paths" in decide_node:
-        assert len(decide_node["router_paths"]) >= 1
-
-    router_edges = [edge for edge in dag_dict["edges"] if edge["is_router_path"]]
-    assert len(router_edges) >= 1
-
-
-def test_structure_to_dict_with_complex_conditions():
-    """Test dictionary conversion for flow with complex conditions."""
-    flow = ComplexFlow()
-    structure = build_flow_structure(flow)
-    dag_dict = structure_to_dict(structure)
-
-    converge_and_node = dag_dict["nodes"]["converge_and"]
-    assert converge_and_node["condition_type"] == "AND"
-    assert "trigger_condition" in converge_and_node
-    assert converge_and_node["trigger_condition"]["type"] == "AND"
-
-    converge_or_node = dag_dict["nodes"]["converge_or"]
-    assert converge_or_node["condition_type"] == "OR"
-
-
 def test_visualize_flow_structure_creates_html():
     """Test that visualization generates valid HTML file."""
     flow = SimpleFlow()
@@ -243,7 +182,7 @@ def test_visualize_flow_structure_creates_assets():
 
     js_content = js_file.read_text(encoding="utf-8")
     assert len(js_content) > 0
-    assert "var nodes" in js_content or "const nodes" in js_content
+    assert "NetworkManager" in js_content
 
 
 def test_visualize_flow_structure_json_data():
@@ -268,22 +207,6 @@ def test_visualize_flow_structure_json_data():
     assert "path_b" in js_content
 
 
-def test_print_structure_summary():
-    """Test printing flow structure summary."""
-    flow = ComplexFlow()
-    structure = build_flow_structure(flow)
-
-    output = print_structure_summary(structure)
-
-    assert "Total nodes:" in output
-    assert "Total edges:" in output
-    assert "Start methods:" in output
-    assert "Router methods:" in output
-
-    assert "start_a" in output
-    assert "start_b" in output
-
-
 def test_node_metadata_includes_source_info():
     """Test that nodes include source code and line number information."""
     flow = SimpleFlow()
@@ -364,8 +287,7 @@ def test_visualization_handles_special_characters():
 
     assert len(structure["nodes"]) == 2
 
-    dag_dict = structure_to_dict(structure)
-    json_str = json.dumps(dag_dict)
+    json_str = json.dumps(structure)
     assert json_str is not None
     assert "method_with_underscore" in json_str
     assert "another_method_123" in json_str
@@ -390,7 +312,6 @@ def test_topological_path_counting():
     """Test that topological path counting is accurate."""
     flow = ComplexFlow()
     structure = build_flow_structure(flow)
-    dag_dict = structure_to_dict(structure)
 
     assert len(structure["nodes"]) > 0
     assert len(structure["edges"]) > 0

lib/crewai/tests/test_task.py

@@ -340,7 +340,7 @@ def test_output_pydantic_hierarchical():
     )
     result = crew.kickoff()
     assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 0}
+    assert result.to_dict() == {"score": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -599,7 +599,7 @@ def test_output_pydantic_to_another_task():
     assert isinstance(pydantic_result, ScoreOutput), (
         "Expected pydantic result to be of type ScoreOutput"
     )
-    assert pydantic_result.score == 4
+    assert pydantic_result.score == 5
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -630,7 +630,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 4}' == result.json
+    assert '{"score": 3}' == result.json
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

lib/crewai/tests/test_task_guardrails.py

@@ -181,7 +181,7 @@ def test_task_guardrail_process_output(task_output):
     result = guardrail(task_output)
     assert result[0] is False
 
-    assert "exceeding the guardrail limit of fewer than" in result[1].lower()
+    assert result[1] == "The task result contains more than 10 words, violating the guardrail. The text provided contains about 21 words."
 
     guardrail = LLMGuardrail(
         description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
@@ -252,10 +252,7 @@ def test_guardrail_emits_events(sample_agent):
             {
                 "success": False,
                 "result": None,
-                "error": "The task result does not comply with the guardrail because none of "
-                "the listed authors are from Italy. All authors mentioned are from "
-                "different countries, including Germany, the UK, the USA, and others, "
-                "which violates the requirement that authors must be Italian.",
+                "error": "The output indicates that none of the authors mentioned are from Italy, while the guardrail requires authors to be from Italy. Therefore, the output does not comply with the guardrail.",
                 "retry_count": 0,
             },
             {"success": True, "result": result.raw, "error": None, "retry_count": 1},

lib/crewai/tests/utilities/test_converter.py

@@ -227,28 +227,22 @@ def test_get_conversion_instructions_gpt() -> None:
     with patch.object(LLM, "supports_function_calling") as supports_function_calling:
         supports_function_calling.return_value = True
         instructions = get_conversion_instructions(SimpleModel, llm)
-        model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        expected_instructions = (
-            "Please convert the following text into valid JSON.\n\n"
-            "Output ONLY the valid JSON and nothing else.\n\n"
-            "Use this format exactly:\n```json\n"
-            f"{model_schema}\n```"
-        )
-        assert instructions == expected_instructions
+        # Now using OpenAPI schema format for all models
+        assert "Ensure your final answer strictly adheres to the following OpenAPI schema:" in instructions
+        assert '"type": "json_schema"' in instructions
+        assert '"name": "SimpleModel"' in instructions
+        assert "Do not include the OpenAPI schema in the final output" in instructions
 
 
 def test_get_conversion_instructions_non_gpt() -> None:
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     with patch.object(LLM, "supports_function_calling", return_value=False):
         instructions = get_conversion_instructions(SimpleModel, llm)
-        # Check that the JSON schema is properly formatted
-        assert "Please convert the following text into valid JSON" in instructions
-        assert "Output ONLY the valid JSON and nothing else" in instructions
-        assert "Use this format exactly" in instructions
-        assert "```json" in instructions
-        assert '"type": "object"' in instructions
-        assert '"properties"' in instructions
-        assert "'type': 'json_schema'" not in instructions
+        # Now using OpenAPI schema format for all models
+        assert "Ensure your final answer strictly adheres to the following OpenAPI schema:" in instructions
+        assert '"type": "json_schema"' in instructions
+        assert '"name": "SimpleModel"' in instructions
+        assert "Do not include the OpenAPI schema in the final output" in instructions
 
 
 # Tests for is_gpt

lib/crewai/tests/utilities/test_tool_output_truncation.py

@@ -0,0 +1,204 @@
+"""Tests for tool output truncation functionality."""
+
+import pytest
+
+from crewai.agents.parser import AgentAction, AgentFinish
+from crewai.tools.tool_types import ToolResult
+from crewai.utilities.agent_utils import (
+    estimate_token_count,
+    handle_agent_action_core,
+    truncate_tool_output,
+)
+
+
+class TestEstimateTokenCount:
+    """Tests for estimate_token_count function."""
+
+    def test_empty_string(self):
+        """Test token count estimation for empty string."""
+        assert estimate_token_count("") == 0
+
+    def test_short_string(self):
+        """Test token count estimation for short string."""
+        text = "Hello world"
+        assert estimate_token_count(text) == len(text) // 4
+
+    def test_long_string(self):
+        """Test token count estimation for long string."""
+        text = "a" * 10000
+        assert estimate_token_count(text) == 2500
+
+
+class TestTruncateToolOutput:
+    """Tests for truncate_tool_output function."""
+
+    def test_no_truncation_needed(self):
+        """Test that small outputs are not truncated."""
+        output = "Small output"
+        result = truncate_tool_output(output, max_tokens=100)
+        assert result == output
+        assert "[Tool output truncated" not in result
+
+    def test_truncation_applied(self):
+        """Test that large outputs are truncated."""
+        output = "a" * 20000
+        result = truncate_tool_output(output, max_tokens=1000)
+        assert len(result) < len(output)
+        assert "[Tool output truncated" in result
+        assert "showing first 1000" in result
+
+    def test_truncation_message_format(self):
+        """Test that truncation message has correct format."""
+        output = "a" * 20000
+        result = truncate_tool_output(output, max_tokens=1000, tool_name="search")
+        assert "[Tool output truncated:" in result
+        assert "Please refine your query" in result
+
+    def test_very_small_max_tokens(self):
+        """Test truncation with very small max_tokens."""
+        output = "a" * 1000
+        result = truncate_tool_output(output, max_tokens=10)
+        assert "[Tool output truncated" in result
+
+    def test_exact_boundary(self):
+        """Test truncation at exact token boundary."""
+        output = "a" * 400
+        result = truncate_tool_output(output, max_tokens=100)
+        assert result == output
+
+
+class TestHandleAgentActionCore:
+    """Tests for handle_agent_action_core with tool output truncation."""
+
+    def test_small_tool_output_not_truncated(self):
+        """Test that small tool outputs are not truncated."""
+        formatted_answer = AgentAction(
+            text="Thought: I need to search",
+            tool="search",
+            tool_input={"query": "test"},
+            thought="I need to search",
+        )
+        tool_result = ToolResult(result="Small result", result_as_answer=False)
+
+        result = handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            max_tool_output_tokens=1000,
+        )
+
+        assert isinstance(result, AgentAction)
+        assert "Small result" in result.text
+        assert "[Tool output truncated" not in result.text
+
+    def test_large_tool_output_truncated(self):
+        """Test that large tool outputs are truncated."""
+        formatted_answer = AgentAction(
+            text="Thought: I need to search",
+            tool="search",
+            tool_input={"query": "test"},
+            thought="I need to search",
+        )
+        large_output = "a" * 20000
+        tool_result = ToolResult(result=large_output, result_as_answer=False)
+
+        result = handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            max_tool_output_tokens=1000,
+        )
+
+        assert isinstance(result, AgentAction)
+        assert "[Tool output truncated" in result.text
+        assert len(result.result) < len(large_output)
+
+    def test_truncation_with_result_as_answer(self):
+        """Test that truncation works with result_as_answer=True."""
+        formatted_answer = AgentAction(
+            text="Thought: I need to search",
+            tool="search",
+            tool_input={"query": "test"},
+            thought="I need to search",
+        )
+        large_output = "a" * 20000
+        tool_result = ToolResult(result=large_output, result_as_answer=True)
+
+        result = handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            max_tool_output_tokens=1000,
+        )
+
+        assert isinstance(result, AgentFinish)
+        assert "[Tool output truncated" in result.output
+        assert len(result.output) < len(large_output)
+
+    def test_custom_max_tokens(self):
+        """Test that custom max_tool_output_tokens is respected."""
+        formatted_answer = AgentAction(
+            text="Thought: I need to search",
+            tool="search",
+            tool_input={"query": "test"},
+            thought="I need to search",
+        )
+        large_output = "a" * 10000
+        tool_result = ToolResult(result=large_output, result_as_answer=False)
+
+        result = handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            max_tool_output_tokens=500,
+        )
+
+        assert isinstance(result, AgentAction)
+        assert "[Tool output truncated" in result.text
+        assert "showing first 500" in result.text
+
+    def test_step_callback_called(self):
+        """Test that step_callback is called even with truncation."""
+        formatted_answer = AgentAction(
+            text="Thought: I need to search",
+            tool="search",
+            tool_input={"query": "test"},
+            thought="I need to search",
+        )
+        tool_result = ToolResult(result="a" * 20000, result_as_answer=False)
+
+        callback_called = []
+
+        def step_callback(result):
+            callback_called.append(result)
+
+        handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            step_callback=step_callback,
+            max_tool_output_tokens=1000,
+        )
+
+        assert len(callback_called) == 1
+        assert callback_called[0] == tool_result
+
+    def test_show_logs_called(self):
+        """Test that show_logs is called even with truncation."""
+        formatted_answer = AgentAction(
+            text="Thought: I need to search",
+            tool="search",
+            tool_input={"query": "test"},
+            thought="I need to search",
+        )
+        tool_result = ToolResult(result="a" * 20000, result_as_answer=False)
+
+        logs_called = []
+
+        def show_logs(answer):
+            logs_called.append(answer)
+
+        handle_agent_action_core(
+            formatted_answer=formatted_answer,
+            tool_result=tool_result,
+            show_logs=show_logs,
+            max_tool_output_tokens=1000,
+        )
+
+        assert len(logs_called) == 1
+        assert isinstance(logs_called[0], AgentAction)

pyproject.toml

@@ -117,13 +117,7 @@ warn_return_any = true
 show_error_codes = true
 warn_unused_ignores = true
 python_version = "3.12"
-exclude = [
-    "lib/crewai/src/crewai/cli/templates",
-    "lib/crewai/tests/",
-    # crewai-tools
-    "lib/crewai-tools/tests/",
-    "lib/crewai/src/crewai/experimental/a2a"
-]
+exclude = "(?x)(^lib/crewai/src/crewai/cli/templates/ | ^lib/crewai/tests/ | ^lib/crewai-tools/tests/)"
 plugins = ["pydantic.mypy", "crewai.mypy"]