replace print by logger

* fix: update document ID handling in ChromaDB utility functions to use SHA-256 hashing and include index for uniqueness

* test: add tests for hash-based ID generation in ChromaDB utility functions

* drop idx for preventing dups, upsert should handle dups

* fix: update document ID extraction logic in ChromaDB utility functions to check for doc_id at the top level of the document

* fix: enhance document ID generation in ChromaDB utility functions to deduplicate documents and ensure unique hash-based IDs without suffixes

* fix: improve error handling and document ID generation in ChromaDB utility functions to ensure robust processing and uniqueness

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py

@@ -10,7 +10,7 @@ from crewai_tools.tools.crewai_platform_tools.misc import (
     get_platform_api_base_url,
     get_platform_integration_token,
 )
-
+from crewai.utilities.logger import Logger
 
 class CrewaiPlatformToolBuilder:
     def __init__(
@@ -20,6 +20,7 @@ class CrewaiPlatformToolBuilder:
         self._apps = apps
         self._actions_schema = {}  # type: ignore[var-annotated]
         self._tools = None
+        self._logger = Logger(verbose=True)
 
     def tools(self) -> list[BaseTool]:
         if self._tools is None:
@@ -30,6 +31,7 @@ class CrewaiPlatformToolBuilder:
     def _fetch_actions(self):
         actions_url = f"{get_platform_api_base_url()}/actions"
         headers = {"Authorization": f"Bearer {get_platform_integration_token()}"}
+        self._logger.log("info", f"Fetch actions: {actions_url}, {headers}, {','.join(self._apps)}")
 
         try:
             response = requests.get(
@@ -39,11 +41,13 @@ class CrewaiPlatformToolBuilder:
                 params={"apps": ",".join(self._apps)},
             )
             response.raise_for_status()
-        except Exception:
+        except Exception as e:
+            self._logger.log("error", f"Error fetching actions: {e!s}")
             return
 
-        raw_data = response.json()
 
+        raw_data = response.json()
+        print("->>>>> raw_data:", raw_data)
         self._actions_schema = {}
         action_categories = raw_data.get("actions", {})
 

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

@@ -640,6 +640,7 @@ class Agent(BaseAgent):
 
     def get_platform_tools(self, apps: list[PlatformAppOrAction]) -> list[BaseTool]:
         try:
+            self._logger.log("info", f"Start get_platform_tools: {apps}")
             from crewai_tools import (
                 CrewaiPlatformTools,
             )

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/lite_agent.py

@@ -1,6 +1,7 @@
 import asyncio
 from collections.abc import Callable
 import inspect
+import json
 from typing import (
     Any,
     Literal,
@@ -58,7 +59,11 @@ 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, get_i18n
@@ -241,7 +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.
 
@@ -249,6 +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.
@@ -269,9 +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(
@@ -289,7 +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,
@@ -303,15 +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",
                 )
 
@@ -400,8 +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
@@ -422,21 +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] = [
@@ -506,6 +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,

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

@@ -7,7 +7,14 @@ outbound and inbound messages at the transport level.
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Generic, TypeVar
+from typing import TYPE_CHECKING, Any, Generic, TypeVar
+
+from pydantic_core import core_schema
+
+
+if TYPE_CHECKING:
+    from pydantic import GetCoreSchemaHandler
+    from pydantic_core import CoreSchema
 
 
 T = TypeVar("T")
@@ -25,6 +32,7 @@ class BaseInterceptor(ABC, Generic[T, U]):
         U: Inbound message type (e.g., httpx.Response)
 
     Example:
+        >>> import httpx
         >>> class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
         ...     def on_outbound(self, message: httpx.Request) -> httpx.Request:
         ...         message.headers["X-Custom-Header"] = "value"
@@ -80,3 +88,46 @@ class BaseInterceptor(ABC, Generic[T, U]):
             Modified message object.
         """
         raise NotImplementedError
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: Any, _handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        """Generate Pydantic core schema for BaseInterceptor.
+
+        This allows the generic BaseInterceptor to be used in Pydantic models
+        without requiring arbitrary_types_allowed=True. The schema validates
+        that the value is an instance of BaseInterceptor.
+
+        Args:
+            _source_type: The source type being validated (unused).
+            _handler: Handler for generating schemas (unused).
+
+        Returns:
+            A Pydantic core schema that validates BaseInterceptor instances.
+        """
+        return core_schema.no_info_plain_validator_function(
+            _validate_interceptor,
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                lambda x: x, return_schema=core_schema.any_schema()
+            ),
+        )
+
+
+def _validate_interceptor(value: Any) -> BaseInterceptor[T, U]:
+    """Validate that the value is a BaseInterceptor instance.
+
+    Args:
+        value: The value to validate.
+
+    Returns:
+        The validated BaseInterceptor instance.
+
+    Raises:
+        ValueError: If the value is not a BaseInterceptor instance.
+    """
+    if not isinstance(value, BaseInterceptor):
+        raise ValueError(
+            f"Expected BaseInterceptor instance, got {type(value).__name__}"
+        )
+    return value

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

@@ -6,16 +6,53 @@ to enable request/response modification at the transport level.
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, TypedDict
 
-import httpx
+from httpx import (
+    AsyncHTTPTransport as _AsyncHTTPTransport,
+    HTTPTransport as _HTTPTransport,
+)
+from typing_extensions import NotRequired, Unpack
 
 
 if TYPE_CHECKING:
+    from ssl import SSLContext
+
+    from httpx import Limits, Request, Response
+    from httpx._types import CertTypes, ProxyTypes
+
     from crewai.llms.hooks.base import BaseInterceptor
 
 
-class HTTPTransport(httpx.HTTPTransport):
+class HTTPTransportKwargs(TypedDict):
+    """Typed dictionary for httpx.HTTPTransport initialization parameters.
+
+    These parameters configure the underlying HTTP transport behavior including
+    SSL verification, proxies, connection limits, and low-level socket options.
+    """
+
+    verify: bool | str | SSLContext
+    cert: NotRequired[CertTypes | None]
+    trust_env: bool
+    http1: bool
+    http2: bool
+    limits: Limits
+    proxy: NotRequired[ProxyTypes | None]
+    uds: NotRequired[str | None]
+    local_address: NotRequired[str | None]
+    retries: int
+    socket_options: NotRequired[
+        Iterable[
+            tuple[int, int, int]
+            | tuple[int, int, bytes | bytearray]
+            | tuple[int, int, None, int]
+        ]
+        | None
+    ]
+
+
+class HTTPTransport(_HTTPTransport):
     """HTTP transport that uses an interceptor for request/response modification.
 
     This transport is used internally when a user provides a BaseInterceptor.
@@ -25,19 +62,19 @@ class HTTPTransport(httpx.HTTPTransport):
 
     def __init__(
         self,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response],
-        **kwargs: Any,
+        interceptor: BaseInterceptor[Request, Response],
+        **kwargs: Unpack[HTTPTransportKwargs],
     ) -> None:
         """Initialize transport with interceptor.
 
         Args:
             interceptor: HTTP interceptor for modifying raw request/response objects.
-            **kwargs: Additional arguments passed to httpx.HTTPTransport.
+            **kwargs: HTTPTransport configuration parameters (verify, cert, proxy, etc.).
         """
         super().__init__(**kwargs)
         self.interceptor = interceptor
 
-    def handle_request(self, request: httpx.Request) -> httpx.Response:
+    def handle_request(self, request: Request) -> Response:
         """Handle request with interception.
 
         Args:
@@ -51,7 +88,7 @@ class HTTPTransport(httpx.HTTPTransport):
         return self.interceptor.on_inbound(response)
 
 
-class AsyncHTTPransport(httpx.AsyncHTTPTransport):
+class AsyncHTTPTransport(_AsyncHTTPTransport):
     """Async HTTP transport that uses an interceptor for request/response modification.
 
     This transport is used internally when a user provides a BaseInterceptor.
@@ -61,19 +98,19 @@ class AsyncHTTPransport(httpx.AsyncHTTPTransport):
 
     def __init__(
         self,
-        interceptor: BaseInterceptor[httpx.Request, httpx.Response],
-        **kwargs: Any,
+        interceptor: BaseInterceptor[Request, Response],
+        **kwargs: Unpack[HTTPTransportKwargs],
     ) -> None:
         """Initialize async transport with interceptor.
 
         Args:
             interceptor: HTTP interceptor for modifying raw request/response objects.
-            **kwargs: Additional arguments passed to httpx.AsyncHTTPTransport.
+            **kwargs: HTTPTransport configuration parameters (verify, cert, proxy, etc.).
         """
         super().__init__(**kwargs)
         self.interceptor = interceptor
 
-    async def handle_async_request(self, request: httpx.Request) -> httpx.Response:
+    async def handle_async_request(self, request: Request) -> Response:
         """Handle async request with interception.
 
         Args:

lib/crewai/src/crewai/rag/chromadb/utils.py

@@ -67,31 +67,44 @@ def _prepare_documents_for_chromadb(
     ids: list[str] = []
     texts: list[str] = []
     metadatas: list[Mapping[str, str | int | float | bool]] = []
+    seen_ids: dict[str, int] = {}
+
+    try:
+        for doc in documents:
+            if "doc_id" in doc:
+                doc_id = str(doc["doc_id"])
+            else:
+                metadata = doc.get("metadata")
+                if metadata and isinstance(metadata, dict) and "doc_id" in metadata:
+                    doc_id = str(metadata["doc_id"])
+                else:
+                    content_for_hash = doc["content"]
+                    if metadata:
+                        metadata_str = json.dumps(metadata, sort_keys=True)
+                        content_for_hash = f"{content_for_hash}|{metadata_str}"
+                    doc_id = hashlib.sha256(content_for_hash.encode()).hexdigest()
 
-    for doc in documents:
-        if "doc_id" in doc:
-            ids.append(doc["doc_id"])
-        else:
-            content_for_hash = doc["content"]
             metadata = doc.get("metadata")
             if metadata:
-                metadata_str = json.dumps(metadata, sort_keys=True)
-                content_for_hash = f"{content_for_hash}|{metadata_str}"
-
-            content_hash = hashlib.blake2b(
-                content_for_hash.encode(), digest_size=32
-            ).hexdigest()
-            ids.append(content_hash)
-
-        texts.append(doc["content"])
-        metadata = doc.get("metadata")
-        if metadata:
-            if isinstance(metadata, list):
-                metadatas.append(metadata[0] if metadata and metadata[0] else {})
+                if isinstance(metadata, list):
+                    processed_metadata = metadata[0] if metadata and metadata[0] else {}
+                else:
+                    processed_metadata = metadata
             else:
-                metadatas.append(metadata)
-        else:
-            metadatas.append({})
+                processed_metadata = {}
+
+            if doc_id in seen_ids:
+                idx = seen_ids[doc_id]
+                texts[idx] = doc["content"]
+                metadatas[idx] = processed_metadata
+            else:
+                idx = len(ids)
+                ids.append(doc_id)
+                texts.append(doc["content"])
+                metadatas.append(processed_metadata)
+                seen_ids[doc_id] = idx
+    except Exception as e:
+        raise ValueError(f"Error preparing documents for ChromaDB: {e}") from e
 
     return PreparedDocuments(ids, texts, metadatas)
 

lib/crewai/src/crewai/task.py

@@ -525,7 +525,11 @@ class Task(BaseModel):
                 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,

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/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/tests/agents/test_agent.py

@@ -2117,15 +2117,14 @@ def test_agent_with_only_crewai_knowledge():
         goal="Provide information based on knowledge sources",
         backstory="You have access to specific knowledge sources.",
         llm=LLM(
-            model="openrouter/openai/gpt-4o-mini",
-            api_key=os.getenv("OPENROUTER_API_KEY"),
+            model="gpt-4o-mini",
         ),
     )
 
     # Create a task that requires the agent to use the knowledge
     task = Task(
         description="What is Vidit's favorite color?",
-        expected_output="Vidit's favorclearite color.",
+        expected_output="Vidit's favorite color.",
         agent=agent,
     )
 

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
     )
 
 

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+      failed validation: Error while validating the task output: The guardrail result
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+      grow quicker and with less resource input.\n\n5. **Plant Communication**: Studies
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+      and volatile organic compounds. This research is leading to better understanding
+      of plant ecology and could have implications for agriculture practices and pest
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+      designs, employing biophilic principles to enhance urban ecosystems. This includes
+      the integration of green roofs and living walls, which provide aesthetic benefits
+      while improving air quality and biodiversity.\n\n\nTry again, making sure to
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lib/crewai/tests/knowledge/test_knowledge.py

@@ -601,3 +601,81 @@ def test_file_path_validation():
         match="file_path/file_paths must be a Path, str, or a list of these types",
     ):
         PDFKnowledgeSource()
+
+
+def test_hash_based_id_generation_without_doc_id(mock_vector_db):
+    """Test that documents without doc_id generate hash-based IDs. Duplicates are deduplicated before upsert."""
+    import hashlib
+    import json
+    from crewai.rag.chromadb.utils import _prepare_documents_for_chromadb
+    from crewai.rag.types import BaseRecord
+
+    documents: list[BaseRecord] = [
+        {"content": "First document content", "metadata": {"source": "test1", "category": "research"}},
+        {"content": "Second document content", "metadata": {"source": "test2", "category": "research"}},
+        {"content": "Third document content"},  # No metadata
+    ]
+
+    result = _prepare_documents_for_chromadb(documents)
+
+    assert len(result.ids) == 3
+
+    # Unique documents should get 64-character hex hashes (no suffix)
+    for doc_id in result.ids:
+        assert len(doc_id) == 64, f"ID should be 64 characters: {doc_id}"
+        assert all(c in "0123456789abcdef" for c in doc_id), f"ID should be hex: {doc_id}"
+
+    # Different documents should have different hashes
+    assert result.ids[0] != result.ids[1] != result.ids[2]
+
+    # Verify hashes match expected values
+    expected_hash_1 = hashlib.sha256(
+        f"First document content|{json.dumps({'category': 'research', 'source': 'test1'}, sort_keys=True)}".encode()
+    ).hexdigest()
+    assert result.ids[0] == expected_hash_1, "First document hash should match expected"
+
+    expected_hash_3 = hashlib.sha256("Third document content".encode()).hexdigest()
+    assert result.ids[2] == expected_hash_3, "Third document hash should match expected"
+
+    # Test that duplicate documents are deduplicated (same ID, only one sent)
+    duplicate_documents: list[BaseRecord] = [
+        {"content": "Same content", "metadata": {"source": "test"}},
+        {"content": "Same content", "metadata": {"source": "test"}},
+        {"content": "Same content", "metadata": {"source": "test"}},
+    ]
+    duplicate_result = _prepare_documents_for_chromadb(duplicate_documents)
+    # Duplicates should be deduplicated - only one ID should remain
+    assert len(duplicate_result.ids) == 1, "Duplicate documents should be deduplicated"
+    assert len(duplicate_result.ids[0]) == 64, "Deduplicated ID should be clean hash"
+    # Verify it's the expected hash
+    expected_hash = hashlib.sha256(
+        f"Same content|{json.dumps({'source': 'test'}, sort_keys=True)}".encode()
+    ).hexdigest()
+    assert duplicate_result.ids[0] == expected_hash, "Deduplicated ID should match expected hash"
+
+
+def test_hash_based_id_generation_with_doc_id_in_metadata(mock_vector_db):
+    """Test that documents with doc_id in metadata use the doc_id directly, not hash-based."""
+    from crewai.rag.chromadb.utils import _prepare_documents_for_chromadb
+    from crewai.rag.types import BaseRecord
+
+    documents_with_doc_id: list[BaseRecord] = [
+        {"content": "First document", "metadata": {"doc_id": "custom-id-1", "source": "test1"}},
+        {"content": "Second document", "metadata": {"doc_id": "custom-id-2"}},
+    ]
+
+    documents_without_doc_id: list[BaseRecord] = [
+        {"content": "First document", "metadata": {"source": "test1"}},
+        {"content": "Second document"},
+    ]
+
+    result_with_doc_id = _prepare_documents_for_chromadb(documents_with_doc_id)
+    result_without_doc_id = _prepare_documents_for_chromadb(documents_without_doc_id)
+
+    assert result_with_doc_id.ids == ["custom-id-1", "custom-id-2"]
+
+    assert len(result_without_doc_id.ids) == 2
+    # Unique documents get 64-character hashes
+    for doc_id in result_without_doc_id.ids:
+        assert len(doc_id) == 64, "ID should be 64 characters"
+        assert all(c in "0123456789abcdef" for c in doc_id), "ID should be hex"

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

@@ -6,7 +6,7 @@ import httpx
 import pytest
 
 from crewai.llms.hooks.base import BaseInterceptor
-from crewai.llms.hooks.transport import AsyncHTTPransport, HTTPTransport
+from crewai.llms.hooks.transport import AsyncHTTPTransport, HTTPTransport
 
 
 class TrackingInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
@@ -128,7 +128,7 @@ class TestAsyncHTTPTransport:
     def test_async_transport_instantiation(self) -> None:
         """Test that async transport can be instantiated with interceptor."""
         interceptor = TrackingInterceptor()
-        transport = AsyncHTTPransport(interceptor=interceptor)
+        transport = AsyncHTTPTransport(interceptor=interceptor)
 
         assert transport.interceptor is interceptor
 
@@ -136,13 +136,13 @@ class TestAsyncHTTPTransport:
         """Test that async transport requires interceptor parameter."""
         # AsyncHTTPransport requires an interceptor parameter
         with pytest.raises(TypeError):
-            AsyncHTTPransport()
+            AsyncHTTPTransport()
 
     @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)
+        transport = AsyncHTTPTransport(interceptor=interceptor)
 
         # Create a mock parent transport that returns a response
         mock_response = httpx.Response(200, json={"success": True})
@@ -217,7 +217,7 @@ class TestTransportIntegration:
     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)
+        transport = AsyncHTTPTransport(interceptor=interceptor)
 
         mock_response = httpx.Response(200)
 

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_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