wip

* Clean up to match enterprise

* add additional params to LLM calls

* make sure additional params are getting passed to llm

* update docs

* drop print

docs/concepts/llms.mdx

@@ -465,11 +465,22 @@ Learn how to get the most out of your LLM configuration:
     # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
+    ## GET CREDENTIALS 
+    file_path = 'path/to/vertex_ai_service_account.json'
+
+    # Load the JSON file
+    with open(file_path, 'r') as file:
+        vertex_credentials = json.load(file)
+
+    # Convert to JSON string
+    vertex_credentials_json = json.dumps(vertex_credentials)
+
     Example usage:
     ```python Code
     llm = LLM(
         model="gemini/gemini-1.5-pro-latest",
-        temperature=0.7
+        temperature=0.7,
+        vertex_credentials=vertex_credentials_json
     )
     ```
   </Accordion>

pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pydantic>=2.4.2",
     "openai>=1.13.3",
     "litellm==1.59.8",
-    "instructor>=1.3.3",
+    "instructor>=1.7.2",
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -95,18 +95,29 @@ class CrewAgentExecutorMixin:
                 pass
 
     def _ask_human_input(self, final_answer: str) -> str:
-        """Prompt human input for final decision making."""
+        """Prompt human input with mode-appropriate messaging."""
         self._printer.print(
             content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
         )
 
-        self._printer.print(
-            content=(
+        # Training mode prompt (single iteration)
+        if self.crew and getattr(self.crew, "_train", False):
+            prompt = (
                 "\n\n=====\n"
-                "## Please provide feedback on the Final Result and the Agent's actions. "
-                "Respond with 'looks good' or a similar phrase when you're satisfied.\n"
+                "## TRAINING MODE: Provide feedback to improve the agent's performance.\n"
+                "This will be used to train better versions of the agent.\n"
+                "Please provide detailed feedback about the result quality and reasoning process.\n"
                 "=====\n"
-            ),
-            color="bold_yellow",
-        )
+            )
+        # Regular human-in-the-loop prompt (multiple iterations)
+        else:
+            prompt = (
+                "\n\n=====\n"
+                "## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
+                "Respond with 'looks good' to accept or provide specific improvement requests.\n"
+                "You can provide multiple rounds of feedback until satisfied.\n"
+                "=====\n"
+            )
+
+        self._printer.print(content=prompt, color="bold_yellow")
         return input()

src/crewai/agents/crew_agent_executor.py

@@ -100,6 +100,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         try:
             formatted_answer = self._invoke_loop()
+        except AssertionError:
+            self._printer.print(
+                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                color="red",
+            )
+            raise
         except Exception as e:
             if e.__class__.__module__.startswith("litellm"):
                 # Do not retry on litellm errors
@@ -115,7 +121,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    def _invoke_loop(self):
+    def _invoke_loop(self) -> AgentFinish:
         """
         Main loop to invoke the agent's thought process until it reaches a conclusion
         or the maximum number of iterations is reached.
@@ -161,6 +167,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             finally:
                 self.iterations += 1
 
+        # During the invoke loop, formatted_answer alternates between AgentAction
+        # (when the agent is using tools) and eventually becomes AgentFinish
+        # (when the agent reaches a final answer). This assertion confirms we've
+        # reached a final answer and helps type checking understand this transition.
+        assert isinstance(formatted_answer, AgentFinish)
         self._show_logs(formatted_answer)
         return formatted_answer
 
@@ -292,8 +303,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             self._printer.print(
                 content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
             )
+            description = (
+                getattr(self.task, "description") if self.task else "Not Found"
+            )
             self._printer.print(
-                content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
+                content=f"\033[95m## Task:\033[00m \033[92m{description}\033[00m"
             )
 
     def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
@@ -418,58 +432,50 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
 
     def _handle_crew_training_output(
-        self, result: AgentFinish, human_feedback: str | None = None
+        self, result: AgentFinish, human_feedback: Optional[str] = None
     ) -> None:
-        """Function to handle the process of the training data."""
+        """Handle the process of saving training data."""
         agent_id = str(self.agent.id)  # type: ignore
+        train_iteration = (
+            getattr(self.crew, "_train_iteration", None) if self.crew else None
+        )
+
+        if train_iteration is None or not isinstance(train_iteration, int):
+            self._printer.print(
+                content="Invalid or missing train iteration. Cannot save training data.",
+                color="red",
+            )
+            return
 
-        # Load training data
         training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
-        training_data = training_handler.load()
+        training_data = training_handler.load() or {}
 
-        # Check if training data exists, human input is not requested, and self.crew is valid
-        if training_data and not self.ask_for_human_input:
-            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
-                train_iteration = self.crew._train_iteration
-                if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration][
-                        "improved_output"
-                    ] = result.output
-                    training_handler.save(training_data)
-                else:
-                    self._printer.print(
-                        content="Invalid train iteration type or agent_id not in training data.",
-                        color="red",
-                    )
-            else:
-                self._printer.print(
-                    content="Crew is None or does not have _train_iteration attribute.",
-                    color="red",
-                )
+        # Initialize or retrieve agent's training data
+        agent_training_data = training_data.get(agent_id, {})
 
-        if self.ask_for_human_input and human_feedback is not None:
-            training_data = {
+        if human_feedback is not None:
+            # Save initial output and human feedback
+            agent_training_data[train_iteration] = {
                 "initial_output": result.output,
                 "human_feedback": human_feedback,
-                "agent": agent_id,
-                "agent_role": self.agent.role,  # type: ignore
             }
-            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
-                train_iteration = self.crew._train_iteration
-                if isinstance(train_iteration, int):
-                    CrewTrainingHandler(TRAINING_DATA_FILE).append(
-                        train_iteration, agent_id, training_data
-                    )
-                else:
-                    self._printer.print(
-                        content="Invalid train iteration type. Expected int.",
-                        color="red",
-                    )
+        else:
+            # Save improved output
+            if train_iteration in agent_training_data:
+                agent_training_data[train_iteration]["improved_output"] = result.output
             else:
                 self._printer.print(
-                    content="Crew is None or does not have _train_iteration attribute.",
+                    content=(
+                        f"No existing training data for agent {agent_id} and iteration "
+                        f"{train_iteration}. Cannot save improved output."
+                    ),
                     color="red",
                 )
+                return
+
+        # Update the training data and save
+        training_data[agent_id] = agent_training_data
+        training_handler.save(training_data)
 
     def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
         prompt = prompt.replace("{input}", inputs["input"])
@@ -485,82 +491,111 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return {"role": role, "content": prompt}
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
-        """
-        Handles the human feedback loop, allowing the user to provide feedback
-        on the agent's output and determining if additional iterations are needed.
+        """Handle human feedback with different flows for training vs regular use.
 
-        Parameters:
-            formatted_answer (AgentFinish): The initial output from the agent.
+        Args:
+            formatted_answer: The initial AgentFinish result to get feedback on
 
         Returns:
-            AgentFinish: The final output after incorporating human feedback.
+            AgentFinish: The final answer after processing feedback
         """
+        human_feedback = self._ask_human_input(formatted_answer.output)
+
+        if self._is_training_mode():
+            return self._handle_training_feedback(formatted_answer, human_feedback)
+
+        return self._handle_regular_feedback(formatted_answer, human_feedback)
+
+    def _is_training_mode(self) -> bool:
+        """Check if crew is in training mode."""
+        return bool(self.crew and self.crew._train)
+
+    def _handle_training_feedback(
+        self, initial_answer: AgentFinish, feedback: str
+    ) -> AgentFinish:
+        """Process feedback for training scenarios with single iteration."""
+        self._printer.print(
+            content="\nProcessing training feedback.\n",
+            color="yellow",
+        )
+        self._handle_crew_training_output(initial_answer, feedback)
+        self.messages.append(
+            self._format_msg(
+                self._i18n.slice("feedback_instructions").format(feedback=feedback)
+            )
+        )
+        improved_answer = self._invoke_loop()
+        self._handle_crew_training_output(improved_answer)
+        self.ask_for_human_input = False
+        return improved_answer
+
+    def _handle_regular_feedback(
+        self, current_answer: AgentFinish, initial_feedback: str
+    ) -> AgentFinish:
+        """Process feedback for regular use with potential multiple iterations."""
+        feedback = initial_feedback
+        answer = current_answer
+
         while self.ask_for_human_input:
-            human_feedback = self._ask_human_input(formatted_answer.output)
+            response = self._get_llm_feedback_response(feedback)
 
-            if self.crew and self.crew._train:
-                self._handle_crew_training_output(formatted_answer, human_feedback)
-
-            # Make an LLM call to verify if additional changes are requested based on human feedback
-            additional_changes_prompt = self._i18n.slice(
-                "human_feedback_classification"
-            ).format(feedback=human_feedback)
-
-            retry_count = 0
-            llm_call_successful = False
-            additional_changes_response = None
-
-            while retry_count < MAX_LLM_RETRY and not llm_call_successful:
-                try:
-                    additional_changes_response = (
-                        self.llm.call(
-                            [
-                                self._format_msg(
-                                    additional_changes_prompt, role="system"
-                                )
-                            ],
-                            callbacks=self.callbacks,
-                        )
-                        .strip()
-                        .lower()
-                    )
-                    llm_call_successful = True
-                except Exception as e:
-                    retry_count += 1
-
-                    self._printer.print(
-                        content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
-                        color="red",
-                    )
-
-            if not llm_call_successful:
-                self._printer.print(
-                    content="Error processing feedback after multiple attempts.",
-                    color="red",
-                )
+            if not self._feedback_requires_changes(response):
                 self.ask_for_human_input = False
-                break
-
-            if additional_changes_response == "false":
-                self.ask_for_human_input = False
-            elif additional_changes_response == "true":
-                self.ask_for_human_input = True
-                # Add human feedback to messages
-                self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
-                # Invoke the loop again with updated messages
-                formatted_answer = self._invoke_loop()
-
-                if self.crew and self.crew._train:
-                    self._handle_crew_training_output(formatted_answer)
             else:
-                # Unexpected response
-                self._printer.print(
-                    content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
-                    color="red",
-                )
-                self.ask_for_human_input = False
+                answer = self._process_feedback_iteration(feedback)
+                feedback = self._ask_human_input(answer.output)
 
-        return formatted_answer
+        return answer
+
+    def _get_llm_feedback_response(self, feedback: str) -> Optional[str]:
+        """Get LLM classification of whether feedback requires changes."""
+        prompt = self._i18n.slice("human_feedback_classification").format(
+            feedback=feedback
+        )
+        message = self._format_msg(prompt, role="system")
+
+        for retry in range(MAX_LLM_RETRY):
+            try:
+                response = self.llm.call([message], callbacks=self.callbacks)
+                return response.strip().lower() if response else None
+            except Exception as error:
+                self._log_feedback_error(retry, error)
+
+        self._log_max_retries_exceeded()
+        return None
+
+    def _feedback_requires_changes(self, response: Optional[str]) -> bool:
+        """Determine if feedback response indicates need for changes."""
+        return response == "true" if response else False
+
+    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
+        """Process a single feedback iteration."""
+        self.messages.append(
+            self._format_msg(
+                self._i18n.slice("feedback_instructions").format(feedback=feedback)
+            )
+        )
+        return self._invoke_loop()
+
+    def _log_feedback_error(self, retry_count: int, error: Exception) -> None:
+        """Log feedback processing errors."""
+        self._printer.print(
+            content=(
+                f"Error processing feedback: {error}. "
+                f"Retrying... ({retry_count + 1}/{MAX_LLM_RETRY})"
+            ),
+            color="red",
+        )
+
+    def _log_max_retries_exceeded(self) -> None:
+        """Log when max retries for feedback processing are exceeded."""
+        self._printer.print(
+            content=(
+                f"Failed to process feedback after {MAX_LLM_RETRY} attempts. "
+                "Ending feedback loop."
+            ),
+            color="red",
+        )
 
     def _handle_max_iterations_exceeded(self, formatted_answer):
         """

src/crewai/crew.py

@@ -494,21 +494,26 @@ class Crew(BaseModel):
         train_crew = self.copy()
         train_crew._setup_for_training(filename)
 
-        for n_iteration in range(n_iterations):
-            train_crew._train_iteration = n_iteration
-            train_crew.kickoff(inputs=inputs)
+        try:
+            for n_iteration in range(n_iterations):
+                train_crew._train_iteration = n_iteration
+                train_crew.kickoff(inputs=inputs)
 
-        training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
+            training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
-        for agent in train_crew.agents:
-            if training_data.get(str(agent.id)):
-                result = TaskEvaluator(agent).evaluate_training_data(
-                    training_data=training_data, agent_id=str(agent.id)
-                )
-
-                CrewTrainingHandler(filename).save_trained_data(
-                    agent_id=str(agent.role), trained_data=result.model_dump()
-                )
+            for agent in train_crew.agents:
+                if training_data.get(str(agent.id)):
+                    result = TaskEvaluator(agent).evaluate_training_data(
+                        training_data=training_data, agent_id=str(agent.id)
+                    )
+                    CrewTrainingHandler(filename).save_trained_data(
+                        agent_id=str(agent.role), trained_data=result.model_dump()
+                    )
+        except Exception as e:
+            self._logger.log("error", f"Training failed: {e}", color="red")
+            CrewTrainingHandler(TRAINING_DATA_FILE).clear()
+            CrewTrainingHandler(filename).clear()
+            raise
 
     def kickoff(
         self,

src/crewai/llm.py

@@ -7,7 +7,10 @@ import warnings
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union, cast
 
+import instructor
 from dotenv import load_dotenv
+from openai.types.chat import ChatCompletionMessageParam
+from pydantic import BaseModel
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
@@ -133,9 +136,11 @@ class LLM:
         logprobs: Optional[int] = None,
         top_logprobs: Optional[int] = None,
         base_url: Optional[str] = None,
+        api_base: Optional[str] = None,
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        **kwargs,
     ):
         self.model = model
         self.timeout = timeout
@@ -152,10 +157,12 @@ class LLM:
         self.logprobs = logprobs
         self.top_logprobs = top_logprobs
         self.base_url = base_url
+        self.api_base = api_base
         self.api_version = api_version
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
+        self.additional_params = kwargs
 
         litellm.drop_params = True
 
@@ -178,34 +185,63 @@ class LLM:
         available_functions: Optional[Dict[str, Any]] = None,
     ) -> str:
         """
-        High-level llm call method that:
-          1) Accepts either a string or a list of messages
-          2) Converts string input to the required message format
-          3) Calls litellm.completion
-          4) Handles function/tool calls if any
-          5) Returns the final text response or tool result
+        High-level LLM call method that handles:
+        1. Multiple input formats (string or message list)
+        2. Structured responses via Instructor integration
+        3. Tool/function calling with optional structured output
+        4. Callback integration
 
         Parameters:
-        - messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
-          - If a string is provided, it will be converted into a message list with a single entry.
-          - If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
-        - tools (Optional[List[dict]]): A list of tool schemas for function calling.
-        - callbacks (Optional[List[Any]]): A list of callback functions to be executed.
-        - available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
+            messages: Input prompt(s) as either:
+                - String (converted to single user message)
+                - List of message dicts with 'role' and 'content'
+
+            tools: List of tool schemas for function calling
+            callbacks: List of callback handlers
+            available_functions: Mapping of function names to callables
+            response_format: Pydantic model for structured responses
 
         Returns:
-        - str: The final text response from the LLM or the result of a tool function call.
+            str: Can be:
+            - Plain text response
+            - Structured response (if response_format provided)
+            - Tool function result (raw or structured)
+
+        Behavior:
+        - With response_format and no tools: Direct structured response
+        - With tools: Initial LLM call → Tool execution → Optional secondary structured call
+        - Without tools/response_format: Standard text completion
 
         Examples:
-        ---------
-        # Example 1: Using a string input
-        response = llm.call("Return the name of a random city in the world.")
-        print(response)
+            # Basic text completion
+            llm.call("Hello world")
 
-        # Example 2: Using a list of messages
-        messages = [{"role": "user", "content": "What is the capital of France?"}]
-        response = llm.call(messages)
-        print(response)
+            # Structured response without tools
+            class City(BaseModel):
+                name: str
+                population: int
+
+            response = llm.call(
+                "Name a major US city",
+                response_format=City
+            )
+            print(response.name)  # Structured access
+
+            # Tool usage with raw output
+            llm.call(
+                "What's 5 squared?",
+                tools=[math_tools],
+                available_functions={"square": square_number}
+            )
+
+            # Tool usage with structured output
+            response = llm.call(
+                "Analyze this data",
+                tools=[data_tools],
+                available_functions={"analyze": analyze_data},
+                response_format=AnalysisResult
+            )
+            print(response.metrics)  # Structured access
         """
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
@@ -214,35 +250,54 @@ class LLM:
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
+            # Prepare the parameters for the completion call.
+            params = {
+                "model": self.model,
+                "messages": messages,
+                "timeout": self.timeout,
+                "temperature": self.temperature,
+                "top_p": self.top_p,
+                "n": self.n,
+                "stop": self.stop,
+                "max_tokens": self.max_tokens or self.max_completion_tokens,
+                "presence_penalty": self.presence_penalty,
+                "frequency_penalty": self.frequency_penalty,
+                "logit_bias": self.logit_bias,
+                "seed": self.seed,
+                "logprobs": self.logprobs,
+                "top_logprobs": self.top_logprobs,
+                "api_base": self.api_base,
+                "base_url": self.base_url,
+                "api_version": self.api_version,
+                "api_key": self.api_key,
+                "stream": False,
+                "tools": tools,
+                **self.additional_params,
+            }
+
+            # Remove any keys with None values.
+            params = {k: v for k, v in params.items() if v is not None}
+
+            # --- Direct structured response if no tools are provided.
+            if self.response_format is not None and (tools is None or len(tools) == 0):
+                print("Direct structured response")
+                try:
+                    # Cast messages to required type and remove model param
+                    params["messages"] = cast(
+                        List[ChatCompletionMessageParam], messages
+                    )
+                    params.pop("model", None)
+
+                    client = instructor.from_litellm(litellm.completion)
+                    response = client.chat.completions.create(**params)
+                    return response
+                except Exception as e:
+                    logging.error(f"LiteLLM call failed: {str(e)}")
+                    raise
+
+            # --- Standard flow with potential tool calls.
             try:
-                # --- 1) Prepare the parameters for the completion call
-                params = {
-                    "model": self.model,
-                    "messages": messages,
-                    "timeout": self.timeout,
-                    "temperature": self.temperature,
-                    "top_p": self.top_p,
-                    "n": self.n,
-                    "stop": self.stop,
-                    "max_tokens": self.max_tokens or self.max_completion_tokens,
-                    "presence_penalty": self.presence_penalty,
-                    "frequency_penalty": self.frequency_penalty,
-                    "logit_bias": self.logit_bias,
-                    "response_format": self.response_format,
-                    "seed": self.seed,
-                    "logprobs": self.logprobs,
-                    "top_logprobs": self.top_logprobs,
-                    "api_base": self.base_url,
-                    "api_version": self.api_version,
-                    "api_key": self.api_key,
-                    "stream": False,
-                    "tools": tools,
-                }
-
-                # Remove None values from params
-                params = {k: v for k, v in params.items() if v is not None}
-
-                # --- 2) Make the completion call
+                print("NOT DIRECT STRUCTURED RESPONSE")
                 response = litellm.completion(**params)
                 response_message = cast(Choices, cast(ModelResponse, response).choices)[
                     0
@@ -250,7 +305,6 @@ class LLM:
                 text_response = response_message.content or ""
                 tool_calls = getattr(response_message, "tool_calls", [])
 
-                # --- 3) Handle callbacks with usage info
                 if callbacks and len(callbacks) > 0:
                     for callback in callbacks:
                         if hasattr(callback, "log_success_event"):
@@ -263,11 +317,11 @@ class LLM:
                                     end_time=0,
                                 )
 
-                # --- 4) If no tool calls, return the text response
+                # If no tool call is requested or available_functions is not provided, return the text response.
                 if not tool_calls or not available_functions:
                     return text_response
 
-                # --- 5) Handle the tool call
+                # --- Handle tool calls.
                 tool_call = tool_calls[0]
                 function_name = tool_call.function.name
 
@@ -280,22 +334,40 @@ class LLM:
 
                     fn = available_functions[function_name]
                     try:
-                        # Call the actual tool function
                         result = fn(**function_args)
-                        return result
-
                     except Exception as e:
                         logging.error(
                             f"Error executing function '{function_name}': {e}"
                         )
                         return text_response
 
+                    # If a structured response is requested, perform a secondary call using the tool result.
+                    if self.response_format is not None:
+                        new_params = dict(params)
+                        # Cast tool result message to required type
+                        new_params["messages"] = cast(
+                            List[ChatCompletionMessageParam],
+                            [{"role": "user", "content": result}],
+                        )
+                        new_params.pop("model", None)
+                        if "tools" in new_params:
+                            del new_params["tools"]
+                        try:
+                            client = instructor.from_litellm(litellm.completion)
+                            final_response = client.chat.completions.create(
+                                **new_params, response_model=response_format
+                            )
+                            return final_response
+                        except Exception as e:
+                            logging.error(f"LiteLLM structured call failed: {e}")
+                            return result
+                    else:
+                        return result
                 else:
                     logging.warning(
                         f"Tool call requested unknown function '{function_name}'"
                     )
                     return text_response
-
             except Exception as e:
                 if not LLMContextLengthExceededException(
                     str(e)

src/crewai/translations/en.json

@@ -24,7 +24,8 @@
     "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.",
     "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
-    "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."
+    "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."
   },
   "errors": {
     "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",

src/crewai/utilities/evaluators/task_evaluator.py

@@ -92,13 +92,34 @@ class TaskEvaluator:
         """
 
         output_training_data = training_data[agent_id]
-
         final_aggregated_data = ""
-        for _, data in output_training_data.items():
+
+        for iteration, data in output_training_data.items():
+            improved_output = data.get("improved_output")
+            initial_output = data.get("initial_output")
+            human_feedback = data.get("human_feedback")
+
+            if not all([improved_output, initial_output, human_feedback]):
+                missing_fields = [
+                    field
+                    for field in ["improved_output", "initial_output", "human_feedback"]
+                    if not data.get(field)
+                ]
+                error_msg = (
+                    f"Critical training data error: Missing fields ({', '.join(missing_fields)}) "
+                    f"for agent {agent_id} in iteration {iteration}.\n"
+                    "This indicates a broken training process. "
+                    "Cannot proceed with evaluation.\n"
+                    "Please check your training implementation."
+                )
+                raise ValueError(error_msg)
+
             final_aggregated_data += (
-                f"Initial Output:\n{data.get('initial_output', '')}\n\n"
-                f"Human Feedback:\n{data.get('human_feedback', '')}\n\n"
-                f"Improved Output:\n{data.get('improved_output', '')}\n\n"
+                f"Iteration: {iteration}\n"
+                f"Initial Output:\n{initial_output}\n\n"
+                f"Human Feedback:\n{human_feedback}\n\n"
+                f"Improved Output:\n{improved_output}\n\n"
+                "------------------------------------------------\n\n"
             )
 
         evaluation_query = (

src/crewai/utilities/llm_utils.py

@@ -53,6 +53,7 @@ def create_llm(
         timeout: Optional[float] = getattr(llm_value, "timeout", None)
         api_key: Optional[str] = getattr(llm_value, "api_key", None)
         base_url: Optional[str] = getattr(llm_value, "base_url", None)
+        api_base: Optional[str] = getattr(llm_value, "api_base", None)
 
         created_llm = LLM(
             model=model,
@@ -62,6 +63,7 @@ def create_llm(
             timeout=timeout,
             api_key=api_key,
             base_url=base_url,
+            api_base=api_base,
         )
         return created_llm
     except Exception as e:
@@ -101,8 +103,18 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     callbacks: List[Any] = []
 
     # Optional base URL from env
-    api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get("OPENAI_BASE_URL")
-    if api_base:
+    base_url = (
+        os.environ.get("BASE_URL")
+        or os.environ.get("OPENAI_API_BASE")
+        or os.environ.get("OPENAI_BASE_URL")
+    )
+
+    api_base = os.environ.get("API_BASE") or os.environ.get("AZURE_API_BASE")
+
+    # Synchronize base_url and api_base if one is populated and the other is not
+    if base_url and not api_base:
+        api_base = base_url
+    elif api_base and not base_url:
         base_url = api_base
 
     # Initialize llm_params dictionary
@@ -115,6 +127,7 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
         "timeout": timeout,
         "api_key": api_key,
         "base_url": base_url,
+        "api_base": api_base,
         "api_version": api_version,
         "presence_penalty": presence_penalty,
         "frequency_penalty": frequency_penalty,

src/crewai/utilities/training_handler.py

@@ -1,3 +1,5 @@
+import os
+
 from crewai.utilities.file_handler import PickleHandler
 
 
@@ -29,3 +31,8 @@ class CrewTrainingHandler(PickleHandler):
             data[agent_id] = {train_iteration: new_data}
 
         self.save(data)
+
+    def clear(self) -> None:
+        """Clear the training data by removing the file or resetting its contents."""
+        if os.path.exists(self.file_path):
+            self.save({})

tests/llm_test.py

@@ -1,4 +1,5 @@
 from time import sleep
+from unittest.mock import MagicMock, patch
 
 import pytest
 
@@ -154,3 +155,50 @@ def test_llm_call_with_tool_and_message_list():
 
     assert isinstance(result, int)
     assert result == 25
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_passes_additional_params():
+    llm = LLM(
+        model="gpt-4o-mini",
+        vertex_credentials="test_credentials",
+        vertex_project="test_project",
+    )
+
+    messages = [{"role": "user", "content": "Hello, world!"}]
+
+    with patch("litellm.completion") as mocked_completion:
+        # Create mocks for response structure
+        mock_message = MagicMock()
+        mock_message.content = "Test response"
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {
+            "prompt_tokens": 5,
+            "completion_tokens": 5,
+            "total_tokens": 10,
+        }
+
+        # Set up the mocked completion to return the mock response
+        mocked_completion.return_value = mock_response
+
+        result = llm.call(messages)
+
+        # Assert that litellm.completion was called once
+        mocked_completion.assert_called_once()
+
+        # Retrieve the actual arguments with which litellm.completion was called
+        _, kwargs = mocked_completion.call_args
+
+        # Check that the additional_params were passed to litellm.completion
+        assert kwargs["vertex_credentials"] == "test_credentials"
+        assert kwargs["vertex_project"] == "test_project"
+
+        # Also verify that other expected parameters are present
+        assert kwargs["model"] == "gpt-4o-mini"
+        assert kwargs["messages"] == messages
+
+        # Check the result from llm.call
+        assert result == "Test response"

tests/utilities/evaluators/test_task_evaluator.py

@@ -48,9 +48,9 @@ def test_evaluate_training_data(converter_mock):
             mock.call(
                 llm=original_agent.llm,
                 text="Assess the quality of the training data based on the llm output, human feedback , and llm "
-                "output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
-                "1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman "
-                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- Provide "
+                "output improved result.\n\nIteration: data1\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
+                "1\n\nImproved Output:\nImproved output 1\n\n------------------------------------------------\n\nIteration: data2\nInitial Output:\nInitial output 2\n\nHuman "
+                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\n------------------------------------------------\n\nPlease provide:\n- Provide "
                 "a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's "
                 "performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific "
                 "action items for future tasks. Ensure all key and specificpoints from the human feedback are "

uv.lock

@@ -736,7 +736,7 @@ requires-dist = [
     { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.32.1" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
-    { name = "instructor", specifier = ">=1.3.3" },
+    { name = "instructor", specifier = ">=1.7.2" },
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
@@ -1961,7 +1961,7 @@ wheels = [
 
 [[package]]
 name = "instructor"
-version = "1.6.3"
+version = "1.7.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -1971,13 +1971,14 @@ dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "pydantic-core" },
+    { name = "requests" },
     { name = "rich" },
     { name = "tenacity" },
     { name = "typer" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b8/e6/21969fe0de9d278979872240b6af17510af8bd5020f6845891719c1d3eef/instructor-1.6.3.tar.gz", hash = "sha256:399cd90e30b5bc7cbd47acd7399c9c4e84926a96c20c8b5d00c5a04b41ed41ab", size = 56708 }
+sdist = { url = "https://files.pythonhosted.org/packages/63/ba/692739c76959191aa7e5f0fccda871b36548355f4a09c8733687e64e62b0/instructor-1.7.2.tar.gz", hash = "sha256:6c01b2b159766df24865dc81f7bf8457cbda88a3c0bbc810da3467d19b185ed2", size = 66200177 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/10/98/c96bf0b1656173d06cd6c3a5adaf3ac429f86d5d696ae8e90e6eb15e89be/instructor-1.6.3-py3-none-any.whl", hash = "sha256:a8f973fea621c0188009b65a3429a526c24aeb249fc24100b605ea496e92d622", size = 69447 },
+    { url = "https://files.pythonhosted.org/packages/c5/82/fd319382c1a33d7021cf151007b4cbd5daddf09d9ca5fb670e476668f9fc/instructor-1.7.2-py3-none-any.whl", hash = "sha256:cb43d27f6d7631c31762b936b2fcb44d2a3f9d8a020430a0f4d3484604ffb95b", size = 71353 },
 ]
 
 [[package]]
@@ -2028,46 +2029,46 @@ wheels = [
 
 [[package]]
 name = "jiter"
-version = "0.5.0"
+version = "0.8.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d7/1a/aa64be757afc614484b370a4d9fc1747dc9237b37ce464f7f9d9ca2a3d38/jiter-0.5.0.tar.gz", hash = "sha256:1d916ba875bcab5c5f7d927df998c4cb694d27dceddf3392e58beaf10563368a", size = 158300 }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/70/90bc7bd3932e651486861df5c8ffea4ca7c77d28e8532ddefe2abc561a53/jiter-0.8.2.tar.gz", hash = "sha256:cd73d3e740666d0e639f678adb176fad25c1bcbdae88d8d7b857e1783bb4212d", size = 163007 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/af/09/f659fc67d6aaa82c56432c4a7cc8365fff763acbf1c8f24121076617f207/jiter-0.5.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b599f4e89b3def9a94091e6ee52e1d7ad7bc33e238ebb9c4c63f211d74822c3f", size = 284126 },
-    { url = "https://files.pythonhosted.org/packages/07/2d/5bdaddfefc44f91af0f3340e75ef327950d790c9f86490757ac8b395c074/jiter-0.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2a063f71c4b06225543dddadbe09d203dc0c95ba352d8b85f1221173480a71d5", size = 299265 },
-    { url = "https://files.pythonhosted.org/packages/74/bd/964485231deaec8caa6599f3f27c8787a54e9f9373ae80dcfbda2ad79c02/jiter-0.5.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:acc0d5b8b3dd12e91dd184b87273f864b363dfabc90ef29a1092d269f18c7e28", size = 332178 },
-    { url = "https://files.pythonhosted.org/packages/cf/4f/6353179174db10254549bbf2eb2c7ea102e59e0460ee374adb12071c274d/jiter-0.5.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c22541f0b672f4d741382a97c65609332a783501551445ab2df137ada01e019e", size = 342533 },
-    { url = "https://files.pythonhosted.org/packages/76/6f/21576071b8b056ef743129b9dacf9da65e328b58766f3d1ea265e966f000/jiter-0.5.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:63314832e302cc10d8dfbda0333a384bf4bcfce80d65fe99b0f3c0da8945a91a", size = 363469 },
-    { url = "https://files.pythonhosted.org/packages/73/a1/9ef99a279c72a031dbe8a4085db41e3521ae01ab0058651d6ccc809a5e93/jiter-0.5.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a25fbd8a5a58061e433d6fae6d5298777c0814a8bcefa1e5ecfff20c594bd749", size = 379078 },
-    { url = "https://files.pythonhosted.org/packages/41/6a/c038077509d67fe876c724bfe9ad15334593851a7def0d84518172bdd44a/jiter-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:503b2c27d87dfff5ab717a8200fbbcf4714516c9d85558048b1fc14d2de7d8dc", size = 318943 },
-    { url = "https://files.pythonhosted.org/packages/67/0d/d82673814eb38c208b7881581df596e680f8c2c003e2b80c25ca58975ee4/jiter-0.5.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6d1f3d27cce923713933a844872d213d244e09b53ec99b7a7fdf73d543529d6d", size = 357394 },
-    { url = "https://files.pythonhosted.org/packages/56/9e/cbd8f6612346c38cc42e41e35cda19ce78f5b12e4106d1186e8e95ee839b/jiter-0.5.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:c95980207b3998f2c3b3098f357994d3fd7661121f30669ca7cb945f09510a87", size = 511080 },
-    { url = "https://files.pythonhosted.org/packages/ff/33/135c0c33565b6d5c3010d047710837427dd24c9adbc9ca090f3f92df446e/jiter-0.5.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:afa66939d834b0ce063f57d9895e8036ffc41c4bd90e4a99631e5f261d9b518e", size = 492827 },
-    { url = "https://files.pythonhosted.org/packages/68/c1/491a8ef682508edbaf2a32e41c1b1e34064078b369b0c2d141170999d1c9/jiter-0.5.0-cp310-none-win32.whl", hash = "sha256:f16ca8f10e62f25fd81d5310e852df6649af17824146ca74647a018424ddeccf", size = 195081 },
-    { url = "https://files.pythonhosted.org/packages/31/20/8cda4faa9571affea6130b150289522a22329778bdfa45a7aab4e7edff95/jiter-0.5.0-cp310-none-win_amd64.whl", hash = "sha256:b2950e4798e82dd9176935ef6a55cf6a448b5c71515a556da3f6b811a7844f1e", size = 190977 },
-    { url = "https://files.pythonhosted.org/packages/94/5f/3ac960ed598726aae46edea916e6df4df7ff6fe084bc60774b95cf3154e6/jiter-0.5.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:d4c8e1ed0ef31ad29cae5ea16b9e41529eb50a7fba70600008e9f8de6376d553", size = 284131 },
-    { url = "https://files.pythonhosted.org/packages/03/eb/2308fa5f5c14c97c4c7720fef9465f1fa0771826cddb4eec9866bdd88846/jiter-0.5.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c6f16e21276074a12d8421692515b3fd6d2ea9c94fd0734c39a12960a20e85f3", size = 299310 },
-    { url = "https://files.pythonhosted.org/packages/3c/f6/dba34ca10b44715fa5302b8e8d2113f72eb00a9297ddf3fa0ae4fd22d1d1/jiter-0.5.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5280e68e7740c8c128d3ae5ab63335ce6d1fb6603d3b809637b11713487af9e6", size = 332282 },
-    { url = "https://files.pythonhosted.org/packages/69/f7/64e0a7439790ec47f7681adb3871c9d9c45fff771102490bbee5e92c00b7/jiter-0.5.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:583c57fc30cc1fec360e66323aadd7fc3edeec01289bfafc35d3b9dcb29495e4", size = 342370 },
-    { url = "https://files.pythonhosted.org/packages/55/31/1efbfff2ae8e4d919144c53db19b828049ad0622a670be3bbea94a86282c/jiter-0.5.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:26351cc14507bdf466b5f99aba3df3143a59da75799bf64a53a3ad3155ecded9", size = 363591 },
-    { url = "https://files.pythonhosted.org/packages/30/c3/7ab2ca2276426a7398c6dfb651e38dbc81954c79a3bfbc36c514d8599499/jiter-0.5.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4829df14d656b3fb87e50ae8b48253a8851c707da9f30d45aacab2aa2ba2d614", size = 378551 },
-    { url = "https://files.pythonhosted.org/packages/47/e7/5d88031cd743c62199b125181a591b1671df3ff2f6e102df85c58d8f7d31/jiter-0.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a42a4bdcf7307b86cb863b2fb9bb55029b422d8f86276a50487982d99eed7c6e", size = 319152 },
-    { url = "https://files.pythonhosted.org/packages/4c/2d/09ea58e1adca9f0359f3d41ef44a1a18e59518d7c43a21f4ece9e72e28c0/jiter-0.5.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:04d461ad0aebf696f8da13c99bc1b3e06f66ecf6cfd56254cc402f6385231c06", size = 357377 },
-    { url = "https://files.pythonhosted.org/packages/7d/2f/83ff1058cb56fc3ff73e0d3c6440703ddc9cdb7f759b00cfbde8228fc435/jiter-0.5.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e6375923c5f19888c9226582a124b77b622f8fd0018b843c45eeb19d9701c403", size = 511091 },
-    { url = "https://files.pythonhosted.org/packages/ae/c9/4f85f97c9894382ab457382337aea0012711baaa17f2ed55c0ff25f3668a/jiter-0.5.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:2cec323a853c24fd0472517113768c92ae0be8f8c384ef4441d3632da8baa646", size = 492948 },
-    { url = "https://files.pythonhosted.org/packages/4d/f2/2e987e0eb465e064c5f52c2f29c8d955452e3b316746e326269263bfb1b7/jiter-0.5.0-cp311-none-win32.whl", hash = "sha256:aa1db0967130b5cab63dfe4d6ff547c88b2a394c3410db64744d491df7f069bb", size = 195183 },
-    { url = "https://files.pythonhosted.org/packages/ab/59/05d1c3203c349b37c4dd28b02b9b4e5915a7bcbd9319173b4548a67d2e93/jiter-0.5.0-cp311-none-win_amd64.whl", hash = "sha256:aa9d2b85b2ed7dc7697597dcfaac66e63c1b3028652f751c81c65a9f220899ae", size = 191032 },
-    { url = "https://files.pythonhosted.org/packages/aa/bd/c3950e2c478161e131bed8cb67c36aed418190e2a961a1c981e69954e54b/jiter-0.5.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:9f664e7351604f91dcdd557603c57fc0d551bc65cc0a732fdacbf73ad335049a", size = 283511 },
-    { url = "https://files.pythonhosted.org/packages/80/1c/8ce58d8c37a589eeaaa5d07d131fd31043886f5e77ab50c00a66d869a361/jiter-0.5.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:044f2f1148b5248ad2c8c3afb43430dccf676c5a5834d2f5089a4e6c5bbd64df", size = 296974 },
-    { url = "https://files.pythonhosted.org/packages/4d/b8/6faeff9eed8952bed93a77ea1cffae7b946795b88eafd1a60e87a67b09e0/jiter-0.5.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:702e3520384c88b6e270c55c772d4bd6d7b150608dcc94dea87ceba1b6391248", size = 331897 },
-    { url = "https://files.pythonhosted.org/packages/4f/54/1d9a2209b46d39ce6f0cef3ad87c462f9c50312ab84585e6bd5541292b35/jiter-0.5.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:528d742dcde73fad9d63e8242c036ab4a84389a56e04efd854062b660f559544", size = 342962 },
-    { url = "https://files.pythonhosted.org/packages/2a/de/90360be7fc54b2b4c2dfe79eb4ed1f659fce9c96682e6a0be4bbe71371f7/jiter-0.5.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8cf80e5fe6ab582c82f0c3331df27a7e1565e2dcf06265afd5173d809cdbf9ba", size = 363844 },
-    { url = "https://files.pythonhosted.org/packages/ba/ad/ef32b173191b7a53ea8a6757b80723cba321f8469834825e8c71c96bde17/jiter-0.5.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:44dfc9ddfb9b51a5626568ef4e55ada462b7328996294fe4d36de02fce42721f", size = 378709 },
-    { url = "https://files.pythonhosted.org/packages/07/de/353ce53743c0defbbbd652e89c106a97dbbac4eb42c95920b74b5056b93a/jiter-0.5.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c451f7922992751a936b96c5f5b9bb9312243d9b754c34b33d0cb72c84669f4e", size = 319038 },
-    { url = "https://files.pythonhosted.org/packages/3f/92/42d47310bf9530b9dece9e2d7c6d51cf419af5586ededaf5e66622d160e2/jiter-0.5.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:308fce789a2f093dca1ff91ac391f11a9f99c35369117ad5a5c6c4903e1b3e3a", size = 357763 },
-    { url = "https://files.pythonhosted.org/packages/bd/8c/2bb76a9a84474d48fdd133d3445db8a4413da4e87c23879d917e000a9d87/jiter-0.5.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7f5ad4a7c6b0d90776fdefa294f662e8a86871e601309643de30bf94bb93a64e", size = 511031 },
-    { url = "https://files.pythonhosted.org/packages/33/4f/9f23d79c0795e0a8e56e7988e8785c2dcda27e0ed37977256d50c77c6a19/jiter-0.5.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ea189db75f8eca08807d02ae27929e890c7d47599ce3d0a6a5d41f2419ecf338", size = 493042 },
-    { url = "https://files.pythonhosted.org/packages/df/67/8a4f975aa834b8aecdb6b131422390173928fd47f42f269dcc32034ab432/jiter-0.5.0-cp312-none-win32.whl", hash = "sha256:e3bbe3910c724b877846186c25fe3c802e105a2c1fc2b57d6688b9f8772026e4", size = 195405 },
-    { url = "https://files.pythonhosted.org/packages/15/81/296b1e25c43db67848728cdab34ac3eb5c5cbb4955ceb3f51ae60d4a5e3d/jiter-0.5.0-cp312-none-win_amd64.whl", hash = "sha256:a586832f70c3f1481732919215f36d41c59ca080fa27a65cf23d9490e75b2ef5", size = 189720 },
+    { url = "https://files.pythonhosted.org/packages/f2/f3/8c11e0e87bd5934c414f9b1cfae3cbfd4a938d4669d57cb427e1c4d11a7f/jiter-0.8.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ca8577f6a413abe29b079bc30f907894d7eb07a865c4df69475e868d73e71c7b", size = 303381 },
+    { url = "https://files.pythonhosted.org/packages/ea/28/4cd3f0bcbf40e946bc6a62a82c951afc386a25673d3d8d5ee461f1559bbe/jiter-0.8.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b25bd626bde7fb51534190c7e3cb97cee89ee76b76d7585580e22f34f5e3f393", size = 311718 },
+    { url = "https://files.pythonhosted.org/packages/0d/17/57acab00507e60bd954eaec0837d9d7b119b4117ff49b8a62f2b646f32ed/jiter-0.8.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5c826a221851a8dc028eb6d7d6429ba03184fa3c7e83ae01cd6d3bd1d4bd17d", size = 335465 },
+    { url = "https://files.pythonhosted.org/packages/74/b9/1a3ddd2bc95ae17c815b021521020f40c60b32137730126bada962ef32b4/jiter-0.8.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d35c864c2dff13dfd79fb070fc4fc6235d7b9b359efe340e1261deb21b9fcb66", size = 355570 },
+    { url = "https://files.pythonhosted.org/packages/78/69/6d29e2296a934199a7d0dde673ecccf98c9c8db44caf0248b3f2b65483cb/jiter-0.8.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f557c55bc2b7676e74d39d19bcb8775ca295c7a028246175d6a8b431e70835e5", size = 381383 },
+    { url = "https://files.pythonhosted.org/packages/22/d7/fbc4c3fb1bf65f9be22a32759b539f88e897aeb13fe84ab0266e4423487a/jiter-0.8.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:580ccf358539153db147e40751a0b41688a5ceb275e6f3e93d91c9467f42b2e3", size = 390454 },
+    { url = "https://files.pythonhosted.org/packages/4d/a0/3993cda2e267fe679b45d0bcc2cef0b4504b0aa810659cdae9737d6bace9/jiter-0.8.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:af102d3372e917cffce49b521e4c32c497515119dc7bd8a75665e90a718bbf08", size = 345039 },
+    { url = "https://files.pythonhosted.org/packages/b9/ef/69c18562b4c09ce88fab5df1dcaf643f6b1a8b970b65216e7221169b81c4/jiter-0.8.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cadcc978f82397d515bb2683fc0d50103acff2a180552654bb92d6045dec2c49", size = 376200 },
+    { url = "https://files.pythonhosted.org/packages/4d/17/0b5a8de46a6ab4d836f70934036278b49b8530c292b29dde3483326d4555/jiter-0.8.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:ba5bdf56969cad2019d4e8ffd3f879b5fdc792624129741d3d83fc832fef8c7d", size = 511158 },
+    { url = "https://files.pythonhosted.org/packages/6c/b2/c401a0a2554b36c9e6d6e4876b43790d75139cf3936f0222e675cbc23451/jiter-0.8.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:3b94a33a241bee9e34b8481cdcaa3d5c2116f575e0226e421bed3f7a6ea71cff", size = 503956 },
+    { url = "https://files.pythonhosted.org/packages/d4/02/a0291ed7d72c0ac130f172354ee3cf0b2556b69584de391463a8ee534f40/jiter-0.8.2-cp310-cp310-win32.whl", hash = "sha256:6e5337bf454abddd91bd048ce0dca5134056fc99ca0205258766db35d0a2ea43", size = 202846 },
+    { url = "https://files.pythonhosted.org/packages/ad/20/8c988831ae4bf437e29f1671e198fc99ba8fe49f2895f23789acad1d1811/jiter-0.8.2-cp310-cp310-win_amd64.whl", hash = "sha256:4a9220497ca0cb1fe94e3f334f65b9b5102a0b8147646118f020d8ce1de70105", size = 204414 },
+    { url = "https://files.pythonhosted.org/packages/cb/b0/c1a7caa7f9dc5f1f6cfa08722867790fe2d3645d6e7170ca280e6e52d163/jiter-0.8.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:2dd61c5afc88a4fda7d8b2cf03ae5947c6ac7516d32b7a15bf4b49569a5c076b", size = 303666 },
+    { url = "https://files.pythonhosted.org/packages/f5/97/0468bc9eeae43079aaa5feb9267964e496bf13133d469cfdc135498f8dd0/jiter-0.8.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a6c710d657c8d1d2adbbb5c0b0c6bfcec28fd35bd6b5f016395f9ac43e878a15", size = 311934 },
+    { url = "https://files.pythonhosted.org/packages/e5/69/64058e18263d9a5f1e10f90c436853616d5f047d997c37c7b2df11b085ec/jiter-0.8.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9584de0cd306072635fe4b89742bf26feae858a0683b399ad0c2509011b9dc0", size = 335506 },
+    { url = "https://files.pythonhosted.org/packages/9d/14/b747f9a77b8c0542141d77ca1e2a7523e854754af2c339ac89a8b66527d6/jiter-0.8.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5a90a923338531b7970abb063cfc087eebae6ef8ec8139762007188f6bc69a9f", size = 355849 },
+    { url = "https://files.pythonhosted.org/packages/53/e2/98a08161db7cc9d0e39bc385415890928ff09709034982f48eccfca40733/jiter-0.8.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d21974d246ed0181558087cd9f76e84e8321091ebfb3a93d4c341479a736f099", size = 381700 },
+    { url = "https://files.pythonhosted.org/packages/7a/38/1674672954d35bce3b1c9af99d5849f9256ac8f5b672e020ac7821581206/jiter-0.8.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:32475a42b2ea7b344069dc1e81445cfc00b9d0e3ca837f0523072432332e9f74", size = 389710 },
+    { url = "https://files.pythonhosted.org/packages/f8/9b/92f9da9a9e107d019bcf883cd9125fa1690079f323f5a9d5c6986eeec3c0/jiter-0.8.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b9931fd36ee513c26b5bf08c940b0ac875de175341cbdd4fa3be109f0492586", size = 345553 },
+    { url = "https://files.pythonhosted.org/packages/44/a6/6d030003394e9659cd0d7136bbeabd82e869849ceccddc34d40abbbbb269/jiter-0.8.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ce0820f4a3a59ddced7fce696d86a096d5cc48d32a4183483a17671a61edfddc", size = 376388 },
+    { url = "https://files.pythonhosted.org/packages/ad/8d/87b09e648e4aca5f9af89e3ab3cfb93db2d1e633b2f2931ede8dabd9b19a/jiter-0.8.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:8ffc86ae5e3e6a93765d49d1ab47b6075a9c978a2b3b80f0f32628f39caa0c88", size = 511226 },
+    { url = "https://files.pythonhosted.org/packages/77/95/8008ebe4cdc82eac1c97864a8042ca7e383ed67e0ec17bfd03797045c727/jiter-0.8.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5127dc1abd809431172bc3fbe8168d6b90556a30bb10acd5ded41c3cfd6f43b6", size = 504134 },
+    { url = "https://files.pythonhosted.org/packages/26/0d/3056a74de13e8b2562e4d526de6dac2f65d91ace63a8234deb9284a1d24d/jiter-0.8.2-cp311-cp311-win32.whl", hash = "sha256:66227a2c7b575720c1871c8800d3a0122bb8ee94edb43a5685aa9aceb2782d44", size = 203103 },
+    { url = "https://files.pythonhosted.org/packages/4e/1e/7f96b798f356e531ffc0f53dd2f37185fac60fae4d6c612bbbd4639b90aa/jiter-0.8.2-cp311-cp311-win_amd64.whl", hash = "sha256:cde031d8413842a1e7501e9129b8e676e62a657f8ec8166e18a70d94d4682855", size = 206717 },
+    { url = "https://files.pythonhosted.org/packages/a1/17/c8747af8ea4e045f57d6cfd6fc180752cab9bc3de0e8a0c9ca4e8af333b1/jiter-0.8.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:e6ec2be506e7d6f9527dae9ff4b7f54e68ea44a0ef6b098256ddf895218a2f8f", size = 302027 },
+    { url = "https://files.pythonhosted.org/packages/3c/c1/6da849640cd35a41e91085723b76acc818d4b7d92b0b6e5111736ce1dd10/jiter-0.8.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:76e324da7b5da060287c54f2fabd3db5f76468006c811831f051942bf68c9d44", size = 310326 },
+    { url = "https://files.pythonhosted.org/packages/06/99/a2bf660d8ccffee9ad7ed46b4f860d2108a148d0ea36043fd16f4dc37e94/jiter-0.8.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:180a8aea058f7535d1c84183c0362c710f4750bef66630c05f40c93c2b152a0f", size = 334242 },
+    { url = "https://files.pythonhosted.org/packages/a7/5f/cea1c17864828731f11427b9d1ab7f24764dbd9aaf4648a7f851164d2718/jiter-0.8.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:025337859077b41548bdcbabe38698bcd93cfe10b06ff66617a48ff92c9aec60", size = 356654 },
+    { url = "https://files.pythonhosted.org/packages/e9/13/62774b7e5e7f5d5043efe1d0f94ead66e6d0f894ae010adb56b3f788de71/jiter-0.8.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ecff0dc14f409599bbcafa7e470c00b80f17abc14d1405d38ab02e4b42e55b57", size = 379967 },
+    { url = "https://files.pythonhosted.org/packages/ec/fb/096b34c553bb0bd3f2289d5013dcad6074948b8d55212aa13a10d44c5326/jiter-0.8.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ffd9fee7d0775ebaba131f7ca2e2d83839a62ad65e8e02fe2bd8fc975cedeb9e", size = 389252 },
+    { url = "https://files.pythonhosted.org/packages/17/61/beea645c0bf398ced8b199e377b61eb999d8e46e053bb285c91c3d3eaab0/jiter-0.8.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:14601dcac4889e0a1c75ccf6a0e4baf70dbc75041e51bcf8d0e9274519df6887", size = 345490 },
+    { url = "https://files.pythonhosted.org/packages/d5/df/834aa17ad5dcc3cf0118821da0a0cf1589ea7db9832589278553640366bc/jiter-0.8.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:92249669925bc1c54fcd2ec73f70f2c1d6a817928480ee1c65af5f6b81cdf12d", size = 376991 },
+    { url = "https://files.pythonhosted.org/packages/67/80/87d140399d382fb4ea5b3d56e7ecaa4efdca17cd7411ff904c1517855314/jiter-0.8.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e725edd0929fa79f8349ab4ec7f81c714df51dc4e991539a578e5018fa4a7152", size = 510822 },
+    { url = "https://files.pythonhosted.org/packages/5c/37/3394bb47bac1ad2cb0465601f86828a0518d07828a650722e55268cdb7e6/jiter-0.8.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:bf55846c7b7a680eebaf9c3c48d630e1bf51bdf76c68a5f654b8524335b0ad29", size = 503730 },
+    { url = "https://files.pythonhosted.org/packages/f9/e2/253fc1fa59103bb4e3aa0665d6ceb1818df1cd7bf3eb492c4dad229b1cd4/jiter-0.8.2-cp312-cp312-win32.whl", hash = "sha256:7efe4853ecd3d6110301665a5178b9856be7e2a9485f49d91aa4d737ad2ae49e", size = 203375 },
+    { url = "https://files.pythonhosted.org/packages/41/69/6d4bbe66b3b3b4507e47aa1dd5d075919ad242b4b1115b3f80eecd443687/jiter-0.8.2-cp312-cp312-win_amd64.whl", hash = "sha256:83c0efd80b29695058d0fd2fa8a556490dbce9804eac3e281f373bbc99045f6c", size = 204740 },
 ]
 
 [[package]]