feat: change log to inside function

.gitignore

@@ -14,5 +14,4 @@ test.py
 rc-tests/*
 *.pkl
 temp/*
-.vscode/*
-crew_tasks_output.json
+.vscode/*

docs/core-concepts/Crews.md

@@ -155,23 +155,4 @@ for async_result in async_results:
     print(async_result)
 ```
 
-### Replaying from specific task:
-You can now replay from a specific task using our cli command replay.
-
-The replay_from_tasks feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the task name for the replay process.
-
-Kickoffs will now create a `crew_tasks_ouput.json` file with the output of the tasks which you use to retrieve the task id to replay.
-
-
-### Replaying from specific task Using the CLI
-To use the replay feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-
-```shell
-crewai replay -t <task_id>
-```
-
 These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs

src/crewai/cli/cli.py

@@ -1,11 +1,8 @@
 import click
 import pkg_resources
 
-
 from .create_crew import create_crew
 from .train_crew import train_crew
-from .replay_from_task import replay_task_command
-from .list_task_outputs import show_task_outputs
 
 
 @click.group()
@@ -51,32 +48,5 @@ def train(n_iterations: int):
     train_crew(n_iterations)
 
 
-@crewai.command()
-@click.option(
-    "-t",
-    "--task_id",
-    type=str,
-    help="Replay the crew from this task ID, including all subsequent tasks.",
-)
-def replay(task_id: str) -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-        task_id (str): The ID of the task to replay from.
-    """
-    try:
-        click.echo(f"Replaying the crew from task {task_id}")
-        replay_task_command(task_id)
-    except Exception as e:
-        click.echo(f"An error occurred while replaying: {e}", err=True)
-
-
-@crewai.command()
-def list_completed_tasks_ids():
-    """List all task outputs saved from crew_tasks_output.json."""
-    show_task_outputs()
-
-
 if __name__ == "__main__":
     crewai()

src/crewai/cli/list_task_outputs.py

@@ -1,34 +0,0 @@
-import subprocess
-import click
-from pathlib import Path
-import json
-
-
-def show_task_outputs() -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-      task_id (str): The ID of the task to replay from.
-    """
-
-    try:
-        file_path = Path("crew_tasks_output.json")
-        if not file_path.exists():
-            click.echo("crew_tasks_output.json not found.")
-            return
-
-        with open(file_path, "r") as f:
-            tasks = json.load(f)
-
-        for index, task in enumerate(tasks):
-            click.echo(f"Task {index + 1}: {task['task_id']}")
-            click.echo(f"Description: {task['output']['description']}")
-            click.echo("---")
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while replaying the task: {e}", err=True)
-        click.echo(e.output, err=True)
-
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/replay_from_task.py

@@ -1,24 +0,0 @@
-import subprocess
-import click
-
-
-def replay_task_command(task_id: str) -> None:
-    """
-    Replay the crew execution from a specific task.
-
-    Args:
-      task_id (str): The ID of the task to replay from.
-    """
-    command = ["poetry", "run", "replay_from_task", task_id]
-
-    try:
-        result = subprocess.run(command, capture_output=False, text=True, check=True)
-        if result.stderr:
-            click.echo(result.stderr, err=True)
-
-    except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while replaying the task: {e}", err=True)
-        click.echo(e.output, err=True)
-
-    except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/templates/main.py

@@ -21,13 +21,3 @@ def train():
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
-
-def replay_from_task():
-    """
-    Replay the crew execution from a specific task.
-    """
-    try:
-        {{crew_name}}Crew().crew().replay_from_task(task_id=sys.argv[1])
-
-    except Exception as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")

src/crewai/cli/templates/pyproject.toml

@@ -11,7 +11,6 @@ crewai = { extras = ["tools"], version = "^0.35.8" }
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
 train = "{{folder_name}}.main:train"
-replay = "{{folder_name}}.main:replay_from_task"
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/crew.py

@@ -1,7 +1,6 @@
 import asyncio
 import json
 import uuid
-from concurrent.futures import Future
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from langchain_core.callbacks import BaseCallbackHandler
@@ -21,30 +20,17 @@ from pydantic_core import PydanticCustomError
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.cache import CacheHandler
-from crewai.crews.crew_output import CrewOutput
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.process import Process
 from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import (
-    CREW_TASKS_OUTPUT_FILE,
-    TRAINED_AGENTS_DATA_FILE,
-    TRAINING_DATA_FILE,
-)
+from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
-from crewai.utilities.task_output_handler import (
-    ExecutionLog,
-    TaskOutputJsonHandler,
-)
-from crewai.utilities.formatter import (
-    aggregate_raw_outputs_from_task_outputs,
-    aggregate_raw_outputs_from_tasks,
-)
+from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 try:
@@ -72,6 +58,7 @@ class Crew(BaseModel):
         max_rpm: Maximum number of requests per minute for the crew execution to be respected.
         prompt_file: Path to the prompt json file to be used for the crew.
         id: A unique identifier for the crew instance.
+        full_output: Whether the crew should return the full output with all tasks outputs and token usage metrics or just the final output.
         task_callback: Callback to be executed after each task for every agents execution.
         step_callback: Callback to be executed after each step for every agents execution.
         share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
@@ -88,10 +75,6 @@ class Crew(BaseModel):
     _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
-    _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
-    _logging_color: str = PrivateAttr(
-        default="bold_purple",
-    )
 
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -111,6 +94,10 @@ class Crew(BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
+    full_output: Optional[bool] = Field(
+        default=False,
+        description="Whether the crew should return the full output with all tasks outputs and token usage metrics or just the final output.",
+    )
     manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
@@ -147,13 +134,9 @@ class Crew(BaseModel):
         default=False,
         description="output_log_file",
     )
-    task_execution_output_json_files: Optional[List[str]] = Field(
-        default=None,
-        description="List of file paths for task execution JSON files.",
-    )
-    execution_logs: List[ExecutionLog] = Field(
-        default=[],
-        description="List of execution logs for tasks",
+    planning: Optional[bool] = Field(
+        default=False,
+        description="Plan the crew execution and add the plan to the crew.",
     )
 
     @field_validator("id", mode="before")
@@ -274,63 +257,6 @@ class Crew(BaseModel):
 
         return self
 
-    @model_validator(mode="after")
-    def validate_end_with_at_most_one_async_task(self):
-        """Validates that the crew ends with at most one asynchronous task."""
-        final_async_task_count = 0
-
-        # Traverse tasks backward
-        for task in reversed(self.tasks):
-            if task.async_execution:
-                final_async_task_count += 1
-            else:
-                break  # Stop traversing as soon as a non-async task is encountered
-
-        if final_async_task_count > 1:
-            raise PydanticCustomError(
-                "async_task_count",
-                "The crew must end with at most one asynchronous task.",
-                {},
-            )
-
-        return self
-
-    @model_validator(mode="after")
-    def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
-        """
-        Validates that if a task is set to be executed asynchronously,
-        it cannot include other asynchronous tasks in its context unless
-        separated by a synchronous task.
-        """
-        for i, task in enumerate(self.tasks):
-            if task.async_execution and task.context:
-                for context_task in task.context:
-                    if context_task.async_execution:
-                        for j in range(i - 1, -1, -1):
-                            if self.tasks[j] == context_task:
-                                raise ValueError(
-                                    f"Task '{task.description}' is asynchronous and cannot include other sequential asynchronous tasks in its context."
-                                )
-                            if not self.tasks[j].async_execution:
-                                break
-        return self
-
-    @model_validator(mode="after")
-    def validate_context_no_future_tasks(self):
-        """Validates that a task's context does not include future tasks."""
-        task_indices = {id(task): i for i, task in enumerate(self.tasks)}
-
-        for task in self.tasks:
-            if task.context:
-                for context_task in task.context:
-                    if id(context_task) not in task_indices:
-                        continue  # Skip context tasks not in the main tasks list
-                    if task_indices[id(context_task)] > task_indices[id(task)]:
-                        raise ValueError(
-                            f"Task '{task.description}' has a context dependency on a future task '{context_task.description}', which is not allowed."
-                        )
-        return self
-
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
 
@@ -393,17 +319,12 @@ class Crew(BaseModel):
 
     def kickoff(
         self,
-        inputs: Optional[Dict[str, Any]] = None,
-    ) -> CrewOutput:
+        inputs: Optional[Dict[str, Any]] = {},
+    ) -> Union[str, Dict[str, Any]]:
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
-        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).initialize_file()
-        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).reset()
-        self._logging_color = "bold_purple"
 
-        if inputs is not None:
-            self._inputs = inputs
-            self._interpolate_inputs(inputs)
+        self._interpolate_inputs(inputs)  # type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
         self._set_tasks_callbacks()
 
         i18n = I18N(prompt_file=self.prompt_file)
@@ -424,12 +345,16 @@ class Crew(BaseModel):
 
             agent.create_agent_executor()
 
+        if self.planning:
+            self._handle_crew_planning()
+
         metrics = []
 
         if self.process == Process.sequential:
             result = self._run_sequential_process()
         elif self.process == Process.hierarchical:
-            result = self._run_hierarchical_process()
+            result, manager_metrics = self._run_hierarchical_process()  # type: ignore # Incompatible types in assignment (expression has type "str | dict[str, Any]", variable has type "str")
+            metrics.append(manager_metrics)
         else:
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
@@ -442,9 +367,11 @@ class Crew(BaseModel):
 
         return result
 
-    def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
+    def kickoff_for_each(
+        self, inputs: List[Dict[str, Any]]
+    ) -> List[Union[str, Dict[str, Any]]]:
         """Executes the Crew's workflow for each input in the list and aggregates results."""
-        results: List[CrewOutput] = []
+        results = []
 
         # Initialize the parent crew's usage metrics
         total_usage_metrics = {
@@ -468,11 +395,13 @@ class Crew(BaseModel):
         self.usage_metrics = total_usage_metrics
         return results
 
-    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = {}) -> CrewOutput:
+    async def kickoff_async(
+        self, inputs: Optional[Dict[str, Any]] = {}
+    ) -> Union[str, Dict]:
         """Asynchronous kickoff method to start the crew execution."""
         return await asyncio.to_thread(self.kickoff, inputs)
 
-    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[CrewOutput]:
+    async def kickoff_for_each_async(self, inputs: List[Dict]) -> List[Any]:
         crew_copies = [self.copy() for _ in inputs]
 
         async def run_crew(crew, input_data):
@@ -482,10 +411,6 @@ class Crew(BaseModel):
             asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
             for i in range(len(inputs))
         ]
-        tasks = [
-            asyncio.create_task(run_crew(crew_copies[i], inputs[i]))
-            for i in range(len(inputs))
-        ]
 
         results = await asyncio.gather(*tasks)
 
@@ -502,66 +427,60 @@ class Crew(BaseModel):
 
         self.usage_metrics = total_usage_metrics
 
-        total_usage_metrics = {
-            "total_tokens": 0,
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "successful_requests": 0,
-        }
-        for crew in crew_copies:
-            if crew.usage_metrics:
-                for key in total_usage_metrics:
-                    total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
-
-        self.usage_metrics = total_usage_metrics
-
         return results
 
-    def _store_execution_log(
-        self,
-        task: Task,
-        output: TaskOutput,
-        task_index: int,
-        was_replayed: bool = False,
-    ):
-        if self._inputs:
-            inputs = self._inputs
-        else:
-            inputs = {}
+    def _handle_crew_planning(self):
+        """Handles the Crew planning."""
+        self._logger.log("info", "Planning the crew execution")
+        result = CrewPlanner(self.tasks)._handle_crew_planning()
 
-        log = ExecutionLog(
-            task_id=str(task.id),
-            expected_output=task.expected_output,
-            output={
-                "description": output.description,
-                "summary": output.summary,
-                "raw": output.raw,
-                "pydantic": output.pydantic,
-                "json_dict": output.json_dict,
-                "output_format": output.output_format,
-                "agent": output.agent,
-            },
-            task_index=task_index,
-            inputs=inputs,
-            was_replayed=was_replayed,
-        )
-        if task_index < len(self.execution_logs):
-            self.execution_logs[task_index] = log
-        else:
-            self.execution_logs.append(log)
+        for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
+            task.description += step_plan
 
-        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).update(task_index, log)
-
-    def _run_sequential_process(self) -> CrewOutput:
+    def _run_sequential_process(self) -> Union[str, Dict[str, Any]]:
         """Executes tasks sequentially and returns the final output."""
-        return self._execute_tasks(self.tasks)
+        task_output = None
 
-    def _run_hierarchical_process(self) -> CrewOutput:
+        for task in self.tasks:
+            if task.agent and task.agent.allow_delegation:
+                agents_for_delegation = [
+                    agent for agent in self.agents if agent != task.agent
+                ]
+                if len(self.agents) > 1 and len(agents_for_delegation) > 0:
+                    task.tools += task.agent.get_delegation_tools(agents_for_delegation)
+
+            role = task.agent.role if task.agent is not None else "None"
+            self._logger.log("debug", f"== Working Agent: {role}", color="bold_purple")
+            self._logger.log(
+                "info", f"== Starting Task: {task.description}", color="bold_purple"
+            )
+
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=role, task=task.description, status="started"
+                )
+            output = task.execute(context=task_output)
+
+            if not task.async_execution:
+                task_output = output
+
+            role = task.agent.role if task.agent is not None else "None"
+            self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
+
+            if self.output_log_file:
+                self._file_handler.log(agent=role, task=task_output, status="completed")
+
+        self._finish_execution(task_output)
+
+        token_usage = self.calculate_usage_metrics()
+
+        return self._format_output(task_output if task_output else "", token_usage)
+
+    def _run_hierarchical_process(
+        self,
+    ) -> Tuple[Union[str, Dict[str, Any]], Dict[str, Any]]:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
-        self._create_manager_agent()
-        return self._execute_tasks(self.tasks, self.manager_agent)
 
-    def _create_manager_agent(self):
         i18n = I18N(prompt_file=self.prompt_file)
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
@@ -580,199 +499,38 @@ class Crew(BaseModel):
             )
             self.manager_agent = manager
 
-    def _execute_tasks(
-        self,
-        tasks: List[Task],
-        manager: Optional[BaseAgent] = None,
-        start_index: Optional[int] = 0,
-        was_replayed: bool = False,
-    ) -> CrewOutput:
-        """Executes tasks sequentially and returns the final output.
+        task_output = None
 
-        Args:
-            tasks (List[Task]): List of tasks to execute
-            manager (Optional[BaseAgent], optional): Manager agent to use for delegation. Defaults to None.
+        for task in self.tasks:
+            self._logger.log("debug", f"Working Agent: {manager.role}")
+            self._logger.log("info", f"Starting Task: {task.description}")
 
-        Returns:
-            CrewOutput: Final output of the crew
-        """
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task.description, status="started"
+                )
 
-        task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
-        last_sync_output: Optional[TaskOutput] = None
-
-        for task_index, task in enumerate(tasks):
-            if start_index is not None and task_index < start_index:
-                if task.output:
-                    if task.async_execution:
-                        task_outputs.append(task.output)
-                    else:
-                        task_outputs = [task.output]
-                        last_sync_output = task.output
-                continue
-
-            self._prepare_task(task, manager)
-            if self.process == Process.hierarchical:
-                agent_to_use = manager
+            if task.agent:
+                manager.tools = task.agent.get_delegation_tools([task.agent])
             else:
-                agent_to_use = task.agent
-            if agent_to_use is None:
-                raise ValueError(
-                    f"No agent available for task: {task.description}. Ensure that either the task has an assigned agent or a manager agent is provided."
-                )
-            self._log_task_start(task, agent_to_use)
-
-            if task.async_execution:
-                context = self._get_context(
-                    task, [last_sync_output] if last_sync_output else []
-                )
-                future = task.execute_async(
-                    agent=agent_to_use,
-                    context=context,
-                    tools=agent_to_use.tools,
-                )
-                futures.append((task, future, task_index))
-            else:
-                if futures:
-                    task_outputs.extend(
-                        self._process_async_tasks(futures, was_replayed)
-                    )
-                    futures.clear()
-
-                context = self._get_context(task, task_outputs)
-                task_output = task.execute_sync(
-                    agent=agent_to_use,
-                    context=context,
-                    tools=agent_to_use.tools,
-                )
-                task_outputs = [task_output]
-                self._process_task_result(task, task_output)
-                self._store_execution_log(task, task_output, task_index, was_replayed)
-
-        if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
-
-        return self._create_crew_output(task_outputs)
-
-    def _prepare_task(self, task: Task, manager: Optional[BaseAgent]):
-        if self.process == Process.hierarchical:
-            self._update_manager_tools(task, manager)
-        elif task.agent and task.agent.allow_delegation:
-            self._add_delegation_tools(task)
-
-    def _add_delegation_tools(self, task: Task):
-        agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
-        if len(self.agents) > 1 and agents_for_delegation:
-            task.tools += task.agent.get_delegation_tools(agents_for_delegation)  # type: ignore
-
-    def _log_task_start(self, task: Task, agent: Optional[BaseAgent]):
-        color = self._logging_color
-        role = agent.role if agent else "None"
-        self._logger.log("debug", f"== Working Agent: {role}", color=color)
-        self._logger.log("info", f"== Starting Task: {task.description}", color=color)
-        if self.output_log_file:
-            self._file_handler.log(agent=role, task=task.description, status="started")
-
-    def _update_manager_tools(self, task: Task, manager: Optional[BaseAgent]):
-        if task.agent and manager:
-            manager.tools = task.agent.get_delegation_tools([task.agent])
-        if manager:
-            manager.tools = manager.get_delegation_tools(self.agents)
-
-    def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
-        context = (
-            aggregate_raw_outputs_from_tasks(task.context)
-            if task.context
-            else aggregate_raw_outputs_from_task_outputs(task_outputs)
-        )
-        return context
-
-    def _process_task_result(self, task: Task, output: TaskOutput) -> None:
-        role = task.agent.role if task.agent is not None else "None"
-        self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
-        if self.output_log_file:
-            self._file_handler.log(agent=role, task=output, status="completed")
-
-    def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
-        if len(task_outputs) != 1:
-            raise ValueError(
-                "Something went wrong. Kickoff should return only one task output."
+                manager.tools = manager.get_delegation_tools(self.agents)
+            task_output = task.execute(
+                agent=manager, context=task_output, tools=manager.tools
             )
-        final_task_output = task_outputs[0]
-        final_string_output = final_task_output.raw
-        self._finish_execution(final_string_output)
+
+            self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
+            if self.output_log_file:
+                self._file_handler.log(
+                    agent=manager.role, task=task_output, status="completed"
+                )
+
+        self._finish_execution(task_output)
+
         token_usage = self.calculate_usage_metrics()
 
-        return CrewOutput(
-            raw=final_task_output.raw,
-            pydantic=final_task_output.pydantic,
-            json_dict=final_task_output.json_dict,
-            tasks_output=[task.output for task in self.tasks if task.output],
-            token_usage=token_usage,
-        )
-
-    def _process_async_tasks(
-        self,
-        futures: List[Tuple[Task, Future[TaskOutput], int]],
-        was_replayed: bool = False,
-    ) -> List[TaskOutput]:
-        task_outputs = []
-        for future_task, future, task_index in futures:
-            task_output = future.result()
-            task_outputs.append(task_output)
-            self._process_task_result(future_task, task_output)
-            self._store_execution_log(
-                future_task, task_output, task_index, was_replayed
-            )
-        return task_outputs
-
-    def _find_task_index(
-        self, task_id: str, stored_outputs: List[Any]
-    ) -> Optional[int]:
-        return next(
-            (
-                index
-                for (index, d) in enumerate(stored_outputs)
-                if d["task_id"] == str(task_id)
-            ),
-            None,
-        )
-
-    def replay_from_task(
-        self, task_id: str, inputs: Optional[Dict[str, Any]] = None
-    ) -> CrewOutput:
-        stored_outputs = TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).load()
-        start_index = self._find_task_index(task_id, stored_outputs)
-
-        if start_index is None:
-            raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
-
-        replay_inputs = (
-            inputs if inputs is not None else stored_outputs[start_index]["inputs"]
-        )
-        self._inputs = replay_inputs
-
-        if replay_inputs:
-            self._interpolate_inputs(replay_inputs)
-
-        if self.process == Process.hierarchical:
-            self._create_manager_agent()
-
-        for i in range(start_index):
-            stored_output = stored_outputs[i]["output"]
-            task_output = TaskOutput(
-                description=stored_output["description"],
-                agent=stored_output["agent"],
-                raw=stored_output["raw"],
-                pydantic=stored_output["pydantic"],
-                json_dict=stored_output["json_dict"],
-                output_format=stored_output["output_format"],
-            )
-            self.tasks[i].output = task_output
-
-        self._logging_color = "bold_blue"
-        result = self._execute_tasks(self.tasks, self.manager_agent, start_index, True)
-        return result
+        return self._format_output(
+            task_output if task_output else "", token_usage
+        ), token_usage
 
     def copy(self):
         """Create a deep copy of the Crew."""
@@ -824,15 +582,31 @@ class Crew(BaseModel):
         for agent in self.agents:
             agent.interpolate_inputs(inputs)
 
-    def _finish_execution(self, final_string_output: str) -> None:
+    def _format_output(
+        self, output: str, token_usage: Optional[Dict[str, Any]] = None
+    ) -> Union[str, Dict[str, Any]]:
+        """
+        Formats the output of the crew execution.
+        If full_output is True, then returned data type will be a dictionary else returned outputs are string
+        """
+
+        if self.full_output:
+            return {
+                "final_output": output,
+                "tasks_outputs": [task.output for task in self.tasks if task],
+                "usage_metrics": token_usage,
+            }
+        else:
+            return output
+
+    def _finish_execution(self, output) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
         if agentops:
             agentops.end_session(
-                end_state="Success",
-                end_state_reason="Finished Execution",
+                end_state="Success", end_state_reason="Finished Execution"
             )
-        self._telemetry.end_crew(self, final_string_output)
+        self._telemetry.end_crew(self, output)
 
     def calculate_usage_metrics(self) -> Dict[str, int]:
         """Calculates and returns the usage metrics."""

src/crewai/crews/__init__.py

@@ -1 +0,0 @@
-from .crew_output import CrewOutput

src/crewai/crews/crew_output.py

@@ -1,58 +0,0 @@
-import json
-from typing import Any, Dict, Optional
-
-from pydantic import BaseModel, Field
-
-from crewai.tasks.output_format import OutputFormat
-from crewai.tasks.task_output import TaskOutput
-
-
-class CrewOutput(BaseModel):
-    """Class that represents the result of a crew."""
-
-    raw: str = Field(description="Raw output of crew", default="")
-    pydantic: Optional[BaseModel] = Field(
-        description="Pydantic output of Crew", default=None
-    )
-    json_dict: Optional[Dict[str, Any]] = Field(
-        description="JSON dict output of Crew", default=None
-    )
-    tasks_output: list[TaskOutput] = Field(
-        description="Output of each task", default=[]
-    )
-    token_usage: Dict[str, Any] = Field(
-        description="Processed token summary", default={}
-    )
-
-    # @property
-    # def pydantic(self) -> Optional[BaseModel]:
-    #     # Check if the final task output included a pydantic model
-    #     if self.tasks_output[-1].output_format != OutputFormat.PYDANTIC:
-    #         raise ValueError(
-    #             "No pydantic model found in the final task. Please make sure to set the output_pydantic property in the final task in your crew."
-    #         )
-
-    #     return self._pydantic
-
-    @property
-    def json(self) -> Optional[str]:
-        if self.tasks_output[-1].output_format != OutputFormat.JSON:
-            raise ValueError(
-                "No JSON output found in the final task. Please make sure to set the output_json property in the final task in your crew."
-            )
-
-        return json.dumps(self.json_dict)
-
-    def to_dict(self) -> Dict[str, Any]:
-        if self.json_dict:
-            return self.json_dict
-        if self.pydantic:
-            return self.pydantic.model_dump()
-        raise ValueError("No output to convert to dictionary")
-
-    def __str__(self):
-        if self.pydantic:
-            return str(self.pydantic)
-        if self.json_dict:
-            return str(self.json_dict)
-        return self.raw

src/crewai/task.py

@@ -1,11 +1,9 @@
-import json
 import os
 import re
-import threading
 import uuid
-from concurrent.futures import Future
+from concurrent.futures import Future, ThreadPoolExecutor
 from copy import copy
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Type, Union
 
 from langchain_openai import ChatOpenAI
 from opentelemetry.trace import Span
@@ -13,7 +11,6 @@ from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
 from crewai.utilities.converter import Converter, ConverterError
@@ -109,7 +106,7 @@ class Task(BaseModel):
     _execution_span: Span | None = None
     _original_description: str | None = None
     _original_expected_output: str | None = None
-    _thread: threading.Thread | None = None
+    _future: Future | None = None
 
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})
@@ -164,75 +161,80 @@ class Task(BaseModel):
             )
         return self
 
-    def execute_sync(
-        self,
-        agent: Optional[BaseAgent] = None,
-        context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
-    ) -> TaskOutput:
-        """Execute the task synchronously."""
-        return self._execute_core(agent, context, tools)
+    def wait_for_completion(self) -> str | BaseModel:
+        """Wait for asynchronous task completion and return the output."""
+        assert self.async_execution, "Task is not set to be executed asynchronously."
 
-    def execute_async(
+        if self._future:
+            self._future.result()  # Wait for the future to complete
+            self._future = None
+
+        assert self.output, "Task output is not set."
+
+        return self.output.exported_output
+
+    def execute(
         self,
         agent: BaseAgent | None = None,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-    ) -> Future[TaskOutput]:
-        """Execute the task asynchronously."""
-        future: Future[TaskOutput] = Future()
-        threading.Thread(
-            target=self._execute_task_async, args=(agent, context, tools, future)
-        ).start()
-        return future
+    ) -> Any:
+        """Execute the task.
 
-    def _execute_task_async(
-        self,
-        agent: Optional[BaseAgent],
-        context: Optional[str],
-        tools: Optional[List[Any]],
-        future: Future[TaskOutput],
-    ) -> None:
-        """Execute the task asynchronously with context handling."""
-        result = self._execute_core(agent, context, tools)
-        future.set_result(result)
+        Returns:
+            Output of the task.
+        """
 
-    def _execute_core(
-        self,
-        agent: Optional[BaseAgent],
-        context: Optional[str],
-        tools: Optional[List[Any]],
-    ) -> TaskOutput:
-        """Run the core execution logic of the task."""
-        self.agent = agent
         agent = agent or self.agent
         if not agent:
             raise Exception(
-                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
+                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly "
+                "and should be executed in a Crew using a specific process that support that, like hierarchical."
             )
 
         self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
 
-        self.prompt_context = context
-        tools = tools or self.tools or []
+        if self.context:
+            internal_context = []
+            for task in self.context:
+                if task.async_execution:
+                    task.wait_for_completion()
+                if task.output:
+                    internal_context.append(task.output.raw_output)
+            context = "\n".join(internal_context)
 
+        self.prompt_context = context
+        tools = tools or self.tools
+
+        if self.async_execution:
+            with ThreadPoolExecutor() as executor:
+                self._future = executor.submit(
+                    self._execute, agent, self, context, tools
+                )
+            return None
+        else:
+            result = self._execute(
+                task=self,
+                agent=agent,
+                context=context,
+                tools=tools,
+            )
+            return result
+
+    def _execute(self, agent: "BaseAgent", task, context, tools) -> str | None:
         result = agent.execute_task(
-            task=self,
+            task=task,
             context=context,
             tools=tools,
         )
+        exported_output = self._export_output(result)
 
-        pydantic_output, json_output = self._export_output(result)
-
-        task_output = TaskOutput(
+        self.output = TaskOutput(
             description=self.description,
-            raw=result,
-            pydantic=pydantic_output,
-            json_dict=json_output,
+            exported_output=exported_output,
+            raw_output=result,
             agent=agent.role,
-            output_format=self._get_output_format(),
         )
-        self.output = task_output
 
         if self.callback:
             self.callback(self.output)
@@ -241,17 +243,7 @@ class Task(BaseModel):
             self._telemetry.task_ended(self._execution_span, self)
             self._execution_span = None
 
-        if self.output_file:
-            content = (
-                json_output
-                if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
-            )
-            self._save_file(content)
-
-        return task_output
+        return exported_output
 
     def prompt(self) -> str:
         """Prompt the task.
@@ -286,7 +278,7 @@ class Task(BaseModel):
         """Increment the delegations counter."""
         self.delegations += 1
 
-    def copy(self, agents: List["BaseAgent"]) -> "Task":
+    def copy(self, agents: Optional[List["BaseAgent"]] = None) -> "Task":  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
         """Create a deep copy of the Task."""
         exclude = {
             "id",
@@ -303,7 +295,7 @@ class Task(BaseModel):
         )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
-            return next((agent for agent in agents if agent.role == role), None)
+            return next((agent for agent in agents if agent.role == role), None)  # type: ignore # Item "None" of "list[BaseAgent] | None" has no attribute "__iter__" (not iterable)
 
         cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
         cloned_tools = copy(self.tools) if self.tools else []
@@ -317,113 +309,72 @@ class Task(BaseModel):
 
         return copied_task
 
-    def _create_converter(self, *args, **kwargs) -> Converter:
-        """Create a converter instance."""
-        converter = self.agent.get_output_converter(*args, **kwargs)
+    def _create_converter(self, *args, **kwargs) -> Converter:  # type: ignore
+        converter = self.agent.get_output_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+            *args, **kwargs
+        )
         if self.converter_cls:
-            converter = self.converter_cls(*args, **kwargs)
+            converter = self.converter_cls(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+                *args, **kwargs
+            )
         return converter
 
-    def _export_output(
-        self, result: str
-    ) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]:
-        pydantic_output: Optional[BaseModel] = None
-        json_output: Optional[Dict[str, Any]] = None
+    def _export_output(self, result: str) -> Any:  # TODO: Refactor and fix type hints
+        exported_result = result
+        instructions = "I'm gonna convert this raw text into valid JSON."
 
         if self.output_pydantic or self.output_json:
-            model_output = self._convert_to_model(result)
-            pydantic_output = (
-                model_output if isinstance(model_output, BaseModel) else None
-            )
-            if isinstance(model_output, str):
-                try:
-                    json_output = json.loads(model_output)
-                except json.JSONDecodeError:
-                    json_output = None
-            else:
-                json_output = model_output if isinstance(model_output, dict) else None
+            model = self.output_pydantic or self.output_json
 
-        return pydantic_output, json_output
-
-    def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
-        model = self.output_pydantic or self.output_json
-        if model is None:
-            return result
-
-        try:
-            return self._validate_model(result, model)
-        except Exception:
-            return self._handle_partial_json(result, model)
-
-    def _validate_model(
-        self, result: str, model: Type[BaseModel]
-    ) -> Union[dict, BaseModel]:
-        exported_result = model.model_validate_json(result)
-        if self.output_json:
-            return exported_result.model_dump()
-        return exported_result
-
-    def _handle_partial_json(
-        self, result: str, model: Type[BaseModel]
-    ) -> Union[dict, BaseModel, str]:
-        match = re.search(r"({.*})", result, re.DOTALL)
-        if match:
+            # try to convert task_output directly to pydantic/json
             try:
-                exported_result = model.model_validate_json(match.group(0))
+                exported_result = model.model_validate_json(result)  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
                 if self.output_json:
-                    return exported_result.model_dump()
+                    return exported_result.model_dump()  # type: ignore # "str" has no attribute "model_dump"
                 return exported_result
             except Exception:
-                pass
+                # sometimes the response contains valid JSON in the middle of text
+                match = re.search(r"({.*})", result, re.DOTALL)
+                if match:
+                    try:
+                        exported_result = model.model_validate_json(match.group(0))  # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
+                        if self.output_json:
+                            return exported_result.model_dump()  # type: ignore # "str" has no attribute "model_dump"
+                        return exported_result
+                    except Exception:
+                        pass
 
-        return self._convert_with_instructions(result, model)
+            llm = getattr(self.agent, "function_calling_llm", None) or self.agent.llm  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
+            if not self._is_gpt(llm):
+                model_schema = PydanticSchemaParser(model=model).get_schema()  # type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
+                instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
 
-    def _convert_with_instructions(
-        self, result: str, model: Type[BaseModel]
-    ) -> Union[dict, BaseModel, str]:
-        llm = self.agent.function_calling_llm or self.agent.llm  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
-        instructions = self._get_conversion_instructions(model, llm)
-
-        converter = self._create_converter(
-            llm=llm, text=result, model=model, instructions=instructions
-        )
-        exported_result = (
-            converter.to_pydantic() if self.output_pydantic else converter.to_json()
-        )
-
-        if isinstance(exported_result, ConverterError):
-            Printer().print(
-                content=f"{exported_result.message} Using raw output instead.",
-                color="red",
+            converter = self._create_converter(  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "get_output_converter"
+                llm=llm, text=result, model=model, instructions=instructions
             )
-            return result
+
+            if self.output_pydantic:
+                exported_result = converter.to_pydantic()
+            elif self.output_json:
+                exported_result = converter.to_json()
+
+            if isinstance(exported_result, ConverterError):
+                Printer().print(
+                    content=f"{exported_result.message} Using raw output instead.",
+                    color="red",
+                )
+                exported_result = result
+
+        if self.output_file:
+            content = (
+                exported_result
+                if not self.output_pydantic
+                else exported_result.model_dump_json()  # type: ignore # "str" has no attribute "json"
+            )
+            self._save_file(content)
 
         return exported_result
 
-    def _get_output_format(self) -> OutputFormat:
-        if self.output_json:
-            return OutputFormat.JSON
-        if self.output_pydantic:
-            return OutputFormat.PYDANTIC
-        return OutputFormat.RAW
-
-    def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
-        instructions = "I'm gonna convert this raw text into valid JSON."
-        if not self._is_gpt(llm):
-            model_schema = PydanticSchemaParser(model=model).get_schema()
-            instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
-        return instructions
-
-    def _save_output(self, content: str) -> None:
-        if not self.output_file:
-            raise Exception("Output file path is not set.")
-
-        directory = os.path.dirname(self.output_file)
-        if directory and not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(self.output_file, "w", encoding="utf-8") as file:
-            file.write(content)
-
     def _is_gpt(self, llm) -> bool:
         return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
 

src/crewai/tasks/__init__.py

@@ -1,4 +0,0 @@
-from crewai.tasks.output_format import OutputFormat
-from crewai.tasks.task_output import TaskOutput
-
-__all__ = ["OutputFormat", "TaskOutput"]

src/crewai/tasks/output_format.py

@@ -1,9 +0,0 @@
-from enum import Enum
-
-
-class OutputFormat(str, Enum):
-    """Enum that represents the output format of a task."""
-
-    JSON = "json"
-    PYDANTIC = "pydantic"
-    RAW = "raw"

src/crewai/tasks/task_output.py

@@ -1,74 +1,24 @@
-import json
-from typing import Any, Dict, Optional
+from typing import Optional, Union
 
 from pydantic import BaseModel, Field, model_validator
 
-from crewai.tasks.output_format import OutputFormat
-
 
 class TaskOutput(BaseModel):
     """Class that represents the result of a task."""
 
     description: str = Field(description="Description of the task")
     summary: Optional[str] = Field(description="Summary of the task", default=None)
-    raw: str = Field(description="Raw output of the task", default="")
-    pydantic: Optional[BaseModel] = Field(
-        description="Pydantic output of task", default=None
-    )
-    json_dict: Optional[Dict[str, Any]] = Field(
-        description="JSON dictionary of task", default=None
+    exported_output: Union[str, BaseModel] = Field(
+        description="Output of the task", default=None
     )
     agent: str = Field(description="Agent that executed the task")
-    output_format: OutputFormat = Field(
-        description="Output format of the task", default=OutputFormat.RAW
-    )
+    raw_output: str = Field(description="Result of the task")
 
     @model_validator(mode="after")
     def set_summary(self):
-        """Set the summary field based on the description."""
         excerpt = " ".join(self.description.split(" ")[:10])
         self.summary = f"{excerpt}..."
         return self
 
-    # @property
-    # def pydantic(self) -> Optional[BaseModel]:
-    #     # Check if the final task output included a pydantic model
-    #     if self.output_format != OutputFormat.PYDANTIC:
-    #         raise ValueError(
-    #             """
-    #             Invalid output format requested.
-    #             If you would like to access the pydantic model,
-    #             please make sure to set the output_pydantic property for the task.
-    #             """
-    #         )
-
-    #     return self._pydantic
-
-    @property
-    def json(self) -> Optional[str]:
-        if self.output_format != OutputFormat.JSON:
-            raise ValueError(
-                """
-                Invalid output format requested.
-                If you would like to access the JSON output,
-                please make sure to set the output_json property for the task
-                """
-            )
-
-        return json.dumps(self.json_dict)
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert json_output and pydantic_output to a dictionary."""
-        output_dict = {}
-        if self.json_dict:
-            output_dict.update(self.json_dict)
-        if self.pydantic:
-            output_dict.update(self.pydantic.model_dump())
-        return output_dict
-
-    def __str__(self) -> str:
-        if self.pydantic:
-            return str(self.pydantic)
-        if self.json_dict:
-            return str(self.json_dict)
-        return self.raw
+    def result(self):
+        return self.exported_output

src/crewai/telemetry/telemetry.py

@@ -337,7 +337,7 @@ class Telemetry:
             except Exception:
                 pass
 
-    def end_crew(self, crew, final_string_output):
+    def end_crew(self, crew, output):
         if (self.ready) and (crew.share_crew):
             try:
                 self._add_attribute(
@@ -345,9 +345,7 @@ class Telemetry:
                     "crewai_version",
                     pkg_resources.get_distribution("crewai").version,
                 )
-                self._add_attribute(
-                    crew._execution_span, "crew_output", final_string_output
-                )
+                self._add_attribute(crew._execution_span, "crew_output", output)
                 self._add_attribute(
                     crew._execution_span,
                     "crew_tasks_output",

src/crewai/utilities/constants.py

@@ -1,3 +1,2 @@
 TRAINING_DATA_FILE = "training_data.pkl"
 TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
-CREW_TASKS_OUTPUT_FILE = "crew_tasks_output.json"

src/crewai/utilities/crew_json_encoder.py

@@ -1,31 +0,0 @@
-from datetime import datetime
-import json
-from uuid import UUID
-from pydantic import BaseModel
-
-
-class CrewJSONEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, BaseModel):
-            return self._handle_pydantic_model(obj)
-        elif isinstance(obj, UUID):
-            return str(obj)
-
-        elif isinstance(obj, datetime):
-            return obj.isoformat()
-        return super().default(obj)
-
-    def _handle_pydantic_model(self, obj):
-        try:
-            data = obj.model_dump()
-            # Remove circular references
-            for key, value in data.items():
-                if isinstance(value, BaseModel):
-                    data[key] = str(
-                        value
-                    )  # Convert nested models to string representation
-            return data
-        except RecursionError:
-            return str(
-                obj
-            )  # Fall back to string representation if circular reference is detected

src/crewai/utilities/file_handler.py

@@ -1,7 +1,5 @@
 import os
 import pickle
-
-
 from datetime import datetime
 
 

src/crewai/utilities/formatter.py

@@ -1,20 +0,0 @@
-from typing import List
-
-from crewai.task import Task
-from crewai.tasks.task_output import TaskOutput
-
-
-def aggregate_raw_outputs_from_task_outputs(task_outputs: List[TaskOutput]) -> str:
-    """Generate string context from the task outputs."""
-    dividers = "\n\n----------\n\n"
-
-    # Join task outputs with dividers
-    context = dividers.join(output.raw for output in task_outputs)
-    return context
-
-
-def aggregate_raw_outputs_from_tasks(tasks: List[Task]) -> str:
-    """Generate string context from the tasks."""
-    task_outputs = [task.output for task in tasks if task.output is not None]
-
-    return aggregate_raw_outputs_from_task_outputs(task_outputs)

src/crewai/utilities/planning_handler.py

@@ -0,0 +1,64 @@
+from typing import List
+
+from pydantic import BaseModel
+
+from crewai.agent import Agent
+from crewai.task import Task
+
+
+class PlannerTaskPydanticOutput(BaseModel):
+    list_of_plans_per_task: List[str]
+
+
+class CrewPlanner:
+    def __init__(self, tasks: List[Task]):
+        self.tasks = tasks
+
+    def _handle_crew_planning(self):
+        """Handles the Crew planning by creating detailed step-by-step plans for each task."""
+        planning_agent = self._create_planning_agent()
+        tasks_summary = self._create_tasks_summary()
+
+        planner_task = self._create_planner_task(planning_agent, tasks_summary)
+
+        return planner_task.execute()
+
+    def _create_planning_agent(self) -> Agent:
+        """Creates the planning agent for the crew planning."""
+        return Agent(
+            role="Task Execution Planner",
+            goal=(
+                "Your goal is to create an extremely detailed, step-by-step plan based on the tasks and tools "
+                "available to each agent so that they can perform the tasks in an exemplary manner"
+            ),
+            backstory="Planner agent for crew planning",
+        )
+
+    def _create_planner_task(self, planning_agent: Agent, tasks_summary: str) -> Task:
+        """Creates the planner task using the given agent and tasks summary."""
+        return Task(
+            description=(
+                f"Based on these tasks summary: {tasks_summary} \n Create the most descriptive plan based on the tasks "
+                "descriptions, tools available, and agents' goals for them to execute their goals with perfection."
+            ),
+            expected_output="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
+            agent=planning_agent,
+            output_pydantic=PlannerTaskPydanticOutput,
+        )
+
+    def _create_tasks_summary(self) -> str:
+        """Creates a summary of all tasks."""
+        tasks_summary = []
+        for idx, task in enumerate(self.tasks):
+            tasks_summary.append(
+                f"""
+                Task Number {idx + 1} - {task.description}
+                "task_description": {task.description}
+                "task_expected_output": {task.expected_output}
+                "agent": {task.agent.role if task.agent else "None"}
+                "agent_goal": {task.agent.goal if task.agent else "None"}
+                "task_tools": {task.tools}
+                "agent_tools": {task.agent.tools if task.agent else "None"}
+                """
+            )
+        return " ".join(tasks_summary)

src/crewai/utilities/printer.py

@@ -8,8 +8,6 @@ class Printer:
             self._print_bold_green(content)
         elif color == "bold_purple":
             self._print_bold_purple(content)
-        elif color == "bold_blue":
-            self._print_bold_blue(content)
         else:
             print(content)
 
@@ -24,6 +22,3 @@ class Printer:
 
     def _print_red(self, content):
         print("\033[91m {}\033[00m".format(content))
-
-    def _print_bold_blue(self, content):
-        print("\033[1m\033[94m {}\033[00m".format(content))

src/crewai/utilities/task_output_handler.py

@@ -1,69 +0,0 @@
-import json
-import os
-
-from pydantic import BaseModel, Field
-from datetime import datetime
-from typing import Dict, Any, Optional, List
-from crewai.utilities.crew_json_encoder import CrewJSONEncoder
-
-
-class ExecutionLog(BaseModel):
-    task_id: str
-    expected_output: Optional[str] = None
-    output: Dict[str, Any]
-    timestamp: datetime = Field(default_factory=datetime.now)
-    task_index: int
-    inputs: Dict[str, Any] = Field(default_factory=dict)
-    was_replayed: bool = False
-
-    def __getitem__(self, key: str) -> Any:
-        return getattr(self, key)
-
-
-class TaskOutputJsonHandler:
-    def __init__(self, file_name: str) -> None:
-        self.file_path = os.path.join(os.getcwd(), file_name)
-
-    def initialize_file(self) -> None:
-        if not os.path.exists(self.file_path) or os.path.getsize(self.file_path) == 0:
-            with open(self.file_path, "w") as file:
-                json.dump([], file)
-
-    def update(self, task_index: int, log: ExecutionLog):
-        logs = self.load()
-        if task_index < len(logs):
-            logs[task_index] = log
-        else:
-            logs.append(log)
-        self.save(logs)
-
-    def save(self, logs: List[ExecutionLog]):
-        with open(self.file_path, "w") as file:
-            json.dump(logs, file, indent=2, cls=CrewJSONEncoder)
-
-    def reset(self):
-        """Reset the JSON file by creating an empty file."""
-        with open(self.file_path, "w") as f:
-            json.dump([], f)
-
-    def load(self) -> List[ExecutionLog]:
-        try:
-            if (
-                not os.path.exists(self.file_path)
-                or os.path.getsize(self.file_path) == 0
-            ):
-                return []
-
-            with open(self.file_path, "r") as file:
-                return json.load(file)
-        except FileNotFoundError:
-            print(f"File {self.file_path} not found. Returning empty list.")
-            return []
-        except json.JSONDecodeError:
-            print(
-                f"Error decoding JSON from file {self.file_path}. Returning empty list."
-            )
-            return []
-        except Exception as e:
-            print(f"An unexpected error occurred: {e}")
-            return []

tests/agent_test.py

@@ -631,9 +631,8 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
 
     crew = Crew(agents=[agent1, agent2], tasks=tasks)
     result = crew.kickoff()
-    print("LOWER RESULT", result.raw)
-    assert "bye" not in result.raw.lower()
-    assert "hi" in result.raw.lower() or "hello" in result.raw.lower()
+    assert "bye" not in result.lower()
+    assert "hi" in result.lower() or "hello" in result.lower()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -645,7 +644,7 @@ def test_agent_step_callback():
     with patch.object(StepCallback, "callback") as callback:
 
         @tool
-        def learn_about_AI(topic) -> str:
+        def learn_about_AI(topic) -> float:
             """Useful for when you need to learn about AI to write an paragraph about it."""
             return "AI is a very broad field."
 
@@ -679,7 +678,7 @@ def test_agent_function_calling_llm():
     with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
 
         @tool
-        def learn_about_AI(topic) -> str:
+        def learn_about_AI(topic) -> float:
             """Useful for when you need to learn about AI to write an paragraph about it."""
             return "AI is a very broad field."
 
@@ -751,8 +750,7 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
     crew = Crew(agents=[agent1], tasks=tasks)
 
     result = crew.kickoff()
-    print("RESULT: ", result.raw)
-    assert result.raw == "Howdy!"
+    assert result == "Howdy!"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

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tests/task_test.py

@@ -81,7 +81,7 @@ def test_task_prompt_includes_expected_output():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync(agent=researcher)
+        task.execute()
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -104,13 +104,11 @@ def test_task_callback():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync(agent=researcher)
+        task.execute()
         task_completed.assert_called_once_with(task.output)
 
 
 def test_task_callback_returns_task_ouput():
-    from crewai.tasks.output_format import OutputFormat
-
     researcher = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -129,7 +127,7 @@ def test_task_callback_returns_task_ouput():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "exported_ok"
-        task.execute_sync(agent=researcher)
+        task.execute()
         # Ensure the callback is called with a TaskOutput object serialized to JSON
         task_completed.assert_called_once()
         callback_data = task_completed.call_args[0][0]
@@ -142,12 +140,10 @@ def test_task_callback_returns_task_ouput():
         output_dict = json.loads(callback_data)
         expected_output = {
             "description": task.description,
-            "raw": "exported_ok",
-            "pydantic": None,
-            "json_dict": None,
+            "exported_output": "exported_ok",
+            "raw_output": "exported_ok",
             "agent": researcher.role,
             "summary": "Give me a list of 5 interesting ideas to explore...",
-            "output_format": OutputFormat.RAW,
         }
         assert output_dict == expected_output
 
@@ -166,7 +162,7 @@ def test_execute_with_agent():
     )
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
-        task.execute_sync(agent=researcher)
+        task.execute(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -186,7 +182,7 @@ def test_async_execution():
     )
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
-        task.execute_async(agent=researcher)
+        task.execute(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -204,7 +200,7 @@ def test_multiple_output_type_error():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic_sequential():
+def test_output_pydantic():
     class ScoreOutput(BaseModel):
         score: int
 
@@ -222,46 +218,13 @@ def test_output_pydantic_sequential():
         agent=scorer,
     )
 
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
+    crew = Crew(agents=[scorer], tasks=[task])
     result = crew.kickoff()
-    assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
+    assert isinstance(result, ScoreOutput)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic_hierarchical():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4o"),
-    )
-    result = crew.kickoff()
-    assert isinstance(result.pydantic, ScoreOutput)
-    assert result.to_dict() == {"score": 4}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_sequential():
+def test_output_json():
     class ScoreOutput(BaseModel):
         score: int
 
@@ -279,126 +242,9 @@ def test_output_json_sequential():
         agent=scorer,
     )
 
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
+    crew = Crew(agents=[scorer], tasks=[task])
     result = crew.kickoff()
-    assert '{"score": 4}' == result.json
-    assert result.to_dict() == {"score": 4}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_hierarchical():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4o"),
-    )
-    result = crew.kickoff()
-    assert '{"score": 4}' == result.json
-    assert result.to_dict() == {"score": 4}
-
-
-def test_json_property_without_output_json():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,  # Using output_pydantic instead of output_json
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
-    result = crew.kickoff()
-
-    with pytest.raises(ValueError) as excinfo:
-        _ = result.json  # Attempt to access the json property
-
-    assert "No JSON output found in the final task." in str(excinfo.value)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_dict_sequential():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task], process=Process.sequential)
-    result = crew.kickoff()
-    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 4}
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_dict_hierarchical():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4o"),
-    )
-    result = crew.kickoff()
-    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 4}
+    assert '{\n  "score": 4\n}' == result
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -434,11 +280,7 @@ def test_output_pydantic_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=2)
     result = crew.kickoff()
-    pydantic_result = result.pydantic
-    assert isinstance(
-        pydantic_result, ScoreOutput
-    ), "Expected pydantic result to be of type ScoreOutput"
-    assert 5 == pydantic_result.score
+    assert 5 == result.score
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -469,7 +311,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 5}' == result.json
+    assert '{\n  "score": 5\n}' == result
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -521,7 +363,7 @@ def test_save_task_json_output():
     with patch.object(Task, "_save_file") as save_file:
         save_file.return_value = None
         crew.kickoff()
-        save_file.assert_called_once_with({"score": 4})
+        save_file.assert_called_once_with('{\n  "score": 4\n}')
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -714,80 +556,3 @@ def test_interpolate_inputs():
         == "Give me a list of 5 interesting ideas about ML to explore for an article, what makes them unique and interesting."
     )
     assert task.expected_output == "Bullet point list of 5 interesting ideas about ML."
-
-
-def test_task_output_str_with_pydantic():
-    from crewai.tasks.output_format import OutputFormat
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    score_output = ScoreOutput(score=4)
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        pydantic=score_output,
-        output_format=OutputFormat.PYDANTIC,
-    )
-
-    assert str(task_output) == str(score_output)
-
-
-def test_task_output_str_with_json_dict():
-    from crewai.tasks.output_format import OutputFormat
-
-    json_dict = {"score": 4}
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        json_dict=json_dict,
-        output_format=OutputFormat.JSON,
-    )
-
-    assert str(task_output) == str(json_dict)
-
-
-def test_task_output_str_with_raw():
-    from crewai.tasks.output_format import OutputFormat
-
-    raw_output = "Raw task output"
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        raw=raw_output,
-        output_format=OutputFormat.RAW,
-    )
-
-    assert str(task_output) == raw_output
-
-
-def test_task_output_str_with_pydantic_and_json_dict():
-    from crewai.tasks.output_format import OutputFormat
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    score_output = ScoreOutput(score=4)
-    json_dict = {"score": 4}
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        pydantic=score_output,
-        json_dict=json_dict,
-        output_format=OutputFormat.PYDANTIC,
-    )
-
-    # When both pydantic and json_dict are present, pydantic should take precedence
-    assert str(task_output) == str(score_output)
-
-
-def test_task_output_str_with_none():
-    from crewai.tasks.output_format import OutputFormat
-
-    task_output = TaskOutput(
-        description="Test task",
-        agent="Test Agent",
-        output_format=OutputFormat.RAW,
-    )
-
-    assert str(task_output) == ""