easier cli command name

.gitignore

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

docs/core-concepts/Crews.md

@@ -155,4 +155,23 @@ 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_from_tasks 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

@@ -1,8 +1,10 @@
 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
 
 
 @click.group()
@@ -48,5 +50,26 @@ 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)
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/replay_from_task.py

@@ -0,0 +1,24 @@
+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

@@ -21,3 +21,13 @@ 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

@@ -11,6 +11,7 @@ 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

@@ -31,8 +31,16 @@ 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 TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
+from crewai.utilities.constants import (
+    CREW_TASKS_OUTPUT_FILE,
+    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,
@@ -80,6 +88,10 @@ 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)
@@ -135,6 +147,14 @@ 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",
+    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -377,7 +397,12 @@ class Crew(BaseModel):
     ) -> CrewOutput:
         """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._set_tasks_callbacks()
 
@@ -404,7 +429,7 @@ class Crew(BaseModel):
         if self.process == Process.sequential:
             result = self._run_sequential_process()
         elif self.process == Process.hierarchical:
-            result = self._run_hierarchical_process()  # type: ignore # Incompatible types in assignment (expression has type "str | dict[str, Any]", variable has type "str")
+            result = self._run_hierarchical_process()
         else:
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
@@ -492,103 +517,51 @@ class Crew(BaseModel):
 
         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 = {}
+
+        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)
+
+        TaskOutputJsonHandler(CREW_TASKS_OUTPUT_FILE).update(task_index, log)
+
     def _run_sequential_process(self) -> CrewOutput:
         """Executes tasks sequentially and returns the final output."""
-        task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+        return self._execute_tasks(self.tasks)
 
-        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"
-                )
-
-            if task.async_execution:
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
-                )
-                future = task.execute_async(
-                    agent=task.agent, context=context, tools=task.tools
-                )
-                futures.append((task, future))
-            else:
-                # Before executing a synchronous task, wait for all async tasks to complete
-                if futures:
-                    # Clear task_outputs before processing async tasks
-                    task_outputs = []
-                    for future_task, future in futures:
-                        task_output = future.result()
-                        task_outputs.append(task_output)
-                        self._process_task_result(future_task, task_output)
-
-                    # Clear the futures list after processing all async results
-                    futures.clear()
-
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
-                )
-                task_output = task.execute_sync(
-                    agent=task.agent, context=context, tools=task.tools
-                )
-                task_outputs = [task_output]
-                self._process_task_result(task, task_output)
-
-        if futures:
-            # Clear task_outputs before processing async tasks
-            task_outputs = []
-            for future_task, future in futures:
-                task_output = future.result()
-                task_outputs.append(task_output)
-                self._process_task_result(future_task, task_output)
-
-        # Important: There should only be one task output in the list
-        #            If there are more or 0, something went wrong.
-        if len(task_outputs) != 1:
-            raise ValueError(
-                "Something went wrong. Kickoff should return only one task output."
-            )
-
-        final_task_output = task_outputs[0]
-
-        final_string_output = final_task_output.raw
-        self._finish_execution(final_string_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_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")
-
-    # TODO: @joao, Breaking change. Changed return type. Usage metrics is included in crewoutput
     def _run_hierarchical_process(self) -> CrewOutput:
         """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
@@ -607,74 +580,127 @@ 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.
+
+        Args:
+            tasks (List[Task]): List of tasks to execute
+            manager (Optional[BaseAgent], optional): Manager agent to use for delegation. Defaults to None.
+
+        Returns:
+            CrewOutput: Final output of the crew
+        """
+
         task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
+        last_sync_output: Optional[TaskOutput] = None
 
-        # TODO: IF USER OVERRIDE THE CONTEXT, PASS THAT
-        for task in self.tasks:
-            self._logger.log("debug", f"Working Agent: {manager.role}")
-            self._logger.log("info", f"Starting Task: {task.description}")
+        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
 
-            if self.output_log_file:
-                self._file_handler.log(
-                    agent=manager.role, task=task.description, status="started"
+            self._prepare_task(task, manager)
+            if self.process == Process.hierarchical:
+                agent_to_use = manager
+            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 = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
+                context = self._get_context(
+                    task, [last_sync_output] if last_sync_output else []
                 )
                 future = task.execute_async(
-                    agent=manager, context=context, tools=manager.tools
+                    agent=agent_to_use,
+                    context=context,
+                    tools=agent_to_use.tools,
                 )
-                futures.append((task, future))
+                futures.append((task, future, task_index))
             else:
-                # Before executing a synchronous task, wait for all async tasks to complete
                 if futures:
-                    # Clear task_outputs before processing async tasks
-                    task_outputs = []
-                    for future_task, future in futures:
-                        task_output = future.result()
-                        task_outputs.append(task_output)
-                        self._process_task_result(future_task, task_output)
-
-                    # Clear the futures list after processing all async results
+                    task_outputs.extend(
+                        self._process_async_tasks(futures, was_replayed)
+                    )
                     futures.clear()
 
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
-                )
+                context = self._get_context(task, task_outputs)
                 task_output = task.execute_sync(
-                    agent=manager, context=context, tools=manager.tools
+                    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)
 
-        # Process any remaining async results
         if futures:
-            # Clear task_outputs before processing async tasks
-            task_outputs = []
-            for future_task, future in futures:
-                task_output = future.result()
-                task_outputs.append(task_output)
-                self._process_task_result(future_task, task_output)
+            task_outputs = self._process_async_tasks(futures, was_replayed)
 
-        # Important: There should only be one task output in the list
-        #            If there are more or 0, something went wrong.
+        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."
             )
-
         final_task_output = task_outputs[0]
-
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
-
         token_usage = self.calculate_usage_metrics()
 
         return CrewOutput(
@@ -685,6 +711,69 @@ class Crew(BaseModel):
             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
+
     def copy(self):
         """Create a deep copy of the Crew."""
 

src/crewai/crews/crew_output.py

@@ -24,8 +24,6 @@ class CrewOutput(BaseModel):
         description="Processed token summary", default={}
     )
 
-    # TODO: Joao - Adding this safety check breakes when people want to see
-    #                   The full output of a CrewOutput.
     # @property
     # def pydantic(self) -> Optional[BaseModel]:
     #     # Check if the final task output included a pydantic model

src/crewai/task.py

@@ -204,6 +204,7 @@ class Task(BaseModel):
         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(
@@ -244,7 +245,9 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json() if pydantic_output else result
+                else pydantic_output.model_dump_json()
+                if pydantic_output
+                else result
             )
             self._save_file(content)
 
@@ -378,7 +381,7 @@ class Task(BaseModel):
     def _convert_with_instructions(
         self, result: str, model: Type[BaseModel]
     ) -> Union[dict, BaseModel, str]:
-        llm = self.agent.function_calling_llm or self.agent.llm
+        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(

src/crewai/tasks/task_output.py

@@ -11,9 +11,7 @@ class TaskOutput(BaseModel):
 
     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=""
-    )  # TODO: @joao: breaking change, by renaming raw_output to raw, but now consistent with CrewOutput
+    raw: str = Field(description="Raw output of the task", default="")
     pydantic: Optional[BaseModel] = Field(
         description="Pydantic output of task", default=None
     )
@@ -32,8 +30,6 @@ class TaskOutput(BaseModel):
         self.summary = f"{excerpt}..."
         return self
 
-    # TODO: Joao - Adding this safety check breakes when people want to see
-    #                   The full output of a TaskOutput or CrewOutput.
     # @property
     # def pydantic(self) -> Optional[BaseModel]:
     #     # Check if the final task output included a pydantic model

src/crewai/utilities/constants.py

@@ -1,2 +1,3 @@
 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

@@ -0,0 +1,31 @@
+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

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

src/crewai/utilities/printer.py

@@ -8,6 +8,8 @@ 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)
 
@@ -22,3 +24,6 @@ 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

@@ -0,0 +1,69 @@
+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/cassettes/test_replay_feature.yaml

@@ -0,0 +1,697 @@
+interactions:
+- request:
+    body: '{"messages": [{"content": "You are Researcher. You''re an expert researcher,
+      specialized in technology, software engineering, AI and startups. You work as
+      a freelancer and is now working on doing research and analysis for a new customer.\nYour
+      personal goal is: Make the best research and analysis on content about AI and
+      AI agentsTo give my best complete final answer to the task use the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: my best complete
+      final answer to the task.\nYour final answer must be the great and the most
+      complete as possible, it must be outcome described.\n\nI MUST use these formats,
+      my job depends on it!\nCurrent Task: Generate a list of 5 interesting ideas
+      to explore for an article, where each bulletpoint is under 15 words.\n\nThis
+      is the expect criteria for your final answer: Bullet point list of 5 important
+      events. No additional commentary. \n you MUST return the actual complete content
+      as the final answer, not a summary.\n\nBegin! This is VERY important to you,
+      use the tools available and give your best Final Answer, your job depends on
+      it!\n\nThought:\n", "role": "user"}], "model": "gpt-4o", "n": 1, "stop": ["\nObservation"],
+      "stream": true, "temperature": 0.7}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate, br
+      connection:
+      - keep-alive
+      content-length:
+      - '1237'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.35.3
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.35.3
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.11.5
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: 'data: {"id":"chatcmpl-9j7QxB9Y779gwWcC1EK2JtcMZ7vCS","object":"chat.completion.chunk","created":1720540363,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
+
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+        data: {"id":"chatcmpl-9j7QxB9Y779gwWcC1EK2JtcMZ7vCS","object":"chat.completion.chunk","created":1720540363,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
+
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+        data: {"id":"chatcmpl-9j7QxB9Y779gwWcC1EK2JtcMZ7vCS","object":"chat.completion.chunk","created":1720540363,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
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+
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+
+
+        data: {"id":"chatcmpl-9j7QxB9Y779gwWcC1EK2JtcMZ7vCS","object":"chat.completion.chunk","created":1720540363,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
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+
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+
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+
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+        data: {"id":"chatcmpl-9kF4iAwQ2K0M2fy86qpF660mrclmY","object":"chat.completion.chunk","created":1720808064,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d33f7b429e","choices":[{"index":0,"delta":{"content":"
+        John"},"logprobs":null,"finish_reason":null}]}
+
+
+        data: {"id":"chatcmpl-9kF4iAwQ2K0M2fy86qpF660mrclmY","object":"chat.completion.chunk","created":1720808064,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_d33f7b429e","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}
+
+
+        data: [DONE]
+
+
+        '
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 8a2303443f0cce48-SJC
+      Connection:
+      - keep-alive
+      Content-Type:
+      - text/event-stream; charset=utf-8
+      Date:
+      - Fri, 12 Jul 2024 18:14:24 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '86'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains
+      x-ratelimit-limit-requests:
+      - '10000'
+      x-ratelimit-limit-tokens:
+      - '22000000'
+      x-ratelimit-remaining-requests:
+      - '9999'
+      x-ratelimit-remaining-tokens:
+      - '21999791'
+      x-ratelimit-reset-requests:
+      - 6ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_649687e24793d5b5782ecf58bc76386a
+    status:
+      code: 200
+      message: OK
+version: 1

tests/crew_test.py

@@ -1,5 +1,6 @@
 """Test Agent creation and execution basic functionality."""
 
+import os
 import json
 from concurrent.futures import Future
 from unittest import mock
@@ -15,8 +16,10 @@ from crewai.crews.crew_output import CrewOutput
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
 from crewai.task import Task
+from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities import Logger, RPMController
+from crewai.utilities.constants import CREW_TASKS_OUTPUT_FILE
 
 ceo = Agent(
     role="CEO",
@@ -136,7 +139,6 @@ def test_async_task_cannot_include_sequential_async_tasks_in_context():
 
 
 def test_context_no_future_tasks():
-
     task2 = Task(
         description="Task 2",
         expected_output="output",
@@ -584,7 +586,6 @@ def test_sequential_async_task_execution_completion():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_single_task_with_async_execution():
-
     researcher_agent = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -740,7 +741,6 @@ def test_async_task_execution_call_count():
     ) as mock_execute_sync, patch.object(
         Task, "execute_async", return_value=mock_future
     ) as mock_execute_async:
-
         crew.kickoff()
 
         assert mock_execute_async.call_count == 2
@@ -1148,7 +1148,6 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegation_is_not_enabled_if_there_are_only_one_agent():
-
     researcher = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -1380,7 +1379,6 @@ def test_crew_does_not_interpolate_without_inputs():
             interpolate_task_inputs.assert_not_called()
 
 
-# TODO: Ask @joao if we want to start throwing errors if inputs are not provided
 # def test_crew_partial_inputs():
 #     agent = Agent(
 #         role="{topic} Researcher",
@@ -1404,7 +1402,6 @@ def test_crew_does_not_interpolate_without_inputs():
 #     assert crew.agents[0].backstory == "You have a lot of experience with AI."
 
 
-# TODO: If we do want ot throw errors if we are missing inputs. Add in this test.
 # def test_crew_invalid_inputs():
 #     agent = Agent(
 #         role="{topic} Researcher",
@@ -1806,3 +1803,472 @@ def test__setup_for_training():
 
     for agent in agents:
         assert agent.allow_delegation is False
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_feature():
+    list_ideas = Task(
+        description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
+        expected_output="Bullet point list of 5 important events. No additional commentary.",
+        agent=researcher,
+    )
+    write = Task(
+        description="Write a sentence about the events",
+        expected_output="A sentence about the events",
+        agent=writer,
+        context=[list_ideas],
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[list_ideas, write],
+        process=Process.sequential,
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.return_value = TaskOutput(
+            description="Mock description",
+            raw="Mocked output for list of ideas",
+            agent="Researcher",
+            json_dict=None,
+            output_format=OutputFormat.RAW,
+            pydantic=None,
+            summary="Mocked output for list of ideas",
+        )
+
+        crew.kickoff()
+        crew.replay_from_task(str(write.id))
+        # Ensure context was passed correctly
+        assert mock_execute_task.call_count == 3
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_replay_from_task_error():
+    task = Task(
+        description="Come up with a list of 5 interesting ideas to explore for an article",
+        expected_output="5 bullet points with a paragraph for each idea.",
+        agent=researcher,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task],
+    )
+
+    with pytest.raises(TypeError) as e:
+        crew.replay_from_task()  # type: ignore purposefully throwing err
+        assert "task_id is required" in str(e)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_task_output_file_creation():
+    agent = Agent(
+        role="Content Writer",
+        goal="Write engaging content on various topics.",
+        backstory="You have a background in journalism and creative writing.",
+    )
+
+    task = Task(
+        description="Write a detailed article about AI in healthcare.",
+        expected_output="A 1 paragraph article about AI.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.return_value = TaskOutput(
+            description="Write about AI in healthcare.",
+            raw="Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatment plans, and streamlining administrative tasks.",
+            agent="Content Writer",
+            json_dict=None,
+            output_format=OutputFormat.RAW,
+            pydantic=None,
+            summary="Write about AI in healthcare...",
+        )
+
+        crew.kickoff()
+
+        # Check if the crew_tasks_output.json file is created
+        assert os.path.exists(CREW_TASKS_OUTPUT_FILE)
+
+        # Clean up the file after test
+        if os.path.exists(CREW_TASKS_OUTPUT_FILE):
+            os.remove(CREW_TASKS_OUTPUT_FILE)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_without_output_tasks_json():
+    agent = Agent(
+        role="Technical Writer",
+        goal="Write detailed technical documentation.",
+        backstory="You have a background in software engineering and technical writing.",
+    )
+
+    task = Task(
+        description="Document the process of setting up a Python project.",
+        expected_output="A step-by-step guide on setting up a Python project.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.return_value = TaskOutput(
+            description="Document the process of setting up a Python project.",
+            raw="To set up a Python project, first create a virtual environment...",
+            agent="Technical Writer",
+            json_dict=None,
+            output_format=OutputFormat.RAW,
+            pydantic=None,
+            summary="Document the process of setting up a Python project...",
+        )
+
+        if os.path.exists(CREW_TASKS_OUTPUT_FILE):
+            os.remove(CREW_TASKS_OUTPUT_FILE)
+
+        with pytest.raises(ValueError):
+            crew.replay_from_task(str(task.id))
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_task_with_context():
+    agent1 = Agent(
+        role="Researcher",
+        goal="Research AI advancements.",
+        backstory="You are an expert in AI research.",
+    )
+    agent2 = Agent(
+        role="Writer",
+        goal="Write detailed articles on AI.",
+        backstory="You have a background in journalism and AI.",
+    )
+
+    task1 = Task(
+        description="Research the latest advancements in AI.",
+        expected_output="A detailed report on AI advancements.",
+        agent=agent1,
+    )
+    task2 = Task(
+        description="Summarize the AI advancements report.",
+        expected_output="A summary of the AI advancements report.",
+        agent=agent2,
+    )
+    task3 = Task(
+        description="Write an article based on the AI advancements summary.",
+        expected_output="An article on AI advancements.",
+        agent=agent2,
+    )
+    task4 = Task(
+        description="Create a presentation based on the AI advancements article.",
+        expected_output="A presentation on AI advancements.",
+        agent=agent2,
+        context=[task1],
+    )
+
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[task1, task2, task3, task4],
+        process=Process.sequential,
+    )
+
+    mock_task_output1 = TaskOutput(
+        description="Research the latest advancements in AI.",
+        raw="Detailed report on AI advancements...",
+        agent="Researcher",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Detailed report on AI advancements...",
+    )
+    mock_task_output2 = TaskOutput(
+        description="Summarize the AI advancements report.",
+        raw="Summary of the AI advancements report...",
+        agent="Writer",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Summary of the AI advancements report...",
+    )
+    mock_task_output3 = TaskOutput(
+        description="Write an article based on the AI advancements summary.",
+        raw="Article on AI advancements...",
+        agent="Writer",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Article on AI advancements...",
+    )
+    mock_task_output4 = TaskOutput(
+        description="Create a presentation based on the AI advancements article.",
+        raw="Presentation on AI advancements...",
+        agent="Writer",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Presentation on AI advancements...",
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.side_effect = [
+            mock_task_output1,
+            mock_task_output2,
+            mock_task_output3,
+            mock_task_output4,
+        ]
+
+        crew.kickoff()
+
+        # Check if the crew_tasks_output.json file is created
+        assert os.path.exists(CREW_TASKS_OUTPUT_FILE)
+
+        # Replay task4 and ensure it uses task1's context properly
+        with patch.object(Task, "execute_sync") as mock_replay_task:
+            mock_replay_task.return_value = mock_task_output4
+
+            replayed_output = crew.replay_from_task(str(task4.id))
+            assert replayed_output.raw == "Presentation on AI advancements..."
+
+        # Clean up the file after test
+        if os.path.exists(CREW_TASKS_OUTPUT_FILE):
+            os.remove(CREW_TASKS_OUTPUT_FILE)
+
+
+def test_replay_from_task_with_context():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(
+        description="Context Task", expected_output="Say Task Output", agent=agent
+    )
+    task2 = Task(
+        description="Test Task", expected_output="Say Hi", agent=agent, context=[task1]
+    )
+
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+
+    with patch(
+        "crewai.utilities.task_output_handler.TaskOutputJsonHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {},
+            },
+        ],
+    ):
+        crew.replay_from_task(str(task2.id))
+
+        assert crew.tasks[1].context[0].output.raw == "context raw output"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_with_invalid_task_id():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(
+        description="Context Task", expected_output="Say Task Output", agent=agent
+    )
+    task2 = Task(
+        description="Test Task", expected_output="Say Hi", agent=agent, context=[task1]
+    )
+
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+
+    with patch(
+        "crewai.utilities.task_output_handler.TaskOutputJsonHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {},
+            },
+        ],
+    ):
+        with pytest.raises(
+            ValueError,
+            match="Task with id bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d not found in the crew's tasks.",
+        ):
+            crew.replay_from_task("bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d")
+
+
+@patch.object(Crew, "_interpolate_inputs")
+def test_replay_interpolates_inputs_properly(mock_interpolate_inputs):
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(description="Context Task", expected_output="Say {name}", agent=agent)
+    task2 = Task(
+        description="Test Task",
+        expected_output="Say Hi to {name}",
+        agent=agent,
+        context=[task1],
+    )
+
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+    crew.kickoff(inputs={"name": "John"})
+
+    with patch(
+        "crewai.utilities.task_output_handler.TaskOutputJsonHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {"name": "John"},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {"name": "John"},
+            },
+        ],
+    ):
+        crew.replay_from_task(str(task2.id))
+        assert crew._inputs == {"name": "John"}
+        assert mock_interpolate_inputs.call_count == 2
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_from_task_setup_context():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(description="Context Task", expected_output="Say {name}", agent=agent)
+    task2 = Task(
+        description="Test Task",
+        expected_output="Say Hi to {name}",
+        agent=agent,
+    )
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+    with patch(
+        "crewai.utilities.task_output_handler.TaskOutputJsonHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {"name": "John"},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {"name": "John"},
+            },
+        ],
+    ):
+        crew.replay_from_task(str(task2.id))
+
+        # Check if the first task's output was set correctly
+        assert crew.tasks[0].output is not None
+        assert isinstance(crew.tasks[0].output, TaskOutput)
+        assert crew.tasks[0].output.description == "Context Task Output"
+        assert crew.tasks[0].output.agent == "test_agent"
+        assert crew.tasks[0].output.raw == "context raw output"
+        assert crew.tasks[0].output.output_format == OutputFormat.RAW
+
+        assert crew.tasks[1].prompt_context == "context raw output"

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()
+        task.execute_sync(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -104,7 +104,7 @@ def test_task_callback():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync()
+        task.execute_sync(agent=researcher)
         task_completed.assert_called_once_with(task.output)
 
 
@@ -129,7 +129,7 @@ def test_task_callback_returns_task_ouput():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "exported_ok"
-        task.execute_sync()
+        task.execute_sync(agent=researcher)
         # 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]
@@ -521,9 +521,7 @@ 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}
-        )  # TODO: @Joao, should this be a dict or a json string?
+        save_file.assert_called_once_with({"score": 4})
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])