replay working for both seq and hier just need tests

src/crewai/crew.py

@@ -33,11 +33,11 @@ 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.crew_json_encoder import CrewJSONEncoder
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.file_handler import TaskOutputJsonHandler
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
+    aggregate_raw_outputs_from_tasks,
 )
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -392,11 +392,10 @@ class Crew(BaseModel):
     ) -> CrewOutput:
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
-        self.execution_logs = []
         self._task_output_handler.reset()
         if inputs is not None:
             self._inputs = inputs
-            # self._interpolate_inputs(inputs)
+            self._interpolate_inputs(inputs)
         self._set_tasks_callbacks()
 
         i18n = I18N(prompt_file=self.prompt_file)
@@ -422,7 +421,7 @@ 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."
@@ -510,7 +509,13 @@ class Crew(BaseModel):
 
         return results
 
-    def _store_execution_log(self, task: Task, output, task_index):
+    def _store_execution_log(
+        self,
+        task: Task,
+        output: TaskOutput,
+        task_index: int,
+        was_replayed: bool = False,
+    ):
         if self._inputs:
             inputs = self._inputs
         else:
@@ -521,21 +526,28 @@ class Crew(BaseModel):
             "expected_output": task.expected_output,
             "agent_role": task.agent.role if task.agent else "None",
             "output": {
-                "description": task.description,
-                "summary": task.description,
-                "raw_output": output.raw_output,
-                "pydantic_output": output.pydantic_output,
-                "json_output": output.json_output,
-                "agent": task.agent.role if task.agent else "None",
+                "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,
             },
             "timestamp": datetime.now().isoformat(),
             "task_index": task_index,
             "inputs": inputs,
+            "was_replayed": was_replayed,
         }
-        self.execution_logs.append(log)
-        self._task_output_handler.append(log)
+        # Update the existing log or append if it's a new entry
+        if task_index < len(self.execution_logs):
+            self.execution_logs[task_index] = log
+        else:
+            self.execution_logs.append(log)
 
-    def _run_sequential_process(self):
+        self._task_output_handler.update(task_index, log)
+
+    def _run_sequential_process(self) -> CrewOutput:
         """Executes tasks sequentially and returns the final output."""
         self.execution_logs = []
         return self._execute_tasks(self.tasks)
@@ -544,7 +556,7 @@ class Crew(BaseModel):
         self,
         tasks: List[Task],
         manager: Optional[BaseAgent] = None,
-    ):
+    ) -> CrewOutput:
         task_outputs: List[TaskOutput] = []
         futures: List[Tuple[Task, Future[TaskOutput], int]] = []
         for task_index, task in enumerate(tasks):
@@ -576,7 +588,7 @@ class Crew(BaseModel):
                     agent=role, task=task.description, status="started"
                 )
             if task.async_execution:
-                context = aggregate_raw_outputs_from_task_outputs(task_outputs)
+                context = self._set_context(task, task_outputs)
                 if agent_to_use:
                     future = task.execute_async(
                         agent=agent_to_use,
@@ -594,8 +606,7 @@ class Crew(BaseModel):
                     task_outputs = self._process_async_tasks(futures)
                     futures.clear()
 
-                context = aggregate_raw_outputs_from_task_outputs(task_outputs)
-
+                context = self._set_context(task, task_outputs)
                 if agent_to_use:
                     task_output = task.execute_sync(
                         agent=agent_to_use,
@@ -609,7 +620,29 @@ class Crew(BaseModel):
         if futures:
             task_outputs = self._process_async_tasks(futures)
 
-        return task_outputs
+        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,
+        )
+        # return task_outputs
+
+    def _set_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"
@@ -620,14 +653,16 @@ class Crew(BaseModel):
     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)
-
+            self._store_execution_log(
+                future_task, task_output, task_index, was_replayed
+            )
         return task_outputs
 
     def _get_agent(self, role: str) -> Optional[BaseAgent]:
@@ -645,10 +680,31 @@ class Crew(BaseModel):
                 return agent
         return None
 
-    def replay_from_task_easy(self, task_id: str, inputs: Dict[str, Any] | None = None):
-        task_outputs = []
-        stored_outputs = self._load_stored_outputs()
-        start_index = next(
+    def _initialize_execution(self, inputs: Optional[Dict[str, Any]]) -> None:
+        """Initializes the execution by setting up necessary attributes and states."""
+        self._execution_span = self._telemetry.crew_execution_span(self, inputs)
+        # self.execution_logs = []
+        self._task_output_handler.reset()
+        if inputs is not None:
+            self._inputs = inputs
+            self._interpolate_inputs(inputs)
+        self._set_tasks_callbacks()
+        i18n = I18N(prompt_file=self.prompt_file)
+        for agent in self.agents:
+            agent.i18n = i18n
+            agent.crew = self  # type: ignore[attr-defined]
+            if not agent.function_calling_llm:  # type: ignore[attr-defined]
+                agent.function_calling_llm = self.function_calling_llm  # type: ignore[attr-defined]
+            if agent.allow_code_execution:  # type: ignore[attr-defined]
+                agent.tools += agent.get_code_execution_tools()  # type: ignore[attr-defined]
+            if not agent.step_callback:  # type: ignore[attr-defined]
+                agent.step_callback = self.step_callback  # type: ignore[attr-defined]
+            agent.create_agent_executor()
+
+    def _find_task_index(
+        self, task_id: str, stored_outputs: List[Dict[str, Any]]
+    ) -> Optional[int]:
+        return next(
             (
                 index
                 for (index, d) in enumerate(stored_outputs)
@@ -656,80 +712,20 @@ class Crew(BaseModel):
             ),
             None,
         )
-        # todo: depending if its seqential or hierarchial how can we run this properly?
-        if self.process == Process.sequential:
-            for task in self.tasks[start_index:]:
-                context = aggregate_raw_outputs_from_task_outputs(task_outputs)
-                task_output = task.execute_sync(
-                    agent=task.agent, context=context, tools=task.tools
-                )
-                task_outputs.append(task_output)
-                self._process_task_result(task, task_output)
-                self._store_execution_log(task, task_output, start_index)
-                start_index += 1
-
-        elif self.process == Process.hierarchical:
-            manager_task = self.tasks[start_index]
-            context = aggregate_raw_outputs_from_task_outputs(task_outputs)
-            task_output = manager_task.execute_sync(
-                agent=manager_task.agent, context=context, tools=manager_task.tools
-            )
-            task_outputs.append(task_output)
-            self._process_task_result(manager_task, task_output)
-            self._store_execution_log(manager_task, task_output, start_index)
-
-            # Assuming hierarchical process involves manager delegating tasks to other agents
-            for task in self.tasks[start_index + 1 :]:
-                context = aggregate_raw_outputs_from_task_outputs(task_outputs)
-                task_output = task.execute_sync(
-                    agent=task.agent, context=context, tools=task.tools
-                )
-                task_outputs.append(task_output)
-                self._process_task_result(task, task_output)
-                self._store_execution_log(task, task_output, start_index)
-                start_index += 1
-
-        else:
-            raise NotImplementedError(
-                f"The process '{self.process}' is not implemented yet."
-            )
-
-    # this works sequentially
-    def replay_from_task(self, task_id: str, inputs: Dict[str, Any] | None = None):
-        all_tasks = self.tasks.copy()
 
+    def replay_from_task(
+        self, task_id: str, inputs: Dict[str, Any] | None = None
+    ) -> CrewOutput:
         stored_outputs = self._load_stored_outputs()
-        start_index = next(
-            (
-                index
-                for (index, d) in enumerate(stored_outputs)
-                if d["task_id"] == str(task_id)
-            ),
-            None,
-        )
-        # Generate tasks based on what was previously replayed
-        if len(self.tasks) != len(stored_outputs):
-            for output in stored_outputs[start_index:]:
-                matching_index = output["task_index"]
-                matching_task = self.tasks[matching_index]
-                if matching_task:
-                    new_task = matching_task.copy()
-                    new_task.agent = self._get_agent(output["agent_role"])
-                    all_tasks.append(new_task)
+        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.")
 
-        # this handles passing the correct context along and updating following task executions with the new task_ouputs as context
-        stored_output_map: Dict[str, dict] = {
-            log["task_id"]: log["output"] for log in stored_outputs
-        }
-
         task_outputs: List[
             TaskOutput
         ] = []  # will propogate the old outputs first to add context then fill the content with the new task outputs relative to the replay start
         futures: List[Tuple[Task, Future[TaskOutput], int]] = []
-        context = ""
 
         # inputs can be overrided with new passed inputs
         replay_inputs = (
@@ -738,28 +734,46 @@ class Crew(BaseModel):
             else stored_outputs[start_index].get("inputs", {})
         )
 
-        self._inputs = replay_inputs  # overriding
+        self._inputs = replay_inputs
         if replay_inputs:
             self._interpolate_inputs(replay_inputs)
+        if self.process == Process.hierarchical:
+            self._create_manager_agent()
+        for task_index, task in enumerate(self.tasks):
+            if task_index < start_index:  # we are skipping this task
+                stored_output = stored_outputs[task_index]["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"],
+                )
+                task_outputs = [task_output]
+                context = self._set_context(task, task_outputs)
 
-        for task_index, task in enumerate(all_tasks):
-            if task_index < start_index:
-                # Use stored output for tasks before the replay point
-                if task.id in stored_output_map:
-                    stored_output = stored_output_map[task.id]
-                    task_output = TaskOutput(
-                        description=stored_output["description"],
-                        raw_output=stored_output["raw_output"],
-                        pydantic_output=stored_output["pydantic_output"],
-                        json_output=stored_output["json_output"],
-                        agent=stored_output["agent"],
-                    )
-                    task_outputs.append(task_output)
-                    context += (
-                        f"\nTask {task_index + 1} Output:\n{task_output.raw_output}"
-                    )
             else:
-                role = task.agent.role if task.agent is not None else "None"
+                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
+                        )
+
+                if self.process == Process.hierarchical:
+                    if task.agent and self.manager_agent:
+                        self.manager_agent.tools = task.agent.get_delegation_tools(
+                            [task.agent]
+                        )
+                    if self.manager_agent:
+                        self.manager_agent.tools = (
+                            self.manager_agent.get_delegation_tools(self.agents)
+                        )
+                agent_to_use = task.agent if task.agent else self.manager_agent
+                role = agent_to_use.role if agent_to_use is not None else "None"
                 log_color = "bold_blue"
                 self._logger.log(
                     "debug", f"Replaying Working Agent: {role}", color=log_color
@@ -776,38 +790,67 @@ class Crew(BaseModel):
                     )
                 # Execute task for replay and subsequent tasks
                 if task.async_execution:
+                    context = self._set_context(task, task_outputs)
                     future = task.execute_async(
-                        agent=task.agent, context=context, tools=task.tools
+                        agent=agent_to_use, context=context, tools=task.tools
                     )
                     futures.append((task, future, task_index))
                 else:
                     if futures:
-                        async_outputs = self._process_async_tasks(futures)
-                        task_outputs.extend(async_outputs)
-                        for output in async_outputs:
-                            context += (
-                                f"\nTask {task_index + 1} Output:\n{output.raw_output}"
-                            )
+                        task_outputs = self._process_async_tasks(futures, True)
                         futures.clear()
+
+                    context = self._set_context(task, task_outputs)
+
                     task_output = task.execute_sync(
-                        agent=task.agent, context=context, tools=task.tools
+                        agent=agent_to_use, context=context, tools=task.tools
                     )
-                    task_outputs.append(task_output)
+                    task_outputs = [task_output]
                     self._process_task_result(task, task_output)
-                    self._store_execution_log(task, task_output, task_index)
-                    context += (
-                        f"\nTask {task_index + 1} Output:\n{task_output.raw_output}"
+                    self._store_execution_log(
+                        task, task_output, task_index, was_replayed=True
                     )
 
         # Process any remaining async tasks
         if futures:
-            async_outputs = self._process_async_tasks(futures)
-            task_outputs.extend(async_outputs)
-        # Calculate usage metrics
+            task_outputs = self._process_async_tasks(futures, True)
+
+        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()
 
-        # Format and return the final output
-        return self._format_output(task_outputs, token_usage)
+        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 _create_manager_agent(self):
+        i18n = I18N(prompt_file=self.prompt_file)
+        if self.manager_agent is not None:
+            self.manager_agent.allow_delegation = True
+            manager = self.manager_agent
+            if manager.tools is not None and len(manager.tools) > 0:
+                raise Exception("Manager agent should not have tools")
+            manager.tools = self.manager_agent.get_delegation_tools(self.agents)
+        else:
+            manager = Agent(
+                role=i18n.retrieve("hierarchical_manager_agent", "role"),
+                goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
+                backstory=i18n.retrieve("hierarchical_manager_agent", "backstory"),
+                tools=AgentTools(agents=self.agents).tools(),
+                llm=self.manager_llm,
+                verbose=self.verbose,
+            )
+            self.manager_agent = manager
 
     def _load_stored_outputs(self) -> List[Dict]:
         try:
@@ -826,37 +869,7 @@ class Crew(BaseModel):
             )
             return []
 
-    def save_execution_logs(self, filename: str | None = None):
-        """Save execution logs to a file."""
-        if filename:
-            self._log_file = filename
-        try:
-            with open(self._log_file, "w") as f:
-                json.dump(self.execution_logs, f, indent=2, cls=CrewJSONEncoder)
-        except Exception as e:
-            self._logger.log("error", f"Failed to save execution logs: {str(e)}")
-
-    def load_execution_logs(self, filename: str | None = None):
-        """Load execution logs from a file."""
-        if filename:
-            self._log_file = filename
-        try:
-            with open(self._log_file, "r") as f:
-                self.execution_logs = json.load(f)
-        except FileNotFoundError:
-            self._logger.log(
-                "warning",
-                f"Log file {self._log_file} not found. Starting with empty logs.",
-            )
-            self.execution_logs = []
-        except json.JSONDecodeError:
-            self._logger.log(
-                "error",
-                f"Failed to parse log file {self._log_file}. Starting with empty logs.",
-            )
-            self.execution_logs = []
-
-    def _run_hierarchical_process(self) -> Tuple[CrewOutput, Dict[str, Any]]:
+    def _run_hierarchical_process(self) -> CrewOutput:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
         i18n = I18N(prompt_file=self.prompt_file)
         if self.manager_agent is not None:
@@ -878,16 +891,6 @@ class Crew(BaseModel):
 
         return self._execute_tasks(self.tasks, manager)
 
-        # final_string_output = aggregate_raw_outputs_from_task_outputs(task_outputs)
-        # self._finish_execution(final_string_output)
-
-        # token_usage = self.calculate_usage_metrics()
-
-        # return (
-        #     self._format_output(task_outputs, token_usage),
-        #     token_usage,
-        # )
-
     def copy(self):
         """Create a deep copy of the Crew."""
 

src/crewai/utilities/file_handler.py

@@ -1,8 +1,10 @@
 import os
 import pickle
-from datetime import datetime
 import json
 
+from datetime import datetime
+from typing import Dict, Any, List
+
 from crewai.utilities.crew_json_encoder import CrewJSONEncoder
 
 
@@ -97,6 +99,18 @@ class TaskOutputJsonHandler:
             json.dump(file_data, file, indent=2, cls=CrewJSONEncoder)
             file.truncate()
 
+    def update(self, task_index: int, log: Dict[str, Any]):
+        logs = self.load()
+        if task_index < len(logs):
+            logs[task_index] = log
+        else:
+            logs.append(log)
+        self.save(logs)
+
+    def save(self, logs: List[Dict[str, Any]]):
+        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:

tests/crew_test.py

@@ -15,6 +15,7 @@ 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
 
@@ -1827,17 +1828,17 @@ def test_replay_feature():
     with patch.object(Task, "execute_sync") as mock_execute_task:
         mock_execute_task.return_value = TaskOutput(
             description="Mock description",
-            raw_output="Mocked output for list of ideas",
+            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
-        context_passed = mock_execute_task.call_args_list[1][1]["context"]
-        assert (
-            "Mocked output for list of ideas" in context_passed
-        )  # ensure context passed
         assert mock_execute_task.call_count == 3