WIP: working replay feat fixing inputs, need tests

.gitignore

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

src/crewai/crew.py

@@ -81,6 +81,7 @@ 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)
 
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -333,7 +334,9 @@ class Crew(BaseModel):
         """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()
@@ -359,7 +362,7 @@ class Crew(BaseModel):
         metrics = []
 
         if self.process == Process.sequential:
-            result = self._run_sequential_process(inputs)
+            result = self._run_sequential_process()
         elif self.process == Process.hierarchical:
             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)
@@ -452,7 +455,11 @@ class Crew(BaseModel):
 
         return results
 
-    def _store_execution_log(self, task, output, task_index, inputs=None):
+    def _store_execution_log(self, task: Task, output, task_index):
+        if self._inputs:
+            inputs = self._inputs
+        else:
+            inputs = {}
         log = {
             "task_id": str(task.id),
             "description": task.description,
@@ -468,19 +475,15 @@ class Crew(BaseModel):
             },
             "timestamp": datetime.now().isoformat(),
             "task_index": task_index,
-            # "output_py": output.pydantic_output,
             "inputs": inputs,
-            # "task": task.model_dump(),
         }
         self.execution_logs.append(log)
         self._task_output_handler.append(log)
 
-    def _run_sequential_process(
-        self, inputs: Dict[str, Any] | None = None
-    ) -> CrewOutput:
+    def _run_sequential_process(self) -> CrewOutput:
         """Executes tasks sequentially and returns the final output."""
         self.execution_logs = []
-        task_outputs = self._execute_tasks(self.tasks, inputs=inputs)
+        task_outputs = self._execute_tasks(self.tasks)
         final_string_output = aggregate_raw_outputs_from_task_outputs(task_outputs)
         self._finish_execution(final_string_output)
         self.save_execution_logs()
@@ -493,10 +496,9 @@ class Crew(BaseModel):
         tasks,
         start_index=0,
         is_replay=False,
-        inputs: Dict[str, Any] | None = None,
     ):
         task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
         for task_index, task in enumerate(tasks[start_index:], start=start_index):
             if task.agent and task.agent.allow_delegation:
                 agents_for_delegation = [
@@ -527,12 +529,10 @@ class Crew(BaseModel):
                 future = task.execute_async(
                     agent=task.agent, context=context, tools=task.tools
                 )
-                futures.append((task, future))
+                futures.append((task, future, task_index))
             else:
                 if futures:
-                    task_outputs = self._process_async_tasks(
-                        futures, task_index, inputs
-                    )
+                    task_outputs = self._process_async_tasks(futures)
                     futures.clear()
 
                 context = aggregate_raw_outputs_from_task_outputs(task_outputs)
@@ -541,10 +541,10 @@ class Crew(BaseModel):
                 )
                 task_outputs = [task_output]
                 self._process_task_result(task, task_output)
-                self._store_execution_log(task, task_output, task_index, inputs)
+                self._store_execution_log(task, task_output, task_index)
 
         if futures:
-            task_outputs = self._process_async_tasks(futures, len(tasks), inputs)
+            task_outputs = self._process_async_tasks(futures)
 
         return task_outputs
 
@@ -556,20 +556,35 @@ class Crew(BaseModel):
 
     def _process_async_tasks(
         self,
-        futures: List[Tuple[Task, Future[TaskOutput]]],
-        task_index: int,
-        inputs: Dict[str, Any] | None = None,
+        futures: List[Tuple[Task, Future[TaskOutput], int]],
     ) -> List[TaskOutput]:
         task_outputs = []
-        for future_task, future in futures:
+        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, inputs)
+            self._store_execution_log(future_task, task_output, task_index)
 
         return task_outputs
 
-    def replay_from_task(self, task_id: str):
+    def _get_agent(self, role: str) -> Optional[BaseAgent]:
+        """
+        Private method to get an agent by role.
+
+        Args:
+            role (str): The role of the agent to retrieve.
+
+        Returns:
+            Optional[BaseAgent]: The agent with the specified role, or None if not found.
+        """
+        for agent in self.agents:
+            if agent.role == role:
+                return agent
+        return None
+
+    def replay_from_task(self, task_id: str, inputs: Dict[str, Any] | None = None):
+        all_tasks = self.tasks.copy()
+
         stored_outputs = self._load_stored_outputs()
         start_index = next(
             (
@@ -579,9 +594,20 @@ class Crew(BaseModel):
             ),
             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)
+
         if start_index is None:
             raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
-        # Create a map of task ID to stored output
+
+        # 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
         }
@@ -589,13 +615,21 @@ class Crew(BaseModel):
         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]]] = []
+        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
         context = ""
 
-        inputs = stored_outputs[start_index].get("inputs", {})
-        if inputs is not None:
-            self._interpolate_inputs(inputs)
-        for task_index, task in enumerate(self.tasks):
+        # inputs can be overrided with new passed inputs
+        replay_inputs = (
+            inputs
+            if inputs is not None
+            else stored_outputs[start_index].get("inputs", {})
+        )
+
+        self._inputs = replay_inputs  # overriding
+        if replay_inputs:
+            self._interpolate_inputs(replay_inputs)
+
+        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:
@@ -632,12 +666,10 @@ class Crew(BaseModel):
                     future = task.execute_async(
                         agent=task.agent, context=context, tools=task.tools
                     )
-                    futures.append((task, future))
+                    futures.append((task, future, task_index))
                 else:
                     if futures:
-                        async_outputs = self._process_async_tasks(
-                            futures, task_index, inputs
-                        )
+                        async_outputs = self._process_async_tasks(futures)
                         task_outputs.extend(async_outputs)
                         for output in async_outputs:
                             context += (
@@ -649,14 +681,14 @@ class Crew(BaseModel):
                     )
                     task_outputs.append(task_output)
                     self._process_task_result(task, task_output)
-                    self._store_execution_log(task, task_output, task_index, inputs)
+                    self._store_execution_log(task, task_output, task_index)
                     context += (
                         f"\nTask {task_index + 1} Output:\n{task_output.raw_output}"
                     )
 
         # Process any remaining async tasks
         if futures:
-            async_outputs = self._process_async_tasks(futures, len(self.tasks), inputs)
+            async_outputs = self._process_async_tasks(futures)
             task_outputs.extend(async_outputs)
         # Calculate usage metrics
         token_usage = self.calculate_usage_metrics()

src/crewai/task.py

@@ -306,6 +306,8 @@ class Task(BaseModel):
         )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
+            if agents is None:
+                return None
             return next((agent for agent in agents if agent.role == role), None)
 
         cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None

src/crewai/utilities/crew_json_encoder.py

@@ -6,12 +6,26 @@ from pydantic import BaseModel
 
 class CrewJSONEncoder(json.JSONEncoder):
     def default(self, obj):
-        if isinstance(obj, datetime):
-            return obj.isoformat()
-        if isinstance(obj, UUID):
-            return str(obj)
         if isinstance(obj, BaseModel):
-            return obj.model_dump()
-        if hasattr(obj, "__dict__"):
-            return obj.__dict__
-        return str(obj)
+            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

@@ -97,6 +97,11 @@ class TaskOutputJsonHandler:
             json.dump(file_data, file, indent=2, cls=CrewJSONEncoder)
             file.truncate()
 
+    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:
         if not os.path.exists(self.file_path) or os.path.getsize(self.file_path) == 0:
             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}]}
+
+
+        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}]}
+
+
+        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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+        I"},"logprobs":null,"finish_reason":null}]}
+
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+
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+
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+        great"},"logprobs":null,"finish_reason":null}]}
+
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+        answer"},"logprobs":null,"finish_reason":null}]}
+
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+
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+
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+        Answer"},"logprobs":null,"finish_reason":null}]}
+
+
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+
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+        \n"},"logprobs":null,"finish_reason":null}]}
+
+
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+
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+        Ethical"},"logprobs":null,"finish_reason":null}]}
+
+
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+        implications"},"logprobs":null,"finish_reason":null}]}
+
+
+        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":"
+        of"},"logprobs":null,"finish_reason":null}]}
+
+
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+        AI"},"logprobs":null,"finish_reason":null}]}
+
+
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+        decision"},"logprobs":null,"finish_reason":null}]}
+
+
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+
+
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+        in"},"logprobs":null,"finish_reason":null}]}
+
+
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+        healthcare"},"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}]}
+
+
+        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":"
+        AI"},"logprobs":null,"finish_reason":null}]}
+
+
+        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":"
+        agents"},"logprobs":null,"finish_reason":null}]}
+
+
+        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":"
+        revolution"},"logprobs":null,"finish_reason":null}]}
+
+
+        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":"izing"},"logprobs":null,"finish_reason":null}]}
+
+
+        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":"
+        customer"},"logprobs":null,"finish_reason":null}]}
+
+
+        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":"
+        service"},"logprobs":null,"finish_reason":null}]}
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tests/crew_test.py

@@ -559,7 +559,6 @@ def test_hierarchical_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",
@@ -713,7 +712,6 @@ 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
@@ -1138,8 +1136,6 @@ 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():
-    from unittest.mock import patch
-
     researcher = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -1825,3 +1821,68 @@ def test__setup_for_training():
 # TODO: TEST EXPORT OUTPUT TASK WITH PYDANTIC
 # TODO: TEST EXPORT OUTPUT TASK WITH JSON
 # TODO: TEST EXPORT OUTPUT TASK CALLBACK
+
+
+@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_output="Mocked output for list of ideas",
+            agent="Researcher",
+        )
+
+        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
+
+
+# TODO: ENSURE INPUTS PASSED PROPERLY ONTO OUTPUT_JSON
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_feature_ensure_inputs_passed():
+    agent = Agent(
+        role="{topic} Researcher",
+        goal="Express hot takes on {topic}.",
+        backstory="You have a lot of experience with {topic}.",
+    )
+
+    task = Task(
+        description="Give me an analysis around {topic}.",
+        expected_output="{points} bullet points about {topic}.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    inputs = {"topic": "AI", "points": 5}
+    crew._interpolate_inputs(inputs=inputs)  # Manual call for now
+    # TODO: ENSURE INPUTS PASSED PROPERLY ONTO OUTPUT_JSON
+    # TODO: PATCH _store_execution_log WITH ATTR _inputs
+
+    # assert crew.tasks[0].description == "Give me an analysis around AI."
+    # assert crew.tasks[0].expected_output == "5 bullet points about AI."
+    # assert crew.agents[0].role == "AI Researcher"
+    # assert crew.agents[0].goal == "Express hot takes on AI."
+    # assert crew.agents[0].backstory == "You have a lot of experience with AI."