wip

* Fixed core invoke loop logic and relevant tests

* Fix failing tests

* Clean up final print statements

* Additional clean up for PR review

.github/workflows/tests.yml

@@ -1,32 +1,60 @@
 name: Run Tests
 
-on: [pull_request]
+on:
+  pull_request:
+  push:
+    branches:
+      - main
 
 permissions:
   contents: write
 
-env:
-  OPENAI_API_KEY: fake-api-key
-
 jobs:
   tests:
     runs-on: ubuntu-latest
     timeout-minutes: 15
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      MODEL: gpt-4o-mini
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Install uv
+      - name: Install UV
         uses: astral-sh/setup-uv@v3
         with:
           enable-cache: true
 
-
       - name: Set up Python
         run: uv python install 3.12.8
 
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Run tests
+      - name: Run General Tests
-        run: uv run pytest tests -vv
+        run: uv run pytest tests -k "not main_branch_tests" -vv
+
+  main_branch_tests:
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    needs: tests
+    timeout-minutes: 15
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install UV
+        uses: astral-sh/setup-uv@v3
+        with:
+          enable-cache: true
+
+      - name: Set up Python
+        run: uv python install 3.12.8
+
+      - name: Install the project
+        run: uv sync --dev --all-extras
+
+      - name: Run Main Branch Specific Tests
+        run: uv run pytest tests/main_branch_tests -vv

docs/concepts/agents.mdx

@@ -101,6 +101,8 @@ from crewai_tools import SerperDevTool
 class LatestAiDevelopmentCrew():
   """LatestAiDevelopment crew"""
 
+  agents_config = "config/agents.yaml"
+
   @agent
   def researcher(self) -> Agent:
     return Agent(

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -19,15 +19,10 @@ class CrewAgentExecutorMixin:
     agent: Optional["BaseAgent"]
     task: Optional["Task"]
     iterations: int
-    have_forced_answer: bool
     max_iter: int
     _i18n: I18N
     _printer: Printer = Printer()
 
-    def _should_force_answer(self) -> bool:
-        """Determine if a forced answer is required based on iteration count."""
-        return self.iterations >= self.max_iter
-
     def _create_short_term_memory(self, output) -> None:
         """Create and save a short-term memory item if conditions are met."""
         if (

src/crewai/agents/crew_agent_executor.py

@@ -1,7 +1,7 @@
 import json
 import re
 from dataclasses import dataclass
-from typing import Any, Dict, List, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
@@ -50,7 +50,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         original_tools: List[Any] = [],
         function_calling_llm: Any = None,
         respect_context_window: bool = False,
-        request_within_rpm_limit: Any = None,
+        request_within_rpm_limit: Optional[Callable[[], bool]] = None,
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
@@ -77,7 +77,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: List[Dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
-        self.have_forced_answer = False
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
@@ -108,106 +107,151 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    def _invoke_loop(self, formatted_answer=None):
+    def _invoke_loop(self):
-        try:
+        """
+        Main loop to invoke the agent's thought process until it reaches a conclusion
+        or the maximum number of iterations is reached.
+        """
+        formatted_answer = None
         while not isinstance(formatted_answer, AgentFinish):
-                if not self.request_within_rpm_limit or self.request_within_rpm_limit():
-                    answer = self.llm.call(
-                        self.messages,
-                        callbacks=self.callbacks,
-                    )
-
-                    if answer is None or answer == "":
-                        self._printer.print(
-                            content="Received None or empty response from LLM call.",
-                            color="red",
-                        )
-                        raise ValueError(
-                            "Invalid response from LLM call - None or empty."
-                        )
-
-                    if not self.use_stop_words:
             try:
-                            self._format_answer(answer)
+                if self._has_reached_max_iterations():
-                        except OutputParserException as e:
+                    formatted_answer = self._handle_max_iterations_exceeded(
-                            if (
+                        formatted_answer
-                                FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE
+                    )
-                                in e.error
+                    break
-                            ):
-                                answer = answer.split("Observation:")[0].strip()
 
-                    self.iterations += 1
+                self._enforce_rpm_limit()
-                    formatted_answer = self._format_answer(answer)
+
+                answer = self._get_llm_response()
+
+                formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
                     tool_result = self._execute_tool_and_check_finality(
                         formatted_answer
                     )
+                    formatted_answer = self._handle_agent_action(
+                        formatted_answer, tool_result
+                    )
 
-                        # Directly append the result to the messages if the
+                self._invoke_step_callback(formatted_answer)
-                        # tool is "Add image to content" in case of multimodal
+                self._append_message(formatted_answer.text, role="assistant")
-                        # agents
+
-                        if formatted_answer.tool == self._i18n.tools("add_image")["name"]:
+            except OutputParserException as e:
-                            self.messages.append(tool_result.result)
+                formatted_answer = self._handle_output_parser_exception(e)
+
+            except Exception as e:
+                if self._is_context_length_exceeded(e):
+                    self._handle_context_length()
                     continue
-
                 else:
+                    raise e
+
+        self._show_logs(formatted_answer)
+        return formatted_answer
+
+    def _has_reached_max_iterations(self) -> bool:
+        """Check if the maximum number of iterations has been reached."""
+        return self.iterations >= self.max_iter
+
+    def _enforce_rpm_limit(self) -> None:
+        """Enforce the requests per minute (RPM) limit if applicable."""
+        if self.request_within_rpm_limit:
+            self.request_within_rpm_limit()
+
+    def _get_llm_response(self) -> str:
+        """Call the LLM and return the response, handling any invalid responses."""
+        answer = self.llm.call(
+            self.messages,
+            callbacks=self.callbacks,
+        )
+
+        if not answer:
+            self._printer.print(
+                content="Received None or empty response from LLM call.",
+                color="red",
+            )
+            raise ValueError("Invalid response from LLM call - None or empty.")
+
+        return answer
+
+    def _process_llm_response(self, answer: str) -> Union[AgentAction, AgentFinish]:
+        """Process the LLM response and format it into an AgentAction or AgentFinish."""
+        if not self.use_stop_words:
+            try:
+                # Preliminary parsing to check for errors.
+                self._format_answer(answer)
+            except OutputParserException as e:
+                if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
+                    answer = answer.split("Observation:")[0].strip()
+
+        self.iterations += 1
+        return self._format_answer(answer)
+
+    def _handle_agent_action(
+        self, formatted_answer: AgentAction, tool_result: ToolResult
+    ) -> Union[AgentAction, AgentFinish]:
+        """Handle the AgentAction, execute tools, and process the results."""
+        add_image_tool = self._i18n.tools("add_image")
+        if (
+            isinstance(add_image_tool, dict)
+            and formatted_answer.tool.casefold().strip()
+            == add_image_tool.get("name", "").casefold().strip()
+        ):
+            self.messages.append(tool_result.result)
+            return formatted_answer  # Continue the loop
+
         if self.step_callback:
             self.step_callback(tool_result)
 
         formatted_answer.text += f"\nObservation: {tool_result.result}"
-
         formatted_answer.result = tool_result.result
+
         if tool_result.result_as_answer:
             return AgentFinish(
                 thought="",
                 output=tool_result.result,
                 text=formatted_answer.text,
             )
-                        self._show_logs(formatted_answer)
 
+        self._show_logs(formatted_answer)
+        return formatted_answer
+
+    def _invoke_step_callback(self, formatted_answer) -> None:
+        """Invoke the step callback if it exists."""
         if self.step_callback:
             self.step_callback(formatted_answer)
 
-                    if self._should_force_answer():
+    def _append_message(self, text: str, role: str = "assistant") -> None:
-                        if self.have_forced_answer:
+        """Append a message to the message list with the given role."""
-                            return AgentFinish(
+        self.messages.append(self._format_msg(text, role=role))
+
+    def _handle_output_parser_exception(self, e: OutputParserException) -> AgentAction:
+        """Handle OutputParserException by updating messages and formatted_answer."""
+        self.messages.append({"role": "user", "content": e.error})
+
+        formatted_answer = AgentAction(
+            text=e.error,
+            tool="",
+            tool_input="",
             thought="",
-                                output=self._i18n.errors(
-                                    "force_final_answer_error"
-                                ).format(formatted_answer.text),
-                                text=formatted_answer.text,
-                            )
-                        else:
-                            formatted_answer.text += (
-                                f'\n{self._i18n.errors("force_final_answer")}'
-                            )
-                            self.have_forced_answer = True
-                    self.messages.append(
-                        self._format_msg(formatted_answer.text, role="assistant")
         )
 
-        except OutputParserException as e:
-            self.messages.append({"role": "user", "content": e.error})
         if self.iterations > self.log_error_after:
             self._printer.print(
                 content=f"Error parsing LLM output, agent will retry: {e.error}",
                 color="red",
             )
-            return self._invoke_loop(formatted_answer)
 
-        except Exception as e:
-            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
-                str(e)
-            ):
-                self._handle_context_length()
-                return self._invoke_loop(formatted_answer)
-            else:
-                raise e
-
-        self._show_logs(formatted_answer)
         return formatted_answer
 
+    def _is_context_length_exceeded(self, exception: Exception) -> bool:
+        """Check if the exception is due to context length exceeding."""
+        return LLMContextLengthExceededException(
+            str(exception)
+        )._is_context_limit_error(str(exception))
+
     def _show_start_logs(self):
         if self.agent is None:
             raise ValueError("Agent cannot be None")
@@ -487,3 +531,45 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self.ask_for_human_input = False
 
         return formatted_answer
+
+    def _handle_max_iterations_exceeded(self, formatted_answer):
+        """
+        Handles the case when the maximum number of iterations is exceeded.
+        Performs one more LLM call to get the final answer.
+
+        Parameters:
+            formatted_answer: The last formatted answer from the agent.
+
+        Returns:
+            The final formatted answer after exceeding max iterations.
+        """
+        self._printer.print(
+            content="Maximum iterations reached. Requesting final answer.",
+            color="yellow",
+        )
+
+        if formatted_answer and hasattr(formatted_answer, "text"):
+            assistant_message = (
+                formatted_answer.text + f'\n{self._i18n.errors("force_final_answer")}'
+            )
+        else:
+            assistant_message = self._i18n.errors("force_final_answer")
+
+        self.messages.append(self._format_msg(assistant_message, role="assistant"))
+
+        # Perform one more LLM call to get the final answer
+        answer = self.llm.call(
+            self.messages,
+            callbacks=self.callbacks,
+        )
+
+        if answer is None or answer == "":
+            self._printer.print(
+                content="Received None or empty response from LLM call.",
+                color="red",
+            )
+            raise ValueError("Invalid response from LLM call - None or empty.")
+
+        formatted_answer = self._format_answer(answer)
+        # Return the formatted answer, regardless of its type
+        return formatted_answer

src/crewai/cli/constants.py

@@ -17,6 +17,12 @@ ENV_VARS = {
             "key_name": "GEMINI_API_KEY",
         }
     ],
+    "nvidia_nim": [
+        {
+            "prompt": "Enter your NVIDIA API key (press Enter to skip)",
+            "key_name": "NVIDIA_NIM_API_KEY",
+        }
+    ],
     "groq": [
         {
             "prompt": "Enter your GROQ API key (press Enter to skip)",
@@ -98,6 +104,7 @@ PROVIDERS = [
     "openai",
     "anthropic",
     "gemini",
+    "nvidia_nim",
     "groq",
     "ollama",
     "watson",
@@ -121,6 +128,75 @@ MODELS = {
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
     ],
+    "nvidia_nim": [
+        "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
+        "nvidia_nim/nvidia/nemotron-4-mini-hindi-4b-instruct",
+        "nvidia_nim/nvidia/llama-3.1-nemotron-70b-instruct",
+        "nvidia_nim/nvidia/llama3-chatqa-1.5-8b",
+        "nvidia_nim/nvidia/llama3-chatqa-1.5-70b",
+        "nvidia_nim/nvidia/vila",
+        "nvidia_nim/nvidia/neva-22",
+        "nvidia_nim/nvidia/nemotron-mini-4b-instruct",
+        "nvidia_nim/nvidia/usdcode-llama3-70b-instruct",
+        "nvidia_nim/nvidia/nemotron-4-340b-instruct",
+        "nvidia_nim/meta/codellama-70b",
+        "nvidia_nim/meta/llama2-70b",
+        "nvidia_nim/meta/llama3-8b-instruct",
+        "nvidia_nim/meta/llama3-70b-instruct",
+        "nvidia_nim/meta/llama-3.1-8b-instruct",
+        "nvidia_nim/meta/llama-3.1-70b-instruct",
+        "nvidia_nim/meta/llama-3.1-405b-instruct",
+        "nvidia_nim/meta/llama-3.2-1b-instruct",
+        "nvidia_nim/meta/llama-3.2-3b-instruct",
+        "nvidia_nim/meta/llama-3.2-11b-vision-instruct",
+        "nvidia_nim/meta/llama-3.2-90b-vision-instruct",
+        "nvidia_nim/meta/llama-3.1-70b-instruct",
+        "nvidia_nim/google/gemma-7b",
+        "nvidia_nim/google/gemma-2b",
+        "nvidia_nim/google/codegemma-7b",
+        "nvidia_nim/google/codegemma-1.1-7b",
+        "nvidia_nim/google/recurrentgemma-2b",
+        "nvidia_nim/google/gemma-2-9b-it",
+        "nvidia_nim/google/gemma-2-27b-it",
+        "nvidia_nim/google/gemma-2-2b-it",
+        "nvidia_nim/google/deplot",
+        "nvidia_nim/google/paligemma",
+        "nvidia_nim/mistralai/mistral-7b-instruct-v0.2",
+        "nvidia_nim/mistralai/mixtral-8x7b-instruct-v0.1",
+        "nvidia_nim/mistralai/mistral-large",
+        "nvidia_nim/mistralai/mixtral-8x22b-instruct-v0.1",
+        "nvidia_nim/mistralai/mistral-7b-instruct-v0.3",
+        "nvidia_nim/nv-mistralai/mistral-nemo-12b-instruct",
+        "nvidia_nim/mistralai/mamba-codestral-7b-v0.1",
+        "nvidia_nim/microsoft/phi-3-mini-128k-instruct",
+        "nvidia_nim/microsoft/phi-3-mini-4k-instruct",
+        "nvidia_nim/microsoft/phi-3-small-8k-instruct",
+        "nvidia_nim/microsoft/phi-3-small-128k-instruct",
+        "nvidia_nim/microsoft/phi-3-medium-4k-instruct",
+        "nvidia_nim/microsoft/phi-3-medium-128k-instruct",
+        "nvidia_nim/microsoft/phi-3.5-mini-instruct",
+        "nvidia_nim/microsoft/phi-3.5-moe-instruct",
+        "nvidia_nim/microsoft/kosmos-2",
+        "nvidia_nim/microsoft/phi-3-vision-128k-instruct",
+        "nvidia_nim/microsoft/phi-3.5-vision-instruct",
+        "nvidia_nim/databricks/dbrx-instruct",
+        "nvidia_nim/snowflake/arctic",
+        "nvidia_nim/aisingapore/sea-lion-7b-instruct",
+        "nvidia_nim/ibm/granite-8b-code-instruct",
+        "nvidia_nim/ibm/granite-34b-code-instruct",
+        "nvidia_nim/ibm/granite-3.0-8b-instruct",
+        "nvidia_nim/ibm/granite-3.0-3b-a800m-instruct",
+        "nvidia_nim/mediatek/breeze-7b-instruct",
+        "nvidia_nim/upstage/solar-10.7b-instruct",
+        "nvidia_nim/writer/palmyra-med-70b-32k",
+        "nvidia_nim/writer/palmyra-med-70b",
+        "nvidia_nim/writer/palmyra-fin-70b-32k",
+        "nvidia_nim/01-ai/yi-large",
+        "nvidia_nim/deepseek-ai/deepseek-coder-6.7b-instruct",
+        "nvidia_nim/rakuten/rakutenai-7b-instruct",
+        "nvidia_nim/rakuten/rakutenai-7b-chat",
+        "nvidia_nim/baichuan-inc/baichuan2-13b-chat",
+    ],
     "groq": [
         "groq/llama-3.1-8b-instant",
         "groq/llama-3.1-70b-versatile",

src/crewai/llm.py

@@ -76,7 +76,7 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "mixtral-8x7b-32768": 32768,
     "llama-3.3-70b-versatile": 128000,
     "llama-3.3-70b-instruct": 128000,
-    #sambanova
+    # sambanova
     "Meta-Llama-3.3-70B-Instruct": 131072,
     "QwQ-32B-Preview": 8192,
     "Qwen2.5-72B-Instruct": 8192,

src/crewai/translations/en.json

@@ -27,7 +27,7 @@
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals."
   },
   "errors": {
-    "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
+    "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
     "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
     "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
     "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",

src/crewai/utilities/llm_utils.py

@@ -67,7 +67,6 @@ def create_llm(
             api_key=api_key,
             base_url=base_url,
         )
-        print("LLM created with extracted parameters; " f"model='{model}'")
         return created_llm
     except Exception as e:
         print(f"Error instantiating LLM from unknown object type: {e}")

src/crewai/utilities/rpm_controller.py

@@ -8,8 +8,10 @@ from crewai.utilities.logger import Logger
 
 """Controls request rate limiting for API calls."""
 
+
 class RPMController(BaseModel):
     """Manages requests per minute limiting."""
+
     max_rpm: Optional[int] = Field(default=None)
     logger: Logger = Field(default_factory=lambda: Logger(verbose=False))
     _current_rpm: int = PrivateAttr(default=0)

tests/agent_test.py

@@ -565,7 +565,7 @@ def test_agent_moved_on_after_max_iterations():
         task=task,
         tools=[get_final_answer],
     )
-    assert output == "The final answer is 42."
+    assert output == "42"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -574,7 +574,6 @@ def test_agent_respect_the_max_rpm_set(capsys):
     def get_final_answer() -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
-        return 42
 
     agent = Agent(
         role="test role",
@@ -641,15 +640,14 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_without_max_rpm_respet_crew_rpm(capsys):
+def test_agent_without_max_rpm_respects_crew_rpm(capsys):
     from unittest.mock import patch
 
     from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
-        """Get the final answer but don't give it yet, just re-use this
+        """Get the final answer but don't give it yet, just re-use this tool non-stop."""
-        tool non-stop."""
         return 42
 
     agent1 = Agent(
@@ -666,23 +664,30 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
         role="test role2",
         goal="test goal2",
         backstory="test backstory2",
-        max_iter=1,
+        max_iter=5,
         verbose=True,
         allow_delegation=False,
     )
 
     tasks = [
         Task(
-            description="Just say hi.", agent=agent1, expected_output="Your greeting."
+            description="Just say hi.",
+            agent=agent1,
+            expected_output="Your greeting.",
         ),
         Task(
-            description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
+            description=(
+                "NEVER give a Final Answer, unless you are told otherwise, "
+                "instead keep using the `get_final_answer` tool non-stop, "
+                "until you must give your best final answer"
+            ),
             expected_output="The final answer",
             tools=[get_final_answer],
             agent=agent2,
         ),
     ]
 
+    # Set crew's max_rpm to 1 to trigger RPM limit
     crew = Crew(agents=[agent1, agent2], tasks=tasks, max_rpm=1, verbose=True)
 
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:

tests/cassettes/test_agent_custom_max_iterations.yaml

@@ -2,22 +2,22 @@ interactions:
 - request:
     body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
       personal goal is: test goal\nYou ONLY have access to the following tools, and
-      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer(*args:
+      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool
-      Any, **kwargs: Any) -> Any\nTool Description: get_final_answer() - Get the final
+      Arguments: {}\nTool Description: Get the final answer but don''t give it yet,
-      answer but don''t give it yet, just re-use this         tool non-stop. \nTool
+      just re-use this\n        tool non-stop.\n\nUse the following format:\n\nThought:
-      Arguments: {}\n\nUse the following format:\n\nThought: you should always think
+      you should always think about what to do\nAction: the action to take, only one
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tests/e2e_crew_tests.py

@@ -0,0 +1,289 @@
+import asyncio
+import os
+import tempfile
+
+import pytest
+
+from crewai.agent import Agent
+from crewai.crew import Crew
+from crewai.crews.crew_output import CrewOutput
+from crewai.process import Process
+from crewai.task import Task
+from crewai.tasks.conditional_task import ConditionalTask
+
+
+def test_basic_crew_execution(default_agent):
+    """Test basic crew execution using the default agent fixture."""
+
+    # Initialize agents by copying the default agent fixture
+    researcher = default_agent.copy()
+    researcher.role = "Researcher"
+    researcher.goal = "Research the latest advancements in AI."
+    researcher.backstory = "An expert in AI technologies."
+
+    writer = default_agent.copy()
+    writer.role = "Writer"
+    writer.goal = "Write an article based on research findings."
+    writer.backstory = "A professional writer specializing in technology topics."
+
+    # Define tasks
+    research_task = Task(
+        description="Provide a summary of the latest advancements in AI.",
+        expected_output="A detailed summary of recent AI advancements.",
+        agent=researcher,
+    )
+
+    writing_task = Task(
+        description="Write an article based on the research summary.",
+        expected_output="An engaging article on AI advancements.",
+        agent=writer,
+    )
+
+    # Create the crew
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[research_task, writing_task],
+        process=Process.sequential,
+    )
+
+    # Execute the crew
+    result = crew.kickoff()
+
+    # Assertions to verify the result
+    assert result is not None, "Crew execution did not return a result."
+    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
+    assert (
+        "AI advancements" in result.raw
+        or "artificial intelligence" in result.raw.lower()
+    ), "Result does not contain expected content."
+
+
+def test_hierarchical_crew_with_manager(default_llm_config):
+    """Test hierarchical crew execution with a manager agent."""
+
+    # Initialize agents using the default LLM config fixture
+    ceo = Agent(
+        role="CEO",
+        goal="Oversee the project and ensure quality deliverables.",
+        backstory="A seasoned executive with a keen eye for detail.",
+        llm=default_llm_config,
+    )
+
+    developer = Agent(
+        role="Developer",
+        goal="Implement software features as per requirements.",
+        backstory="An experienced software developer.",
+        llm=default_llm_config,
+    )
+
+    tester = Agent(
+        role="Tester",
+        goal="Test software features and report bugs.",
+        backstory="A meticulous QA engineer.",
+        llm=default_llm_config,
+    )
+
+    # Define tasks
+    development_task = Task(
+        description="Develop the new authentication feature.",
+        expected_output="Code implementation of the authentication feature.",
+        agent=developer,
+    )
+
+    testing_task = Task(
+        description="Test the authentication feature for vulnerabilities.",
+        expected_output="A report on any found bugs or vulnerabilities.",
+        agent=tester,
+    )
+
+    # Create the crew with hierarchical process
+    crew = Crew(
+        agents=[ceo, developer, tester],
+        tasks=[development_task, testing_task],
+        process=Process.hierarchical,
+        manager_agent=ceo,
+    )
+
+    # Execute the crew
+    result = crew.kickoff()
+
+    # Assertions to verify the result
+    assert result is not None, "Crew execution did not return a result."
+    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
+    assert (
+        "authentication" in result.raw.lower()
+    ), "Result does not contain expected content."
+
+
+@pytest.mark.asyncio
+async def test_asynchronous_task_execution(default_llm_config):
+    """Test crew execution with asynchronous tasks."""
+
+    # Initialize agent
+    data_processor = Agent(
+        role="Data Processor",
+        goal="Process large datasets efficiently.",
+        backstory="An expert in data processing and analysis.",
+        llm=default_llm_config,
+    )
+
+    # Define tasks with async_execution=True
+    async_task1 = Task(
+        description="Process dataset A asynchronously.",
+        expected_output="Processed results of dataset A.",
+        agent=data_processor,
+        async_execution=True,
+    )
+
+    async_task2 = Task(
+        description="Process dataset B asynchronously.",
+        expected_output="Processed results of dataset B.",
+        agent=data_processor,
+        async_execution=True,
+    )
+
+    # Create the crew
+    crew = Crew(
+        agents=[data_processor],
+        tasks=[async_task1, async_task2],
+        process=Process.sequential,
+    )
+
+    # Execute the crew asynchronously
+    result = await crew.kickoff_async()
+
+    # Assertions to verify the result
+    assert result is not None, "Crew execution did not return a result."
+    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
+    assert (
+        "dataset a" in result.raw.lower() or "dataset b" in result.raw.lower()
+    ), "Result does not contain expected content."
+
+
+def test_crew_with_conditional_task(default_llm_config):
+    """Test crew execution that includes a conditional task."""
+
+    # Initialize agents
+    analyst = Agent(
+        role="Analyst",
+        goal="Analyze data and make decisions based on insights.",
+        backstory="A data analyst with experience in predictive modeling.",
+        llm=default_llm_config,
+    )
+
+    decision_maker = Agent(
+        role="Decision Maker",
+        goal="Make decisions based on analysis.",
+        backstory="An executive responsible for strategic decisions.",
+        llm=default_llm_config,
+    )
+
+    # Define tasks
+    analysis_task = Task(
+        description="Analyze the quarterly financial data.",
+        expected_output="A report highlighting key financial insights.",
+        agent=analyst,
+    )
+
+    decision_task = ConditionalTask(
+        description="If the profit margin is below 10%, recommend cost-cutting measures.",
+        expected_output="Recommendations for reducing costs.",
+        agent=decision_maker,
+        condition=lambda output: "profit margin below 10%" in output.lower(),
+    )
+
+    # Create the crew
+    crew = Crew(
+        agents=[analyst, decision_maker],
+        tasks=[analysis_task, decision_task],
+        process=Process.sequential,
+    )
+
+    # Execute the crew
+    result = crew.kickoff()
+
+    # Assertions to verify the result
+    assert result is not None, "Crew execution did not return a result."
+    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
+    assert len(result.tasks_output) >= 1, "No tasks were executed."
+
+
+def test_crew_with_output_file():
+    """Test crew execution that writes output to a file."""
+
+    # Access the API key from environment variables
+    openai_api_key = os.environ.get("OPENAI_API_KEY")
+    assert openai_api_key, "OPENAI_API_KEY environment variable is not set."
+
+    # Create a temporary directory for output files
+    with tempfile.TemporaryDirectory() as tmpdirname:
+
+        # Initialize agent
+        content_creator = Agent(
+            role="Content Creator",
+            goal="Generate engaging blog content.",
+            backstory="A creative writer with a passion for storytelling.",
+            llm={"provider": "openai", "model": "gpt-4", "api_key": openai_api_key},
+        )
+
+        # Define task with output file
+        output_file_path = f"{tmpdirname}/blog_post.txt"
+        blog_task = Task(
+            description="Write a blog post about the benefits of remote work.",
+            expected_output="An informative and engaging blog post.",
+            agent=content_creator,
+            output_file=output_file_path,
+        )
+
+        # Create the crew
+        crew = Crew(
+            agents=[content_creator],
+            tasks=[blog_task],
+            process=Process.sequential,
+        )
+
+        # Execute the crew
+        crew.kickoff()
+
+        # Assertions to verify the result
+        assert os.path.exists(output_file_path), "Output file was not created."
+
+        # Read the content from the file and perform assertions
+        with open(output_file_path, "r") as file:
+            content = file.read()
+            assert (
+                "remote work" in content.lower()
+            ), "Output file does not contain expected content."
+
+
+def test_invalid_hierarchical_process():
+    """Test that an error is raised when using hierarchical process without a manager agent or manager_llm."""
+    with pytest.raises(ValueError) as exc_info:
+        Crew(
+            agents=[],
+            tasks=[],
+            process=Process.hierarchical,  # Hierarchical process without a manager
+        )
+    assert "manager_llm or manager_agent is required" in str(exc_info.value)
+
+
+def test_crew_with_memory(memory_agent, memory_tasks):
+    """Test crew execution utilizing memory."""
+
+    # Enable memory in the crew
+    crew = Crew(
+        agents=[memory_agent],
+        tasks=memory_tasks,
+        process=Process.sequential,
+        memory=True,  # Enable memory
+    )
+
+    # Execute the crew
+    result = crew.kickoff()
+
+    # Assertions to verify the result
+    assert result is not None, "Crew execution did not return a result."
+    assert isinstance(result, CrewOutput), "Result is not an instance of CrewOutput."
+    assert (
+        "history of ai" in result.raw.lower() and "future of ai" in result.raw.lower()
+    ), "Result does not contain expected content."

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