From 2d2154ed659ab57885c9ad92de9b20adce31bf38 Mon Sep 17 00:00:00 2001 From: Eduardo Chiarotti Date: Fri, 26 Jul 2024 14:23:51 -0300 Subject: [PATCH 01/36] feat: add crew Testing/Evaluating feature (#998) * feat: add crew Testing/evalauting feature * feat: add docs and add unit test * feat: improve testing output table * feat: add tests * feat: fix type checking issue * feat: add raise ValueError when testing if output is not the expected * docs: add docs for Testing * feat: improve tests and fix some issue * feat: back to sync * feat: change opdeai model * feat: fix test --- docs/core-concepts/Testing.md | 41 +++++ mkdocs.yml | 1 + src/crewai/cli/templates/main.py | 2 +- src/crewai/crew.py | 16 +- .../evaluators/crew_evaluator_handler.py | 149 ++++++++++++++++++ tests/crew_test.py | 32 ++++ .../evaluators/test_crew_evaluator_handler.py | 113 +++++++++++++ 7 files changed, 350 insertions(+), 4 deletions(-) create mode 100644 docs/core-concepts/Testing.md create mode 100644 src/crewai/utilities/evaluators/crew_evaluator_handler.py create mode 100644 tests/utilities/evaluators/test_crew_evaluator_handler.py diff --git a/docs/core-concepts/Testing.md b/docs/core-concepts/Testing.md new file mode 100644 index 0000000000..45ababafb0 --- /dev/null +++ b/docs/core-concepts/Testing.md @@ -0,0 +1,41 @@ +--- +title: crewAI Testing +description: Learn how to test your crewAI Crew and evaluate their performance. +--- + +## Introduction + +Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. And with crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities. + +### Using the Testing Feature + +We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. +The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now the only provider available is OpenAI. + +```bash +crewai test +``` + +If you want to run more iterations or use a different model, you can specify the parameters like this: + +```bash +crewai test --n_iterations 5 --model gpt-4o +``` + +What happens when you run the `crewai test` command is that the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run. + +A table of scores at the end will show the performance of the crew in terms of the following metrics: +``` + Task Scores + (1-10 Higher is better) +┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓ +┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃ +┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩ +│ Task 1 │ 10.0 │ 9.0 │ 9.5 │ +│ Task 2 │ 9.0 │ 9.0 │ 9.0 │ +│ Crew │ 9.5 │ 9.0 │ 9.2 │ +└────────────┴───────┴───────┴────────────┘ +``` + +The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole. + diff --git a/mkdocs.yml b/mkdocs.yml index 7caac90b39..6daeba443c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -129,6 +129,7 @@ nav: - Training: 'core-concepts/Training-Crew.md' - Memory: 'core-concepts/Memory.md' - Planning: 'core-concepts/Planning.md' + - Testing: 'core-concepts/Testing.md' - Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md' - Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md' - How to Guides: diff --git a/src/crewai/cli/templates/main.py b/src/crewai/cli/templates/main.py index f300f49120..94ac35b938 100644 --- a/src/crewai/cli/templates/main.py +++ b/src/crewai/cli/templates/main.py @@ -48,7 +48,7 @@ def test(): "topic": "AI LLMs" } try: - {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), model=sys.argv[2], inputs=inputs) + {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs) except Exception as e: raise Exception(f"An error occurred while replaying the crew: {e}") diff --git a/src/crewai/crew.py b/src/crewai/crew.py index e286b47f99..f1f064a3a6 100644 --- a/src/crewai/crew.py +++ b/src/crewai/crew.py @@ -37,6 +37,7 @@ TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE, ) +from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator from crewai.utilities.evaluators.task_evaluator import TaskEvaluator from crewai.utilities.formatter import ( aggregate_raw_outputs_from_task_outputs, @@ -967,10 +968,19 @@ def calculate_usage_metrics(self) -> Dict[str, int]: return total_usage_metrics def test( - self, n_iterations: int, model: str, inputs: Optional[Dict[str, Any]] = None + self, + n_iterations: int, + openai_model_name: str, + inputs: Optional[Dict[str, Any]] = None, ) -> None: - """Test the crew with the given inputs.""" - pass + """Test and evaluate the Crew with the given inputs for n iterations.""" + evaluator = CrewEvaluator(self, openai_model_name) + + for i in range(1, n_iterations + 1): + evaluator.set_iteration(i) + self.kickoff(inputs=inputs) + + evaluator.print_crew_evaluation_result() def __repr__(self): return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})" diff --git a/src/crewai/utilities/evaluators/crew_evaluator_handler.py b/src/crewai/utilities/evaluators/crew_evaluator_handler.py new file mode 100644 index 0000000000..3f1abb8b8c --- /dev/null +++ b/src/crewai/utilities/evaluators/crew_evaluator_handler.py @@ -0,0 +1,149 @@ +from collections import defaultdict + +from langchain_openai import ChatOpenAI +from pydantic import BaseModel, Field +from rich.console import Console +from rich.table import Table + +from crewai.agent import Agent +from crewai.task import Task +from crewai.tasks.task_output import TaskOutput + + +class TaskEvaluationPydanticOutput(BaseModel): + quality: float = Field( + description="A score from 1 to 10 evaluating on completion, quality, and overall performance from the task_description and task_expected_output to the actual Task Output." + ) + + +class CrewEvaluator: + """ + A class to evaluate the performance of the agents in the crew based on the tasks they have performed. + + Attributes: + crew (Crew): The crew of agents to evaluate. + openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted). + tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task. + iteration (int): The current iteration of the evaluation. + """ + + tasks_scores: defaultdict = defaultdict(list) + iteration: int = 0 + + def __init__(self, crew, openai_model_name: str): + self.crew = crew + self.openai_model_name = openai_model_name + self._setup_for_evaluating() + + def _setup_for_evaluating(self) -> None: + """Sets up the crew for evaluating.""" + for task in self.crew.tasks: + task.callback = self.evaluate + + def set_iteration(self, iteration: int) -> None: + self.iteration = iteration + + def _evaluator_agent(self): + return Agent( + role="Task Execution Evaluator", + goal=( + "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance." + ), + backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed", + verbose=False, + llm=ChatOpenAI(model=self.openai_model_name), + ) + + def _evaluation_task( + self, evaluator_agent: Agent, task_to_evaluate: Task, task_output: str + ) -> Task: + return Task( + description=( + "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance." + f"task_description: {task_to_evaluate.description} " + f"task_expected_output: {task_to_evaluate.expected_output} " + f"agent: {task_to_evaluate.agent.role if task_to_evaluate.agent else None} " + f"agent_goal: {task_to_evaluate.agent.goal if task_to_evaluate.agent else None} " + f"Task Output: {task_output}" + ), + expected_output="Evaluation Score from 1 to 10 based on the performance of the agents on the tasks", + agent=evaluator_agent, + output_pydantic=TaskEvaluationPydanticOutput, + ) + + def print_crew_evaluation_result(self) -> None: + """ + Prints the evaluation result of the crew in a table. + A Crew with 2 tasks using the command crewai test -n 2 + will output the following table: + + Task Scores + (1-10 Higher is better) + ┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓ + ┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃ + ┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩ + │ Task 1 │ 10.0 │ 9.0 │ 9.5 │ + │ Task 2 │ 9.0 │ 9.0 │ 9.0 │ + │ Crew │ 9.5 │ 9.0 │ 9.2 │ + └────────────┴───────┴───────┴────────────┘ + """ + task_averages = [ + sum(scores) / len(scores) for scores in zip(*self.tasks_scores.values()) + ] + crew_average = sum(task_averages) / len(task_averages) + + # Create a table + table = Table(title="Tasks Scores \n (1-10 Higher is better)") + + # Add columns for the table + table.add_column("Tasks/Crew") + for run in range(1, len(self.tasks_scores) + 1): + table.add_column(f"Run {run}") + table.add_column("Avg. Total") + + # Add rows for each task + for task_index in range(len(task_averages)): + task_scores = [ + self.tasks_scores[run][task_index] + for run in range(1, len(self.tasks_scores) + 1) + ] + avg_score = task_averages[task_index] + table.add_row( + f"Task {task_index + 1}", *map(str, task_scores), f"{avg_score:.1f}" + ) + + # Add a row for the crew average + crew_scores = [ + sum(self.tasks_scores[run]) / len(self.tasks_scores[run]) + for run in range(1, len(self.tasks_scores) + 1) + ] + table.add_row("Crew", *map(str, crew_scores), f"{crew_average:.1f}") + + # Display the table in the terminal + console = Console() + console.print(table) + + def evaluate(self, task_output: TaskOutput): + """Evaluates the performance of the agents in the crew based on the tasks they have performed.""" + current_task = None + for task in self.crew.tasks: + if task.description == task_output.description: + current_task = task + break + + if not current_task or not task_output: + raise ValueError( + "Task to evaluate and task output are required for evaluation" + ) + + evaluator_agent = self._evaluator_agent() + evaluation_task = self._evaluation_task( + evaluator_agent, current_task, task_output.raw + ) + + evaluation_result = evaluation_task.execute_sync() + + if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput): + self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality) + else: + raise ValueError("Evaluation result is not in the expected format") diff --git a/tests/crew_test.py b/tests/crew_test.py index 73167d566d..e82913c694 100644 --- a/tests/crew_test.py +++ b/tests/crew_test.py @@ -8,6 +8,7 @@ import pydantic_core import pytest + from crewai.agent import Agent from crewai.agents.cache import CacheHandler from crewai.crew import Crew @@ -2499,3 +2500,34 @@ def test_conditional_should_execute(): assert condition_mock.call_count == 1 assert condition_mock() is True assert mock_execute_sync.call_count == 2 + + +@mock.patch("crewai.crew.CrewEvaluator") +@mock.patch("crewai.crew.Crew.kickoff") +def test_crew_testing_function(mock_kickoff, crew_evaluator): + task = Task( + description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", + expected_output="5 bullet points with a paragraph for each idea.", + agent=researcher, + ) + + crew = Crew( + agents=[researcher], + tasks=[task], + ) + n_iterations = 2 + crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"}) + + assert len(mock_kickoff.mock_calls) == n_iterations + mock_kickoff.assert_has_calls( + [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})] + ) + + crew_evaluator.assert_has_calls( + [ + mock.call(crew, "gpt-4o-mini"), + mock.call().set_iteration(1), + mock.call().set_iteration(2), + mock.call().print_crew_evaluation_result(), + ] + ) diff --git a/tests/utilities/evaluators/test_crew_evaluator_handler.py b/tests/utilities/evaluators/test_crew_evaluator_handler.py new file mode 100644 index 0000000000..39fa35c448 --- /dev/null +++ b/tests/utilities/evaluators/test_crew_evaluator_handler.py @@ -0,0 +1,113 @@ +from unittest import mock + +import pytest + +from crewai.agent import Agent +from crewai.crew import Crew +from crewai.task import Task +from crewai.tasks.task_output import TaskOutput +from crewai.utilities.evaluators.crew_evaluator_handler import ( + CrewEvaluator, + TaskEvaluationPydanticOutput, +) + + +class TestCrewEvaluator: + @pytest.fixture + def crew_planner(self): + agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1") + task = Task( + description="Task 1", + expected_output="Output 1", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + + return CrewEvaluator(crew, openai_model_name="gpt-4o-mini") + + def test_setup_for_evaluating(self, crew_planner): + crew_planner._setup_for_evaluating() + assert crew_planner.crew.tasks[0].callback == crew_planner.evaluate + + def test_set_iteration(self, crew_planner): + crew_planner.set_iteration(1) + assert crew_planner.iteration == 1 + + def test_evaluator_agent(self, crew_planner): + agent = crew_planner._evaluator_agent() + assert agent.role == "Task Execution Evaluator" + assert ( + agent.goal + == "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance." + ) + assert ( + agent.backstory + == "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed" + ) + assert agent.verbose is False + assert agent.llm.model_name == "gpt-4o-mini" + + def test_evaluation_task(self, crew_planner): + evaluator_agent = Agent( + role="Evaluator Agent", + goal="Evaluate the performance of the agents in the crew", + backstory="Master in Evaluation", + ) + task_to_evaluate = Task( + description="Task 1", + expected_output="Output 1", + agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"), + ) + task_output = "Task Output 1" + task = crew_planner._evaluation_task( + evaluator_agent, task_to_evaluate, task_output + ) + + assert task.description.startswith( + "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance." + ) + + assert task.agent == evaluator_agent + assert ( + task.description + == "Based on the task description and the expected output, compare and evaluate " + "the performance of the agents in the crew based on the Task Output they have " + "performed using score from 1 to 10 evaluating on completion, quality, and overall " + "performance.task_description: Task 1 task_expected_output: Output 1 " + "agent: Agent 1 agent_goal: Goal 1 Task Output: Task Output 1" + ) + + @mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Console") + @mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Table") + def test_print_crew_evaluation_result(self, table, console, crew_planner): + crew_planner.tasks_scores = { + 1: [10, 9, 8], + 2: [9, 8, 7], + } + + crew_planner.print_crew_evaluation_result() + + table.assert_has_calls( + [ + mock.call(title="Tasks Scores \n (1-10 Higher is better)"), + mock.call().add_column("Tasks/Crew"), + mock.call().add_column("Run 1"), + mock.call().add_column("Run 2"), + mock.call().add_column("Avg. Total"), + mock.call().add_row("Task 1", "10", "9", "9.5"), + mock.call().add_row("Task 2", "9", "8", "8.5"), + mock.call().add_row("Task 3", "8", "7", "7.5"), + mock.call().add_row("Crew", "9.0", "8.0", "8.5"), + ] + ) + console.assert_has_calls([mock.call(), mock.call().print(table())]) + + def test_evaluate(self, crew_planner): + task_output = TaskOutput( + description="Task 1", agent=str(crew_planner.crew.agents[0]) + ) + + with mock.patch.object(Task, "execute_sync") as execute: + execute().pydantic = TaskEvaluationPydanticOutput(quality=9.5) + crew_planner.evaluate(task_output) + assert crew_planner.tasks_scores[0] == [9.5] From 144e6d203f5016f41d5f3f2403311981adb84d90 Mon Sep 17 00:00:00 2001 From: Eduardo Chiarotti Date: Fri, 26 Jul 2024 14:24:29 -0300 Subject: [PATCH 02/36] feat: add ability to set LLM for AgentPLanner on Crew (#1001) * feat: add ability to set LLM for AgentPLanner on Crew * feat: fixes issue on instantiating the ChatOpenAI on the crew * docs: add docs for the planning_llm new parameter * docs: change message to ChatOpenAI llm * feat: add tests --- docs/core-concepts/Crews.md | 1 + docs/core-concepts/Planning.md | 19 ++++++++++++ src/crewai/crew.py | 17 ++++++----- src/crewai/utilities/planning_handler.py | 20 ++++++++++--- tests/utilities/test_planning_handler.py | 38 ++++++++++++++++++++++-- 5 files changed, 80 insertions(+), 15 deletions(-) diff --git a/docs/core-concepts/Crews.md b/docs/core-concepts/Crews.md index 1896c6a386..62c3da657e 100644 --- a/docs/core-concepts/Crews.md +++ b/docs/core-concepts/Crews.md @@ -33,6 +33,7 @@ A crew in crewAI represents a collaborative group of agents working together to | **Manager Callbacks** _(optional)_ | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. | | **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. | | **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description. +| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. | !!! note "Crew Max RPM" The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it. diff --git a/docs/core-concepts/Planning.md b/docs/core-concepts/Planning.md index 810309703f..36ae34437d 100644 --- a/docs/core-concepts/Planning.md +++ b/docs/core-concepts/Planning.md @@ -23,6 +23,25 @@ my_crew = Crew( From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration. +#### Planning LLM + +Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available. + +```python +from crewai import Crew, Agent, Task, Process +from langchain_openai import ChatOpenAI + +# Assemble your crew with planning capabilities and custom LLM +my_crew = Crew( + agents=self.agents, + tasks=self.tasks, + process=Process.sequential, + planning=True, + planning_llm=ChatOpenAI(model="gpt-4o") +) +``` + + ### Example When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks. diff --git a/src/crewai/crew.py b/src/crewai/crew.py index f1f064a3a6..d133c2f478 100644 --- a/src/crewai/crew.py +++ b/src/crewai/crew.py @@ -155,6 +155,10 @@ class Crew(BaseModel): default=False, description="Plan the crew execution and add the plan to the crew.", ) + planning_llm: Optional[Any] = Field( + default=None, + description="Language model that will run the AgentPlanner if planning is True.", + ) task_execution_output_json_files: Optional[List[str]] = Field( default=None, description="List of file paths for task execution JSON files.", @@ -560,15 +564,12 @@ async def run_crew(crew, input_data): def _handle_crew_planning(self): """Handles the Crew planning.""" self._logger.log("info", "Planning the crew execution") - result = CrewPlanner(self.tasks)._handle_crew_planning() + result = CrewPlanner( + tasks=self.tasks, planning_agent_llm=self.planning_llm + )._handle_crew_planning() - if result is not None and hasattr(result, "list_of_plans_per_task"): - for task, step_plan in zip(self.tasks, result.list_of_plans_per_task): - task.description += step_plan - else: - self._logger.log( - "info", "Something went wrong with the planning process of the Crew" - ) + for task, step_plan in zip(self.tasks, result.list_of_plans_per_task): + task.description += step_plan def _store_execution_log( self, diff --git a/src/crewai/utilities/planning_handler.py b/src/crewai/utilities/planning_handler.py index cba1727b99..29b89667e0 100644 --- a/src/crewai/utilities/planning_handler.py +++ b/src/crewai/utilities/planning_handler.py @@ -1,5 +1,6 @@ -from typing import List, Optional +from typing import Any, List, Optional +from langchain_openai import ChatOpenAI from pydantic import BaseModel from crewai.agent import Agent @@ -11,17 +12,27 @@ class PlannerTaskPydanticOutput(BaseModel): class CrewPlanner: - def __init__(self, tasks: List[Task]): + def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None): self.tasks = tasks - def _handle_crew_planning(self) -> Optional[BaseModel]: + if planning_agent_llm is None: + self.planning_agent_llm = ChatOpenAI(model="gpt-4o-mini") + else: + self.planning_agent_llm = planning_agent_llm + + def _handle_crew_planning(self) -> PlannerTaskPydanticOutput: """Handles the Crew planning by creating detailed step-by-step plans for each task.""" planning_agent = self._create_planning_agent() tasks_summary = self._create_tasks_summary() planner_task = self._create_planner_task(planning_agent, tasks_summary) - return planner_task.execute_sync().pydantic + result = planner_task.execute_sync() + + if isinstance(result.pydantic, PlannerTaskPydanticOutput): + return result.pydantic + + raise ValueError("Failed to get the Planning output") def _create_planning_agent(self) -> Agent: """Creates the planning agent for the crew planning.""" @@ -32,6 +43,7 @@ def _create_planning_agent(self) -> Agent: "available to each agent so that they can perform the tasks in an exemplary manner" ), backstory="Planner agent for crew planning", + llm=self.planning_agent_llm, ) def _create_planner_task(self, planning_agent: Agent, tasks_summary: str) -> Task: diff --git a/tests/utilities/test_planning_handler.py b/tests/utilities/test_planning_handler.py index 75bc3e0331..502398fabf 100644 --- a/tests/utilities/test_planning_handler.py +++ b/tests/utilities/test_planning_handler.py @@ -1,10 +1,11 @@ from unittest.mock import patch -from crewai.tasks.task_output import TaskOutput import pytest +from langchain_openai import ChatOpenAI from crewai.agent import Agent from crewai.task import Task +from crewai.tasks.task_output import TaskOutput from crewai.utilities.planning_handler import CrewPlanner, PlannerTaskPydanticOutput @@ -28,7 +29,19 @@ def crew_planner(self): agent=Agent(role="Agent 3", goal="Goal 3", backstory="Backstory 3"), ), ] - return CrewPlanner(tasks) + return CrewPlanner(tasks, None) + + @pytest.fixture + def crew_planner_different_llm(self): + tasks = [ + Task( + description="Task 1", + expected_output="Output 1", + agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"), + ) + ] + planning_agent_llm = ChatOpenAI(model="gpt-3.5-turbo") + return CrewPlanner(tasks, planning_agent_llm) def test_handle_crew_planning(self, crew_planner): with patch.object(Task, "execute_sync") as execute: @@ -40,7 +53,7 @@ def test_handle_crew_planning(self, crew_planner): ), ) result = crew_planner._handle_crew_planning() - + assert crew_planner.planning_agent_llm.model_name == "gpt-4o-mini" assert isinstance(result, PlannerTaskPydanticOutput) assert len(result.list_of_plans_per_task) == len(crew_planner.tasks) execute.assert_called_once() @@ -72,3 +85,22 @@ def test_create_tasks_summary(self, crew_planner): assert isinstance(tasks_summary, str) assert tasks_summary.startswith("\n Task Number 1 - Task 1") assert tasks_summary.endswith('"agent_tools": []\n ') + + def test_handle_crew_planning_different_llm(self, crew_planner_different_llm): + with patch.object(Task, "execute_sync") as execute: + execute.return_value = TaskOutput( + description="Description", + agent="agent", + pydantic=PlannerTaskPydanticOutput(list_of_plans_per_task=["Plan 1"]), + ) + result = crew_planner_different_llm._handle_crew_planning() + + assert ( + crew_planner_different_llm.planning_agent_llm.model_name + == "gpt-3.5-turbo" + ) + assert isinstance(result, PlannerTaskPydanticOutput) + assert len(result.list_of_plans_per_task) == len( + crew_planner_different_llm.tasks + ) + execute.assert_called_once() From 8b513de64cd7cb479a3d6fe655d5b57157b03eb4 Mon Sep 17 00:00:00 2001 From: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Date: Fri, 26 Jul 2024 10:55:51 -0700 Subject: [PATCH 03/36] hierarchical process unblocked for async tasks (#995) * WIP: hierarchical unblock for async tasks * added better test * update name change * added more test and crew manager cleanup * remove prints * code cleanup, no need to pass manager --- src/crewai/crew.py | 48 +- ...w_creation_tasks_with_async_execution.yaml | 21444 ++++++++++++++++ ...al_crew_creation_tasks_with_sync_last.yaml | 4282 +++ tests/crew_test.py | 68 +- 4 files changed, 25796 insertions(+), 46 deletions(-) create mode 100644 tests/cassettes/test_hierarchical_crew_creation_tasks_with_async_execution.yaml create mode 100644 tests/cassettes/test_hierarchical_crew_creation_tasks_with_sync_last.yaml diff --git a/src/crewai/crew.py b/src/crewai/crew.py index d133c2f478..4f29a877d1 100644 --- a/src/crewai/crew.py +++ b/src/crewai/crew.py @@ -271,20 +271,6 @@ def validate_tasks(self): return self - @model_validator(mode="after") - def check_tasks_in_hierarchical_process_not_async(self): - """Validates that the tasks in hierarchical process are not flagged with async_execution.""" - if self.process == Process.hierarchical: - for task in self.tasks: - if task.async_execution: - raise PydanticCustomError( - "async_execution_in_hierarchical_process", - "Hierarchical process error: Tasks cannot be flagged with async_execution.", - {}, - ) - - return self - @model_validator(mode="after") def validate_end_with_at_most_one_async_task(self): """Validates that the crew ends with at most one asynchronous task.""" @@ -607,7 +593,7 @@ def _run_sequential_process(self) -> 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) + return self._execute_tasks(self.tasks) def _create_manager_agent(self): i18n = I18N(prompt_file=self.prompt_file) @@ -631,7 +617,6 @@ def _create_manager_agent(self): def _execute_tasks( self, tasks: List[Task], - manager: Optional[BaseAgent] = None, start_index: Optional[int] = 0, was_replayed: bool = False, ) -> CrewOutput: @@ -659,13 +644,13 @@ def _execute_tasks( last_sync_output = task.output continue - agent_to_use = self._get_agent_to_use(task, manager) + agent_to_use = self._get_agent_to_use(task) 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._prepare_agent_tools(task, manager) + self._prepare_agent_tools(task) self._log_task_start(task, agent_to_use.role) if isinstance(task, ConditionalTask): @@ -731,20 +716,18 @@ def _handle_conditional_task( return skipped_task_output return None - def _prepare_agent_tools(self, task: Task, manager: Optional[BaseAgent]): + def _prepare_agent_tools(self, task: Task): if self.process == Process.hierarchical: - if manager: - self._update_manager_tools(task, manager) + if self.manager_agent: + self._update_manager_tools(task) else: raise ValueError("Manager agent is required for hierarchical process.") elif task.agent and task.agent.allow_delegation: self._add_delegation_tools(task) - def _get_agent_to_use( - self, task: Task, manager: Optional[BaseAgent] - ) -> Optional[BaseAgent]: + def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]: if self.process == Process.hierarchical: - return manager + return self.manager_agent return task.agent def _add_delegation_tools(self, task: Task): @@ -780,11 +763,14 @@ def _log_task_start(self, task: Task, role: str = "None"): if self.output_log_file: self._file_handler.log(agent=role, task=task.description, status="started") - def _update_manager_tools(self, task: Task, manager: BaseAgent): - if task.agent: - manager.tools = task.agent.get_delegation_tools([task.agent]) - else: - manager.tools = manager.get_delegation_tools(self.agents) + def _update_manager_tools(self, task: Task): + if self.manager_agent: + if task.agent: + self.manager_agent.tools = task.agent.get_delegation_tools([task.agent]) + else: + self.manager_agent.tools = self.manager_agent.get_delegation_tools( + self.agents + ) def _get_context(self, task: Task, task_outputs: List[TaskOutput]): context = ( @@ -883,7 +869,7 @@ def replay( self.tasks[i].output = task_output self._logging_color = "bold_blue" - result = self._execute_tasks(self.tasks, self.manager_agent, start_index, True) + result = self._execute_tasks(self.tasks, start_index, True) return result def copy(self): diff --git a/tests/cassettes/test_hierarchical_crew_creation_tasks_with_async_execution.yaml b/tests/cassettes/test_hierarchical_crew_creation_tasks_with_async_execution.yaml new file mode 100644 index 0000000000..42c672e8c2 --- /dev/null +++ b/tests/cassettes/test_hierarchical_crew_creation_tasks_with_async_execution.yaml @@ -0,0 +1,21444 @@ +interactions: +- request: + body: !!binary | + CtrgAwokCiIKDHNlcnZpY2UubmFtZRISChBjcmV3QUktdGVsZW1ldHJ5ErDgAwoSChBjcmV3YWku + dGVsZW1ldHJ5EpACChAri+pFoyQvTAgbGwWAWXzOEggaf8lpEXMgByoOVGFzayBFeGVjdXRpb24w + ATkA6YHGPOrkF0FgjMzNPOrkF0ouCghjcmV3X2tleRIiCiA5YmYyY2RlNmJjNWM0MjAxZDY5Yjli + Y2ZmZjM1YmZiOUoxCgdjcmV3X2lkEiYKJDlmMzQ5YzlmLTk0NmYtNGNjZS05Y2RjLTZkMzc2MzE4 + NzRkZkouCgh0YXNrX2tleRIiCiBjNTAyYzU3NDVjMjc4MWFmNTFiMmYzZWY1ZDYyZmM3NEoxCgd0 + YXNrX2lkEiYKJDM3MzY0OTZmLTEyZGItNDUwYi04ZjZjLWVhYThhNjJiNDJmZnoCGAGFAQABAAAS + 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Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. 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You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. 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You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. They + are diverse enough to appeal to readers with varied interests, from healthcare + and education to ethics and environmental sustainability.\n\n\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: + Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task + to one of the following coworkers: Researcher\nThe input to this tool should + be the coworker, the task you want them to do, and ALL necessary context to + execute the task, they know nothing about the task, so share absolute everything + you know, don''t reference things but instead explain them.\nTool Arguments: + {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': + ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article. To do this effectively, I + should first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have gathered enough information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. 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+ Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + alt-svc: + - h3=":443"; ma=86400 + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '986' + openai-version: + - '2020-10-01' + strict-transport-security: + - max-age=15552000; includeSubDomains; preload + x-ratelimit-limit-requests: + - '10000' + x-ratelimit-limit-tokens: + - '30000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '29996708' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 6ms + x-request-id: + - req_2b9f3e021337d77538b04ec4a7637e9e + status: + code: 200 + message: OK +- request: + body: '{"messages": [{"content": "You are Crew Manager. You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. They + are diverse enough to appeal to readers with varied interests, from healthcare + and education to ethics and environmental sustainability.\n\n\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: + Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task + to one of the following coworkers: Researcher\nThe input to this tool should + be the coworker, the task you want them to do, and ALL necessary context to + execute the task, they know nothing about the task, so share absolute everything + you know, don''t reference things but instead explain them.\nTool Arguments: + {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': + ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article. To do this effectively, I + should first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have gathered enough information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. 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You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. They + are diverse enough to appeal to readers with varied interests, from healthcare + and education to ethics and environmental sustainability.\n\n\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: + Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task + to one of the following coworkers: Researcher\nThe input to this tool should + be the coworker, the task you want them to do, and ALL necessary context to + execute the task, they know nothing about the task, so share absolute everything + you know, don''t reference things but instead explain them.\nTool Arguments: + {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': + ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article. To do this effectively, I + should first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have gathered enough information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: + str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a + specific task to one of the following coworkers: Researcher\nThe input to this + tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolute + everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask 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You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. They + are diverse enough to appeal to readers with varied interests, from healthcare + and education to ethics and environmental sustainability.\n\n\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: + Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task + to one of the following coworkers: Researcher\nThe input to this tool should + be the coworker, the task you want them to do, and ALL necessary context to + execute the task, they know nothing about the task, so share absolute everything + you know, don''t reference things but instead explain them.\nTool Arguments: + {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': + ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article. To do this effectively, I + should first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have gathered enough information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: + str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a + specific task to one of the following coworkers: Researcher\nThe input to this + tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolute + everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + Once I have gathered enough information, I will write the paragraphs.\n\nThought: + I should ask the Researcher for interesting and diverse article ideas first.\n\nAction: + Ask question to coworker\nAction Input: {\"question\": \"Can you brainstorm + at least 5 potential topics for an article? The topics should be diverse and + interesting, suitable for a wide audience. Each topic should have the potential + to captivate a wide readership.\", \"context\": \"We need a list of 5 interesting + ideas to explore for an article. The topics should be engaging and have the + potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. 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You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. They + are diverse enough to appeal to readers with varied interests, from healthcare + and education to ethics and environmental sustainability.\n\n\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: + Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task + to one of the following coworkers: Researcher\nThe input to this tool should + be the coworker, the task you want them to do, and ALL necessary context to + execute the task, they know nothing about the task, so share absolute everything + you know, don''t reference things but instead explain them.\nTool Arguments: + {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': + ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article. To do this effectively, I + should first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have gathered enough information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: + str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a + specific task to one of the following coworkers: Researcher\nThe input to this + tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolute + everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + Once I have gathered enough information, I will write the paragraphs.\n\nThought: + I should ask the Researcher for interesting and diverse article ideas first.\n\nAction: + Ask question to coworker\nAction Input: {\"question\": \"Can you brainstorm + at least 5 potential topics for an article? The topics should be diverse and + interesting, suitable for a wide audience. Each topic should have the potential + to captivate a wide readership.\", \"context\": \"We need a list of 5 interesting + ideas to explore for an article. The topics should be engaging and have the + potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first ask the Researcher for potential topics.\n\nThought: + I should ask the Researcher to brainstorm at least 5 potential topics for an + article. \n\nAction: Ask question to coworker\nAction Input: {\"question\": + \"Can you brainstorm at least 5 potential topics for an article? The topics + should be diverse and interesting, suitable for a wide audience. Each topic + should have the potential to captivate a wide readership.\", \"context\": \"We + need a list of 5 interesting ideas to explore for an article. The topics should + be engaging and have the potential to captivate a wide readership.\", \"coworker\": + \"Researcher\"}\nObservation: I tried reusing the same input, I must stop using + this action input. I''ll try something else instead.\n\n\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: + - '19406' + content-type: + - application/json + cookie: + - __cf_bm=aT37FR9W9IdS7rPqbsl6Q0m4oUkE0fys_6vECBA96v4-1721758507-1.0.1.1-NthguXX58ax_bMOjkhQLDJsTISvWl3uEoaukovAIXU5mRkuorTcCTZRY06BMAl2aJMSccwYXgqwEZT6ANxR8lA; + _cfuvid=96JpT7Rj1Pcdu.fMW.VS39irkf2fy9h9_qQSFTE4ZhY-1721758507706-0.0.1.1-604800000 + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.35.10 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.35.10 + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.11.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: 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[{"content": "You are Crew Manager. You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool + Description: Delegate a specific task to one of the following coworkers: Researcher\nThe + input to this tool should be the coworker, the task you want them to do, and + ALL necessary context to execute the task, they know nothing about the task, + so share absolute everything you know, don''t reference things but instead explain + them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, + ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': + ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': + ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, + coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific + question to one of the following coworkers: Researcher\nThe input to this tool + should be the coworker, the question you have for them, and ALL necessary context + to ask the question properly, they know nothing about the question, so share + absolute everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse + the following format:\n\nThought: you should always think about what to do\nAction: + the action to take, only one name of [Delegate work to coworker, Ask question + to coworker], just the name, exactly as it''s written.\nAction Input: the input + to the action, just a simple python dictionary, enclosed in curly braces, using + \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all + necessary information is gathered:\n\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n\nCurrent Task: Come + up with a list of 5 interesting ideas to explore for an article, then write + one amazing paragraph highlight for each idea that showcases how good an article + about this topic could be. Return the list of ideas with their paragraph and + your notes.\n\nThis is the expect criteria for your final answer: 5 bullet points + with a paragraph for each idea. \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:\nI need to come up with a list of 5 interesting ideas for an + article and write one amazing paragraph highlight for each idea. To do this + effectively, I should first brainstorm potential topics and then delegate the + task of researching these topics to the Researcher. After gathering the necessary + information, I will write the paragraphs.\n\nThought: I should delegate the + task of brainstorming potential topics to the Researcher.\n\nAction: Delegate + work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics for + an article. The topics should be diverse and interesting, suitable for a wide + audience. Come up with at least 5 topics.\", \"context\": \"We need a list of + 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + my best complete final answer to the task.\n\n1. **The Future of AI in Healthcare: + Revolutionizing Patient Care and Diagnostics**\n - Explore how artificial + intelligence is transforming the healthcare industry, from enhancing diagnostic + accuracy to personalizing patient treatment plans. Discuss recent advancements, + success stories, and the potential ethical implications of AI in healthcare.\n\n2. + **AI Agents in Customer Service: Enhancing User Experience and Operational Efficiency**\n - + Investigate the role of AI agents in customer service, focusing on how they + improve user experience and streamline operations for businesses. Highlight + case studies of companies successfully implementing AI agents, the technology + behind them, and future trends.\n\n3. **Ethics and Bias in AI: Navigating the + Challenges and Solutions**\n - Delve into the ethical considerations and biases + inherent in AI systems. Discuss notable instances of AI bias, the impact on + society, and ongoing efforts within the tech community to create more fair and + equitable AI algorithms.\n\n4. **AI and the Future of Work: Preparing for a + New Era of Employment**\n - Examine how AI is reshaping the job market, including + the creation of new roles and the obsolescence of others. Consider the skills + needed for future jobs, the importance of lifelong learning, and the potential + societal impacts of widespread AI adoption in the workplace.\n\n5. **AI in Entertainment: + From Personalized Recommendations to Creative Content Generation**\n - Explore + the influence of AI in the entertainment industry, including its role in personalizing + content recommendations, generating music and art, and even writing scripts. + Discuss the technology behind these innovations, notable projects, and the future + possibilities of AI-driven creativity.\n\nThese topics are designed to be engaging + and relevant to a wide audience, providing insight into the multifaceted impact + of AI on various aspects of society and industry.\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + After gathering the necessary information, I will write the paragraphs.\n\nThought: + I should delegate the task of brainstorming potential topics to the Researcher.\n\nAction: + Delegate work to coworker\nAction Input: {\"task\": \"Brainstorm potential topics + for an article. The topics should be diverse and interesting, suitable for a + wide audience. Come up with at least 5 topics.\", \"context\": \"We need a list + of 5 interesting ideas to explore for an article. The topics should be engaging + and have the potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first brainstorm potential topics and then delegate + the task of researching these topics to the Researcher. Once I have the necessary + information, I will write the paragraphs.\n\nThought: I should ask the Researcher + for interesting and diverse article ideas first, and then later delegate specific + research tasks based on those ideas.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: my best complete final answer to + the task.\n\n1. **The Future of AI in Healthcare: Transforming Patient Care + and Diagnosis**\n - This article would explore how AI is revolutionizing the + healthcare industry. It could cover advancements in medical imaging, predictive + analytics for patient outcomes, personalized medicine, and AI-driven diagnostic + tools. The article could also discuss ethical considerations and potential challenges + in the widespread adoption of AI in healthcare.\n\n2. **AI Agents: Redefining + Customer Service and User Experience**\n - This topic would delve into the + role of AI agents in enhancing customer service across various industries. It + could include case studies of successful AI implementations, the technology + behind AI agents, and the impact on customer satisfaction and business efficiency. + Additionally, the article could explore future trends and developments in AI-driven + customer service.\n\n3. **Ethical AI: Balancing Innovation with Responsibility**\n - + An engaging article on the ethical implications of AI development and deployment. + Topics could include bias in AI algorithms, data privacy concerns, accountability + in AI decision-making, and the importance of transparency. The article could + also highlight initiatives and frameworks that aim to promote ethical AI practices.\n\n4. + **AI in Education: Personalizing Learning and Enhancing Teaching**\n - This + article could investigate how AI technologies are being used to personalize + education and improve learning outcomes. Topics might include adaptive learning + platforms, AI tutors, and the use of AI for grading and feedback. The article + could also discuss the potential impact on teachers and the educational system + as a whole.\n\n5. **The Role of AI in Environmental Sustainability**\n - This + topic would explore how AI is being used to address environmental challenges. + It could cover AI applications in climate modeling, wildlife conservation, energy + management, and pollution control. The article could also look at the potential + benefits and risks of relying on AI for environmental sustainability efforts.\n\nEach + of these topics has the potential to captivate a wide readership by addressing + current trends, technological advancements, and societal impacts of AI. They + are diverse enough to appeal to readers with varied interests, from healthcare + and education to ethics and environmental sustainability.\n\n\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: + Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task + to one of the following coworkers: Researcher\nThe input to this tool should + be the coworker, the task you want them to do, and ALL necessary context to + execute the task, they know nothing about the task, so share absolute everything + you know, don''t reference things but instead explain them.\nTool Arguments: + {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': + ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article. To do this effectively, I + should first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have gathered enough information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\nThought: + I need to come up with a list of 5 interesting ideas for an article and write + one amazing paragraph highlight for each idea. To do this effectively, I should + first brainstorm potential topics and then delegate the task of researching + these topics to the Researcher. Once I have the necessary information, I will + write the paragraphs.\n\nThought: I should ask the Researcher for interesting + and diverse article ideas first.\n\nAction: Ask question to coworker\nAction + Input: {\"question\": \"Can you brainstorm at least 5 potential topics for an + article? The topics should be diverse and interesting, suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: + str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a + specific task to one of the following coworkers: Researcher\nThe input to this + tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolute + everything you know, don''t reference things but instead explain them.\nTool + Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': + {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', + ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool + Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Ask a specific question to one of the following + coworkers: Researcher\nThe input to this tool should be the coworker, the question + you have for them, and ALL necessary context to ask the question properly, they + know nothing about the question, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''question'': {''title'': + ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, + ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following + format:\n\nThought: you should always think about what to do\nAction: the action + to take, only one name of [Delegate work to coworker, Ask question to coworker], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple python dictionary, enclosed in curly braces, using \" to wrap + keys and values.\nObservation: the result of the action\n\nOnce all necessary + information is gathered:\n\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n\nThought: I need to come up + with a list of 5 interesting ideas for an article and write one amazing paragraph + highlight for each idea. To do this effectively, I should first brainstorm potential + topics and then delegate the task of researching these topics to the Researcher. + Once I have gathered enough information, I will write the paragraphs.\n\nThought: + I should ask the Researcher for interesting and diverse article ideas first.\n\nAction: + Ask question to coworker\nAction Input: {\"question\": \"Can you brainstorm + at least 5 potential topics for an article? The topics should be diverse and + interesting, suitable for a wide audience. Each topic should have the potential + to captivate a wide readership.\", \"context\": \"We need a list of 5 interesting + ideas to explore for an article. The topics should be engaging and have the + potential to captivate a wide readership.\", \"coworker\": \"Researcher\"}\nObservation: + I tried reusing the same input, I must stop using this action input. I''ll try + something else instead.\n\n\nThought: I need to come up with a list of 5 interesting + ideas for an article and write one amazing paragraph highlight for each idea. + To achieve this, I should first ask the Researcher for potential topics.\n\nThought: + I should ask the Researcher to brainstorm at least 5 potential topics for an + article. \n\nAction: Ask question to coworker\nAction Input: {\"question\": + \"Can you brainstorm at least 5 potential topics for an article? The topics + should be diverse and interesting, suitable for a wide audience. Each topic + should have the potential to captivate a wide readership.\", \"context\": \"We + need a list of 5 interesting ideas to explore for an article. The topics should + be engaging and have the potential to captivate a wide readership.\", \"coworker\": + \"Researcher\"}\nObservation: I tried reusing the same input, I must stop using + this action input. I''ll try something else instead.\n\n\nThought: It seems + that my previous attempts to ask the Researcher for brainstorming potential + topics were not successful. I need to ensure my request is clear and concise. + Let''s attempt to ask the Researcher again with a slight modification in the + phrasing to ensure clarity.\n\nAction: Ask question to coworker\nAction Input: + {\"question\": \"Can you brainstorm at least 5 potential topics for an article? + The topics should be diverse, interesting, and suitable for a wide audience. + Each topic should have the potential to captivate a wide readership.\", \"context\": + \"We need a list of 5 interesting ideas to explore for an article. The topics + should be engaging and have the potential to captivate a wide readership.\", + \"coworker\": \"Researcher\"}\nObservation: 1. **The Rise of AI in Everyday + Life: How Artificial Intelligence is Transforming Daily Activities**\n - This + topic could explore how AI is being integrated into various aspects of daily + life, from smart home devices and personal assistants to healthcare applications + and autonomous vehicles. The article could highlight real-world examples and + discuss the potential future of AI in everyday tasks.\n\n2. **AI Agents in Customer + Service: Revolutionizing the Way Companies Interact with Consumers**\n - This + article could delve into how AI-powered agents are changing customer service, + improving response times, and providing personalized experiences. It could include + case studies of companies successfully using AI agents, as well as an analysis + of the technology behind these systems.\n\n3. **Ethical Considerations in AI + Development: Balancing Innovation and Responsibility**\n - This topic would + address the ethical implications of AI development, including issues like bias + in algorithms, privacy concerns, and the potential for job displacement. The + article could feature perspectives from industry experts, ethicists, and technologists + on how to create responsible AI.\n\n4. **Startups Leveraging AI: Success Stories + and Lessons Learned**\n - This article could focus on startups that have successfully + integrated AI into their business models. It could include interviews with founders, + an overview of the challenges they faced, and insights into how AI has contributed + to their growth and success.\n\n5. **The Future of AI in Healthcare: From Diagnosis + to Treatment**\n - This topic would explore the transformative potential of + AI in the healthcare industry, from early diagnosis and personalized treatment + plans to drug discovery and robotic surgery. The article could feature cutting-edge + research, current applications, and expert predictions for the future.\n\nThese + topics are designed to be engaging and informative, appealing to a wide audience + with varied interests in AI and technology.\n\n\nYou ONLY have access to the + following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: Delegate work to coworker(task: str, context: str, coworker: Optional[str] + = None, **kwargs)\nTool Description: Delegate a specific task to one of the + following coworkers: Researcher\nThe input to this tool should be the coworker, + the task you want them to do, and ALL necessary context to execute the task, + they know nothing about the task, so share absolute everything you know, don''t + reference things but instead explain them.\nTool Arguments: {''task'': {''title'': + ''Task'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': + ''string''}, ''coworker'': {''title'': ''Coworker'', 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strict-transport-security: + - max-age=15552000; includeSubDomains; preload + x-ratelimit-limit-requests: + - '10000' + x-ratelimit-limit-tokens: + - '30000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '29998588' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 2ms + x-request-id: + - req_486c1f881617f0da27ee9e67b08a6d0d + status: + code: 200 + message: OK +version: 1 diff --git a/tests/crew_test.py b/tests/crew_test.py index e82913c694..b80a103530 100644 --- a/tests/crew_test.py +++ b/tests/crew_test.py @@ -1356,28 +1356,66 @@ def test_hierarchical_crew_creation_tasks_with_agents(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_hierarchical_crew_creation_tasks_with_async_execution(): + """ + Agents are not required for tasks in a hierarchical process but sometimes they are still added + This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task + """ from langchain_openai import ChatOpenAI task = Task( - description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", - expected_output="5 bullet points with a paragraph for each idea.", - async_execution=True, # should throw an error + description="Write one amazing paragraph about AI.", + expected_output="A single paragraph with 4 sentences.", + agent=writer, + async_execution=True, ) - with pytest.raises(pydantic_core._pydantic_core.ValidationError) as exec_info: - Crew( - tasks=[task], - agents=[researcher], - process=Process.hierarchical, - manager_llm=ChatOpenAI(model="gpt-4o"), - ) + crew = Crew( + tasks=[task], + agents=[writer, researcher, ceo], + process=Process.hierarchical, + manager_llm=ChatOpenAI(model="gpt-4o"), + ) - assert ( - exec_info.value.errors()[0]["type"] == "async_execution_in_hierarchical_process" + crew.kickoff() + assert crew.manager_agent is not None + assert crew.manager_agent.tools is not None + assert crew.manager_agent.tools[0].description.startswith( + "Delegate a specific task to one of the following coworkers: Senior Writer\n" ) - assert ( - "Hierarchical process error: Tasks cannot be flagged with async_execution." - in exec_info.value.errors()[0]["msg"] + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_hierarchical_crew_creation_tasks_with_sync_last(): + """ + Agents are not required for tasks in a hierarchical process but sometimes they are still added + This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task + """ + from langchain_openai import ChatOpenAI + + task = Task( + description="Write one amazing paragraph about AI.", + expected_output="A single paragraph with 4 sentences.", + agent=writer, + async_execution=True, + ) + task2 = Task( + description="Write one amazing paragraph about AI.", + expected_output="A single paragraph with 4 sentences.", + async_execution=False, + ) + + crew = Crew( + tasks=[task, task2], + agents=[writer, researcher, ceo], + process=Process.hierarchical, + manager_llm=ChatOpenAI(model="gpt-4o"), + ) + + crew.kickoff() + assert crew.manager_agent is not None + assert crew.manager_agent.tools is not None + assert crew.manager_agent.tools[0].description.startswith( + "Delegate a specific task to one of the following coworkers: Senior Writer, Researcher, CEO\n" ) From 5ac7050f7af17c4f91a7fd186882939c64728bd8 Mon Sep 17 00:00:00 2001 From: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Date: Fri, 26 Jul 2024 10:57:56 -0700 Subject: [PATCH 04/36] Patch/non gpt model pydantic output (#1003) * patching for non-gpt model * removal of json_object tool name assignment * fixed issue for smaller models due to instructions prompt * fixing for ollama llama3 models * closing brackets * removed not used and fixes --- .../utilities/crew_pydantic_output_parser.py | 17 ++++++----------- .../utilities/evaluators/task_evaluator.py | 4 ++-- src/crewai/utilities/pydantic_schema_parser.py | 10 ++++++---- 3 files changed, 14 insertions(+), 17 deletions(-) diff --git a/src/crewai/utilities/crew_pydantic_output_parser.py b/src/crewai/utilities/crew_pydantic_output_parser.py index 54025d5e3a..f4e9cdd18b 100644 --- a/src/crewai/utilities/crew_pydantic_output_parser.py +++ b/src/crewai/utilities/crew_pydantic_output_parser.py @@ -1,5 +1,5 @@ import json -from typing import Any, List, Type, Union +from typing import Any, List, Type import regex from langchain.output_parsers import PydanticOutputParser @@ -7,29 +7,24 @@ from langchain_core.outputs import Generation from langchain_core.pydantic_v1 import ValidationError from pydantic import BaseModel -from pydantic.v1 import BaseModel as V1BaseModel class CrewPydanticOutputParser(PydanticOutputParser): """Parses the text into pydantic models""" - pydantic_object: Union[Type[BaseModel], Type[V1BaseModel]] + pydantic_object: Type[BaseModel] - def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: + def parse_result(self, result: List[Generation]) -> Any: result[0].text = self._transform_in_valid_json(result[0].text) # Treating edge case of function calling llm returning the name instead of tool_name json_object = json.loads(result[0].text) - json_object["tool_name"] = ( - json_object["name"] - if "tool_name" not in json_object - else json_object["tool_name"] - ) + if "tool_name" not in json_object: + json_object["tool_name"] = json_object.get("name", "") result[0].text = json.dumps(json_object) - json_object = super().parse_result(result) try: - return self.pydantic_object.parse_obj(json_object) + return self.pydantic_object.model_validate(json_object) except ValidationError as e: name = self.pydantic_object.__name__ msg = f"Failed to parse {name} from completion {json_object}. Got: {e}" diff --git a/src/crewai/utilities/evaluators/task_evaluator.py b/src/crewai/utilities/evaluators/task_evaluator.py index 04983b07c4..fdb42e1252 100644 --- a/src/crewai/utilities/evaluators/task_evaluator.py +++ b/src/crewai/utilities/evaluators/task_evaluator.py @@ -66,11 +66,11 @@ def evaluate(self, task, ouput) -> TaskEvaluation: "- Entities extracted from the task output, if any, their type, description, and relationships" ) - instructions = "I'm gonna convert this raw text into valid JSON." + instructions = "Convert all responses into valid JSON output." if not self._is_gpt(self.llm): model_schema = PydanticSchemaParser(model=TaskEvaluation).get_schema() - instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}" + instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```" converter = Converter( llm=self.llm, diff --git a/src/crewai/utilities/pydantic_schema_parser.py b/src/crewai/utilities/pydantic_schema_parser.py index aa6b18b0eb..9d9cdabe8e 100644 --- a/src/crewai/utilities/pydantic_schema_parser.py +++ b/src/crewai/utilities/pydantic_schema_parser.py @@ -16,11 +16,13 @@ def get_schema(self) -> str: return self._get_model_schema(self.model) def _get_model_schema(self, model, depth=0) -> str: - lines = [] + indent = " " * depth + lines = [f"{indent}{{"] for field_name, field in model.model_fields.items(): field_type_str = self._get_field_type(field, depth + 1) - lines.append(f"{' ' * 4 * depth}- {field_name}: {field_type_str}") - + lines.append(f"{indent} {field_name}: {field_type_str},") + lines[-1] = lines[-1].rstrip(",") # Remove trailing comma from last item + lines.append(f"{indent}}}") return "\n".join(lines) def _get_field_type(self, field, depth) -> str: @@ -35,6 +37,6 @@ def _get_field_type(self, field, depth) -> str: else: return f"List[{list_item_type.__name__}]" elif issubclass(field_type, BaseModel): - return f"\n{self._get_model_schema(field_type, depth)}" + return self._get_model_schema(field_type, depth) else: return field_type.__name__ From 1159140d9fa882f19979cfb693159f9aa525647b Mon Sep 17 00:00:00 2001 From: Lorenze Jay Date: Fri, 26 Jul 2024 13:37:44 -0700 Subject: [PATCH 05/36] fix test due to asserting instructions model_schema change --- tests/utilities/evaluators/test_task_evaluator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/utilities/evaluators/test_task_evaluator.py b/tests/utilities/evaluators/test_task_evaluator.py index 8baebde44e..879acf0ddd 100644 --- a/tests/utilities/evaluators/test_task_evaluator.py +++ b/tests/utilities/evaluators/test_task_evaluator.py @@ -56,8 +56,7 @@ def test_evaluate_training_data(converter_mock): "based on the human feedback\n", model=TrainingTaskEvaluation, instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the " - "following structure, with the following keys:\n- suggestions: List[str]\n- " - "quality: float\n- final_summary: str", + "following structure, with the following keys:\n{\n suggestions: List[str],\n quality: float,\n final_summary: str\n}", ), mock.call().to_pydantic(), ] From da7d8256eb56222ef6341724a16ce61a7692aac6 Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Fri, 26 Jul 2024 17:27:01 -0400 Subject: [PATCH 06/36] Json Task Output Truncation with Escape Characters (#1009) * Fixed special character issue when converting json to models. Added numerous tests to ensure thigns work properly. * Fix linting error and cleaned up tests * Fix customer_converter_cls test failure * Fixed tests. Thank you lorenze for pointing that out. added a few more to ensure converter creation works properly * Address lorenze feedback * Fix linting issues --- src/crewai/task.py | 115 ++----------- src/crewai/utilities/converter.py | 155 +++++++++++++++++ tests/task_test.py | 5 +- tests/utilities/test_converter.py | 266 ++++++++++++++++++++++++++++++ 4 files changed, 437 insertions(+), 104 deletions(-) create mode 100644 tests/utilities/test_converter.py diff --git a/src/crewai/task.py b/src/crewai/task.py index 8b1a05ca0b..017d210833 100644 --- a/src/crewai/task.py +++ b/src/crewai/task.py @@ -1,6 +1,5 @@ import json import os -import re import threading import uuid from concurrent.futures import Future @@ -8,7 +7,6 @@ from hashlib import md5 from typing import Any, Dict, List, Optional, Tuple, Type, Union -from langchain_openai import ChatOpenAI from opentelemetry.trace import Span from pydantic import UUID4, BaseModel, Field, field_validator, model_validator from pydantic_core import PydanticCustomError @@ -17,10 +15,8 @@ from crewai.tasks.output_format import OutputFormat from crewai.tasks.task_output import TaskOutput from crewai.telemetry.telemetry import Telemetry -from crewai.utilities.converter import Converter, ConverterError +from crewai.utilities.converter import Converter, convert_to_model from crewai.utilities.i18n import I18N -from crewai.utilities.printer import Printer -from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser class Task(BaseModel): @@ -254,9 +250,7 @@ def _execute_core( 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) @@ -326,18 +320,6 @@ def get_agent_by_role(role: str) -> Union["BaseAgent", None]: return copied_task - def _create_converter(self, *args, **kwargs) -> Converter: - """Create a converter instance.""" - if self.agent and not self.converter_cls: - converter = self.agent.get_output_converter(*args, **kwargs) - elif self.converter_cls: - converter = self.converter_cls(*args, **kwargs) - - if not converter: - raise Exception("No output converter found or set.") - - return converter - def _export_output( self, result: str ) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]: @@ -345,75 +327,26 @@ def _export_output( json_output: Optional[Dict[str, Any]] = None if self.output_pydantic or self.output_json: - model_output = self._convert_to_model(result) - pydantic_output = ( - model_output if isinstance(model_output, BaseModel) else None + model_output = convert_to_model( + result, + self.output_pydantic, + self.output_json, + self.agent, + self.converter_cls, ) - if isinstance(model_output, str): + + if isinstance(model_output, BaseModel): + pydantic_output = model_output + elif isinstance(model_output, dict): + json_output = model_output + elif isinstance(model_output, str): try: json_output = json.loads(model_output) except json.JSONDecodeError: json_output = None - else: - json_output = model_output if isinstance(model_output, dict) else None return pydantic_output, json_output - def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]: - model = self.output_pydantic or self.output_json - if model is None: - return result - - try: - return self._validate_model(result, model) - except Exception: - return self._handle_partial_json(result, model) - - def _validate_model( - self, result: str, model: Type[BaseModel] - ) -> Union[dict, BaseModel]: - exported_result = model.model_validate_json(result) - if self.output_json: - return exported_result.model_dump() - return exported_result - - def _handle_partial_json( - self, result: str, model: Type[BaseModel] - ) -> Union[dict, BaseModel, str]: - match = re.search(r"({.*})", result, re.DOTALL) - if match: - try: - exported_result = model.model_validate_json(match.group(0)) - if self.output_json: - return exported_result.model_dump() - return exported_result - except Exception: - pass - - return self._convert_with_instructions(result, model) - - def _convert_with_instructions( - self, result: str, model: Type[BaseModel] - ) -> Union[dict, BaseModel, str]: - llm = self.agent.function_calling_llm or self.agent.llm # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm" - instructions = self._get_conversion_instructions(model, llm) - - converter = self._create_converter( - llm=llm, text=result, model=model, instructions=instructions - ) - exported_result = ( - converter.to_pydantic() if self.output_pydantic else converter.to_json() - ) - - if isinstance(exported_result, ConverterError): - Printer().print( - content=f"{exported_result.message} Using raw output instead.", - color="red", - ) - return result - - return exported_result - def _get_output_format(self) -> OutputFormat: if self.output_json: return OutputFormat.JSON @@ -421,26 +354,6 @@ def _get_output_format(self) -> OutputFormat: return OutputFormat.PYDANTIC return OutputFormat.RAW - def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str: - instructions = "I'm gonna convert this raw text into valid JSON." - if not self._is_gpt(llm): - model_schema = PydanticSchemaParser(model=model).get_schema() - instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}" - return instructions - - def _save_output(self, content: str) -> None: - if not self.output_file: - raise Exception("Output file path is not set.") - - directory = os.path.dirname(self.output_file) - if directory and not os.path.exists(directory): - os.makedirs(directory) - with open(self.output_file, "w", encoding="utf-8") as file: - file.write(content) - - def _is_gpt(self, llm) -> bool: - return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None - def _save_file(self, result: Any) -> None: directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None" diff --git a/src/crewai/utilities/converter.py b/src/crewai/utilities/converter.py index f5732f62fa..ec2bace7ef 100644 --- a/src/crewai/utilities/converter.py +++ b/src/crewai/utilities/converter.py @@ -1,9 +1,14 @@ import json +import re +from typing import Any, Optional, Type, Union from langchain.schema import HumanMessage, SystemMessage from langchain_openai import ChatOpenAI +from pydantic import BaseModel, ValidationError from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter +from crewai.utilities.printer import Printer +from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser class ConverterError(Exception): @@ -72,3 +77,153 @@ def _create_chain(self): def is_gpt(self) -> bool: """Return if llm provided is of gpt from openai.""" return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None + + +def convert_to_model( + result: str, + output_pydantic: Optional[Type[BaseModel]], + output_json: Optional[Type[BaseModel]], + agent: Any, + converter_cls: Optional[Type[Converter]] = None, +) -> Union[dict, BaseModel, str]: + model = output_pydantic or output_json + if model is None: + return result + + try: + escaped_result = json.dumps(json.loads(result, strict=False)) + return validate_model(escaped_result, model, bool(output_json)) + except json.JSONDecodeError as e: + Printer().print( + content=f"Error parsing JSON: {e}. Attempting to handle partial JSON.", + color="yellow", + ) + return handle_partial_json( + result, model, bool(output_json), agent, converter_cls + ) + except ValidationError as e: + Printer().print( + content=f"Pydantic validation error: {e}. Attempting to handle partial JSON.", + color="yellow", + ) + return handle_partial_json( + result, model, bool(output_json), agent, converter_cls + ) + except Exception as e: + Printer().print( + content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.", + color="red", + ) + return result + + +def validate_model( + result: str, model: Type[BaseModel], is_json_output: bool +) -> Union[dict, BaseModel]: + exported_result = model.model_validate_json(result) + if is_json_output: + return exported_result.model_dump() + return exported_result + + +def handle_partial_json( + result: str, + model: Type[BaseModel], + is_json_output: bool, + agent: Any, + converter_cls: Optional[Type[Converter]] = None, +) -> Union[dict, BaseModel, str]: + match = re.search(r"({.*})", result, re.DOTALL) + if match: + try: + exported_result = model.model_validate_json(match.group(0)) + if is_json_output: + return exported_result.model_dump() + return exported_result + except json.JSONDecodeError as e: + Printer().print( + content=f"Error parsing JSON: {e}. The extracted JSON-like string is not valid JSON. Attempting alternative conversion method.", + color="yellow", + ) + except ValidationError as e: + Printer().print( + content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.", + color="yellow", + ) + except Exception as e: + Printer().print( + content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.", + color="red", + ) + + return convert_with_instructions( + result, model, is_json_output, agent, converter_cls + ) + + +def convert_with_instructions( + result: str, + model: Type[BaseModel], + is_json_output: bool, + agent: Any, + converter_cls: Optional[Type[Converter]] = None, +) -> Union[dict, BaseModel, str]: + llm = agent.function_calling_llm or agent.llm + instructions = get_conversion_instructions(model, llm) + + converter = create_converter( + agent=agent, + converter_cls=converter_cls, + llm=llm, + text=result, + model=model, + instructions=instructions, + ) + exported_result = ( + converter.to_pydantic() if not is_json_output else converter.to_json() + ) + + if isinstance(exported_result, ConverterError): + Printer().print( + content=f"{exported_result.message} Using raw output instead.", + color="red", + ) + return result + + return exported_result + + +def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str: + instructions = "I'm gonna convert this raw text into valid JSON." + if not is_gpt(llm): + model_schema = PydanticSchemaParser(model=model).get_schema() + instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}" + return instructions + + +def is_gpt(llm: Any) -> bool: + from langchain_openai import ChatOpenAI + + return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None + + +def create_converter( + agent: Optional[Any] = None, + converter_cls: Optional[Type[Converter]] = None, + *args, + **kwargs, +) -> Converter: + if agent and not converter_cls: + if hasattr(agent, "get_output_converter"): + converter = agent.get_output_converter(*args, **kwargs) + else: + raise AttributeError("Agent does not have a 'get_output_converter' method") + elif converter_cls: + converter = converter_cls(*args, **kwargs) + else: + raise ValueError("Either agent or converter_cls must be provided") + + if not converter: + raise Exception("No output converter found or set.") + + return converter diff --git a/tests/task_test.py b/tests/task_test.py index 6458ea3587..412e42dcad 100644 --- a/tests/task_test.py +++ b/tests/task_test.py @@ -5,13 +5,12 @@ from unittest.mock import MagicMock, patch import pytest -from pydantic import BaseModel -from pydantic_core import ValidationError - from crewai import Agent, Crew, Process, Task from crewai.tasks.conditional_task import ConditionalTask from crewai.tasks.task_output import TaskOutput from crewai.utilities.converter import Converter +from pydantic import BaseModel +from pydantic_core import ValidationError def test_task_tool_reflect_agent_tools(): diff --git a/tests/utilities/test_converter.py b/tests/utilities/test_converter.py new file mode 100644 index 0000000000..c6d5bcc1cc --- /dev/null +++ b/tests/utilities/test_converter.py @@ -0,0 +1,266 @@ +import json +from unittest.mock import MagicMock, Mock, patch + +import pytest +from crewai.utilities.converter import ( + Converter, + ConverterError, + convert_to_model, + convert_with_instructions, + create_converter, + get_conversion_instructions, + handle_partial_json, + is_gpt, + validate_model, +) +from pydantic import BaseModel + + +# Sample Pydantic models for testing +class EmailResponse(BaseModel): + previous_message_content: str + + +class EmailResponses(BaseModel): + responses: list[EmailResponse] + + +class SimpleModel(BaseModel): + name: str + age: int + + +class NestedModel(BaseModel): + id: int + data: SimpleModel + + +# Fixtures +@pytest.fixture +def mock_agent(): + agent = Mock() + agent.function_calling_llm = None + agent.llm = Mock() + return agent + + +# Tests for convert_to_model +def test_convert_to_model_with_valid_json(): + result = '{"name": "John", "age": 30}' + output = convert_to_model(result, SimpleModel, None, None) + assert isinstance(output, SimpleModel) + assert output.name == "John" + assert output.age == 30 + + +def test_convert_to_model_with_invalid_json(): + result = '{"name": "John", "age": "thirty"}' + with patch("crewai.utilities.converter.handle_partial_json") as mock_handle: + mock_handle.return_value = "Fallback result" + output = convert_to_model(result, SimpleModel, None, None) + assert output == "Fallback result" + + +def test_convert_to_model_with_no_model(): + result = "Plain text" + output = convert_to_model(result, None, None, None) + assert output == "Plain text" + + +def test_convert_to_model_with_special_characters(): + json_string_test = """ + { + "responses": [ + { + "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on" + } + ] + } + """ + output = convert_to_model(json_string_test, EmailResponses, None, None) + assert isinstance(output, EmailResponses) + assert len(output.responses) == 1 + assert ( + output.responses[0].previous_message_content + == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on" + ) + + +def test_convert_to_model_with_escaped_special_characters(): + json_string_test = json.dumps( + { + "responses": [ + { + "previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on" + } + ] + } + ) + output = convert_to_model(json_string_test, EmailResponses, None, None) + assert isinstance(output, EmailResponses) + assert len(output.responses) == 1 + assert ( + output.responses[0].previous_message_content + == "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on" + ) + + +def test_convert_to_model_with_multiple_special_characters(): + json_string_test = """ + { + "responses": [ + { + "previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline" + } + ] + } + """ + output = convert_to_model(json_string_test, EmailResponses, None, None) + assert isinstance(output, EmailResponses) + assert len(output.responses) == 1 + assert ( + output.responses[0].previous_message_content + == "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline" + ) + + +# Tests for validate_model +def test_validate_model_pydantic_output(): + result = '{"name": "Alice", "age": 25}' + output = validate_model(result, SimpleModel, False) + assert isinstance(output, SimpleModel) + assert output.name == "Alice" + assert output.age == 25 + + +def test_validate_model_json_output(): + result = '{"name": "Bob", "age": 40}' + output = validate_model(result, SimpleModel, True) + assert isinstance(output, dict) + assert output == {"name": "Bob", "age": 40} + + +# Tests for handle_partial_json +def test_handle_partial_json_with_valid_partial(): + result = 'Some text {"name": "Charlie", "age": 35} more text' + output = handle_partial_json(result, SimpleModel, False, None) + assert isinstance(output, SimpleModel) + assert output.name == "Charlie" + assert output.age == 35 + + +def test_handle_partial_json_with_invalid_partial(mock_agent): + result = "No valid JSON here" + with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert: + mock_convert.return_value = "Converted result" + output = handle_partial_json(result, SimpleModel, False, mock_agent) + assert output == "Converted result" + + +# Tests for convert_with_instructions +@patch("crewai.utilities.converter.create_converter") +@patch("crewai.utilities.converter.get_conversion_instructions") +def test_convert_with_instructions_success( + mock_get_instructions, mock_create_converter, mock_agent +): + mock_get_instructions.return_value = "Instructions" + mock_converter = Mock() + mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50) + mock_create_converter.return_value = mock_converter + + result = "Some text to convert" + output = convert_with_instructions(result, SimpleModel, False, mock_agent) + + assert isinstance(output, SimpleModel) + assert output.name == "David" + assert output.age == 50 + + +@patch("crewai.utilities.converter.create_converter") +@patch("crewai.utilities.converter.get_conversion_instructions") +def test_convert_with_instructions_failure( + mock_get_instructions, mock_create_converter, mock_agent +): + mock_get_instructions.return_value = "Instructions" + mock_converter = Mock() + mock_converter.to_pydantic.return_value = ConverterError("Conversion failed") + mock_create_converter.return_value = mock_converter + + result = "Some text to convert" + with patch("crewai.utilities.converter.Printer") as mock_printer: + output = convert_with_instructions(result, SimpleModel, False, mock_agent) + assert output == result + mock_printer.return_value.print.assert_called_once() + + +# Tests for get_conversion_instructions +def test_get_conversion_instructions_gpt(): + mock_llm = Mock() + mock_llm.openai_api_base = None + with patch("crewai.utilities.converter.is_gpt", return_value=True): + instructions = get_conversion_instructions(SimpleModel, mock_llm) + assert instructions == "I'm gonna convert this raw text into valid JSON." + + +def test_get_conversion_instructions_non_gpt(): + mock_llm = Mock() + with patch("crewai.utilities.converter.is_gpt", return_value=False): + with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser: + mock_parser.return_value.get_schema.return_value = "Sample schema" + instructions = get_conversion_instructions(SimpleModel, mock_llm) + assert "Sample schema" in instructions + + +# Tests for is_gpt +def test_is_gpt_true(): + from langchain_openai import ChatOpenAI + + mock_llm = Mock(spec=ChatOpenAI) + mock_llm.openai_api_base = None + assert is_gpt(mock_llm) is True + + +def test_is_gpt_false(): + mock_llm = Mock() + assert is_gpt(mock_llm) is False + + +class CustomConverter(Converter): + pass + + +def test_create_converter_with_mock_agent(): + mock_agent = MagicMock() + mock_agent.get_output_converter.return_value = MagicMock(spec=Converter) + + converter = create_converter( + agent=mock_agent, + llm=Mock(), + text="Sample", + model=SimpleModel, + instructions="Convert", + ) + + assert isinstance(converter, Converter) + mock_agent.get_output_converter.assert_called_once() + + +def test_create_converter_with_custom_converter(): + converter = create_converter( + converter_cls=CustomConverter, + llm=Mock(), + text="Sample", + model=SimpleModel, + instructions="Convert", + ) + + assert isinstance(converter, CustomConverter) + + +def test_create_converter_fails_without_agent_or_converter_cls(): + with pytest.raises( + ValueError, match="Either agent or converter_cls must be provided" + ): + create_converter( + llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert" + ) From 99d023c5f33636be1ff9eb473aa761eb8a4a36c9 Mon Sep 17 00:00:00 2001 From: ResearchAI <46435010+wuloi@users.noreply.github.com> Date: Sat, 27 Jul 2024 05:40:47 +0800 Subject: [PATCH 07/36] Update reset_memories_command.py (#974) --- src/crewai/cli/reset_memories_command.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/crewai/cli/reset_memories_command.py b/src/crewai/cli/reset_memories_command.py index 68d82a92ab..c4808594fa 100644 --- a/src/crewai/cli/reset_memories_command.py +++ b/src/crewai/cli/reset_memories_command.py @@ -9,10 +9,14 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: """ - Replay the crew execution from a specific task. + Reset the crew memories. Args: - task_id (str): The ID of the task to replay from. + long (bool): Whether to reset the long-term memory. + short (bool): Whether to reset the short-term memory. + entity (bool): Whether to reset the entity memory. + kickoff_outputs (bool): Whether to reset the latest kickoff task outputs. + all (bool): Whether to reset all memories. """ try: From 9018e2ab6a5dbf78774c35b874c6054e3ea383f3 Mon Sep 17 00:00:00 2001 From: Rip&Tear <84775494+theCyberTech@users.noreply.github.com> Date: Sun, 28 Jul 2024 22:55:09 +0800 Subject: [PATCH 08/36] Docs update (#1008) * Updated Docs: New Getting started section + content update / addition * fixed indentation issue * Minor updates to fix typos --------- Co-authored-by: theCyberTech --- .../Installing-CrewAI.md | 3 + ...t-a-New-CrewAI-Project-Template-Method.md} | 55 +++++++++++- .../how-to/Creating-a-Crew-and-kick-it-off.md | 84 ------------------- docs/index.md | 13 +++ 4 files changed, 68 insertions(+), 87 deletions(-) rename docs/{how-to => getting-started}/Installing-CrewAI.md (91%) rename docs/{how-to/Start-a-New-CrewAI-Project.md => getting-started/Start-a-New-CrewAI-Project-Template-Method.md} (65%) delete mode 100644 docs/how-to/Creating-a-Crew-and-kick-it-off.md diff --git a/docs/how-to/Installing-CrewAI.md b/docs/getting-started/Installing-CrewAI.md similarity index 91% rename from docs/how-to/Installing-CrewAI.md rename to docs/getting-started/Installing-CrewAI.md index 5a347df322..8bf58ee015 100644 --- a/docs/how-to/Installing-CrewAI.md +++ b/docs/getting-started/Installing-CrewAI.md @@ -18,4 +18,7 @@ pip install crewai # Install the main crewAI package and the tools package # that includes a series of helpful tools for your agents pip install 'crewai[tools]' + +# Alternatively, you can also use: +pip install crewai crewai-tools ``` \ No newline at end of file diff --git a/docs/how-to/Start-a-New-CrewAI-Project.md b/docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md similarity index 65% rename from docs/how-to/Start-a-New-CrewAI-Project.md rename to docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md index 9fb5cb63c2..0e6fcc4468 100644 --- a/docs/how-to/Start-a-New-CrewAI-Project.md +++ b/docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md @@ -1,5 +1,5 @@ --- -title: Starting a New CrewAI Project +title: Starting a New CrewAI Project - Using Template description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods. --- @@ -7,13 +7,62 @@ description: A comprehensive guide to starting a new CrewAI project, including t Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started. +Beforre we start there are a couple of things to note: + +1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run. +2. The preferred way of setting up CrewAI is using the `crewai create` command.This will create a new project folder and install a skeleton template for you to work on. + ## Prerequisites -We assume you have already installed CrewAI. If not, please refer to the [installation guide](https://docs.crewai.com/how-to/Installing-CrewAI/) to install CrewAI and its dependencies. +Before getting started with CrewAI, make sure that you have installed it via pip: + +```shell +$ pip install crewai crewi-tools +``` + +### Virtual Environemnts +It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version: + +1. Use venv (Python's built-in virtual environment tool): + venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups. + + To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html). + +2. Use Conda (A Python virtual environment manager): + Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way. + + To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html). + +3. Use Poetry (A Python package manager and dependency management tool): + Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies. + Poetry is CrewAI's prefered tool for package / dependancy management in CrewAI. + +### Code IDEs + +Most users of CrewAI a Code Editor / Integrated Development Environment (IDE) for building there Crews. You can use any code IDE of your choice. Seee below for some popular options for Code Editors / Integrated Development Environments (IDE): + +- [Visual Studio Code](https://code.visualstudio.com/) - Most popular +- [PyCharm](https://www.jetbrains.com/pycharm/) +- [Cursor AI](https://cursor.com) + +Pick one that suits your style and needs. ## Creating a New Project +In this example we will be using Venv as our virtual environment manager. + +To setup a virtual environment, run the following CLI command: + +```shell +$ python3 -m venv +``` + +Activate your virtual environment by running the following CLI command: + +```shell +$ source /bin/activate +``` -To create a new project, run the following CLI command: +Now, to create a new CrewAI project, run the following CLI command: ```shell $ crewai create diff --git a/docs/how-to/Creating-a-Crew-and-kick-it-off.md b/docs/how-to/Creating-a-Crew-and-kick-it-off.md deleted file mode 100644 index 7200d75d4f..0000000000 --- a/docs/how-to/Creating-a-Crew-and-kick-it-off.md +++ /dev/null @@ -1,84 +0,0 @@ ---- -title: Assembling and Activating Your CrewAI Team -description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, code execution, integration with third-party agents, and improved task management. ---- - -## Introduction -Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience, including code execution capabilities, integration with third-party agents, and advanced task management. - -## Step 0: Installation -Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13. - -```shell -pip install crewai -pip install 'crewai[tools]' -``` - -## Step 1: Assemble Your Agents -Define your agents with distinct roles, backstories, and enhanced capabilities. The Agent class now supports a wide range of attributes for fine-tuned control over agent behavior and interactions, including code execution and integration with third-party agents. - -```python -import os -from langchain.llms import OpenAI -from crewai import Agent -from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool - -os.environ["OPENAI_API_KEY"] = "Your OpenAI Key" -os.environ["SERPER_API_KEY"] = "Your Serper Key" -os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key" -os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id" - -search_tool = SerperDevTool() -browser_tool = BrowserbaseLoadTool() -exa_search_tool = EXASearchTool() - -# Creating a senior researcher agent with advanced configurations -researcher = Agent( - role='Senior Researcher', - goal='Uncover groundbreaking technologies in {topic}', - backstory=("Driven by curiosity, you're at the forefront of innovation, " - "eager to explore and share knowledge that could change the world."), - memory=True, - verbose=True, - allow_delegation=False, - tools=[search_tool, browser_tool], - allow_code_execution=False, # New attribute for enabling code execution - max_iter=15, # Maximum number of iterations for task execution - max_rpm=100, # Maximum requests per minute - max_execution_time=3600, # Maximum execution time in seconds - system_template="Your custom system template here", # Custom system template - prompt_template="Your custom prompt template here", # Custom prompt template - response_template="Your custom response template here", # Custom response template -) - -# Creating a writer agent with custom tools and specific configurations -writer = Agent( - role='Writer', - goal='Narrate compelling tech stories about {topic}', - backstory=("With a flair for simplifying complex topics, you craft engaging " - "narratives that captivate and educate, bringing new discoveries to light."), - verbose=True, - allow_delegation=False, - memory=True, - tools=[exa_search_tool], - function_calling_llm=OpenAI(model_name="gpt-3.5-turbo"), # Separate LLM for function calling -) - -# Setting a specific manager agent -manager = Agent( - role='Manager', - goal='Ensure the smooth operation and coordination of the team', - verbose=True, - backstory=( - "As a seasoned project manager, you excel in organizing " - "tasks, managing timelines, and ensuring the team stays on track." - ), - allow_code_execution=True, # Enable code execution for the manager -) -``` - -### New Agent Attributes and Features - -1. `allow_code_execution`: Enable or disable code execution capabilities for the agent (default is False). -2. `max_execution_time`: Set a maximum execution time (in seconds) for the agent to complete a task. -3. `function_calling_llm`: Specify a separate language model for function calling. \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 77cdd9852c..003df16d0b 100644 --- a/docs/index.md +++ b/docs/index.md @@ -5,6 +5,19 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
+

Core Concepts