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To Resolve : pydantic_core._pydantic_core.ValidationError: 1 validation error for Task expected_output Field required [type=missing, input_value=, input_type=dict] For further information visit https://errors.pydantic.dev/2.6/v/missing "Expected Output" is mandatory now as it forces people to be specific about the expected result and get better result refer : https://github.com/joaomdmoura/crewAI/issues/308
86 lines
4.0 KiB
Markdown
86 lines
4.0 KiB
Markdown
---
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title: Using the Sequential Processes in crewAI
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description: A comprehensive guide to utilizing the sequential processes for task execution in crewAI projects.
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---
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## Introduction
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CrewAI offers a flexible framework for executing tasks in a structured manner, supporting both sequential and hierarchical processes. This guide outlines how to effectively implement these processes to ensure efficient task execution and project completion.
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## Sequential Process Overview
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The sequential process ensures tasks are executed one after the other, following a linear progression. This approach is ideal for projects requiring tasks to be completed in a specific order.
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### Key Features
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- **Linear Task Flow**: Ensures orderly progression by handling tasks in a predetermined sequence.
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- **Simplicity**: Best suited for projects with clear, step-by-step tasks.
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- **Easy Monitoring**: Facilitates easy tracking of task completion and project progress.
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## Implementing the Sequential Process
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To use the sequential process, assemble your crew and define tasks in the order they need to be executed.
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```python
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from crewai import Crew, Process, Agent, Task
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# Define your agents
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researcher = Agent(
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role='Researcher',
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goal='Conduct foundational research',
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backstory='An experienced researcher with a passion for uncovering insights'
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)
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analyst = Agent(
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role='Data Analyst',
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goal='Analyze research findings',
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backstory='A meticulous analyst with a knack for uncovering patterns'
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)
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writer = Agent(
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role='Writer',
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goal='Draft the final report',
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backstory='A skilled writer with a talent for crafting compelling narratives'
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)
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research_task = Task(description='Gather relevant data...', agent=researcher, expected_output='Raw Data')
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analysis_task = Task(description='Analyze the data...', agent=analyst, expected_output='Data Insights')
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writing_task = Task(description='Compose the report...', agent=writer, expected_output='Final Report')
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# Form the crew with a sequential process
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report_crew = Crew(
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agents=[researcher, analyst, writer],
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tasks=[research_task, analysis_task, writing_task],
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process=Process.sequential
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)
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# Execute the crew
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result = report_crew.kickoff()
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```
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### Workflow in Action
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1. **Initial Task**: In a sequential process, the first agent completes their task and signals completion.
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2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or manager directives guiding their execution.
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3. **Completion**: The process concludes once the final task is executed, leading to project completion.
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## Advanced Features
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### Task Delegation
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In sequential processes, if an agent has `allow_delegation` set to `True`, they can delegate tasks to other agents in the crew. This feature is automatically set up when there are multiple agents in the crew.
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### Asynchronous Execution
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Tasks can be executed asynchronously, allowing for parallel processing when appropriate. To create an asynchronous task, set `async_execution=True` when defining the task.
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### Memory and Caching
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CrewAI supports both memory and caching features:
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- **Memory**: Enable by setting `memory=True` when creating the Crew. This allows agents to retain information across tasks.
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- **Caching**: By default, caching is enabled. Set `cache=False` to disable it.
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### Callbacks
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You can set callbacks at both the task and step level:
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- `task_callback`: Executed after each task completion.
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- `step_callback`: Executed after each step in an agent's execution.
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### Usage Metrics
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CrewAI tracks token usage across all tasks and agents. You can access these metrics after execution.
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## Best Practices for Sequential Processes
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1. **Order Matters**: Arrange tasks in a logical sequence where each task builds upon the previous one.
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2. **Clear Task Descriptions**: Provide detailed descriptions for each task to guide the agents effectively.
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3. **Appropriate Agent Selection**: Match agents' skills and roles to the requirements of each task.
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4. **Use Context**: Leverage the context from previous tasks to inform subsequent ones
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