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智能体推理是一项允许智能体在执行前反思任务并创建计划的功能。这有助于智能体更有条理地处理任务，并确保它们为执行指定工作做好了准备。
要为智能体启用推理，只需在创建智能体时设置 reasoning=True
from crewai import Agent
agent = Agent(
role="Data Analyst",
goal="Analyze complex datasets and provide insights",
backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
reasoning=True, # Enable reasoning
max_reasoning_attempts=3 # Optional: Set a maximum number of reasoning attempts
)
工作原理
启用推理后，在执行任务之前，智能体会：
- 对任务进行反思并制定详细计划
- 评估是否准备好执行该任务
- 根据需要细化计划，直到准备就绪或达到 max_reasoning_attempts
- 在执行前将推理计划注入任务描述中
此过程有助于智能体将复杂任务分解为可管理的步骤，并在开始之前识别潜在挑战。
配置选项
在继续执行之前优化计划的最大尝试次数。如果为 None（默认值），智能体将持续优化直到准备就绪。
from crewai import Agent, Task, Crew
# Create an agent with reasoning enabled
analyst = Agent(
role="Data Analyst",
goal="Analyze data and provide insights",
backstory="You are an expert data analyst.",
reasoning=True,
max_reasoning_attempts=3 # Optional: Set a limit on reasoning attempts
)
# Create a task
analysis_task = Task(
description="Analyze the provided sales data and identify key trends.",
expected_output="A report highlighting the top 3 sales trends.",
agent=analyst
)
# Create a crew and run the task
crew = Crew(agents=[analyst], tasks=[analysis_task])
result = crew.kickoff()
print(result)
错误处理
推理过程旨在保持健壮性，并内置了错误处理机制。如果推理过程中发生错误，智能体将在没有推理计划的情况下继续执行任务。这确保了即使推理过程失败，任务仍然可以执行。 以下是如何在代码中处理潜在错误的方法：from crewai import Agent, Task
import logging
# Set up logging to capture any reasoning errors
logging.basicConfig(level=logging.INFO)
# Create an agent with reasoning enabled
agent = Agent(
role="Data Analyst",
goal="Analyze data and provide insights",
reasoning=True,
max_reasoning_attempts=3
)
# Create a task
task = Task(
description="Analyze the provided sales data and identify key trends.",
expected_output="A report highlighting the top 3 sales trends.",
agent=agent
)
# Execute the task
# If an error occurs during reasoning, it will be logged and execution will continue
result = agent.execute_task(task)
推理输出示例
这是一个针对数据分析任务的推理计划示例
Task: Analyze the provided sales data and identify key trends.
Reasoning Plan:
I'll analyze the sales data to identify the top 3 trends.
1. Understanding of the task:
I need to analyze sales data to identify key trends that would be valuable for business decision-making.
2. Key steps I'll take:
- First, I'll examine the data structure to understand what fields are available
- Then I'll perform exploratory data analysis to identify patterns
- Next, I'll analyze sales by time periods to identify temporal trends
- I'll also analyze sales by product categories and customer segments
- Finally, I'll identify the top 3 most significant trends
3. Approach to challenges:
- If the data has missing values, I'll decide whether to fill or filter them
- If the data has outliers, I'll investigate whether they're valid data points or errors
- If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
4. Use of available tools:
- I'll use data analysis tools to explore and visualize the data
- I'll use statistical tools to identify significant patterns
- I'll use knowledge retrieval to access relevant information about sales analysis
5. Expected outcome:
A concise report highlighting the top 3 sales trends with supporting evidence from the data.
READY: I am ready to execute the task.
