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* docs(cli): document device-code login and config reset guidance; renumber sections * docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections) * docs: Fix webhook documentation to include meta dict in all webhook payloads - Add note explaining that meta objects from kickoff requests are included in all webhook payloads - Update webhook examples to show proper payload structure including meta field - Fix webhook examples to match actual API implementation - Apply changes to English, Korean, and Portuguese documentation Resolves the documentation gap where meta dict passing to webhooks was not documented despite being implemented in the API. * WIP: CrewAI docs theme, changelog, GEO, localization * docs(cli): fix merge markers; ensure mode: "wide"; convert ASCII tables to Markdown (en/pt-BR/ko) * docs: add group icons across locales; split Automation/Integrations; update tools overviews and links
54 lines
1.6 KiB
Plaintext
54 lines
1.6 KiB
Plaintext
---
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title: Kickoff Crew for Each
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description: Kickoff Crew for Each Item in a List
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icon: at
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mode: "wide"
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---
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## Introduction
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CrewAI provides the ability to kickoff a crew for each item in a list, allowing you to execute the crew for each item in the list.
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This feature is particularly useful when you need to perform the same set of tasks for multiple items.
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## Kicking Off a Crew for Each Item
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To kickoff a crew for each item in a list, use the `kickoff_for_each()` method.
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This method executes the crew for each item in the list, allowing you to process multiple items efficiently.
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Here's an example of how to kickoff a crew for each item in a list:
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```python Code
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from crewai import Crew, Agent, Task
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# Create an agent with code execution enabled
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coding_agent = Agent(
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role="Python Data Analyst",
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goal="Analyze data and provide insights using Python",
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backstory="You are an experienced data analyst with strong Python skills.",
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allow_code_execution=True
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)
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# Create a task that requires code execution
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data_analysis_task = Task(
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description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
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agent=coding_agent,
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expected_output="The average age calculated from the dataset"
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)
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# Create a crew and add the task
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analysis_crew = Crew(
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agents=[coding_agent],
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tasks=[data_analysis_task],
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verbose=True,
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memory=False
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)
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datasets = [
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{ "ages": [25, 30, 35, 40, 45] },
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{ "ages": [20, 25, 30, 35, 40] },
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{ "ages": [30, 35, 40, 45, 50] }
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]
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# Execute the crew
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result = analysis_crew.kickoff_for_each(inputs=datasets)
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``` |