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* docs: Fix major memory system documentation issues - Remove misleading deprecation warnings, fix confusing comments, clearly separate three memory approaches, provide accurate examples that match implementation * fix: Correct broken image paths in README - Update crewai_logo.png and asset.png paths to point to docs/images/ directory instead of docs/ directly * docs: Add system prompt transparency and customization guide - Add 'Understanding Default System Instructions' section to address black-box concerns - Document what CrewAI automatically injects into prompts - Provide code examples to inspect complete system prompts - Show 3 methods to override default instructions - Include observability integration examples with Langfuse - Add best practices for production prompt management * docs: Fix implementation accuracy issues in memory documentation - Fix Ollama embedding URL parameter and remove unsupported Cohere input_type parameter * docs: Reference observability docs instead of showing specific tool examples * docs: Reorganize knowledge documentation for better developer experience - Move quickstart examples right after overview for immediate hands-on experience - Create logical learning progression: basics → configuration → advanced → troubleshooting - Add comprehensive agent vs crew knowledge guide with working examples - Consolidate debugging and troubleshooting in dedicated section - Organize best practices by topic in accordion format - Improve content flow from simple concepts to advanced features - Ensure all examples are grounded in actual codebase implementation * docs: enhance custom LLM documentation with comprehensive examples and accurate imports * docs: reorganize observability tools into dedicated section with comprehensive overview and improved navigation * docs: rename how-to section to learn and add comprehensive overview page * docs: finalize documentation reorganization and update navigation labels * docs: enhance README with comprehensive badges, navigation links, and getting started video
89 lines
2.6 KiB
Plaintext
89 lines
2.6 KiB
Plaintext
---
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title: Conditional Tasks
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description: Learn how to use conditional tasks in a crewAI kickoff
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icon: diagram-subtask
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---
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## Introduction
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Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks.
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This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.
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## Example Usage
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```python Code
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from typing import List
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from pydantic import BaseModel
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from crewai import Agent, Crew
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from crewai.tasks.conditional_task import ConditionalTask
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from crewai.tasks.task_output import TaskOutput
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from crewai.task import Task
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from crewai_tools import SerperDevTool
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# Define a condition function for the conditional task
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# If false, the task will be skipped, if true, then execute the task.
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def is_data_missing(output: TaskOutput) -> bool:
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return len(output.pydantic.events) < 10 # this will skip this task
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# Define the agents
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data_fetcher_agent = Agent(
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role="Data Fetcher",
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goal="Fetch data online using Serper tool",
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backstory="Backstory 1",
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verbose=True,
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tools=[SerperDevTool()]
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)
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data_processor_agent = Agent(
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role="Data Processor",
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goal="Process fetched data",
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backstory="Backstory 2",
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verbose=True
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)
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summary_generator_agent = Agent(
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role="Summary Generator",
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goal="Generate summary from fetched data",
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backstory="Backstory 3",
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verbose=True
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)
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class EventOutput(BaseModel):
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events: List[str]
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task1 = Task(
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description="Fetch data about events in San Francisco using Serper tool",
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expected_output="List of 10 things to do in SF this week",
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agent=data_fetcher_agent,
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output_pydantic=EventOutput,
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)
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conditional_task = ConditionalTask(
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description="""
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Check if data is missing. If we have less than 10 events,
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fetch more events using Serper tool so that
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we have a total of 10 events in SF this week..
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""",
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expected_output="List of 10 Things to do in SF this week",
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condition=is_data_missing,
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agent=data_processor_agent,
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)
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task3 = Task(
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description="Generate summary of events in San Francisco from fetched data",
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expected_output="A complete report on the customer and their customers and competitors, including their demographics, preferences, market positioning and audience engagement.",
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agent=summary_generator_agent,
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)
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# Create a crew with the tasks
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crew = Crew(
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agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
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tasks=[task1, conditional_task, task3],
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verbose=True,
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planning=True
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)
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# Run the crew
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result = crew.kickoff()
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print("results", result)
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``` |