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* Add enterprise deployment documentation to CLI docs * Update CrewAI Enterprise documentation with comprehensive guides for Traces, Tool Repository, Webhook Streaming, and FAQ structure * Add Enterprise documentation images * Update Enterprise introduction with visual CardGroups and Steps components
181 lines
11 KiB
Plaintext
181 lines
11 KiB
Plaintext
---
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title: FAQs
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description: "Frequently asked questions about CrewAI Enterprise"
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icon: "code"
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---
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<AccordionGroup>
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<Accordion title="How is task execution handled in the hierarchical process?">
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In the hierarchical process, a manager agent is automatically created and coordinates the workflow, delegating tasks and validating outcomes for
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streamlined and effective execution. The manager agent utilizes tools to facilitate task delegation and execution by agents under the manager's guidance.
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The manager LLM is crucial for the hierarchical process and must be set up correctly for proper function.
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</Accordion>
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<Accordion title="Where can I get the latest CrewAI documentation?">
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The most up-to-date documentation for CrewAI is available on our official documentation website; https://docs.crewai.com/
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<Card href="https://docs.crewai.com/" icon="books">CrewAI Docs</Card>
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</Accordion>
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<Accordion title="What are the key differences between Hierarchical and Sequential Processes in CrewAI?">
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#### Hierarchical Process:
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Tasks are delegated and executed based on a structured chain of command.
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A manager language model (`manager_llm`) must be specified for the manager agent.
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Manager agent oversees task execution, planning, delegation, and validation.
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Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities.
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#### Sequential Process:
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Tasks are executed one after another, ensuring tasks are completed in an orderly progression.
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Output of one task serves as context for the next.
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Task execution follows the predefined order in the task list.
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#### Which Process is Better Suited for Complex Projects?
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The hierarchical process is better suited for complex projects because it allows for:
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- **Dynamic task allocation and delegation**: Manager agent can assign tasks based on agent capabilities, allowing for efficient resource utilization.
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- **Structured validation and oversight**: Manager agent reviews task outputs and ensures task completion, increasing reliability and accuracy.
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- **Complex task management**: Assigning tools at the agent level allows for precise control over tool availability, facilitating the execution of intricate tasks.
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</Accordion>
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<Accordion title="What are the benefits of using memory in the CrewAI framework?">
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- **Adaptive Learning**: Crews become more efficient over time, adapting to new information and refining their approach to tasks.
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- **Enhanced Personalization**: Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
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- **Improved Problem Solving**: Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
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</Accordion>
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<Accordion title="What is the purpose of setting a maximum RPM limit for an agent?">
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Setting a maximum RPM limit for an agent prevents the agent from making too many requests to external services, which can help to avoid rate limits and improve performance.
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</Accordion>
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<Accordion title="What role does human input play in the execution of tasks within a CrewAI crew?">
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It allows agents to request additional information or clarification when necessary.
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This feature is crucial in complex decision-making processes or when agents require more details to complete a task effectively.
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To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
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This input can provide extra context, clarify ambiguities, or validate the agent's output.
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</Accordion>
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<Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
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CrewAI provides a range of advanced customization options to tailor and enhance agent behavior and capabilities:
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- **Language Model Customization**: Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities.
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- **Performance and Debugging Settings**: Adjust an agent's performance and monitor its operations for efficient task execution.
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- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization.
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- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`).
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- **Maximum Iterations for Task Execution**: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions.
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- **Delegation and Autonomy**: Control an agent's ability to delegate or ask questions, tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is set to True, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be disabled to suit specific operational requirements.
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- **Human Input in Agent Execution**: Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
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</Accordion>
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<Accordion title="In what scenarios is human input particularly useful in agent execution?">
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Human input is particularly useful in agent execution when:
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- **Agents require additional information or clarification**: When agents encounter ambiguity or incomplete data, human input can provide the necessary context to complete the task effectively.
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- **Agents need to make complex or sensitive decisions**: Human input can assist agents in ethical or nuanced decision-making, ensuring responsible and informed outcomes.
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- **Oversight and validation of agent output**: Human input can help validate the results generated by agents, ensuring accuracy and preventing any misinterpretation or errors.
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- **Customizing agent behavior**: Human input can provide feedback on agent responses, allowing users to refine the agent's behavior and responses over time.
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- **Identifying and resolving errors or limitations**: Human input can help identify and address any errors or limitations in the agent's capabilities, enabling continuous improvement and optimization.
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</Accordion>
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<Accordion title="What are the different types of memory that are available in crewAI?">
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The different types of memory available in CrewAI are:
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- `short-term memory`
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- `long-term memory`
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- `entity memory`
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- `contextual memory`
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Learn more about the different types of memory here:
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<Card href="https://docs.crewai.com/concepts/memory" icon="brain">CrewAI Memory</Card>
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</Accordion>
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<Accordion title="How can I create custom tools for my CrewAI agents?">
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You can create custom tools by subclassing the `BaseTool` class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from `BaseTool`, specifying the name, description, and the `_run` method for operational logic. The tool decorator allows you to create a `Tool` object directly with the required attributes and a functional logic.
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Click here for more details:
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<Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools</Card>
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</Accordion>
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<Accordion title="How do I use Output Pydantic in a Task?">
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To use Output Pydantic in a task, you need to define the expected output of the task as a Pydantic model. Here's an example:
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<Steps>
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<Step title="Define a Pydantic model">
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First, you need to define a Pydantic model. For instance, let's create a simple model for a user:
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```python
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from pydantic import BaseModel
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class User(BaseModel):
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name: str
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age: int
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```
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</Step>
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<Step title="Then, when creating a task, specify the expected output as this Pydantic model:">
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```python
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from crewai import Task, Crew, Agent
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# Import the User model
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from my_models import User
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# Create a task with Output Pydantic
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task = Task(
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description="Create a user with the provided name and age",
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expected_output=User, # This is the Pydantic model
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agent=agent,
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tools=[tool1, tool2]
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)
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```
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</Step>
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<Step title="In your agent, make sure to set the output_pydantic attribute to the Pydantic model you're using:">
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```python
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from crewai import Agent
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# Import the User model
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from my_models import User
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# Create an agent with Output Pydantic
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agent = Agent(
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role='User Creator',
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goal='Create users',
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backstory='I am skilled in creating user accounts',
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tools=[tool1, tool2],
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output_pydantic=User
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)
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```
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</Step>
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<Step title="When executing the crew, the output of the task will be a User object:">
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```python
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from crewai import Crew
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# Create a crew with the agent and task
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crew = Crew(agents=[agent], tasks=[task])
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# Kick off the crew
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result = crew.kickoff()
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# The output of the task will be a User object
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print(result.tasks[0].output)
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```
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</Step>
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</Steps>
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Here's a tutorial on how to consistently get structured outputs from your agents:
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<Frame>
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<iframe
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height="400"
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width="100%"
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src="https://www.youtube.com/embed/dNpKQk5uxHw"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen></iframe>
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</Frame>
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</Accordion>
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</AccordionGroup> |