Self-Managed Crew
The Self-Managed Crew component lets you organize multiple AI agents and assign them structured tasks managed by a lead agent and LLM, enabling dynamic, multi-agent workflows.
Component description
How the Self-Managed Crew component works
SelfManaged Crew Component
The SelfManaged Crew component represents a collaborative group of AI agents, designed to work together to achieve complex goals by dividing and managing tasks. This component is ideal when you want to create workflows where multiple agents, each with distinct capabilities, can coordinate, execute, and manage hierarchical or multi-step tasks autonomously.
What the Component Does
- Orchestrates a crew of agents: You can define multiple agents, each potentially with different specializations or roles, to work together as a team.
- Task assignment and management: Tasks can be structured hierarchically, allowing for complex workflows to be broken down and assigned to the appropriate agents.
- Manager agent support: Optionally assign a specific agent to act as a manager, overseeing the crew and ensuring tasks are allocated and completed effectively.
- Customizable LLM for management: Specify a language model (LLM) for the manager agent, allowing for advanced coordination, reasoning, and communication within the crew.
Inputs
The SelfManaged Crew component offers a range of configurable inputs to tailor the teamwork and task management to your needs:
| Input Name | Type(s) | Description | Required | Multiple |
|---|---|---|---|---|
| Agents | Agent | List of agents forming the crew. | No | Yes |
| Manager Agent | Agent | An optional agent to manage the crew and delegate tasks. | No | No |
| Manager LLM | BaseChatModel | Language model for the manager agent, used to generate text and reasoning for coordination. | No | No |
| Tasks | HierarchicalTask | List of hierarchical tasks the crew should perform. | No | Yes |
| Max RPM | Integer | Maximum requests per minute (default: 100) to control execution rate. | No | No |
| Show Progress | Boolean | If enabled, shows detailed progress of each agent during execution. | No | No |
| Cache | Boolean | Enables caching of results for efficiency (default: enabled). | No | No |
Additional Input Details
- Show Progress: When set to true, you can monitor exactly what each agent is doing at every step, which is particularly useful for debugging or process transparency.
- Max RPM (Requests per Minute): Useful for rate-limiting when interacting with APIs or external services.
Output
- Output: The component outputs a Message object, typically containing the results of the coordinated crew task execution. This can include the completion status, data generated, or final reports compiled by the crew.
Use Cases & Benefits
- Complex Task Automation: Perfect for scenarios where a single agent is not sufficient, and multiple agents with varying skills need to collaborate.
- Hierarchical Workflows: Useful for breaking down large problems into sub-tasks, assigning them to specialists, and aggregating results.
- Human-Like Project Management: Mimics real-world team dynamics, with a “manager” agent overseeing the process, providing oversight, and making decisions.
- Process Transparency: Enable progress tracking to see the inner workings of your AI crew, helpful for both development and production monitoring.
- Efficiency: Caching and rate-limiting options help optimize performance and control resource usage.
When to Use
Consider using the SelfManaged Crew component when your AI workflow requires:
- Multiple agents collaborating on interdependent tasks.
- Task breakdown and delegation mimicking human project management.
- Clear tracking and reporting of progress and results.
Further Documentation
For more detailed examples and advanced setups, refer to the official documentation.
Examples of flow templates using Self-Managed Crew component
To help you get started quickly, we have prepared several example flow templates that demonstrate how to use the Self-Managed Crew component effectively. These templates showcase different use cases and best practices, making it easier for you to understand and implement the component in your own projects.
Frequently asked questions
- What is the Self-Managed Crew component?
It enables you to group multiple AI agents and assign them tasks, with a manager agent coordinating their collaboration. This structure is ideal for automating complex, multi-step workflows.
- How does the manager agent function?
The manager agent acts as the central coordinator, distributing tasks among agents and leveraging an LLM to generate and manage task instructions.
- What kind of tasks can be handled?
You can define hierarchical or multi-level tasks that require collaboration between several specialized agents, making it suitable for advanced workflow automation.
- Can I monitor agent progress?
Yes, you can enable progress tracking to see what each agent is working on during execution.
- Is caching supported?
Yes, the component can cache results to optimize performance and reduce redundant processing.
Try Self-Managed Crew in FlowHunt
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