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The Sequential Crew component is designed to organize and execute multiple AI agent tasks in a strict sequence, one after the other. This structure is particularly useful for building workflows where the output of one task can inform or trigger the next, ensuring a controlled and logical progression through your AI pipeline.
What Does This Component Do?
Sequential Crew acts as a manager for a group of agents, ensuring that their assigned tasks are executed in a specific order. Each agent completes its task before the next one begins, allowing for clear task dependencies and step-by-step workflow construction. This is ideal for scenarios where tasks must be performed in a certain sequence, such as data preprocessing, analysis, and reporting.
Key Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| Tasks | List of SequentialTask | No | The set of tasks (each handled by an agent) to be executed in sequence. |
| Max RPM | Integer | No | Maximum requests per minute (RPM) allowed for task execution (default: 100). |
| Show Progress of Agent | Boolean | No | If enabled, displays detailed progress of each agent as they perform their tasks. |
| Cache | Boolean | No | When enabled, caches the results to improve efficiency if the same tasks are rerun. |
- Tasks: This is where you define and input the list of tasks for the crew to execute. Each task should follow the
SequentialTaskformat. - Max RPM: Controls how many tasks can be processed per minute, which is useful for rate-limiting or adhering to external service limits.
- Show Progress: When set to
true, provides transparency into the execution flow by showing what each agent is doing at any moment. - Cache: Enables caching, allowing the system to reuse results from previous runs to save on computation and time.
Output
- Output: The component outputs a
Messageobject which typically contains the final result after all tasks have been executed in sequence.
Why Use Sequential Crew?
- Task Dependency Management: Ensures that each task is executed only after the previous one finishes, making it easy to build dependent, multi-step workflows.
- Transparency & Debugging: The ability to show agent progress helps you to monitor, debug, and optimize your workflow.
- Efficiency: The caching option saves computation by preventing redundant task execution.
- Rate Control: By setting Max RPM, you avoid overwhelming external APIs or hitting rate limits.
Example Use Cases
- Data ingestion → data cleaning → data analysis → report generation
- Multi-step document processing (e.g., OCR → translation → summarization)
- Any workflow where outputs from one step are required as inputs for the next
Documentation
For detailed usage instructions, visit the Sequential Crew documentation .
Summary:
The Sequential Crew component is essential for orchestrating ordered, multi-step AI workflows, providing control, efficiency, and transparency as your agents work together in sequence.
