In this tutorial, we guide you through the design and implementation of an advanced Supervisor Agent Framework using CrewAI and the Google Gemini model. We create specialized agents like researchers, analysts, writers, and reviewers, each with distinct roles, and place them under a supervisor agent who oversees and coordinates their work. By combining structured task configurations, hierarchical workflows, and built-in tools, we create a system where each agent has a clear role, and the supervisor ensures quality and coherence throughout the project lifecycle. You can find the full code here.

We begin by installing necessary libraries and defining a `TaskPriority` enum to assign urgency and importance levels to tasks. The `TaskConfig` data class captures each task’s intent, expected output, priority, and runtime requirements, standardizing work flow through the system. Our `SupervisorFramework` class initializes the supervisor framework, setting up specialized agents and a supervisor agent using the Google Gemini model.

The framework includes methods to create specialized agents: `create_research_agent()`, `create_analyst_agent()`, `create_writer_agent()`, and `create_reviewer_agent()`. Each agent has a unique role, goal, backstory, and is equipped with the Gemini model and optional tools like Serper for web search. The `create_supervisor_agent()` method creates the main supervisor agent, responsible for coordinating team efforts, managing workflows, and ensuring project success.

The `setup_agents()` method initializes all agents in the framework, while `create_task_workflow()` generates a comprehensive task workflow based on a given topic and task configurations. This method creates tasks for research, analysis, writing, and review, with the supervisor task overseeing the entire workflow. The `execute_project()` method runs the project using the supervisor framework, allowing you to choose between hierarchical and sequential process types.

We also provide a `create_sample_task_configs()` function that defines default task blueprints for research, analysis, writing, and review, ensuring agents understand their tasks’ criticality and expected outputs. Finally, the `demo_supervisor_framework()` function showcases the full workflow, initializing the framework, executing a sample project, and displaying task progress, execution results, and usage metrics.

In conclusion, the Supervisor Framework enables systematic management of complex projects by utilizing multiple specialized agents working in unison. It allows for coordinated workflows, with the supervisor ensuring quality and alignment at every stage. This setup equips us to handle real-world projects more efficiently, turning abstract goals into actionable, high-quality deliverables. You can find the full codes here, and explore our GitHub page for tutorials, codes, and notebooks. Don’t forget to follow us on Twitter, join our 100k+ ML SubReddit, and subscribe to our newsletter for more updates.

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