Multi-Agent System — Crew.AI

🌈 Abstract

The article discusses multi-agent systems, which are LLM (Large Language Model) applications that are changing the automation landscape with intelligent bots. It focuses on the Crew.AI framework for building multi-agent applications.

🙋 Q&A

[01] Multi-Agent System

1. What is a multi-agent system in the context of language models and AI? A multi-agent system in the context of language models and AI involves multiple independent actors, each powered by language models, collaborating in a specific way.

2. What are the key components of the Crew.AI architecture? The key components of the Crew.AI architecture are:

• Tools: Utilities or equipment that the agents use to perform specific tasks
• Tasks: The specific tasks that need to be executed by the agents
• Agents: The individual team members with specific roles, backgrounds, goals, and memory
• Crew: The team of agents working together to achieve a particular goal
• Process: The workflow or strategy the crew follows to complete tasks, such as sequential, hierarchical, or consensual

3. What are the three process strategies defined by the Crew.AI framework? The three process strategies defined by the Crew.AI framework are:

• Sequential: Executes tasks in a specific order
• Hierarchical: Organizes tasks in a hierarchy and delegates them in a hierarchical fashion
• Consensual (Planned): Involves agents collaborating and making decisions democratically

[02] Building a Multi-Agent Blogger Application

1. What are the two agents implemented in the example application? The two agents implemented in the example application are:

• Researcher: Searches the web for relevant content on a given topic
• Blogger: Converts the collected content into a blog post in Markdown format

2. What tools are provided to the Researcher agent to perform the task_search? The Researcher agent is provided with two tools to perform the task_search:

• DuckDuckGo search tool
• Web search tool using Serper

3. How are the agents and tasks configured in the Crew.AI application? The agents (Researcher and Blogger) are configured with specific roles, goals, and backstories. The tasks (task_search and task_post) are defined and assigned to the respective agents. The Crew object is then created, which brings the agents and tasks together, and the sequential process strategy is applied.

4. What is the output of the multi-agent blogger application? The output of the multi-agent blogger application is a Markdown-formatted blog post on the given topic, generated by the collaborative effort of the Researcher and Blogger agents.