With Large Language Models (LLMs) like ChatGPT, OpenAI has witnessed a surge in enterprise and user adoption, currently raking in around $80 million in monthly revenue. According to a recent report by The Information, the San Francisco-based company is reportedly on pace to hit $1 billion in annual revenue.
Last time we delved into AutoGPT and GPT-Engineering, the early mainstream open-source LLM-based AI agents designed to automate complex tasks. While promising, these systems had their fair share of issues: inconsistent results, performance bottlenecks, and limitations in handling multifaceted demands. They show proficiency in code generation, but their capabilities often stop there. They lack critical project management functionalities like PRD generation, technical design generation, and API interface prototyping.
Enter MetaGPT— a Multi-agent system that utilizes Large Language models by Sirui Hong fuses Standardized Operating Procedures (SOPs) with LLM-based multi-agent systems. This emerging paradigm disrupts the existing limitations of LLMs in fostering effective collaboration and task decomposition in complex, real-world applications.
The beauty of MetaGPT lies in its structuring. It capitalizes on meta-programming techniques to manipulate, analyze, and transform code in real-time. The aim? To actualize an agile, flexible software architecture that can adapt to dynamic programming tasks.
Agile Development
SOPs act as a meta-function here, coordinating agents to auto-generate code based on defined inputs. In simple terms, it’s as if you’ve turned a highly coordinated team of software engineers into an adaptable, intelligent software system.
Understanding MetaGPT Framework
MetaGPT Framework (Link to Framework)
Foundational & Collaboration Layers
MetaGPT’s architecture is divided into two layers: the Foundational Components Layer and the Collaboration Layer.
- Foundational Components Layer: This layer focuses on individual agent operations and facilitates system-wide information exchange. It introduces core building blocks such as Environment, Memory, Roles, Actions, and Tools. The Environment sets the stage for shared workspaces and communication pathways, while Memory serves as the historical data archive. Roles encapsulate domain-specific expertise, Actions execute modular tasks, and Tools offer common services. This layer essentially serves as the operating system for the agents.
- Collaboration Layer: Built on top of foundational components, this layer manages and streamlines the collaborative efforts of individual agents. It introduces two mechanisms: Knowledge Sharing and Encapsulating Workflows.
