MetaGPT is an open source multiagent framework orchestrating the use of human procedural knowledge and AI agents powered by large language models (LLMs) to develop a diverse range of software solutions. Acting as an AI software company, MetaGPT features specialized AI agents that perform roles like those in traditional software companies.
MetaGPT is the proprietary technology of DeepWisdom founded by Chenglin Wu. The framework gained popularity quickly on GitHub, garnering interest around MetaGPT’s underlying goal to propel the advancement of natural language programming using multiagent systems (MAS).
The popular research paper “MetaGPT: Metaprogramming for A Multi-Agent Collaborative Framework” explains the framework’s orchestration of agent collaboration. As research surrounding LLM orchestration and AI agents continues, MetaGPT is an early contributor offering a straightforward approach to multiagent collaboration by using familiar workflows.
What is multiagent collaboration?
Agents acting within a multiagent system (MAS) remain autonomous but also cooperate and coordinate in agentic structures, or frameworks such as MetaGPT.1 A MAS coordinates individual agents to operate and interact within their environment to complete complex tasks to achieve a common goal. This idea is a part of collective intelligence, a popular concept sometimes related to AGI.
A multiagent system consists of a network of problem-solving agents that work together to tackle challenges beyond the capacity of any single agent.2 One of the key challenges in designing an effective MAS is coordinating the agents to ensure their collaboration on a shared objective. Successful collaboration relies on each agent’s ability to solve individual tasks, as well as their ability to interact effectively with one another.3
The considerations in planning a MAS environment include:
The limitations that other agents’ activities create for an agent’s decision-making,
The constraints that arise from an agent’s commitments to others regarding its available actions,
And the unpredictable evolution of the world that is caused by external, unmodeled agents.4
One approach to this design challenge is the orchestration of agent collaboration by explicitly modeling teamwork. This multicollaborative design pattern breaks down a complex prompt and delegates the abstracted tasks to agents who execute them based on their specialized roles.5
Within MetaGPT’s multiagent collaborative framework, a team of AI agents operates according to a structured workflow guided by standard operating procedures (SOPs) that define distinct roles and instructions. By integrating communication protocols and streamlined workflows within LLM-driven multiagent systems, users can create applications with a single line of input.
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