Introduction to Meta GPT
Meta GPT is a new framework for multi-agent collaboration that is making a lot of noise on GitHub. It is a new type of AI-based system agents that can access and interact with one or more applications. Building an advanced system based on large English models is both very difficult and unsafe. One of the main issues is that a large English model is susceptible to hallucinations, which happens when the model spits out facts or information that is completely nonsensical. This is where Meta GPT comes in
Meta GPT is a new paper and open-source work developed by Sirui Hong and Xiawu Zheng and Jonathan Chen and Yuheng Cheng and Jinlin Wang and Ceyao Zhang and Zili Wang and Steven KaShing Yau and Zijuan Lin and Liyang Zhou and Chenyu Ran and Lingfeng Xiao and Chenglin Wu.
This new design scheme allows the system to better understand the user’s goal and mitigate the hallucinations by dividing a complex task into multiple easier ones.
Problems with Large AI Models
The main problem with LLM is hallucination, which becomes cascading when naively chaining multiple intelligent agents, resulting in a failure to effectively address complex problems. To solve this problem, MetaGPT incorporates efficient human workflows as a meta programming approach into LLM-based multi-agent collaboration. Specifically, MetaGPT encodes Standardized Operating Procedures (SOPs) into prompts to enhance structured coordination. By doing so, MetaGPT transforms GPTs into engineers, architects, and managers and aims to solve complex tasks through multi-agent collaboration
What is Meta GPT?
Meta GPT is a new paper and open-source work developed by Sirui Hong and the team. It is a framework for combining or chaining large English models and mitigating hallucination risks by integrating human-standardized operation processors, or subs, into the chaining process. This new design scheme allows the system to better understand the user’s goal and mitigate the hallucinations by dividing a complex task into multiple easier ones.
Comparison with other AI models
Meta GPT is different from other AI models because it replaces each with an effective human being. Instead of talking to each other, the different GPT models will generate standardized documents for the following GPT model, which will limit possible hallucinations when conversations flow freely. This is in contrast to other AI models like GPT-3, which can generate text but cannot interact with other applications.
What are SOPs and how does MetaGPT use them to coordinate multi-agent systems?
Standardized Operating Procedures (SOPs) are a set of instructions that describe how to perform a task or activity in a consistent and repeatable manner. MetaGPT uses SOPs to coordinate multi-agent systems by encoding them into prompts that foster structured coordination. SOPs serve as a strategy for organizing the collaboration of multi-agents, which enhances the efficiency of their cooperative efforts. MetaGPT analyzes human workflows to extract SOPs that capture essential procedural knowledge. By doing so, MetaGPT aims to seamlessly integrate human workflows into collaborative problem-solving. SOPs are necessary for multifaceted processes to be effective, and well-structured SOPs are crucial for improving the design and structure of LLM-based multi-agent systems and increasing their efficacy and application.
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MetaGPT Example
Meta GPT has a functional layer where the different models can exchange together, and their discussions are saved into memory for efficiency and to allow them to look back and access all the information. Each agent has a personality and a role, and each has its own goal and constraints for the project that are defined by the user. Then each agent sequentially follows a five-step process to produce the SOP or work for the next agent to use and build upon. For example, in building a Flappy Bird game, one agent could be responsible for defining the game, while another agent could be responsible for designing the different levels.
MetaGpt is available on HuggingFace, for you to do a test run.
Conclusion
Meta GPT is an impressive work from the researchers, and this multi-agent process might be the most challenging but most interesting use case of large English models. It can efficiently chain multiple agents to mitigate hallucinations and solve complex tasks with a single prompt and user directives. The use of SOPS and human-defined objectives and constraints further reduces the risk of hallucinations. The code for Meta GPT is fully open-sourced, and the paper is linked below for those interested in learning more.
FAQ
What are some examples of tasks that can be completed with MultiAgent Collaboration?
Multi-agent collaboration is a powerful tool that can be used to complete complex tasks efficiently. Here are some examples of tasks that can be completed with multi-agent collaboration:
- Building a video game: Multi-agent collaboration can be used to build a video game, from brainstorming the idea to designing the different levels, difficulty code, and QA testing. Each agent would be responsible for a specific task, such as defining the game, designing the levels, or testing the game.
- Writing a research paper: Multi-agent collaboration can be used to write a research paper from start to finish. Each agent would be responsible for a specific task, such as researching the topic, writing the introduction, or editing the paper.
- Designing a website: Multi-agent collaboration can be used to design a website from start to finish. Each agent would be responsible for a specific task, such as designing the layout, coding the website, or testing the website.
- Creating a marketing campaign: Multi-agent collaboration can be used to create a marketing campaign from start to finish. Each agent would be responsible for a specific task, such as designing the campaign, creating the content, or analyzing the results.
What is the main idea behind MetaGPT?
MetaGPT is a meta programming framework that aims to enable collaboration in multi-agent systems by leveraging human procedural knowledge to enhance robustness, reduce errors, and engineer software solutions for complex tasks.
How does MetaGPT differ from other large language models?
MetaGPT differs from other large language models in several ways, including:
- Incorporating human workflows: MetaGPT leverages Standardized Operating Procedures (SOPs) to enhance structured coordination in multi-agent systems. By doing so, MetaGPT aims to seamlessly integrate human workflows into collaborative problem-solving.
- Multi-agent collaboration: MetaGPT is designed to solve complex tasks through multi-agent collaboration, which is a departure from existing LLM-based multi-agent works that primarily focus on solving simple dialogue tasks.
- Project management functionalities: Unlike other LLM-based AI agents that lack critical project management functionalities like PRD generation, technical design generation, and API interface prototyping, MetaGPT fuses SOPs with LLM-based multi-agent systems to provide these functionalities.
- Addressing the LLM hallucination problem: MetaGPT addresses the LLM hallucination problem, which becomes cascading when naively chaining multiple intelligent agents, resulting in a failure to effectively address complex problems. To solve this problem, MetaGPT incorporates efficient human workflows as a meta programming approach into LLM-based multi-agent collaboration.
How does MetaGPT incorporate human workflows into its programming approach?
MetaGPT incorporates human workflows into its programming approach by leveraging Standardized Operating Procedures (SOPs) to enhance structured coordination in multi-agent systems.
Is the code for MetaGPT open-sourced?
Yes, the code for MetaGPT is fully open-sourced.
Who can benefit from using MetaGPT?
MetaGPT can benefit anyone who needs to complete complex tasks efficiently, such as software developers, researchers, designers, and marketers.
