Use MetaGPT to build Your Own Software Development Company
Use LLM agents as Product Manager, Architect, Project Manager, Coder, etc.
TL;DR: MetaGPT is a new multi-agent collaboration approach that uses meta-programming and human workflows to solve complex tasks. It encodes SOPs for structured coordination and assigns roles to agents, mirroring an assembly line. MetaGPT outperforms other methods in problem-solving and is cost-effective, costing $0.2 for an example and $2.0 for a full project.
Multi-agent systems driven by large language models (LLMs) have made remarkable progress. However, despite these advancements, there are still limitations when it comes to solving complex tasks. Enter MetaGPT, a revolutionary approach to multi-agent collaboration that incorporates meta-programming and human workflows.
The Problem with Existing LLM Agents
Existing LLM agents have been successful in solving simple dialogue-based tasks, but complex tasks have remained elusive. One of the main reasons for this is the LLM hallucination problem, where the model generates information that is not grounded in reality. This can lead to errors and inaccuracies, making it difficult to solve complex tasks effectively.
MetaGPT: A New Approach to Multi-Agent Collaboration
MetaGPT offers a solution to this problem by incorporating efficient human workflows as a meta-programming approach into LLM-based multi-agent collaboration. It encodes Standardized Operating Procedures (SOPs) into prompts, enhancing structured coordination and ensuring that tasks are executed consistently and accurately. This approach mandates modular outputs, empowering agents with domain expertise comparable to human professionals, to validate outputs and minimize compounded errors.
The Role of SOPs in MetaGPT
SOPs play a crucial role in supporting task decomposition and efficient coordination within MetaGPT. They outline responsibilities among team members and set standards for component outputs, ensuring that tasks are executed consistently and accurately, in alignment with the defined roles and quality standards. By encoding SOPs into the agent architecture using role-based action specifications, MetaGPT agents can generate standard actions output, such as high-quality requirements documents, design artefacts, flowcharts, and interface specifications.
The Assembly Line Paradigm in MetaGPT
MetaGPT leverages the assembly line paradigm to assign diverse roles to various agents, establishing a framework that can effectively and cohesively deconstruct complex multi-agent collaborative problems. This approach mirrors the infrastructure of human workplaces that facilitate team collaboration, allowing agents to actively observe and retrieve relevant information, a more efficient approach compared to passively receiving data through dialogue.
MetaGPT's Personas and Their Roles
Internally, MetaGPT includes the personas of product managers, architects, project managers, and engineers. These personas provide the entire process of a software company along with carefully orchestrated SPOs. By incorporating human domain knowledge into multi-agent systems, MetaGPT creates new opportunities to tackle complex real-world challenges.
MetaGPT in Action: A Practical Example
Imagine a scenario where Alice, a Product Manager, writes a product requirement document containing product goals, user stories, competitive analysis, and more. After a review, Alice posts her work into the "WritePRD" category. Bob, an Architect, subscribes to this category and drafts a system design based on Alice's requirements. Bob posts his work in the "WriteDesign" category, which Eve, a Project Manager, subscribes to. Eve breaks down the project into simpler tasks and lists out project dependencies. Finally, Alex, an Engineer, writes the code based on the provided information. This process ensures that MetaGPT can successfully generate error-free code on the first attempt.
MetaGPT's Performance Compared to Other Methods
MetaGPT significantly outperforms other methods, including professional code generation frameworks such as CodeX, CodeT, and the most powerful language model currently available, GPT-4. The primary distinction between MetaGPT and earlier LLM-based approaches lies in the multi-agent interaction and the incorporation of human standard operating procedures. The results, based on HumanEval and MBPP benchmarks, showcase the enhanced human-like problem-solving capacity of the development team of agents.
MetaGPT is not only innovative but also cost-effective. It costs approximately $0.2 (in GPT-4 API fees) to generate one example with analysis and design in MetaGPT, and around $2.0 for a full project. This makes it an affordable solution for businesses looking to leverage the power of multi-agent collaboration and LLMs for their software development needs.
Find the code here.
MetaGPT's innovative approach to multi-agent collaboration offers a promising solution to the limitations of existing LLM agents. By incorporating human workflows and SOPs, MetaGPT can effectively tackle complex tasks and generate more coherent and correct solutions compared to existing chat-based multi-agent systems. The potential of integrating human domain knowledge into multi-agent systems opens up new opportunities to address complex real-world challenges.
In the ever-evolving world of automated task-solving, MetaGPT stands out as a game-changer, offering a glimpse into the future of multi-agent collaboration and problem-solving.