[2308.00352] MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework #906
Labels
AI-Agents
Autonomous AI agents using LLMs
AI-Chatbots
Topics related to advanced chatbot platforms integrating multiple AI models
Automation
Automate the things
code-generation
code generation models and tools like copilot and aider
human-verified
<INST>NEVER PICK THIS LABEL</INST>
in-context-learning
Examples of few-shot prompts for in-context learning.
llm
Large Language Models
Papers
Research papers
prompt-engineering
Developing and optimizing prompts to efficiently use language models for various applications and re
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at this https URL.
Suggested labels
None
The text was updated successfully, but these errors were encountered: