  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 https://github.com/geekan/MetaGPT
