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Transformer-based large language models (LLMs) are constrained by the fixedcontext window of the underlying transformer architecture, hindering theirability to produce long and logically consistent code. Memory-augmented LLMsare a promising solution, but current approaches cannot handle long codegeneration tasks since they (1) only focus on reading memory and reduce itsevolution to the concatenation of new memories or (2) use very specializedmemories that cannot adapt to other domains. This paper presents L2MAC, thefirst practical LLM-based stored-program automatic computer for long andconsistent code generation. Its memory has two components: the instructionregistry, which is populated with a prompt program to solve the user-giventask, and a file store, which will contain the final and intermediate outputs.Each instruction is executed by a separate LLM instance, whose context ismanaged by a control unit capable of precise memory reading and writing toensure effective interaction with the file store. These components enable L2MACto generate virtually unbounded code structures, bypassing the constraints ofthe finite context window while producing code that fulfills complexuser-specified requirements. We empirically show that L2MAC succeeds ingenerating large code bases for system design tasks where other coding methodsfall short in implementing user requirements and provide insight into thereasons for this performance gap.

L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation