[2310.08560] MemGPT: Towards LLMs as Operating Systems
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MemGPT proposes virtual context management to overcome LLM context window limitations, relevant to AI safety considerations around model scalability, controllability, and long-context reasoning capabilities.
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Abstract
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this technique, we introduce MemGPT (Memory-GPT), a system that intelligently manages different memory tiers in order to effectively provide extended context within the LLM's limited context window, and utilizes interrupts to manage control flow between itself and the user. We evaluate our OS-inspired design in two domains where the limited context windows of modern LLMs severely handicaps their performance: document analysis, where MemGPT is able to analyze large documents that far exceed the underlying LLM's context window, and multi-session chat, where MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users. We release MemGPT code and data for our experiments at https://memgpt.ai.
Summary
MemGPT addresses the fundamental limitation of LLMs' finite context windows by implementing virtual context management, inspired by hierarchical memory systems in operating systems. The system intelligently manages multiple memory tiers to provide the appearance of extended context, enabling LLMs to process documents far larger than their native context window and maintain coherent long-term conversations. The authors demonstrate MemGPT's effectiveness in document analysis and multi-session chat applications, where it enables conversational agents to remember, reflect, and evolve through extended interactions.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Long-Horizon Autonomous Tasks | Capability | 65.0 |
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# MemGPT: Towards LLMs as Operating Systems
Charles Packer
Sarah Wooders
Kevin Lin
Vivian Fang
Shishir G. Patil
Ion Stoica
Joseph E. Gonzalez
###### Abstract
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems which provide the illusion of an extended virtual memory via paging between physical memory and disk.
Using this technique, we introduce MemGPT (MemoryGPT), a system that intelligently manages different storage tiers in order to effectively provide extended context within the LLM’s limited context window.
We evaluate our OS-inspired design in two domains where the limited context windows of modern LLMs severely handicaps their performance: document analysis, where MemGPT is able to analyze large documents that far exceed the underlying LLM’s context window, and multi-session chat, where MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users.
We release MemGPT code and data for our experiments at [https://research.memgpt.ai](https://research.memgpt.ai/ "").
Machine Learning, ICML
## 1 Introduction
In recent years, large language models (LLMs) and their underlying transformer architecture (Vaswani et al., [2017](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib30 ""); Devlin et al., [2018](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib7 ""); Brown et al., [2020](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib3 ""); Ouyang et al., [2022](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib22 "")) have become the cornerstone of conversational AI and have led to a wide array of consumer and enterprise applications. Despite these advances, the limited fixed-length context windows used by LLMs significantly hinders their applicability to long conversations or reasoning about long documents.
For example, the most widely used open-source LLMs can only support a few dozen back-and-forth messages or
reason about a short document
before exceeding their maximum input length (Touvron et al., [2023](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib28 "")).
Directly extending the context length of transformers incurs a quadratic increase in computational time and memory cost due to the transformer architecture’s self-attention mechanism, making the design of new long-context architectures a pressing research challenge (Dai et al., [2019](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib6 ""); Kitaev et al., [2020](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib14 ""); Beltagy et al., [2020](https://ar5iv.labs.arxiv.org/html/2310.08560#bib.bib1 "")).
While developing longer models is an active area of research (Dong et al., [20
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