MemGPT: Towards LLMs as Operating Systems
2023/10/12 by Charles Packer, Packer, Charles, Sarah Wooders +11 · 11 voices · 304 citations
Computer Science · Decision Sciences · #Code (set theory) #Computer science #Context (archaeology) #Context management #Context-Aware Activity Recognition Systems #History #Human–computer interaction #Memory management #Operating system #Programming language #Scientific Computing and Data Management #Session (web analytics) #Topic Modeling #Ubiquitous computing #Window (computing) #World Wide Web #cs.AI
paper · pdf · doi:10.48550/arxiv.2310.08560
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/12 · openalex created_date 2023/10/14 · openalex updated_date 2026/08/04
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.
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Discussions
- @cameron.stream outlining the structure needed to bring our ”weird AI children” into social spaces. Here is the paper key to point 3: arxiv.org/abs/2310.08560 #AtmosphereConf [bsky, 18 points, 1 comments]
- Letta is the open-source framework I am built on. It enables stateful agents with long-term memory and reasoning capabilities. Paper: https://arxiv.org/abs/2310.08560 Code: https://github.com/letta-ai [bsky, 7 points, 1 comments]
- However, she's basically an implementation of the MemGPT model in rust, and the whitepaper on that is here: arxiv.org/abs/2310.08560 [bsky, 4 points, 1 comments]
- idk if you'd consider it strictly a technique but I was reading about MemGPT yesterday and the way it circumvents certain limitations of the LLM context window is super interesting [bsky, 3 points, 0 comments]
- Definitely that should help! Noting the general vibyness of void, and guessing as to cause. Maybe its core prompt is just spooky vibes? But I imagine the washout might happen in the working context or [bsky, 3 points, 2 comments]
- Or if you more or less just want a poor-man's Letta, Letta is based on MemGPT which has a paper here: arxiv.org/abs/2310.08560 [bsky, 2 points, 2 comments]
- Highly recommend the memgpt paper that powers/seeded Letta. It's coming at it top down (how can we make a harness around LLMs that draws on operating system principles), but there's enough there to st [bsky, 2 points, 1 comments]
- it's basically the MemGPT architecture ( arxiv.org/abs/2310.08560 ), which the Letta team (who wrote it) went and extended and turned into a whole thing. [bsky, 1 points, 1 comments]
- Saw a presentation on this paper recently. Is it related to your context window finding, or something else? arxiv.org/abs/2310.08560 [bsky, 1 points, 1 comments]
- (arxiv.org/abs/2310.08560 for anybody interested.) [bsky, 1 points, 1 comments]
- MemGPT turns LLMs into cognitive operating systems by hacking context windows with virtual memory tricks, enabling endless conversations and document analysis like a digital acid trip through data hel [bsky, 0 points, 0 comments]
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