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# MemGPT
By Charles Avery Packer
Brief Introduction
## Get Started

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<!DOCTYPE html>
<html>
<head>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-BFMDYSTN1L"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-BFMDYSTN1L');
</script>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, shrink-to-fit=no">
<title>MemGPT</title>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.5.0/css/bootstrap.min.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Source+Sans+Pro">
<link rel="stylesheet" href="assets/css/Highlight-Clean.css">
<link rel="stylesheet" href="assets/css/styles.css">
<link rel="stylesheet" href="assets/css/Team-Clean.css">
</head>
<body>
<div class="highlight-clean" style="padding-bottom: 10px;">
<div class="container">
<h1 class="text-center">MemGPT: Towards An OS For LLMs</h1>
</div>
<div><p></p></div>
<div class="container" style="max-width: 768px;">
<div class="row">
<div class="col-md-12">
<h4 class="text-center" style="margin: 0px;">
<a class="text-center" href="https://people.eecs.berkeley.edu/~cpacker/">Charles Avery Packer</a>
</h4>
<h4></h4>
<h5 class="text-center">UC Berkeley</h5>
</div>
</div>
</div>
<div><p></p></div>
<div class="buttons" style="margin-bottom: 8px;">
<a class="btn btn-primary" role="button" href="https://memgpt.ai">Paper (Coming Soon)</a>
<a class="btn btn-primary" role="button" href="https://memgpt.ai">Discord (Coming Soon)</a>
<a class="btn btn-primary" role="button" href="https://github.com/cpacker/MemGPT">GitHub</a>
</div>
<div><p></p></div>
<div>
<div class="container" style="max-width: 768px;">
<div class="row">
<div class="col-md-12">
<h3 class="text-center">Teach LLMs to manage their own memory and achieve unbounded context!</h3>
</div>
</div>
</div>
</div>
<div><p></p></div>
<div class="container" style="max-width: 768px;">
<div class="row">
<div class="col-md-12 text-center"><img src="assets/img/memgpt-system-diagram.png"
style="width: 100%;margin-bottom: 8px;" alt="MemGPT system overview">
<em>In MemGPT (components shaded), a fixed-context LLM is augmented with a tiered memory system and a set of functions
that allow it to manage its own memory.
The LLM inputs the text (tokens) in main memory, and outputs text that is interpreted by a parser,
resulting either in a yield or a function call.
MemGPT uses functions to move data between main memory and disk memory.
When the LLM generates a function call, it can request immediate return of execution to chain together functions.
In the case of a yield, the LLM will not be run again until the next external event trigger
(e.g. a user message or scheduled interrupt).
</em>
</div>
</div>
</div>
</div>
<div><p></p></div>
<hr style="max-width: 768px;">
<div class="container" style="max-width: 768px;">
<div class="row">
<div class="col-md-12">
<h2>Overview</h2>
<ul>
<li>LLMs are increasingly being used for perpetual chats. </li>
<li>Challenge: Limited context length makes perpetual chat challenging.</li>
<li>Inspired by memory management in OS, MemGPT uses RAM (in-context), and Disk (Archival) memory to effeciently manage
the limited context length. </li>
<li><mark>With MemGPT, we demonstrate that LLMs can be taught to manage their own memory!</mark>
</li>
</ul>
</div>
</div>
<div><p></p></div>
<hr style="max-width: 768px;">
<div class="container" style="max-width: 768px;">
<div class="row">
<div class="col-md-12">
<h2>Abstract</h2>
<p>
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.
Drawing inspiration from the hierarchical memory systems in traditional operating systems,
we introduce MemGPT (Memory-GPT).
Similar to how an operating system (OS) provides the illusion of large memory resources through
data movement between fast and slow memory,
MemGPT manages tiers of memory to effectively manage extended context within the language model'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 limit, and multi-session chat, where MemGPT enables
conversational agents to remember, reflect, and evolve dynamically through long-term interactions with their users.
<br>
</p>
</div>
</div>
</div>
<hr style="max-width: 768px;">
<div class="container" style="max-width: 768px;">
<div class="row">
<div class="col-md-12">
<h2>Citation</h2>
<code>
@inproceedings{pacher2023memgpt,<br>
&nbsp; title={MemGPT: Towards an OS for LLMs} <br>
&nbsp; author={Packer, Charles Avery} <br>
&nbsp; year={2023},<br>
}
</code></div>
</div>
</div>
</body>
</html>