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Shishir Patil
2023-10-11 11:53:37 -07:00
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<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://memgpt.ai">Discord</a>
<a class="btn btn-primary" role="button" href="https://github.com/cpacker/MemGPT">GitHub</a>
</div>
<div><p></p></div>
@@ -50,7 +50,7 @@
<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>
<h3 class="text-center">Teach LLMs to manage their own memory for unbounded context!</h3>
</div>
</div>
@@ -61,14 +61,12 @@
<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>In MemGPT, 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 takes as input the text in main context (capped at the size of the standard LLM context window), 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
context and external context. 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>
@@ -97,18 +95,20 @@
<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.
Large language models (LLMs) have revolutionized AI but are constrained by
limited context windows, hindering their utility in tasks like extended conversa-
tions 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 man-
ages 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 per-
formance: 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 dynam-
ically through long-term interactions with their users. Code and Data is available at https://memgpt.ai
<br>
</p>
</div>
@@ -121,10 +121,11 @@
<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>
@inproceedings{packer2023memgpt,<br>
&nbsp; title={{MemGPT}: Towards LLMs as Operating Systems} <br>
&nbsp; author={Packer, Charles and Fang, Vivian and Patil, Shishir G. <br>
&nbsp; and Lin, Kevin and Wooders, Sarah and Gonzalez, Joseph E.} <br>
&nbsp; year={2023}<br>
}
</code></div>
</div>