- Charles Packer + Charles Packer, + Vivian Fang, + Shishir G. Patil, + Kevin Lin, + Sarah Wooders, + Joseph E. Gonzalez +
UC Berkeley
@@ -41,8 +47,8 @@
@@ -62,7 +68,7 @@
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
+ The LLM inputs text in it's 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
@@ -122,9 +128,9 @@
Citation
@inproceedings{packer2023memgpt,
- title={{MemGPT}: Towards LLMs as Operating Systems}
+ title={{MemGPT}: Towards LLMs as Operating Systems},
author={Packer, Charles and Fang, Vivian and Patil, Shishir G.
- and Lin, Kevin and Wooders, Sarah and Gonzalez, Joseph E.}
+ and Lin, Kevin and Wooders, Sarah and Gonzalez, Joseph E.},
year={2023}
}