MemGPT

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MemGPT - the research paper

MemGPT is the name of a research paper that popularized several of the key concepts behind the “LLM Operating System (OS)“:

1. Memory management: In MemGPT, an LLM OS moves data in and out of the context window of the LLM to manage its memory.
2. Memory hierarchy: The “LLM OS” divides the LLM’s memory (aka its “virtual context”, similar to ”virtual memory” in computer systems) into two parts: the in-context memory, and out-of-context memory.
3. Self-editing memory via tool calling: In MemGPT, the “OS” that manages memory is itself an LLM. The LLM moves data in and out of the context window using designated memory-editing tools.
4. Multi-step reasoning using heartbeats: MemGPT supports multi-step reasoning (allowing the agent to take multiple steps in sequence) via the concept of “heartbeats”. Whenever the LLM outputs a tool call, it has to option to request a heartbeat by setting the keyword argument request_heartbeat to true. If the LLM requests a heartbeat, the LLM OS continues execution in a loop, allowing the LLM to “think” again.

You can read more about the MemGPT memory hierarchy and memory management system in our memory concepts guide.

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MemGPT - the agent architecture

MemGPT also refers to a particular agent architecture that was popularized by the paper and adopted widely by other LLM chatbots:

1. Chat-focused core memory: The core memory of a MemGPT agent is split into two parts - the agent’s own persona, and the user information. Because the MemGPT agent has self-editing memory, it can update its own personality over time, as well as update the user information as it learns new facts about the user.
2. Vector database archival memory: By default, the archival memory connected to a MemGPT agent is backed by a vector database, such as Chroma or pgvector. Because in MemGPT all connections to memory are driven by tools, it’s simple to exchange archival memory to be powered by a more traditional database (you can even make archival memory a flatfile if you want!).

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Creating MemGPT agents in the Letta framework

Because Letta was created out of the original MemGPT open source project, it’s extremely easy to make MemGPT agents inside of Letta (the default Letta agent architecture is a MemGPT agent). See our agents overview for a tutorial on how to create MemGPT agents with Letta.

The Letta framework also allow you to make agent architectures beyond MemGPT that differ significantly from the architecture proposed in the research paper - for example, agents with multiple logical threads (e.g. a “concious” and a “subconcious”), or agents with more advanced memory types (e.g. task memory).

Additionally, the Letta framework also allows you to expose your agents as services (over REST APIs) - so you can use the Letta framework to power your AI applications.