MemGPT

Teaching LLMs memory management for unbounded context

Quick setup

Install dependencies:

pip install -r requirements.txt

Add your OpenAI API key to your environment:

export OPENAI_API_KEY=YOUR_API_KEY

What is MemGPT?

MemoryGPT (or MemGPT in short) is a system that intelligently manages different memory tiers in LLMs in order to effectively provide extended context within the LLM's limited context window. For example, MemGPT knows when to push critical information to a vector database and when to retrieve it later in the chat, enabling perpetual conversations. Learn more about MemGPT in our paper.

Try MemGPT in your CLI

To run MemGPT for as a conversation agent in CLI mode, simply run main.py:

python3 main.py

To create a new starter user or starter persona (that MemGPT gets initialized with), create a new .txt file in /memgpt/humans/examples or /memgpt/personas/examples, then use the --persona or --human flag when running main.py. For example:

# assuming you created a new file /memgpt/humans/examples/me.txt
python main.py --human me.txt

main.py flags

--persona
  load a specific persona file
--human
  load a specific human file
--first
  allows you to send the first message in the chat (by default, MemGPT will send the first message)
--debug
  enables debugging output

Interactive CLI commands

While using MemGPT via the CLI you can run various commands:

/save
  save a checkpoint of the current agent/conversation state
/load
  load a saved checkpoint
/dump
  view the current message log (see the contents of main context)
/memory
  print the current contents of agent memory
/pop
  undo the last message in the conversation
/heartbeat
  send a heartbeat system message to the agent
/memorywarning
  send a memory warning system message to the agent

Support

• By default MemGPT will use gpt-4, so your API key will require gpt-4 API access.

If you have any further questions, or have anything to share, we are excited to hear your feedback!

• For issues and feature requests, please open a GitHub issue.

Datasets

Datasets used in our paper can be downloaded at HuggingFace.