Teaching LLMs memory management for unbounded context 📚🦙

MemGPT

🤖 Create perpetual chatbots with self-editing memory!

🗃️ Chat with your data - talk to your SQL database or your local files!

SQL Database

Local files

📄 You can also talk to docs - for example ask about LlamaIndex!

ChatGPT (GPT-4) when asked the same question:

(Question from run-llama/llama_index#7756)

Quick setup

Join Discord and message the MemGPT bot (in the #memgpt channel). Then run the following commands (messaged to "MemGPT Bot"):

• /profile (to create your profile)
• /key (to enter your OpenAI key)
• /create (to create a MemGPT chatbot)

Make sure your privacy settings on this server are open so that MemGPT Bot can DM you:
MemGPT → Privacy Settings → Direct Messages set to ON

You can see the full list of available commands when you enter / into the message box.

What is MemGPT?

Memory-GPT (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.

Running MemGPT Locally

Install dependencies:

pip install -r requirements.txt

Add your OpenAI API key to your environment:

export OPENAI_API_KEY=YOUR_API_KEY

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
--archival_storage_faiss_path=<ARCHIVAL_STORAGE_FAISS_PATH>
  load in document database (backed by FAISS index)
--archival_storage_files="<ARCHIVAL_STORAGE_FILES_GLOB>"
  pre-load files into archival memory
--archival_storage_sqldb=<SQLDB_PATH>
  load in SQL database

Interactive CLI commands

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

/exit
  exit the CLI
/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

Use MemGPT to talk to your Database!

MemGPT's archival memory let's you load your database and talk to it! To motivate this use-case, we have included a toy example.

Consider the test.db already included in the repository.

To talk to this database, run:

python main_db.py  --archival_storage_sqldb=memgpt/personas/examples/sqldb/test.db

And then you can input the path to your database, and your query.

Please enter the path to the database. test.db
...
Enter your message: How old is Bob?
...
🤖 Bob is 25 years old.

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.