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How to save a memory snapshot and resume it in a new session with MemT 1 0

Store a model's memory state to a file and restore it later without the old chat transcript.

  • Research
  • Advanced
  • 2 hours
  • From the README
  • Any agent

Who it is forA developer building an assistant that must pick up where a user left off after a restart or a new process.

The problemRestarting a conversation normally loses everything the model learned about the user, or forces the app to store and resend full transcripts.

The outcomeA snapshot file in the server's state directory that a new session can load, with zero stored messages and the earlier facts still answerable.

What you need first

  • Linux server with NVIDIA CUDA and Python 3.11 or newer
  • uv installed for the inference install step
  • A complete local MemT-1.0-Nano checkpoint with BF16 weights, configuration and tokenizer assets
  • Two GPUs for the Nano example in the README
  • Write access to ./memt-state on the server for memory snapshots

Steps

  1. Complete the server setup from the persistent memory chat use case so start_memt_nano.sh is running.
  2. In your Python session, call snapshot = session.save_state("memmy_memory.safetensors") to commit pending turns and save memory.
  3. Run session.close() to end the original session.
  4. Start a new MemTSession with state=snapshot["path"] and check that len(restored.messages) prints 0.
  5. Ask a question that depends on the saved facts and confirm the reply uses them.

Prompt for your agent

Using the MemTensor/MemT-1.0 repo and a running MemT-1.0-Nano server, save memory from a session to <snapshot-name>.safetensors, then restore it in a new MemTSession. Expected output: restored.messages has length 0 and a reply to a fact-based question is correct. Verify the returned snapshot path is used as the state argument.

Caveat. Snapshots are saved in the server's configured state directory, so the restored session only works against a server that can read that directory.

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