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Example Conversation

This example is designed to show what MemOS feels like when the retrieval layer is doing useful work instead of just dumping raw context.

Conversation

User: I am building MemOS as a local-first memory layer for LLMs.
User: !remember The forgetting engine is the most novel part and should stay visible in the UI.
User: The dashboard should show a live graph, a retrieval panel, and a decay preview.

Later in the same session:

User: What should the assistant remember about this project?

Retrieved context

[MEMORY CONTEXT]
Use these memories if they improve the answer:
1. [PROJECT] I am building MemOS as a local-first memory layer for LLMs (importance=0.92, last_seen=0h ago)
2. [PREFERENCE] The forgetting engine is the most novel part and should stay visible in the UI (importance=0.84, last_seen=0h ago)
3. [FACT] The dashboard should show a live graph, a retrieval panel, and a decay preview (importance=0.77, last_seen=0h ago)

Why this example works

  • It mixes stable project facts with a design preference, which is exactly where conversational memory becomes more useful than plain document retrieval.
  • The !remember message becomes a pinned memory, so the most strategic product choice remains available even after normal facts decay.
  • The answer the LLM produces can now preserve intent, not just keywords.