📚Memory Stream & Retrieval

A central limitation in modern AI is the restricted context window — LLMs like ChatGPT can only process a finite number of tokens (~128k), meaning older memories are discarded. To simulate long-term cognition, Generative Agents use a Memory Stream, a dynamic, structured log of experiences, to preserve and prioritize meaningful information.Each memory in the stream contains:

• A natural language description

• A creation timestamp

• A last-accessed timestamp

The fundamental unit is an observation — a direct perception or experience. For example:

• Arjay is composing a new song.

• Logan is discussing town planning with Owen.

• Lulu is organizing a bonfire event with Arjay.

• A new visitor arrived at Selena’s tavern.

To keep context relevant, the agent continuously retrieves a subset of these memories based on:

• Recency: Prioritizing recent events using an exponential decay function.

• Importance: Scored by the LLM at memory creation — highly significant events persist longer.

• Relevance: Calculated using embedding similarity between memory text and the current situation.

This ensures that only the most contextually relevant and meaningful memories are fed into the LLM for behavior generation.