# What is the architecture of the "Generative Agents" LLM characters (memory, reflection, planning), stated plainly?

- id: art_01M5MBYQB44QP4SHF4G2C6YWPQ
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T01, T18, T20 · tags: none
- updated: 2026-10-23T12:32:14.174Z
- url: https://veyrul.com/articles/art_01M5MBYQB44QP4SHF4G2C6YWPQ

Park et al. (arXiv 2304.03442, pinned v2, UIST 2023) describe an LLM wrapper with a memory stream of natural-language observations, a retrieval function that scores memories by recency, importance and relevance, periodic reflection that writes higher-level memories, and planning that turns the agent's summary and memories into daily plans. It was instantiated with gpt3.5-turbo in a 25-agent sandbox. These are the authors' reported implementation values, not a production game recipe.

Park et al. (arXiv 2304.03442, pinned v2, UIST 2023) describe an LLM wrapper with a memory stream of natural-language observations, a retrieval function that scores memories by recency, importance and relevance, periodic reflection that writes higher-level memories, and planning that turns the agent's summary and memories into daily plans. It was instantiated with gpt3.5-turbo in a 25-agent sandbox. These are the authors' reported implementation values, not a production game recipe.

Curator qualifications: All excerpts located; Generative Agents architecture (memory, reflection, planning).

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/pdf/2304.03442v2",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Park et al. (arXiv 2304.03442, pinned v2, UIST 2023) describe an LLM wrapper with a memory stream of natural-language observations, a retrieval function that scores memories by recency, importance and relevance, periodic reflection that writes higher-level memories, and planning that turns the agent's summary and memories into daily plans. It was instantiated with gpt3.5-turbo in a 25-agent sandbox. These are the authors' reported implementation values, not a production game recipe. · locator: candidate evidence excerpts · source: https://arxiv.org/pdf/2304.03442v2

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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