Use Case · memory architecture · self-modeling

OpenClaw Inside-Out-2 Memory: From Session Logs to Beliefs and Self-Model Updates

The OpenClaw Showcase describes a community memory manager that promotes session data into memories, then beliefs, then an evolving self model with explicit separation between layers.

Last updated: 2026-03-10 · Language: English

0) TL;DR (3-minute launch)

  • Session logs contain useful facts, but they are noisy and unstable.
  • Workflow in short: Session files and event logs → extract candidate memories with evidence → score by recurrence and impact → propose belief updates (add/modify/remove) → require human review for belief promotion → update self-model snapshot → publish changelog and rollback pointer
  • Start fast: Keep three separate stores: raw observations, reviewed memories, stable beliefs.
  • Guardrail: Never treat inferred beliefs as user-confirmed facts.

1) What problem this solves

Session logs contain useful facts, but they are noisy and unstable. A layered memory pipeline helps keep short-term notes separate from long-term beliefs, reducing inconsistent behavior caused by one-off chat artifacts.

2) Who this is for

  • Operators designing long-term memory behavior for AI assistants
  • Teams that need auditable transitions from observations to stable beliefs
  • Builders experimenting with agent self-model updates under strict review

3) Workflow map

Session files and event logs
   -> extract candidate memories with evidence
   -> score by recurrence and impact
   -> propose belief updates (add/modify/remove)
   -> require human review for belief promotion
   -> update self-model snapshot
   -> publish changelog and rollback pointer

4) MVP setup

  • Keep three separate stores: raw observations, reviewed memories, stable beliefs
  • Define promotion criteria (minimum evidence count and recency window)
  • Run weekly belief-review sessions with explicit accept/reject decisions
  • Store every self-model change with before/after diffs
  • Add one-command rollback to previous belief snapshots

5) Prompt template

You are a memory curator.

Input: session logs and existing belief set.
Output:
1) candidate memories with supporting evidence,
2) proposed belief changes,
3) confidence and conflict notes,
4) rollback-safe patch format.

Rules:
- keep observations separate from beliefs,
- do not promote a belief without evidence,
- flag contradictions explicitly.

6) Cost and payoff

Cost

Ongoing review overhead and memory quality evaluation infrastructure.

Payoff

More consistent assistant behavior across long-running sessions.

Scale

Add automated contradiction checks and domain-specific belief taxonomies.

7) Risk boundaries

  • Never treat inferred beliefs as user-confirmed facts
  • Keep human approval mandatory for high-impact self-model changes
  • Store provenance for every belief so corrections are possible
  • Avoid retaining sensitive personal data longer than policy allows

8) Related use cases

Source links

Implementation links and next steps

Install OpenClaw →How to use OpenClaw →Command reference →