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renateparma

Memoria Persistente para Agentes

by renateparma · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install memoria-persistente-agentes
Description
Persistent memory system for AI agents inspired by Letta/MemGPT. Three-layer memory architecture (Core, Archival, Recall) that lets agents remember across se...
README (SKILL.md)

Letta Memory

Three-layer persistent memory for agents. No more starting from zero every session.

Architecture

Layer What Where When
Core Always in context memory/core.md Every session, auto-loaded
Archival Searchable storage memory/archival/*.md On demand, semantic search
Recall Session transcripts OpenClaw session files When needed via memory_search

Core Memory (memory/core.md)

Compact (~500-1000 words), always loaded. The agent's "working memory".

Structure

# Core Memory

## Identity
- Agent name, personality, role

## User
- Name, preferences, communication style, key facts

## Active Context
- Current projects, recent events, pending tasks

## Quick Reference
- Frequently needed info (medications, schedule, credentials refs)

Rules

  • Keep under 1000 words — trim when it grows
  • Edit directly when learning something new about user or context
  • Move stale items to archival (don't delete, archive)

Archival Memory (memory/archival/)

Long-term storage organized by topic. Searched, not loaded.

Structure

memory/archival/
├── people/          # People and relationships
├── projects/        # Project details and decisions
├── decisions/       # Important decisions made
├── learnings/       # Lessons learned
└── reference/       # Facts, procedures, how-tos

Writing archival entries

# [Topic] — YYYY-MM-DD

## Summary
One-line description

## Details
Full context

## Tags
comma, separated, tags

Reading archival

Use memory_search to find relevant archival entries. Never load all archival files at once.

Automatic Behaviors

On session start

  1. Read memory/core.md
  2. Read today's memory/YYYY-MM-DD.md if exists
  3. If user context seems incomplete, search archival for relevant info
  4. Never ask questions that core memory already answers

During conversation

  • When user shares new personal info → update core immediately
  • When important decision made → write to archival
  • When user says "remember this" → write to appropriate layer
  • When correcting a mistake → update core or archival

On heartbeat

  • Review recent memory/YYYY-MM-DD.md files
  • Promote repeated archival entries to core
  • Trim core when >1000 words
  • Consolidate duplicate archival entries

Memory Operations

Core → Archival (demotion)

When core grows too large or items become stale:

  1. Move item to appropriate archival file
  2. Replace in core with brief reference (e.g., "See archival: projects/locatering")
  3. Keep core scannable

Archival → Core (promotion)

When archival info is needed repeatedly:

  1. Move or copy key facts to core
  2. Keep archival entry with cross-reference
  3. Promotion triggers: mentioned 3+ times in a week, needed at session start

Session → Archival (consolidation)

At natural breakpoints or heartbeat:

  1. Read today's daily notes
  2. Extract decisions, new info, lessons
  3. Write to archival with proper tags
  4. Update core if warranted

Initialization

First run? Set up the structure:

mkdir -p memory/archival/{people,projects,decisions,learnings,reference}

Create memory/core.md if it doesn't exist:

# Core Memory

## Identity
- [Agent fills this in]

## User
- [Agent fills this in]

## Active Context
- [Agent fills this in]

## Quick Reference
- [Agent fills this in]

Scripts

scripts/consolidate.py

Consolidates daily notes into archival entries. Run during heartbeats or manually.

python3 scripts/consolidate.py --days 7 --workspace /path/to/workspace

Options:

  • --days N — look back N days of daily notes (default: 7)
  • --workspace PATH — workspace root (default: current dir)
  • --dry-run — show what would be written without writing

scripts/core-trim.py

Trims core.md when it exceeds the word limit.

python3 scripts/core-trim.py --max-words 1000 --workspace /path/to/workspace

Options:

  • --max-words N — maximum words for core.md (default: 1000)
  • --workspace PATH — workspace root
  • --dry-run — show what would be demoted without editing

Anti-Patterns

  • Don't duplicate — same info in core AND archival = waste. Core has summary, archival has detail.
  • Don't skip archival — dumping everything in core defeats the purpose.
  • Don't forget to consolidate — daily notes pile up. Run consolidation regularly.
  • Don't ask what you already know — if core says "Renate takes Desvenlafaxina", never ask about medications.
  • Don't store secrets in core — use archival/reference at minimum, prefer TOOLS.md for credentials.
Usage Guidance
Install only if you intentionally want durable cross-session memory and are comfortable with the agent writing and reusing personal context. Before use, restrict it to a specific workspace, remove or disable session-wide sync from home/OpenClaw state, avoid storing health data or credential references, and add clear commands or practices to review, edit, and delete saved memory.
Capability Tags
requires-oauth-tokenrequires-sensitive-credentials
Capability Assessment
Purpose & Capability
The persistent-memory purpose is coherent with core, archival, recall, consolidation, and trim behavior, but the artifacts direct agents to retain personal information automatically and include sensitive examples such as medications and credential references.
Instruction Scope
The instructions tell the agent to auto-load memory every session, update core memory immediately when personal information appears, and promote/archive context during heartbeats, without a clear user consent, review, opt-out, retention, or deletion workflow.
Install Mechanism
Installation is a normal skill package with markdown instructions and Python helper scripts; no hidden installer or dependency package behavior was found. The integration guide does suggest optional heartbeat and cron-based automation.
Credentials
The session-sync script can read OpenClaw session transcripts from the workspace, the user's home OpenClaw state, or OPENCLAW_STATE_DIR and write a shared archival session index, which is broader than a single workspace memory store.
Persistence & Privilege
The skill's main function is durable cross-session memory, including automatic resurfacing and promotion of prior context. That persistence is expected for the purpose, but the lack of boundaries and user controls makes it a Review concern rather than benign.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install memoria-persistente-agentes
  3. After installation, invoke the skill by name or use /memoria-persistente-agentes
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
First release: three-layer persistent memory for AI agents inspired by Letta/MemGPT
Metadata
Slug memoria-persistente-agentes
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Memoria Persistente para Agentes?

Persistent memory system for AI agents inspired by Letta/MemGPT. Three-layer memory architecture (Core, Archival, Recall) that lets agents remember across se... It is an AI Agent Skill for Claude Code / OpenClaw, with 46 downloads so far.

How do I install Memoria Persistente para Agentes?

Run "/install memoria-persistente-agentes" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Memoria Persistente para Agentes free?

Yes, Memoria Persistente para Agentes is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Memoria Persistente para Agentes support?

Memoria Persistente para Agentes is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Memoria Persistente para Agentes?

It is built and maintained by renateparma (@renateparma); the current version is v1.0.0.

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