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 sessions. Use when: (1) agent needs to recall user preferences, context, or decisions from past conversations, (2) starting a new session and needing context continuity, (3) user says 'remember this', 'you should know', or 'like last time', (4) agent detects important information worth persisting, (5) periodic memory maintenance during heartbeats. NOT for: simple Q&A with no persistence need.

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

markdown

# 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

text

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

markdown

# [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:

bash

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

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

markdown

# 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.

text

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.

text

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.