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memory-upgrade

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Atualizado28 de maio de 2026 às 19:27

Diagnose and fix broken memory search in OpenClaw. Enables local embeddings, hybrid search (BM25+vector), session transcript indexing, MMR diversity, and temporal decay — all running locally with zero API keys. Use when: memory_search returns empty results, agent has poor cross-session recall, user wants to upgrade their memory system, or after a fresh OpenClaw install.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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profbernardoj

profbernardoj/everclaw-community-branches

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

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SKILL.md

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name	memory-upgrade
description	Diagnose and fix broken memory search in OpenClaw. Enables local embeddings, hybrid search (BM25+vector), session transcript indexing, MMR diversity, and temporal decay — all running locally with zero API keys. Use when: memory_search returns empty results, agent has poor cross-session recall, user wants to upgrade their memory system, or after a fresh OpenClaw install.

Memory Upgrade

Most OpenClaw installs have broken memory search — the memory_search tool returns empty results because no embedding provider is configured. OpenClaw auto-detects OpenAI → Google → Voyage keys; if none exist, embeddings stay disabled silently.

This skill fixes it with fully local inference. No API keys. No data leaves the machine.

Quick Start

# 1. Diagnose
bash scripts/diagnose.sh

# 2. Fix (patches openclaw.json, restart [REDACTED] after)
bash scripts/configure.sh

# 3. Restart [REDACTED]
openclaw [REDACTED] restart

# 4. Verify (waits for indexing, runs test query)
bash scripts/verify.sh

Optional Enhancements

# Organize memory files into clean directory structure
bash scripts/organize.sh

# Add YAML frontmatter tags to untagged files
bash scripts/tag.sh

What Gets Enabled

Feature	Details
Local embeddings	embeddinggemma-300m (~328MB GGUF, auto-downloads)
Hybrid search	BM25 keyword + vector semantic (70/30 weight)
Session transcripts	Past conversations become searchable
MMR diversity	Reduces duplicate/overlapping results (λ=0.7)
Temporal decay	Recent memories rank higher (30-day half-life)
Embedding cache	50k entries, avoids re-embedding unchanged text
File watcher	Auto-reindexes when memory files change

How It Works

Patches agents.defaults.memorySearch in openclaw.json
Uses node-llama-cpp (ships with OpenClaw) for local embeddings
Vector search via sqlite-vec (ships with OpenClaw)
No external dependencies required

Notes

First search after restart may be slow (model loads into memory)
Initial indexing takes 30-120s depending on file count
Embedding model runs on CPU (ARM/x86), ~768-dim vectors
Compatible with existing memory files — no migration needed

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name	memory-upgrade
description	Diagnose and fix broken memory search in OpenClaw. Enables local embeddings, hybrid search (BM25+vector), session transcript indexing, MMR diversity, and temporal decay — all running locally with zero API keys. Use when: memory_search returns empty results, agent has poor cross-session recall, user wants to upgrade their memory system, or after a fresh OpenClaw install.

Memory Upgrade

This skill fixes it with fully local inference. No API keys. No data leaves the machine.

Quick Start

# 1. Diagnose
bash scripts/diagnose.sh

# 2. Fix (patches openclaw.json, restart [REDACTED] after)
bash scripts/configure.sh

# 3. Restart [REDACTED]
openclaw [REDACTED] restart

# 4. Verify (waits for indexing, runs test query)
bash scripts/verify.sh

Optional Enhancements

# Organize memory files into clean directory structure
bash scripts/organize.sh

# Add YAML frontmatter tags to untagged files
bash scripts/tag.sh

What Gets Enabled

Feature	Details
Local embeddings	embeddinggemma-300m (~328MB GGUF, auto-downloads)
Hybrid search	BM25 keyword + vector semantic (70/30 weight)
Session transcripts	Past conversations become searchable
MMR diversity	Reduces duplicate/overlapping results (λ=0.7)
Temporal decay	Recent memories rank higher (30-day half-life)
Embedding cache	50k entries, avoids re-embedding unchanged text
File watcher	Auto-reindexes when memory files change

How It Works

Patches agents.defaults.memorySearch in openclaw.json
Uses node-llama-cpp (ships with OpenClaw) for local embeddings
Vector search via sqlite-vec (ships with OpenClaw)
No external dependencies required

Notes

First search after restart may be slow (model loads into memory)
Initial indexing takes 30-120s depending on file count
Embedding model runs on CPU (ARM/x86), ~768-dim vectors
Compatible with existing memory files — no migration needed