Trending-skills memory-lancedb-pro-openclaw

Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart auto-capture.

install

source · Clone the upstream repo

git clone https://github.com/Aradotso/trending-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/Aradotso/trending-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/memory-lancedb-pro-openclaw" ~/.claude/skills/aradotso-trending-skills-memory-lancedb-pro-openclaw && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/Aradotso/trending-skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/memory-lancedb-pro-openclaw" ~/.openclaw/skills/aradotso-trending-skills-memory-lancedb-pro-openclaw && rm -rf "$T"

manifest: skills/memory-lancedb-pro-openclaw/SKILL.md

source content

memory-lancedb-pro OpenClaw Plugin

Skill by ara.so — Daily 2026 Skills collection.

memory-lancedb-pro

is a production-grade long-term memory plugin for OpenClaw agents. It stores preferences, decisions, and project context in a local LanceDB vector database and automatically recalls relevant memories before each agent reply. Key features: hybrid retrieval (vector + BM25 full-text), cross-encoder reranking, LLM-powered smart extraction (6 categories), Weibull decay-based forgetting, multi-scope isolation (agent/user/project), and a full management CLI.

Installation

Option A: One-Click Setup Script (Recommended)

curl -fsSL https://raw.githubusercontent.com/CortexReach/toolbox/main/memory-lancedb-pro-setup/setup-memory.sh -o setup-memory.sh
bash setup-memory.sh

Flags:

bash setup-memory.sh --dry-run       # Preview changes only
bash setup-memory.sh --beta          # Include pre-release versions
bash setup-memory.sh --uninstall     # Revert config and remove plugin
bash setup-memory.sh --selfcheck-only  # Health checks, no changes

The script handles fresh installs, upgrades from git-cloned versions, invalid config fields, broken CLI fallback, and provider presets (Jina, DashScope, SiliconFlow, OpenAI, Ollama).

Option B: OpenClaw CLI

openclaw plugins install memory-lancedb-pro@beta

Option C: npm

npm i memory-lancedb-pro@beta

Critical: When installing via npm, you must add the plugin's absolute install path to
plugins.load.paths
in
openclaw.json
. This is the most common setup issue.

Minimal Configuration (

openclaw.json

)

{
  "plugins": {
    "load": {
      "paths": ["/absolute/path/to/node_modules/memory-lancedb-pro"]
    },
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "embedding": {
            "provider": "openai-compatible",
            "apiKey": "${OPENAI_API_KEY}",
            "model": "text-embedding-3-small"
          },
          "autoCapture": true,
          "autoRecall": true,
          "smartExtraction": true,
          "extractMinMessages": 2,
          "extractMaxChars": 8000,
          "sessionMemory": { "enabled": false }
        }
      }
    }
  }
}

Why these defaults:

```
autoCapture
```
+
```
smartExtraction
```
→ agent learns from conversations automatically, no manual calls needed
```
autoRecall
```
→ memories injected before each reply
```
extractMinMessages: 2
```
→ triggers in normal two-turn chats
```
sessionMemory.enabled: false
```
→ avoids polluting retrieval with session summaries early on

Full Production Configuration

{
  "plugins": {
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "embedding": {
            "provider": "openai-compatible",
            "apiKey": "${OPENAI_API_KEY}",
            "model": "text-embedding-3-small",
            "baseURL": "https://api.openai.com/v1"
          },
          "reranker": {
            "provider": "jina",
            "apiKey": "${JINA_API_KEY}",
            "model": "jina-reranker-v2-base-multilingual"
          },
          "extraction": {
            "provider": "openai-compatible",
            "apiKey": "${OPENAI_API_KEY}",
            "model": "gpt-4o-mini"
          },
          "autoCapture": true,
          "captureAssistant": false,
          "autoRecall": true,
          "smartExtraction": true,
          "extractMinMessages": 2,
          "extractMaxChars": 8000,
          "enableManagementTools": true,
          "retrieval": {
            "mode": "hybrid",
            "vectorWeight": 0.7,
            "bm25Weight": 0.3,
            "topK": 10
          },
          "rerank": {
            "enabled": true,
            "type": "cross-encoder",
            "candidatePoolSize": 12,
            "minScore": 0.6,
            "hardMinScore": 0.62
          },
          "decay": {
            "enabled": true,
            "model": "weibull",
            "halfLifeDays": 30
          },
          "sessionMemory": { "enabled": false },
          "scopes": {
            "agent": true,
            "user": true,
            "project": true
          }
        }
      }
    }
  }
}

Provider Options for Embedding

Provider	`provider` value	Notes
OpenAI / compatible	`"openai-compatible"`	Requires `apiKey` , optional `baseURL`
Jina	`"jina"`	Requires `apiKey`
Gemini	`"gemini"`	Requires `apiKey`
Ollama	`"ollama"`	Local, zero API cost, set `baseURL`
DashScope	`"dashscope"`	Requires `apiKey`
SiliconFlow	`"siliconflow"`	Requires `apiKey` , free reranker tier

Deployment Plans

Full Power (Jina + OpenAI):

{
  "embedding": { "provider": "jina", "apiKey": "${JINA_API_KEY}", "model": "jina-embeddings-v3" },
  "reranker": { "provider": "jina", "apiKey": "${JINA_API_KEY}", "model": "jina-reranker-v2-base-multilingual" },
  "extraction": { "provider": "openai-compatible", "apiKey": "${OPENAI_API_KEY}", "model": "gpt-4o-mini" }
}

Budget (SiliconFlow free reranker):

{
  "embedding": { "provider": "openai-compatible", "apiKey": "${OPENAI_API_KEY}", "model": "text-embedding-3-small" },
  "reranker": { "provider": "siliconflow", "apiKey": "${SILICONFLOW_API_KEY}", "model": "BAAI/bge-reranker-v2-m3" },
  "extraction": { "provider": "openai-compatible", "apiKey": "${OPENAI_API_KEY}", "model": "gpt-4o-mini" }
}

Fully Local (Ollama, zero API cost):

{
  "embedding": { "provider": "ollama", "baseURL": "http://localhost:11434", "model": "nomic-embed-text" },
  "extraction": { "provider": "ollama", "baseURL": "http://localhost:11434", "model": "llama3" }
}

CLI Reference

Validate config and restart after any changes:

openclaw config validate
openclaw gateway restart
openclaw logs --follow --plain | grep "memory-lancedb-pro"

Expected startup log output:

memory-lancedb-pro: smart extraction enabled
memory-lancedb-pro@1.x.x: plugin registered

Memory Management CLI

# Stats overview
openclaw memory-pro stats

# List memories (with optional scope/filter)
openclaw memory-pro list
openclaw memory-pro list --scope user --limit 20
openclaw memory-pro list --filter "typescript"

# Search memories
openclaw memory-pro search "coding preferences"
openclaw memory-pro search "database decisions" --scope project

# Delete a memory by ID
openclaw memory-pro forget <memory-id>

# Export memories (for backup or migration)
openclaw memory-pro export --scope global --output memories-backup.json
openclaw memory-pro export --scope user --output user-memories.json

# Import memories
openclaw memory-pro import --input memories-backup.json

# Upgrade schema (when upgrading plugin versions)
openclaw memory-pro upgrade --dry-run   # Preview first
openclaw memory-pro upgrade             # Run upgrade

# Plugin info
openclaw plugins info memory-lancedb-pro

MCP Tool API

The plugin exposes MCP tools to the agent. Core tools are always available; management tools require

enableManagementTools: true

in config.

Core Tools (always available)

memory_recall

Retrieve relevant memories for a query.

// Agent usage pattern
const results = await memory_recall({
  query: "user's preferred code style",
  scope: "user",        // "agent" | "user" | "project" | "global"
  topK: 5
});

memory_store

Manually store a memory.

await memory_store({
  content: "User prefers tabs over spaces, always wants error handling",
  category: "preference",   // "profile" | "preference" | "entity" | "event" | "case" | "pattern"
  scope: "user",
  tags: ["coding-style", "typescript"]
});

memory_forget

Delete a specific memory by ID.

await memory_forget({ id: "mem_abc123" });

memory_update

Update an existing memory.

await memory_update({
  id: "mem_abc123",
  content: "User now prefers 2-space indentation (changed from tabs on 2026-03-01)",
  category: "preference"
});

Management Tools (requires

enableManagementTools: true

)

memory_stats

const stats = await memory_stats({ scope: "global" });
// Returns: total count, category breakdown, decay stats, db size

memory_list

const list = await memory_list({ scope: "user", limit: 20, offset: 0 });

self_improvement_log

Log an agent learning event for meta-improvement tracking.

await self_improvement_log({
  event: "user corrected indentation preference",
  context: "User asked me to switch from tabs to spaces",
  improvement: "Updated coding-style preference memory"
});

self_improvement_extract_skill

Extract a reusable pattern from a conversation.

await self_improvement_extract_skill({
  conversation: "...",
  domain: "code-review",
  skillName: "typescript-strict-mode-setup"
});

self_improvement_review

Review and consolidate recent self-improvement logs.

await self_improvement_review({ days: 7 });

Smart Extraction: 6 Memory Categories

When

smartExtraction: true

, the LLM automatically classifies memories into:

Category	What gets stored	Example
`profile`	User identity, background	"User is a senior TypeScript developer"
`preference`	Style, tool, workflow choices	"Prefers functional programming patterns"
`entity`	Projects, people, systems	"Project 'Falcon' uses PostgreSQL + Redis"
`event`	Decisions made, things that happened	"Chose Vite over webpack on 2026-02-15"
`case`	Solutions to specific problems	"Fixed CORS by adding proxy in vite.config.ts"
`pattern`	Recurring behaviors, habits	"Always asks for tests before implementation"

Hybrid Retrieval Internals

With

retrieval.mode: "hybrid"

, every recall runs:

Vector search — semantic similarity via embeddings (weight:
```
vectorWeight
```
, default 0.7)
BM25 full-text search — keyword matching (weight:
```
bm25Weight
```
, default 0.3)
Score fusion — results merged with weighted RRF (Reciprocal Rank Fusion)
Cross-encoder rerank — top
```
candidatePoolSize
```
candidates reranked by a cross-encoder model
Score filtering — results below
```
hardMinScore
```
are dropped

"retrieval": {
  "mode": "hybrid",
  "vectorWeight": 0.7,
  "bm25Weight": 0.3,
  "topK": 10
},
"rerank": {
  "enabled": true,
  "type": "cross-encoder",
  "candidatePoolSize": 12,
  "minScore": 0.6,
  "hardMinScore": 0.62
}

Retrieval mode options:

```
"vector"
```
— pure semantic search only
```
"bm25"
```
— pure keyword search only
```
"hybrid"
```
— both fused (recommended)

Multi-Scope Isolation

Scopes let you isolate memories by context. Enabling all three gives maximum flexibility:

"scopes": {
  "agent": true,    // Memories specific to this agent instance
  "user": true,     // Memories tied to a user identity
  "project": true   // Memories tied to a project/workspace
}

When recalling, specify scope to narrow results:

// Get only project-level memories
await memory_recall({ query: "database choices", scope: "project" });

// Get user preferences across all agents
await memory_recall({ query: "coding style", scope: "user" });

// Global recall across all scopes
await memory_recall({ query: "error handling patterns", scope: "global" });

Weibull Decay Model

Memories naturally fade over time. The decay model prevents stale memories from polluting retrieval.

"decay": {
  "enabled": true,
  "model": "weibull",
  "halfLifeDays": 30
}

Memories accessed frequently get their decay clock reset
Important, repeatedly-recalled memories effectively become permanent
Noise and one-off mentions fade naturally after ~30 days

Upgrading

From pre-v1.1.0

# 1. Backup first — always
openclaw memory-pro export --scope global --output memories-backup-$(date +%Y%m%d).json

# 2. Preview schema changes
openclaw memory-pro upgrade --dry-run

# 3. Run the upgrade
openclaw memory-pro upgrade

# 4. Verify
openclaw memory-pro stats

See

CHANGELOG-v1.1.0.md

in the repo for behavior changes and upgrade rationale.

Troubleshooting

Plugin not loading

# Check plugin is recognized
openclaw plugins info memory-lancedb-pro

# Validate config (catches JSON errors, unknown fields)
openclaw config validate

# Check logs for registration
openclaw logs --follow --plain | grep "memory-lancedb-pro"

Common causes:

Missing or relative
```
plugins.load.paths
```
(must be absolute when using npm install)

plugins.slots.memory

not set to

"memory-lancedb-pro"

Plugin not listed under
```
plugins.entries
```

autoRecall

not injecting memories

By default

autoRecall

false

in some versions — explicitly set it to

true

"autoRecall": true

Also confirm the plugin is bound to the

memory

slot, not just loaded.

Jiti cache issues after upgrade

# Clear jiti transpile cache
rm -rf ~/.openclaw/.cache/jiti
openclaw gateway restart

Memories not being extracted from conversations

Check
```
extractMinMessages
```
— must be ≥ number of turns in the conversation (set to
```
2
```
for normal chats)
Check
```
extractMaxChars
```
— very long contexts may be truncated; increase to
```
12000
```
if needed
Verify extraction LLM config has a valid
```
apiKey
```
and reachable endpoint

Check logs:

openclaw logs --follow --plain | grep "extraction"

Retrieval returns nothing or poor results

Confirm
```
retrieval.mode
```
is
```
"hybrid"
```
not
```
"bm25"
```
alone (BM25 requires indexed content)
Lower
```
rerank.hardMinScore
```
temporarily (try
```
0.4
```
) to see if results exist but are being filtered
Check embedding model is consistent between store and recall operations — changing models requires re-embedding

Environment variable not resolving

Ensure env vars are exported in the shell that runs OpenClaw, or use a

.env

file loaded by your process manager. The

${VAR}

syntax in

openclaw.json

is resolved at startup.

export OPENAI_API_KEY="sk-..."
export JINA_API_KEY="jina_..."
openclaw gateway restart

Telegram Bot Quick Config Import

If using OpenClaw's Telegram integration, send this to the bot to auto-configure:

Help me connect this memory plugin with the most user-friendly configuration:
https://github.com/CortexReach/memory-lancedb-pro

Requirements:
1. Set it as the only active memory plugin
2. Use Jina for embedding
3. Use Jina for reranker
4. Use gpt-4o-mini for the smart-extraction LLM
5. Enable autoCapture, autoRecall, smartExtraction
6. extractMinMessages=2
7. sessionMemory.enabled=false
8. captureAssistant=false
9. retrieval mode=hybrid, vectorWeight=0.7, bm25Weight=0.3
10. rerank=cross-encoder, candidatePoolSize=12, minScore=0.6, hardMinScore=0.62
11. Generate the final openclaw.json config directly, not just an explanation

Resources

GitHub: https://github.com/CortexReach/memory-lancedb-pro
npm: https://www.npmjs.com/package/memory-lancedb-pro
Setup script: https://github.com/CortexReach/toolbox/tree/main/memory-lancedb-pro-setup
Agent skill: https://github.com/CortexReach/memory-lancedb-pro-skill
Video walkthrough (YouTube): https://youtu.be/MtukF1C8epQ
Video walkthrough (Bilibili): https://www.bilibili.com/video/BV1zUf2BGEgn/

Trending-skills memory-lancedb-pro-openclaw

memory-lancedb-pro OpenClaw Plugin

Installation

Option A: One-Click Setup Script (Recommended)

Option B: OpenClaw CLI

Option C: npm

Minimal Configuration (
`openclaw.json`
)

Full Production Configuration

Provider Options for Embedding

Deployment Plans

CLI Reference

Memory Management CLI

MCP Tool API

Core Tools (always available)

`memory_recall`

`memory_store`

`memory_forget`

`memory_update`

Management Tools (requires
`enableManagementTools: true`
)

`memory_stats`

`memory_list`

`self_improvement_log`

`self_improvement_extract_skill`

`self_improvement_review`

Smart Extraction: 6 Memory Categories

Hybrid Retrieval Internals

Multi-Scope Isolation

Weibull Decay Model

Upgrading

From pre-v1.1.0

Troubleshooting

Plugin not loading

`autoRecall`
not injecting memories

Jiti cache issues after upgrade

Memories not being extracted from conversations

Retrieval returns nothing or poor results

Environment variable not resolving

Telegram Bot Quick Config Import

Resources

Trending-skills memory-lancedb-pro-openclaw

memory-lancedb-pro OpenClaw Plugin

Installation

Option A: One-Click Setup Script (Recommended)

Option B: OpenClaw CLI

Option C: npm

Minimal Configuration (openclaw.json)

Full Production Configuration

Provider Options for Embedding

Deployment Plans

CLI Reference

Memory Management CLI

MCP Tool API

Core Tools (always available)

memory_recall

memory_store

memory_forget

memory_update

Management Tools (requires enableManagementTools: true)

memory_stats

memory_list

self_improvement_log

self_improvement_extract_skill

self_improvement_review

Smart Extraction: 6 Memory Categories

Hybrid Retrieval Internals

Multi-Scope Isolation

Weibull Decay Model

Upgrading

From pre-v1.1.0

Troubleshooting

Plugin not loading

autoRecall not injecting memories

Jiti cache issues after upgrade

Memories not being extracted from conversations

Retrieval returns nothing or poor results

Environment variable not resolving

Telegram Bot Quick Config Import

Resources

Minimal Configuration (
`openclaw.json`
)

`memory_recall`

`memory_store`

`memory_forget`

`memory_update`

Management Tools (requires
`enableManagementTools: true`
)

`memory_stats`

`memory_list`

`self_improvement_log`

`self_improvement_extract_skill`

`self_improvement_review`

`autoRecall`
not injecting memories