PaperclipOrg
Claude Agent Skill · by Aradotso

Memory Lancedb Pro Openclaw

Install Memory Lancedb Pro Openclaw skill for Claude Code from aradotso/trending-skills.

Works with Paperclip

How Memory Lancedb Pro Openclaw fits into a Paperclip company.

Memory Lancedb Pro Openclaw drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md554 lines
Expand
---name: memory-lancedb-pro-openclawdescription: 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.triggers:  - help me set up long-term memory for my OpenClaw agent  - configure memory-lancedb-pro plugin  - my agent keeps forgetting things between sessions  - enable hybrid retrieval with BM25 and vector search  - set up cross-encoder reranking for memory  - how do I use memory_recall and memory_store tools  - migrate or upgrade memory-lancedb-pro  - configure multi-scope memory isolation per agent or user--- # memory-lancedb-pro OpenClaw Plugin > Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection. `memory-lancedb-pro` is a production-grade long-term memory plugin for [OpenClaw](https://github.com/openclaw/openclaw) agents. It stores preferences, decisions, and project context in a local [LanceDB](https://lancedb.com) 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) ```bashcurl -fsSL https://raw.githubusercontent.com/CortexReach/toolbox/main/memory-lancedb-pro-setup/setup-memory.sh -o setup-memory.shbash setup-memory.sh``` Flags:```bashbash setup-memory.sh --dry-run       # Preview changes onlybash setup-memory.sh --beta          # Include pre-release versionsbash setup-memory.sh --uninstall     # Revert config and remove pluginbash 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 ```bashopenclaw plugins install memory-lancedb-pro@beta``` ### Option C: npm ```bashnpm 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`) ```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 ```json{  "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": "${OPENAI_API_KEY}",            "model": "gpt-4o-mini"          },          "autoCapture": true,          "captureAssistant": false,          "autoRecall": true,          "smartExtraction": true,          "extractMinMessages": 2,          "extractMaxChars": 8000,          "enableManagementTools": true,          "retrieval": {            "mode": "hybrid",            "vectorWeight": 0.7,            "bm25Weight": 0.3,            "topK": 10          },          "rerank": {            "enabled": true,            "type": "cross-encoder",            "candidatePoolSize": 12,            "minScore": 0.6,            "hardMinScore": 0.62          },          "decay": {            "enabled": true,            "model": "weibull",            "halfLifeDays": 30          },          "sessionMemory": { "enabled": false },          "scopes": {            "agent": true,            "user": true,            "project": true          }        }      }    }  }}``` ### Provider Options for Embedding | Provider | `provider` value | Notes ||---|---|---|| OpenAI / compatible | `"openai-compatible"` | Requires `apiKey`, optional `baseURL` || Jina | `"jina"` | Requires `apiKey` || Gemini | `"gemini"` | Requires `apiKey` || Ollama | `"ollama"` | Local, zero API cost, set `baseURL` || DashScope | `"dashscope"` | Requires `apiKey` || SiliconFlow | `"siliconflow"` | Requires `apiKey`, free reranker tier | ### Deployment Plans **Full Power (Jina + OpenAI):**```json{  "embedding": { "provider": "jina", "apiKey": "${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):**```json{  "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):**```json{  "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: ```bashopenclaw config validateopenclaw gateway restartopenclaw logs --follow --plain | grep "memory-lancedb-pro"``` Expected startup log output:```memory-lancedb-pro: smart extraction enabledmemory-lancedb-pro@1.x.x: plugin registered``` ### Memory Management CLI ```bash# Stats overviewopenclaw memory-pro stats # List memories (with optional scope/filter)openclaw memory-pro listopenclaw memory-pro list --scope user --limit 20openclaw memory-pro list --filter "typescript" # Search memoriesopenclaw memory-pro search "coding preferences"openclaw memory-pro search "database decisions" --scope project # Delete a memory by IDopenclaw memory-pro forget <memory-id> # Export memories (for backup or migration)openclaw memory-pro export --scope global --output memories-backup.jsonopenclaw memory-pro export --scope user --output user-memories.json # Import memoriesopenclaw memory-pro import --input memories-backup.json # Upgrade schema (when upgrading plugin versions)openclaw memory-pro upgrade --dry-run   # Preview firstopenclaw memory-pro upgrade             # Run upgrade # Plugin infoopenclaw 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. ```typescript// Agent usage patternconst results = await memory_recall({  query: "user's preferred code style",  scope: "user",        // "agent" | "user" | "project" | "global"  topK: 5});``` #### `memory_store`Manually store a memory. ```typescriptawait 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. ```typescriptawait memory_forget({ id: "mem_abc123" });``` #### `memory_update`Update an existing memory. ```typescriptawait 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````typescriptconst stats = await memory_stats({ scope: "global" });// Returns: total count, category breakdown, decay stats, db size``` #### `memory_list````typescriptconst list = await memory_list({ scope: "user", limit: 20, offset: 0 });``` #### `self_improvement_log`Log an agent learning event for meta-improvement tracking. ```typescriptawait 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. ```typescriptawait self_improvement_extract_skill({  conversation: "...",  domain: "code-review",  skillName: "typescript-strict-mode-setup"});``` #### `self_improvement_review`Review and consolidate recent self-improvement logs. ```typescriptawait 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: 1. **Vector search** — semantic similarity via embeddings (weight: `vectorWeight`, default 0.7)2. **BM25 full-text search** — keyword matching (weight: `bm25Weight`, default 0.3)3. **Score fusion** — results merged with weighted RRF (Reciprocal Rank Fusion)4. **Cross-encoder rerank** — top `candidatePoolSize` candidates reranked by a cross-encoder model5. **Score filtering** — results below `hardMinScore` are dropped ```json"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: ```json"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: ```typescript// Get only project-level memoriesawait memory_recall({ query: "database choices", scope: "project" }); // Get user preferences across all agentsawait memory_recall({ query: "coding style", scope: "user" }); // Global recall across all scopesawait 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. ```json"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 ```bash# 1. Backup first — alwaysopenclaw memory-pro export --scope global --output memories-backup-$(date +%Y%m%d).json # 2. Preview schema changesopenclaw memory-pro upgrade --dry-run # 3. Run the upgradeopenclaw memory-pro upgrade # 4. Verifyopenclaw memory-pro stats``` See `CHANGELOG-v1.1.0.md` in the repo for behavior changes and upgrade rationale. --- ## Troubleshooting ### Plugin not loading ```bash# Check plugin is recognizedopenclaw plugins info memory-lancedb-pro # Validate config (catches JSON errors, unknown fields)openclaw config validate # Check logs for registrationopenclaw 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` is `false` in some versions — explicitly set it to `true`: ```json"autoRecall": true``` Also confirm the plugin is bound to the `memory` slot, not just loaded. ### Jiti cache issues after upgrade ```bash# Clear jiti transpile cacherm -rf ~/.openclaw/.cache/jitiopenclaw 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 1. Confirm `retrieval.mode` is `"hybrid"` not `"bm25"` alone (BM25 requires indexed content)2. Lower `rerank.hardMinScore` temporarily (try `0.4`) to see if results exist but are being filtered3. 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. ```bashexport 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 plugin2. Use Jina for embedding3. Use Jina for reranker4. Use gpt-4o-mini for the smart-extraction LLM5. Enable autoCapture, autoRecall, smartExtraction6. extractMinMessages=27. sessionMemory.enabled=false8. captureAssistant=false9. retrieval mode=hybrid, vectorWeight=0.7, bm25Weight=0.310. rerank=cross-encoder, candidatePoolSize=12, minScore=0.6, hardMinScore=0.6211. 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/