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 is 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/