memory-lancedb-pro

name

memory-lancedb-pro

description

Comprehensive guide for maintaining, debugging, and upgrading the memory-lancedb-pro OpenClaw plugin — an enhanced LanceDB-backed long-term memory system with hybrid retrieval (Vector + BM25), cross-encoder reranking, multi-scope isolation, noise filtering, adaptive retrieval, and a management CLI. Use this skill when: (1) developing new features or fixing bugs in memory-lancedb-pro, (2) modifying the retrieval pipeline (vector search, BM25, RRF fusion, reranking, scoring stages), (3) adding or changing embedding providers, (4) updating scope/access control logic, (5) modifying agent tools or CLI commands, (6) troubleshooting memory quality issues (noise, duplicates, low recall), (7) working on the JSONL session distillation pipeline, (8) migrating data between memory backends, or (9) understanding the plugin's architecture to plan enhancements.

memory-lancedb-pro Plugin Maintenance Guide

Overview

memory-lancedb-pro is an enhanced long-term memory plugin for OpenClaw. It replaces the built-in memory-lancedb plugin with advanced retrieval capabilities, multi-scope memory isolation, and a management CLI.

Repository: https://github.com/win4r/memory-lancedb-pro License: MIT | Language: TypeScript (ESM) | Runtime: Node.js via OpenClaw Gateway

Architecture

┌─────────────────────────────────────────────────────────┐
│                   index.ts (Entry Point)                │
│  Plugin Registration · Config Parsing · Lifecycle Hooks │
└────────┬──────────┬──────────┬──────────┬───────────────┘
         │          │          │          │
    ┌────▼───┐ ┌────▼───┐ ┌───▼────┐ ┌──▼──────────┐
    │ store  │ │embedder│ │retriever│ │   scopes    │
    │ .ts    │ │ .ts    │ │ .ts    │ │    .ts      │
    └────────┘ └────────┘ └────────┘ └─────────────┘
         │                     │
    ┌────▼───┐           ┌─────▼──────────┐
    │migrate │           │noise-filter.ts │
    │ .ts    │           │adaptive-       │
    └────────┘           │retrieval.ts    │
                         └────────────────┘
    ┌─────────────┐   ┌──────────┐
    │  tools.ts   │   │  cli.ts  │
    │ (Agent API) │   │ (CLI)    │
    └─────────────┘   └──────────┘

File Reference (Quick Navigation)

File	Purpose	Key Exports
index.ts	Plugin entry point. Registers with OpenClaw Plugin API, parses config, mounts lifecycle hooks	memoryLanceDBProPlugin (default), shouldCapture, detectCategory
openclaw.plugin.json	Plugin metadata + full JSON Schema config with uiHints	—
package.json	NPM package. Deps: @lancedb/lancedb, openai, @sinclair/typebox	—
cli.ts	CLI: memory-pro list/search/stats/delete/delete-bulk/export/import/reembed/migrate	createMemoryCLI, registerMemoryCLI
src/store.ts	LanceDB storage layer. Table creation, FTS indexing, CRUD, vector/BM25 search	MemoryStore, MemoryEntry, loadLanceDB
src/embedder.ts	Embedding abstraction. OpenAI-compatible API, task-aware, LRU cache	Embedder, createEmbedder, getVectorDimensions
src/retriever.ts	Hybrid retrieval engine. Full scoring pipeline	MemoryRetriever, createRetriever, DEFAULT_RETRIEVAL_CONFIG
src/scopes.ts	Multi-scope access control	MemoryScopeManager, createScopeManager
src/tools.ts	Agent tool definitions: memory_recall/store/forget/update/stats/list	registerAllMemoryTools
src/noise-filter.ts	Noise filter for low-quality content	isNoise, filterNoise
src/adaptive-retrieval.ts

Core Subsystem Reference

For detailed deep-dives into each subsystem, read the appropriate reference file:

• Retrieval Pipeline (scoring math, RRF fusion, reranking, all scoring stages): See references/retrieval_pipeline.md
• Storage & Data Model (LanceDB schema, FTS indexing, CRUD, vector dim): See references/storage_and_schema.md
• Embedding System (providers, task-aware API, caching, dimensions): See references/embedding_system.md
• Plugin Lifecycle & Config (hooks, registration, config parsing): See references/plugin_lifecycle.md
• Scope System (multi-scope isolation, agent access, patterns): See references/scope_system.md
• Tools & CLI (agent tools, CLI commands, parameters): See references/tools_and_cli.md
• Common Gotchas & Troubleshooting: See references/troubleshooting.md

Development Workflows

Adding a New Embedding Provider

1. Check if it's OpenAI-compatible (most are). If so, no code change needed — just config
2. If the model is not in EMBEDDING_DIMENSIONS map in src/embedder.ts, add it
3. If the provider needs special request fields beyond task and normalized, extend buildPayload() in src/embedder.ts
4. Test with embedder.test() method
5. Document the provider in README.md table

Adding a New Rerank Provider

1. Add provider name to RerankProvider type in src/retriever.ts
2. Add case in buildRerankRequest() for request format (headers + body)
3. Add case in parseRerankResponse() for response parsing
4. Add to rerankProvider enum in openclaw.plugin.json
5. Test with actual API calls — reranker has 5s timeout protection

Adding a New Scoring Stage

1. Create a private apply<StageName>(results: RetrievalResult[]): RetrievalResult[] method in MemoryRetriever
2. Add corresponding config fields to RetrievalConfig interface
3. Insert the stage in the pipeline sequence in both hybridRetrieval() and vectorOnlyRetrieval()
4. Add defaults to DEFAULT_RETRIEVAL_CONFIG
5. Add JSON Schema fields to openclaw.plugin.json
6. Pipeline order: Fusion → Rerank → Recency → Importance → LengthNorm → TimeDecay → HardMin → Noise → MMR

Adding a New Agent Tool

1. Create registerMemory<ToolName>Tool() in src/tools.ts
2. Define parameters with Type.Object() from @sinclair/typebox
3. Use stringEnum() from openclaw/plugin-sdk for enum params
4. Always validate scope access via context.scopeManager
5. Register in registerAllMemoryTools() — decide if core (always) or management (optional)
6. Return { content: [{ type: "text", text }], details: {...} }

Adding a New CLI Command

1. Add command in registerMemoryCLI() in cli.ts
2. Pattern: memory.command("name <args>").description("...").option("--flag", "...").action(async (args, opts) => { ... })
3. Support --json flag for machine-readable output
4. Use process.exit(1) for error cases
5. CLI is registered via api.registerCli() in index.ts

Modifying Auto-Capture Logic

1. shouldCapture(text) in index.ts controls what gets auto-captured
2. MEMORY_TRIGGERS regex array defines trigger patterns (supports EN/CJK)
3. detectCategory(text) classifies captures as preference/fact/decision/entity/other
4. Auto-capture runs in agent_end hook, limited to 3 per turn
5. Duplicate detection threshold: cosine similarity > 0.95

Modifying Auto-Recall Logic

1. Auto-recall uses before_agent_start hook (OFF by default)
2. shouldSkipRetrieval() from src/adaptive-retrieval.ts gates retrieval
3. Injected as <relevant-memories> XML block with UNTRUSTED DATA warning
4. sanitizeForContext() strips HTML, newlines, limits to 300 chars per memory
5. Max 3 memories injected per turn

Key Design Decisions

• autoRecall defaults to OFF — prevents model from echoing injected memory context
• autoCapture defaults to ON — transparent memory accumulation
• sessionMemory defaults to OFF — raw session summaries degrade retrieval quality; use JSONL distillation instead
• LanceDB dynamic import — loaded asynchronously to avoid blocking; cached in singleton promise
• Startup checks are fire-and-forget — gateway binds HTTP port immediately; embedding/retrieval tests run in background with 8s timeout
• Daily JSONL backup — 24h interval, keeps last 7 files, runs 1 min after start
• BM25 score normalization — raw BM25 scores are unbounded, normalized with sigmoid: 1 / (1 + exp(-score/5))
• Update = delete + re-add — LanceDB doesn't support in-place updates
• ID prefix matching — 8+ hex char prefix resolves to full UUID for user convenience
• CJK-aware thresholds — shorter minimum lengths for Chinese/Japanese/Korean text (4–6 chars vs 10–15 for English)
• Env var resolution — ${VAR} syntax resolved at config parse time; gateway service may not inherit shell env

Testing

• Smoke test: node test/cli-smoke.mjs
• Manual verification: openclaw plugins doctor, openclaw memory-pro stats
• Embedding test: embedder.test() returns { success, dimensions, error? }
• Retrieval test: retriever.test() returns { success, mode, hasFtsSupport, error? }