memory-lancedb-pro

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      │

File Reference (Quick Navigation)

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? }