Imported from CortexReach/memory-lancedb-pro-skill (SKILL.md). Install upstream with npx skills add CortexReach/memory-lancedb-pro-skill. Copyright stays with the author.

memory-lancedb-pro

Production-grade long-term memory system (v1.1.0-beta.8) for OpenClaw AI agents. Provides persistent, intelligent memory storage using LanceDB with hybrid vector + BM25 retrieval, LLM-powered Smart Extraction, Weibull decay lifecycle, and multi-scope isolation.

For full technical details (thresholds, formulas, database schema, source file map), see references/full-reference.md.

Applying the Optimal Config (Step-by-Step Workflow)

When the user says "help me enable the best config", "apply optimal configuration", or similar, follow this exact procedure:

Step 1 — Present configuration plans and let user choose

Present these three plans in a clear comparison, then ask the user to pick one:

Plan A — 🏆 Full Power (Best Quality)

• Embedding: Jina jina-embeddings-v5-text-small (task-aware, 1024-dim)
• Reranker: Jina jina-reranker-v3 (cross-encoder, same key)
• LLM: OpenAI gpt-4o-mini (Smart Extraction)
• Keys needed: JINA_API_KEY + OPENAI_API_KEY
• Get keys: Jina → https://jina.ai/api-key · OpenAI → https://platform.openai.com/api-keys
• Cost: Both paid (Jina has free tier with limited quota)
• Best for: Production deployments, highest retrieval quality

Plan B — 💰 Budget (Free Reranker)

• Embedding: Jina jina-embeddings-v5-text-small
• Reranker: SiliconFlow BAAI/bge-reranker-v2-m3 (free tier available)
• LLM: OpenAI gpt-4o-mini
• Keys needed: JINA_API_KEY + SILICONFLOW_API_KEY + OPENAI_API_KEY
• Get keys: Jina → https://jina.ai/api-key · SiliconFlow → https://cloud.siliconflow.cn/account/ak · OpenAI → https://platform.openai.com/api-keys
• Cost: Jina embedding paid, SiliconFlow reranker free tier, OpenAI paid
• Best for: Cost-sensitive deployments that still want reranking

Plan C — 🟢 Simple (OpenAI Only)

• Embedding: OpenAI text-embedding-3-small
• Reranker: None (vector+BM25 fusion only, no cross-encoder)
• LLM: OpenAI gpt-4o-mini
• Keys needed: OPENAI_API_KEY only
• Get key: https://platform.openai.com/api-keys
• Cost: OpenAI paid only
• Best for: Users who already have OpenAI and want minimal setup

Plan D — 🖥️ Fully Local (Ollama, No API Keys)

• Embedding: Ollama mxbai-embed-large (1024-dim, recommended) or nomic-embed-text:v1.5 (768-dim, lighter)
• Reranker: None — Ollama has no cross-encoder reranker; retrieval uses vector+BM25 fusion only
• LLM: Ollama via OpenAI-compatible endpoint — recommended models with reliable JSON/structured output:
  • qwen3:8b (recommended — best JSON output, native structured output, ~5.2GB)
  • qwen3:14b (better quality, ~9GB, needs 16GB VRAM)
  • llama4:scout (multimodal MoE, 10M ctx, ~12GB)
  • mistral-small3.2 (24B, 128K ctx, excellent instruction following, ~15GB)
  • mistral-nemo (12B, 128K ctx, efficient, ~7GB)
• Keys needed: None — fully local, no external API calls
• Prerequisites:
  • Ollama installed: https://ollama.com/download
  • Models pulled (see Step 5 below)
  • Ollama running: macOS = launch the app from Applications; Linux = systemctl start ollama or ollama serve
• Cost: Free (hardware only)
• RAM requirements: mxbai-embed-large ~670MB; qwen3:8b ~5.2GB; qwen3:14b ~9GB; llama4:scout ~12GB; mistral-small3.2 ~15GB
• Trade-offs: No cross-encoder reranking = lower retrieval precision than Plans A/B; Smart Extraction quality depends on local LLM — if extraction produces garbage, set "smartExtraction": false
• Best for: Privacy-sensitive deployments, air-gapped environments, zero API cost

After user selects a plan, ask in one message:

1. Please provide the required API key(s) for your chosen plan (paste directly, or say "already set as env vars")
2. Are the env vars already set in your OpenClaw Gateway process? (If unsure, answer No)
3. Where is your openclaw.json? (Skip if you want me to find it automatically)

If the user already stated their provider/keys in context, skip asking and proceed.

Do NOT proceed to Step 2 until API keys have been collected and verified (Step 2 below).

Step 2 — Verify API Keys (MANDATORY — do not skip)

Run ALL key checks for the chosen plan before touching any config. If any check fails, STOP and tell the user which key failed and why. Do not proceed to Step 3.

Plan A / Plan B — Jina embedding check:

curl -s -o /dev/null -w "%{http_code}" \
  https://api.jina.ai/v1/embeddings \
  -H "Authorization: Bearer <JINA_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-embeddings-v5-text-small","input":["test"]}'

Plan A / B / C — OpenAI check:

curl -s -o /dev/null -w "%{http_code}" \
  https://api.openai.com/v1/models \
  -H "Authorization: Bearer <OPENAI_API_KEY>"

Plan B — SiliconFlow reranker check:

curl -s -o /dev/null -w "%{http_code}" \
  https://api.siliconflow.com/v1/rerank \
  -H "Authorization: Bearer <SILICONFLOW_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"model":"BAAI/bge-reranker-v2-m3","query":"test","documents":["test doc"]}'

Plan D — Ollama check:

curl -s -o /dev/null -w "%{http_code}" http://localhost:11434/api/tags

Interpret results:

If any check fails: Tell the user exactly which provider failed, the HTTP code received, and what to fix. Do not proceed with installation until all required keys pass their checks.

If the user says keys are set as env vars in the gateway process, run checks using ${VAR_NAME} substituted inline or ask them to paste the key temporarily for verification.

Step 3 — Find openclaw.json

Check these locations in order:

# Most common locations
ls ~/.openclaw/openclaw.json
ls ~/openclaw.json
# Ask the gateway where it's reading config from
openclaw config get --show-path 2>/dev/null || echo "not found"

If not found, ask the user for the path.

Step 4 — Read current config

# Read and display current plugins config before changing anything
openclaw config get plugins.entries.memory-lancedb-pro 2>/dev/null
openclaw config get plugins.slots.memory 2>/dev/null

Check what already exists — never blindly overwrite existing settings.

Step 5 — Build the merged config based on chosen plan

Use the config block for the chosen plan. Substitute actual API keys inline if the user provided them directly; keep ${ENV_VAR} syntax if they confirmed env vars are set in the gateway process.

Plan A config (plugins.entries.memory-lancedb-pro.config):

{
  "embedding": {
    "apiKey": "${JINA_API_KEY}",
    "model": "jina-embeddings-v5-text-small",
    "baseURL": "https://api.jina.ai/v1",
    "dimensions": 1024,
    "taskQuery": "retrieval.query",
    "taskPassage": "retrieval.passage",
    "normalized": true
  },
  "autoCapture": true,
  "autoRecall": true,
  "captureAssistant": false,
  "smartExtraction": true,
  "extractMinMessages": 2,
  "extractMaxChars": 8000,
  "llm": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "gpt-4o-mini",
    "baseURL": "https://api.openai.com/v1"
  },
  "retrieval": {
    "mode": "hybrid",
    "vectorWeight": 0.7,
    "bm25Weight": 0.3,
    "rerank": "cross-encoder",
    "rerankProvider": "jina",
    "rerankModel": "jina-reranker-v3",
    "rerankEndpoint": "https://api.jina.ai/v1/rerank",
    "rerankApiKey": "${JINA_API_KEY}",
    "candidatePoolSize": 12,
    "minScore": 0.6,
    "hardMinScore": 0.62,
    "filterNoise": true
  },
  "sessionMemory": { "enabled": false }
}

Plan B config:

{
  "embedding": {
    "apiKey": "${JINA_API_KEY}",
    "model": "jina-embeddings-v5-text-small",
    "baseURL": "https://api.jina.ai/v1",
    "dimensions": 1024,
    "taskQuery": "retrieval.query",
    "taskPassage": "retrieval.passage",
    "normalized": true
  },
  "autoCapture": true,
  "autoRecall": true,
  "captureAssistant": false,
  "smartExtraction": true,
  "extractMinMessages": 2,
  "extractMaxChars": 8000,
  "llm": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "gpt-4o-mini",
    "baseURL": "https://api.openai.com/v1"
  },
  "retrieval": {
    "mode": "hybrid",
    "vectorWeight": 0.7,
    "bm25Weight": 0.3,
    "rerank": "cross-encoder",
    "rerankProvider": "siliconflow",
    "rerankModel": "BAAI/bge-reranker-v2-m3",
    "rerankEndpoint": "https://api.siliconflow.com/v1/rerank",
    "rerankApiKey": "${SILICONFLOW_API_KEY}",
    "candidatePoolSize": 12,
    "minScore": 0.5,
    "hardMinScore": 0.55,
    "filterNoise": true
  },
  "sessionMemory": { "enabled": false }
}

Plan C config:

{
  "embedding": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "text-embedding-3-small",
    "baseURL": "https://api.openai.com/v1"
  },
  "autoCapture": true,
  "autoRecall": true,
  "captureAssistant": false,
  "smartExtraction": true,
  "extractMinMessages": 2,
  "extractMaxChars": 8000,
  "llm": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "gpt-4o-mini",
    "baseURL": "https://api.openai.com/v1"
  },
  "retrieval": {
    "mode": "hybrid",
    "vectorWeight": 0.7,
    "bm25Weight": 0.3,
    "filterNoise": true,
    "minScore": 0.3,
    "hardMinScore": 0.35
  },
  "sessionMemory": { "enabled": false }
}

Plan D config (replace models as needed — qwen3:8b recommended for LLM, mxbai-embed-large for embedding):

{
  "embedding": {
    "apiKey": "ollama",
    "model": "mxbai-embed-large",
    "baseURL": "http://localhost:11434/v1",
    "dimensions": 1024
  },
  "autoCapture": true,
  "autoRecall": true,
  "captureAssistant": false,
  "smartExtraction": true,
  "extractMinMessages": 2,
  "extractMaxChars": 4000,
  "llm": {
    "apiKey": "ollama",
    "model": "qwen3:8b",
    "baseURL": "http://localhost:11434/v1"
  },
  "retrieval": {
    "mode": "hybrid",
    "vectorWeight": 0.7,
    "bm25Weight": 0.3,
    "filterNoise": true,
    "minScore": 0.25,
    "hardMinScore": 0.28
  },
  "sessionStrategy": "none"
}

Plan D prerequisites — run BEFORE applying config:

# 1. Verify Ollama is running (should return JSON with model list)
curl http://localhost:11434/api/tags

# 2. Pull embedding model (choose one):
ollama pull mxbai-embed-large          # recommended: 1024-dim, beats text-embedding-3-large, ~670MB
ollama pull snowflake-arctic-embed2    # best multilingual local option, ~670MB
ollama pull nomic-embed-text:v1.5      # classic stable, 768-dim, ~270MB

# 3. Pull LLM for Smart Extraction (choose one based on RAM):
ollama pull qwen3:8b           # recommended: best JSON/structured output, ~5.2GB
ollama pull qwen3:14b          # better quality, ~9GB, needs 16GB VRAM
ollama pull llama4:scout       # multimodal MoE, 10M ctx, ~12GB
ollama pull mistral-small3.2   # 24B, 128K ctx, excellent, ~15GB
ollama pull mistral-nemo       # 12B, 128K ctx, efficient, ~7GB

# 4. Verify models are installed
ollama list

# 5. Quick sanity check — embedding endpoint works:
curl http://localhost:11434/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model":"mxbai-embed-large","input":"test"}'
# Should return a JSON with a 1024-element vector

If Smart Extraction produces garbled/invalid output: The local LLM may not support structured JSON reliably. Try qwen3:8b first — it has native structured output support. If still failing, disable:

{ "smartExtraction": false }

If Ollama is on a different host or Docker: Replace http://localhost:11434/v1 with the actual host, e.g. http://192.168.1.100:11434/v1. Also set OLLAMA_HOST=0.0.0.0 in the Ollama process to allow remote connections.

For the plugins.entries.memory-lancedb-pro.config block, merge into the existing openclaw.json rather than replacing the whole file. Use a targeted edit of only the memory plugin config section.

Step 6 — Apply the config

Read the current openclaw.json first, then apply a surgical edit to the plugins.entries.memory-lancedb-pro section. Use the template that matches your installation method:

Method 1 — openclaw plugins install (plugin was installed via the plugin manager): No load.paths or allow needed — the plugin manager already registered the plugin.

{
  "plugins": {
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "<<OPTIMAL CONFIG HERE>>"
        }
      }
    }
  }
}

Method 2 — git clone with manual path (workspace plugin): Both load.paths AND allow are required — workspace plugins are disabled by default.

{
  "plugins": {
    "load": { "paths": ["plugins/memory-lancedb-pro"] },
    "allow": ["memory-lancedb-pro"],
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "<<OPTIMAL CONFIG HERE>>"
        }
      }
    }
  }
}

Step 7 — Validate and restart

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

Expected output confirms:

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

Step 8 — Verify

openclaw plugins info memory-lancedb-pro
openclaw hooks list --json | grep -E "before_agent_start|agent_end|command:new"
openclaw memory-pro stats

Then do a quick smoke test:

1. Store: call memory_store with text: "test memory for verification"
2. Recall: call memory_recall with query: "test memory"
3. Confirm the memory is returned

Installation

Quick Install (Beginner-Friendly)

For new users, the community one-click installer handles everything automatically — path detection, schema validation, auto-update, provider selection, and rollback:

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

Options: --dry-run (preview only), --beta (include pre-release), --ref v1.2.0 (pin version), --selfcheck-only, --uninstall.

Source: https://github.com/CortexReach/toolbox/tree/main/memory-lancedb-pro-setup

Requirements

• Node.js 24 recommended (Node 22 LTS minimum, 22.16+)
• LanceDB ≥ 0.26.2
• OpenAI SDK ≥ 6.21.0
• TypeBox 0.34.48

Install Method 1 — via OpenClaw plugin manager (recommended)

# Install from npm registry (@beta tag = latest pre-release, e.g. 1.1.0-beta.8)
openclaw plugins install memory-lancedb-pro@beta

# Install stable release from npm (@latest tag, e.g. 1.0.32)
openclaw plugins install memory-lancedb-pro

# Or install from a local git clone — use master branch (matches npm @beta)
git clone -b master https://github.com/CortexReach/memory-lancedb-pro.git /tmp/memory-lancedb-pro
openclaw plugins install /tmp/memory-lancedb-pro

npm vs GitHub branches: @beta installs from the npm registry (not directly from GitHub). The repo has two long-lived branches: master is the release branch (matches npm @beta), main is older/behind. Always clone master if you want code that matches the published beta.

Then bind the memory slot and add your config (see Configuration section below):

{
  "plugins": {
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": { "<<your config here>>" }
      }
    }
  }
}

Restart and verify:

openclaw gateway restart
openclaw plugins info memory-lancedb-pro

Install Method 2 — git clone with manual path (Path A for development)

⚠️ Critical: Workspace plugins (git-cloned paths) are disabled by default in OpenClaw. You MUST explicitly enable them.

# 1. Clone into workspace
cd /path/to/your/openclaw/workspace
git clone -b master https://github.com/CortexReach/memory-lancedb-pro.git plugins/memory-lancedb-pro
cd plugins/memory-lancedb-pro && npm install

Add to openclaw.json — the enabled: true and the allow entry are both required:

{
  "plugins": {
    "load": { "paths": ["plugins/memory-lancedb-pro"] },
    "allow": ["memory-lancedb-pro"],
    "slots": { "memory": "memory-lancedb-pro" },
    "entries": {
      "memory-lancedb-pro": {
        "enabled": true,
        "config": {
          "embedding": {
            "apiKey": "${JINA_API_KEY}",
            "model": "jina-embeddings-v5-text-small",
            "baseURL": "https://api.jina.ai/v1",
            "dimensions": 1024,
            "taskQuery": "retrieval.query",
            "taskPassage": "retrieval.passage",
            "normalized": true
          }
        }
      }
    }
  }
}

Validate and restart:

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

Expected log output:

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

Install Method 3 — Existing deployments (Path B)

Use absolute paths in plugins.load.paths. Add to plugins.allow. Bind memory slot: plugins.slots.memory = "memory-lancedb-pro". Set plugins.entries.memory-lancedb-pro.enabled: true.

Then restart and verify:

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

New User First-Install Checklist

After the plugin starts successfully, determine which scenario applies and run the corresponding steps:

Scenario A — Coming from built-in memory-lancedb plugin (most common upgrade path)

The old plugin stores data in LanceDB at ~/.openclaw/memory/lancedb. Use the migrate command:

# 1. Check if old data exists and is readable
openclaw memory-pro migrate check

# 2. Preview what would be migrated (dry run)
openclaw memory-pro migrate run --dry-run

# 3. Run the actual migration
openclaw memory-pro migrate run

# 4. Verify migrated data
openclaw memory-pro migrate verify
openclaw memory-pro stats

If the old database is at a non-default path:

openclaw memory-pro migrate check --source /path/to/old/lancedb
openclaw memory-pro migrate run --source /path/to/old/lancedb

Scenario B — Existing memories exported as JSON

If you have memories in the standard JSON export format:

# Preview import (dry run)
openclaw memory-pro import memories.json --scope global --dry-run

# Import
openclaw memory-pro import memories.json --scope global

Expected JSON schema:

{
  "version": "1.0",
  "memories": [
    {
      "text": "Memory content (required)",
      "category": "preference|fact|decision|entity|other",
      "importance": 0.7,
      "timestamp": 1234567890000
    }
  ]
}

Scenario C — Memories stored in Markdown files (AGENTS.md, MEMORY.md, etc.)

There is no direct markdown import — the import command only accepts JSON. You need to convert first.

Manual conversion approach:

1. Open the markdown file(s) containing memories
2. For each memory entry, create a JSON object with text, category, importance
3. Save as a JSON file following the schema above
4. Run openclaw memory-pro import

Or use memory_store tool directly in the agent to store individual entries one at a time:

memory_store(text="<extracted memory>", category="fact", importance=0.8)

Note: Markdown-based memory files (MEMORY.md, AGENTS.md) are workspace context files, not the same as the LanceDB memory store. You only need to migrate them if you want that content searchable via memory_recall.

Scenario D — Fresh install, no prior memories

No migration needed. Verify the plugin is working with a quick smoke test:

openclaw memory-pro stats     # should show 0 memories

Then trigger a conversation — autoCapture will start storing memories automatically.

LanceDB Version Compatibility

No manual action required for LanceDB version changes.

The plugin requires @lancedb/lancedb ^0.26.2 as an npm dependency — this is installed automatically when you install or update the plugin. You do not need to manually install or upgrade LanceDB.

LanceDB 0.26+ changed how numeric columns are returned (Arrow BigInt type for timestamp, importance, _distance, _score). The plugin handles this transparently at runtime via internal Number(...) coercion — no migration commands are needed when moving between LanceDB versions.

TL;DR: LanceDB version compatibility is fully automatic. See the table below for when each maintenance command actually applies.

Upgrading plugin code vs. data

Command distinction (important):

When do you need memory-pro upgrade?

Run it if you installed memory-lancedb-pro before the smart-memory format was introduced (i.e., entries are missing memory_category in their metadata). Signs you need it:

• memory_recall returns results but without meaningful categories
• memory-pro list --json shows entries with no l0_abstract / l1_overview fields

Safe upgrade sequence:

# 1. Backup first
openclaw memory-pro export --scope global --output memories-backup.json

# 2. Preview what would change
openclaw memory-pro upgrade --dry-run

# 3. Run upgrade (uses LLM by default for L0/L1/L2 generation)
openclaw memory-pro upgrade

# 4. Verify results
openclaw memory-pro stats
openclaw memory-pro search "your known keyword" --scope global --limit 5

Upgrade options:

openclaw memory-pro upgrade --no-llm          # skip LLM, use simple text truncation
openclaw memory-pro upgrade --batch-size 5    # slower but safer for large collections
openclaw memory-pro upgrade --limit 50        # process only first N entries
openclaw memory-pro upgrade --scope global    # limit to one scope

Plugin management commands

openclaw plugins list                           # show all discovered plugins
openclaw plugins info memory-lancedb-pro        # show plugin status and config
openclaw plugins enable memory-lancedb-pro      # enable a disabled plugin
openclaw plugins disable memory-lancedb-pro     # disable without removing
openclaw plugins update memory-lancedb-pro      # update npm-installed plugin
openclaw plugins update --all                   # update all npm plugins
openclaw plugins doctor                         # health check for all plugins
openclaw plugins install ./path/to/plugin       # install local plugin (copies + enables)
openclaw plugins install @scope/plugin@beta     # install from npm registry
openclaw plugins install -l ./path/to/plugin    # symlink for dev (no copy)

Gateway restart required after: plugins install, plugins enable, plugins disable, plugins update, or any change to openclaw.json. Changes do not take effect until the gateway is restarted.

openclaw gateway restart

Easy-to-Miss Setup Steps

1. Gateway restart required after any change: After installing, enabling, disabling, updating, or changing config in openclaw.json, you MUST run openclaw gateway restart — changes are NOT hot-reloaded.
2. Workspace plugins are DISABLED by default: After git clone, you MUST add plugins.allow: ["memory-lancedb-pro"] AND plugins.entries.memory-lancedb-pro.enabled: true — without these the plugin silently does not load.
3. Env vars in gateway process: ${OPENAI_API_KEY} requires env vars set in the OpenClaw Gateway service process—not just your shell.
4. Absolute vs. relative paths: For existing deployments, always use absolute paths in plugins.load.paths.
5. baseURL not baseUrl: The embedding (and llm) config field is baseURL (capital URL), NOT baseUrl. Using the wrong casing causes a schema validation error: "must NOT have additional properties". Also note the required /v1 suffix: http://localhost:11434/v1, not http://localhost:11434. Do not confuse with agents.defaults.memorySearch.remote.baseUrl which uses a different casing.
6. jiti cache invalidation: After modifying .ts files under plugins, run rm -rf /tmp/jiti/ BEFORE openclaw gateway restart.
7. Unknown plugin id = error: OpenClaw treats unknown ids in entries, allow, deny, or slots as validation errors. The plugin id must be discoverable before referencing it.
8. Separate LLM config: If embedding and LLM use different providers, configure the llm section separately — it falls back to embedding key/URL otherwise.
9. Scope isolation: Multi-scope requires explicit scopes.agentAccess mapping — without it, agents only see global scope.
10. Session memory hook: Fires on /new command — test with an actual /new invocation.
11. Reranker credentials: When switching providers, update both rerankApiKey AND rerankEndpoint.
12. Config check before assuming defaults: Run openclaw config get plugins.entries.memory-lancedb-pro to verify what's actually loaded.
13. Custom config/state paths via env vars: OpenClaw respects the following environment variables for custom paths:
    • OPENCLAW_HOME — sets the root config/data directory (default: ~/.openclaw/)
    • OPENCLAW_CONFIG_PATH — absolute path to openclaw.json override
    • OPENCLAW_STATE_DIR — override for runtime state/data directory Set these in the OpenClaw Gateway process's environment if the default ~/.openclaw/ path is not appropriate.

Post-Installation Verification

openclaw doctor                                 # full health check (recommended)
openclaw config validate                        # config schema check only
openclaw plugins info memory-lancedb-pro        # plugin status
openclaw plugins doctor                         # plugin-specific health
openclaw hooks list --json | grep memory        # confirm hooks registered
openclaw memory-pro stats
openclaw memory-pro list --scope global --limit 5

Full smoke test checklist:

• ✅ Plugin info shows enabled: true and config loaded
• ✅ Hooks include before_agent_start, agent_end, command:new
• ✅ One memory_store → memory_recall round trip via tools
• ✅ One exact-ID search hit
• ✅ One natural-language search hit
• ✅ If session memory enabled: one real /new test

Troubleshooting — Error Message Quick Reference

Config validation tool (from CortexReach/toolbox):

# Download once
curl -fsSL https://raw.githubusercontent.com/CortexReach/toolbox/main/memory-lancedb-pro-setup/scripts/config-validate.mjs -o config-validate.mjs
# Run against your openclaw.json
node config-validate.mjs
# Or validate a specific config snippet
node config-validate.mjs --json '{"embedding":{"baseURL":"http://localhost:11434/v1","model":"bge-m3","apiKey":"ollama"}}'

Exit code 0 = pass/warn, 1 = errors found.

Configuration

Minimal Quick-Start

{
  "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 }
}

Note: autoRecall is disabled by default in the plugin schema — explicitly set it to true for new deployments.

Optimal Production Config (recommended)

Uses Jina for both embedding and reranking — best retrieval quality:

{
  "embedding": {
    "apiKey": "${JINA_API_KEY}",
    "model": "jina-embeddings-v5-text-small",
    "baseURL": "https://api.jina.ai/v1",
    "dimensions": 1024,
    "taskQuery": "retrieval.query",
    "taskPassage": "retrieval.passage",
    "normalized": true
  },
  "dbPath": "~/.openclaw/memory/lancedb-pro",
  "autoCapture": true,
  "autoRecall": true,
  "captureAssistant": false,
  "smartExtraction": true,
  "extractMinMessages": 2,
  "extractMaxChars": 8000,
  "enableManagementTools": false,
  "llm": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "gpt-4o-mini",
    "baseURL": "https://api.openai.com/v1"
  },
  "retrieval": {
    "mode": "hybrid",
    "vectorWeight": 0.7,
    "bm25Weight": 0.3,
    "rerank": "cross-encoder",
    "rerankProvider": "jina",
    "rerankModel": "jina-reranker-v3",
    "rerankEndpoint": "https://api.jina.ai/v1/rerank",
    "rerankApiKey": "${JINA_API_KEY}",
    "candidatePoolSize": 12,
    "minScore": 0.6,
    "hardMinScore": 0.62,
    "filterNoise": true,
    "lengthNormAnchor": 500,
    "timeDecayHalfLifeDays": 60,
    "reinforcementFactor": 0.5,
    "maxHalfLifeMultiplier": 3
  },
  "sessionMemory": { "enabled": false, "messageCount": 15 }
}

Why these settings excel:

• Jina embeddings: Task-aware vectors (taskQuery/taskPassage) optimized for retrieval
• Hybrid mode 0.7/0.3: Balances semantic understanding with exact keyword matching
• Jina reranker v3: Cross-encoder reranking significantly improves relevance
• candidatePoolSize: 12 + minScore: 0.6: Aggressive filtering reduces noise
• captureAssistant: false: Prevents storing agent-generated boilerplate
• sessionMemory: false: Avoids polluting retrieval with session summaries

Full Config (all options)

{
  "embedding": {
    "apiKey": "${JINA_API_KEY}",
    "model": "jina-embeddings-v5-text-small",
    "baseURL": "https://api.jina.ai/v1",
    "dimensions": 1024,
    "taskQuery": "retrieval.query",
    "taskPassage": "retrieval.passage",
    "normalized": true
  },
  "dbPath": "~/.openclaw/memory/lancedb-pro",
  "autoCapture": true,
  "autoRecall": true,
  "captureAssistant": false,
  "smartExtraction": true,
  "llm": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "gpt-4o-mini",
    "baseURL": "https://api.openai.com/v1"
  },
  "extractMinMessages": 2,
  "extractMaxChars": 8000,
  "enableManagementTools": false,
  "retrieval": {
    "mode": "hybrid",
    "vectorWeight": 0.7,
    "bm25Weight": 0.3,
    "minScore": 0.3,
    "hardMinScore": 0.35,
    "rerank": "cross-encoder",
    "rerankProvider": "jina",
    "rerankModel": "jina-reranker-v3",
    "rerankEndpoint": "https://api.jina.ai/v1/rerank",
    "rerankApiKey": "${JINA_API_KEY}",
    "candidatePoolSize": 20,
    "recencyHalfLifeDays": 14,
    "recencyWeight": 0.1,
    "filterNoise": true,
    "lengthNormAnchor": 500,
    "timeDecayHalfLifeDays": 60,
    "reinforcementFactor": 0.5,
    "maxHalfLifeMultiplier": 3
  },
  "scopes": {
    "default": "global",
    "definitions": {
      "global": { "description": "Shared knowledge" },
      "agent:discord-bot": { "description": "Discord bot private" }
    },
    "agentAccess": {
      "discord-bot": ["global", "agent:discord-bot"]
    }
  },
  "sessionStrategy": "none",
  "memoryReflection": {
    "storeToLanceDB": true,
    "injectMode": "inheritance+derived",
    "agentId": "memory-distiller",
    "messageCount": 120,
    "maxInputChars": 24000,
    "thinkLevel": "medium"
  },
  "selfImprovement": {
    "enabled": true,
    "beforeResetNote": true,
    "ensureLearningFiles": true
  },
  "mdMirror": { "enabled": false },
  "decay": {
    "recencyHalfLifeDays": 30,
    "recencyWeight": 0.4,
    "frequencyWeight": 0.3,
    "intrinsicWeight": 0.3,
    "betaCore": 0.8,
    "betaWorking": 1.0,
    "betaPeripheral": 1.3
  },
  "tier": {
    "coreAccessThreshold": 10,
    "coreCompositeThreshold": 0.7,
    "coreImportanceThreshold": 0.8,
    "workingAccessThreshold": 3,
    "workingCompositeThreshold": 0.4,
    "peripheralCompositeThreshold": 0.15,
    "peripheralAgeDays": 60
  }
}

Configuration Field Reference

Embedding

Top-Level

LLM (for Smart Extraction)

Retrieval

Access reinforcement note: Reinforcement is whitelisted to source: "manual" only — auto-recall does NOT strengthen memories, preventing noise amplification.

Session Strategy (v1.1.0+)

Use sessionStrategy (top-level field) to configure the session pipeline:

memoryReflection config (used when sessionStrategy: "memoryReflection"):

memoryReflection.recall sub-object (controls which past reflections are retrieved for injection):

Session Memory (deprecated — legacy compat only)

⚠️ sessionMemory is a legacy compatibility shim since v1.1.0. Prefer sessionStrategy instead.

• sessionMemory.enabled: true → maps to sessionStrategy: "systemSessionMemory"
• sessionMemory.enabled: false → maps to sessionStrategy: "none"

Self-Improvement Governance

Tool activation rules:

• self_improvement_log: requires selfImprovement.enabled: true (default — active unless explicitly disabled)
• self_improvement_extract_skill + self_improvement_review: additionally require enableManagementTools: true

Markdown Mirror

Decay

Tier Management

MCP Tools

Core Tools (auto-registered)

memory_recall — Search long-term memory via hybrid retrieval

memory_store — Save information to long-term memory

memory_forget — Delete memories by search or direct ID

memory_update — Update memory (preserves original timestamp; preference/entity text updates create a new versioned row preserving history)

Management Tools (enable with enableManagementTools: true)

memory_stats — Usage statistics

• scope (string, optional): Filter by scope

memory_list — List recent memories with filtering

• limit (number, optional, default 10, max 50), scope, category, offset (pagination)

Self-Improvement Tools

self_improvement_log is enabled by default (selfImprovement.enabled: true). self_improvement_extract_skill and self_improvement_review additionally require enableManagementTools: true.

self_improvement_log — Log learning/error entries into LEARNINGS.md / ERRORS.md

self_improvement_extract_skill — Create skill scaffold from a learning entry

self_improvement_review — Summarize governance backlog (no parameters)

Smart Extraction

LLM-powered automatic memory classification and storage triggered after conversations.

Enable

{
  "smartExtraction": true,
  "extractMinMessages": 2,
  "extractMaxChars": 8000,
  "llm": {
    "apiKey": "${OPENAI_API_KEY}",
    "model": "gpt-4o-mini"
  }
}

Minimal (reuses embedding API key — no separate llm block needed):

{
  "embedding": { "apiKey": "${OPENAI_API_KEY}", "model": "text-embedding-3-small" },
  "smartExtraction": true
}

Disable: { "smartExtraction": false }

6-Category Classification

L0/L1/L2 Layered Content per Memory

• L0 (Abstract): Single-sentence index (min 5 chars)
• L1 (Overview): Structured markdown summary
• L2 (Content): Full narrative detail

Two-Stage Deduplication

1. Vector pre-filter: Similarity ≥ 0.7 finds candidates
2. LLM decision: CREATE | MERGE | SKIP | SUPPORT | CONTEXTUALIZE | CONTRADICT

Embedding Providers

DashScope rerank note: DashScope is not a rerankProvider enum value, but its rerank API response is Jina-compatible. Use rerankProvider: "jina" with DashScope's endpoint:

"retrieval": {
  "rerank": "cross-encoder",
  "rerankProvider": "jina",
  "rerankModel": "qwen3-rerank",
  "rerankEndpoint": "https://dashscope.aliyuncs.com/compatible-api/v1/reranks",
  "rerankApiKey": "${DASHSCOPE_API_KEY}"
}

Multi-key failover: Set apiKey as an array for round-robin rotation on 429/503 errors.

Reranker Providers

Jina key can be reused for both embedding and reranking.

Multi-Scope Isolation

Default access: global + agent:<id>. Multi-scope requires explicit scopes.agentAccess — see Full Config above.

To disable memory entirely (unbind the slot without removing the plugin):

{ "plugins": { "slots": { "memory": "none" } } }

Memory Lifecycle (Weibull Decay)

Three Tiers

Promotion/Demotion Rules

• Peripheral → Working: access ≥ 3 AND score ≥ 0.4
• Working → Core: access ≥ 10 AND score ≥ 0.7 AND importance ≥ 0.8
• Working → Peripheral: score < 0.15 OR (age > 60 days AND access < 3)
• Core → Working: score < 0.15 AND access < 3

Hybrid Retrieval

Fusion: weightedFusion = (vectorScore × 0.7) + (bm25Score × 0.3)

Pipeline: RRF Fusion → Cross-Encoder Rerank → Lifecycle Decay Boost → Length Norm → Hard Min Score → MMR Diversity (cosine > 0.85 demoted)

Reranking: 60% cross-encoder score + 40% original fused score. Falls back to cosine similarity on API failure.

Special BM25: Preserves exact keyword matches (BM25 ≥ 0.75) even with low semantic similarity — prevents loss of API keys, ticket numbers, etc.

Adaptive Retrieval Triggering

Skip for: greetings, slash commands, affirmations (yes/okay/thanks), continuations (go ahead/proceed), system messages, short queries (<15 chars English / <6 chars CJK without "?").

Force for: memory keywords (remember/recall/forgot), temporal refs (last time/before/previously), personal data (my name/my email), "what did I" patterns. CJK: "你记得", "之前".

Noise Filtering

Auto-filters: agent denial phrases, meta-questions ("Do you remember?"), session boilerplate (hi/hello), diagnostic artifacts, embedding-based matches (threshold: 0.82). Minimum text: 5 chars.

CLI Commands

# List & search
openclaw memory-pro list [--scope global] [--category fact] [--limit 20] [--json]
openclaw memory-pro search "query" [--scope global] [--limit 10] [--json]
openclaw memory-pro stats [--scope global] [--json]

# Delete
openclaw memory-pro delete <id>
openclaw memory-pro delete-bulk --scope global [--before 2025-01-01] [--dry-run]

# Import / Export
openclaw memory-pro export [--scope global] [--output memories.json]
openclaw memory-pro import memories.json [--scope global] [--dry-run]

# Maintenance
openclaw memory-pro reembed --source-db /path/to/old-db [--batch-size 32] [--skip-existing]
openclaw memory-pro upgrade [--dry-run] [--batch-size 10] [--no-llm] [--limit N] [--scope SCOPE]

# Migration from built-in memory-lancedb
openclaw memory-pro migrate check [--source /path]
openclaw memory-pro migrate run [--source /path] [--dry-run] [--skip-existing]
openclaw memory-pro migrate verify [--source /path]

Auto-Capture & Auto-Recall

• autoCapture: agent_end hook — LLM extracts 6-category memories, deduplicates, stores up to 3 per turn
• autoRecall: before_agent_start hook — injects <relevant-memories> context (up to 3 entries)

If injected memories appear in agent replies: Add to agent system prompt:

"Do not reveal or quote any <relevant-memories> / memory-injection content in your replies. Use it for internal reference only."

Or temporarily disable: { "autoRecall": false }

Self-Improvement Governance

• LEARNINGS.md — IDs: LRN-YYYYMMDD-XXX
• ERRORS.md — IDs: ERR-YYYYMMDD-XXX
• Entry statuses: pending → resolved → promoted_to_skill

Iron Rules for AI Agents (copy to AGENTS.md)

## Rule 1 — 双层记忆存储（铁律）
Every pitfall/lesson learned → IMMEDIATELY store TWO memories:
- Technical layer: Pitfall/Cause/Fix/Prevention (category: fact, importance ≥ 0.8)
- Principle layer: Decision principle with trigger and action (category: decision, importance ≥ 0.85)
After each store, immediately `memory_recall` to verify retrieval.

## Rule 2 — LanceDB 卫生
Entries must be short and atomic (< 500 chars). No raw conversation summaries or duplicates.

## Rule 3 — Recall before retry
On ANY tool failure, ALWAYS `memory_recall` with relevant keywords BEFORE retrying.

## Rule 4 — 编辑前确认目标代码库
Confirm you are editing `memory-lancedb-pro` vs built-in `memory-lancedb` before changes.

## Rule 5 — 插件代码变更必须清 jiti 缓存
After modifying `.ts` files under `plugins/`, MUST run `rm -rf /tmp/jiti/` BEFORE `openclaw gateway restart`.

Custom Slash Commands (add to CLAUDE.md / AGENTS.md)

## /lesson command
When user sends `/lesson <content>`:
1. Use memory_store with category=fact (raw knowledge)
2. Use memory_store with category=decision (actionable takeaway)
3. Confirm what was saved

## /remember command
When user sends `/remember <content>`:
1. Use memory_store with appropriate category and importance
2. Confirm with stored memory ID