Memory Lancedb Pro Skill

This skill should be used when working with memory-lancedb-pro, a production-grade long-term memory MCP plugin for OpenClaw AI agents. Use when installing, configuring, or using any feature of memory-lancedb-pro including Smart Extraction, hybrid retrieval, memory lifecycle management, multi-scope isolation, self-improvement governance, or any MCP memory tools (memory_recall, memory_store, memory_forget, memory_update, memory_stats, memory_list, self_improvement_log, self_improvement_extract_skill, self_improvement_review).

Darcy-Wang@darcy-wang

Install

openclaw skills install @darcy-wang/memory-lancedb-pro-skill

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:

Please provide the required API key(s) for your chosen plan (paste directly, or say "already set as env vars")
Are the env vars already set in your OpenClaw Gateway process? (If unsure, answer No)
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:

bash

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:

bash

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:

bash

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:

bash

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

Interpret results:

HTTP code	Meaning	Action
`200` / `201`	Key valid, quota available	✅ Continue
`401` / `403`	Invalid or expired key	❌ STOP — ask user to check key
`402`	Payment required / no credits	❌ STOP — ask user to top up account
`429`	Rate limited or quota exceeded	❌ STOP — ask user to check billing/quota
`000` / connection refused	Service unreachable	❌ STOP — ask user to check network / Ollama running

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:

bash

# 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

bash

# 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):

json

{
  "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:

json

{
  "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:

json

{
  "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):

json

{
  "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:

bash

# 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:

json

{ "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.

json

{
  "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.

json

{
  "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

bash

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

bash

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:

Store: call memory_store with text: "test memory for verification"
Recall: call memory_recall with query: "test memory"
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:

bash

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)

bash

# 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):

json

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

Restart and verify:

bash

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.

bash

# 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:

json

{
  "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:

bash

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:

bash

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:

bash

# 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:

bash

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:

bash

# 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:

json

{
  "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:

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

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

text

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:

bash

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):

Command	When to use
`openclaw plugins update memory-lancedb-pro`	Update plugin code after a new release (npm-installed only)
`openclaw plugins update --all`	Update all npm-installed plugins at once
`openclaw memory-pro upgrade`	Enrich old memory-lancedb-pro entries that predate the smart-memory schema (missing L0/L1/L2 metadata + 6-category system) — NOT related to LanceDB version
`openclaw memory-pro migrate`	One-time migration from the separate `memory-lancedb` built-in plugin → Pro
`openclaw memory-pro reembed`	Rebuild all embeddings after switching embedding model or provider

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:

bash

# 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:

bash

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

bash

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.
bash
openclaw gateway restart

Easy-to-Miss Setup Steps

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.
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.
Env vars in gateway process: ${OPENAI_API_KEY} requires env vars set in the OpenClaw Gateway service process—not just your shell.
Absolute vs. relative paths: For existing deployments, always use absolute paths in plugins.load.paths.
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.
jiti cache invalidation: After modifying .ts files under plugins, run rm -rf /tmp/jiti/ BEFORE openclaw gateway restart.
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.
Separate LLM config: If embedding and LLM use different providers, configure the llm section separately — it falls back to embedding key/URL otherwise.
Scope isolation: Multi-scope requires explicit scopes.agentAccess mapping — without it, agents only see global scope.
Session memory hook: Fires on /new command — test with an actual /new invocation.
Reranker credentials: When switching providers, update both rerankApiKey AND rerankEndpoint.
Config check before assuming defaults: Run openclaw config get plugins.entries.memory-lancedb-pro to verify what's actually loaded.
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

bash

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):

bash

# 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.

Error message	Root cause	Fix
`must NOT have additional properties` + `config.embedding`	Field name typo in embedding config (e.g. `baseUrl` instead of `baseURL`)	Check all field names against the schema table below — field names are case-sensitive
`must NOT have additional properties` (top-level config)	Unknown top-level field in plugin config	Remove or correct the field
`memory-lancedb-pro: plugin not found` / plugin silently not loading	`plugins.allow` missing (git-clone install) or `enabled: false`	Add `plugins.allow: ["memory-lancedb-pro"]` and set `enabled: true`, then restart
`Unknown plugin id` validation error	Plugin referenced in `entries`/`slots` before it's discoverable	Install/register the plugin first, then add config references
`${OPENAI_API_KEY}` not expanding / auth errors despite env var set	Env var not set in the gateway process environment	Set the env var in the service that runs OpenClaw gateway, not just your shell
Hooks (`before_agent_start`, `agent_end`) not firing	Gateway not restarted after install/config change	Run `openclaw gateway restart`
Embedding errors with Ollama	Wrong `baseURL` format	Must be `http://localhost:11434/v1` (with `/v1`), field must be `baseURL` not `baseUrl`
`memory-pro stats` shows 0 entries after conversation	`autoCapture` false or `extractMinMessages` not reached	Set `autoCapture: true`; need at least `extractMinMessages` (default 2) turns
Memories not injected before agent replies	`autoRecall` is false (schema default)	Explicitly set `"autoRecall": true`
`jiti` cache error after editing plugin `.ts` files	Stale compiled cache	Run `rm -rf /tmp/jiti/` then `openclaw gateway restart`

Configuration

Minimal Quick-Start

json

{
  "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:

json

{
  "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)

json

{
  "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

Field	Type	Default	Description
`apiKey`	string	—	API key (supports `${ENV_VAR}`); array for multi-key failover
`model`	string	—	Model identifier
`baseURL`	string	provider default	API endpoint
`dimensions`	number	provider default	Vector dimensionality
`taskQuery`	string	—	Task hint for query embeddings (`retrieval.query`)
`taskPassage`	string	—	Task hint for passage embeddings (`retrieval.passage`)
`normalized`	boolean	false	Request L2-normalized embeddings
`provider`	string	`openai-compatible`	Provider type selector
`chunking`	boolean	true	Auto-chunk documents exceeding embedding context limits

Top-Level

Field	Type	Default	Description
`dbPath`	string	`~/.openclaw/memory/lancedb-pro`	LanceDB data directory
`autoCapture`	boolean	true	Auto-extract memories after agent replies (via `agent_end` hook)
`autoRecall`	boolean	false (schema default)	Inject memories before agent processing — set to true explicitly
`captureAssistant`	boolean	false	Include assistant messages in extraction
`smartExtraction`	boolean	true	LLM-powered 6-category extraction
`extractMinMessages`	number	2	Min conversation turns before extraction triggers
`extractMaxChars`	number	8000	Max context chars sent to extraction LLM
`enableManagementTools`	boolean	false	Register CLI management tools as agent tools
`autoRecallMinLength`	number	15	Min prompt chars to trigger auto-recall (6 for CJK)
`autoRecallMinRepeated`	number	0	Min turns before same memory can re-inject in same session
`sessionStrategy`	string	`systemSessionMemory`	Session pipeline: `memoryReflection` / `systemSessionMemory` / `none`
`autoRecallTopK`	number	3	Max memories injected per auto-recall (max 20)
`autoRecallSelectionMode`	string	`mmr`	Selection algorithm: `mmr` / `legacy` / `setwise-v2`
`autoRecallCategories`	array	`["preference","fact","decision","entity","other"]`	Categories eligible for auto-recall injection
`autoRecallExcludeReflection`	boolean	true	Exclude reflection-type memories from auto-recall
`autoRecallMaxAgeDays`	number	30	Max age (days) of memories considered for auto-recall
`autoRecallMaxEntriesPerKey`	number	10	Max entries per scope key in auto-recall results

LLM (for Smart Extraction)

Field	Type	Default	Description
`llm.apiKey`	string	falls back to `embedding.apiKey`	LLM API key
`llm.model`	string	`openai/gpt-oss-120b`	LLM model for extraction
`llm.baseURL`	string	falls back to `embedding.baseURL`	LLM endpoint

Retrieval

Field	Type	Default	Description
`mode`	string	`hybrid`	`hybrid` / `vector` (`bm25`-only mode does not exist in schema)
`vectorWeight`	number	0.7	Weight for vector search
`bm25Weight`	number	0.3	Weight for BM25 full-text search
`minScore`	number	0.3	Minimum relevance threshold
`hardMinScore`	number	0.35	Hard cutoff post-reranking
`rerank`	string	`cross-encoder`	Reranking strategy: `cross-encoder` / `lightweight` / `none`
`rerankProvider`	string	`jina`	`jina` / `siliconflow` / `voyage` / `pinecone` / `vllm` (Docker Model Runner)
`rerankModel`	string	`jina-reranker-v3`	Reranker model name
`rerankEndpoint`	string	provider default	Reranker API URL
`rerankApiKey`	string	—	Reranker API key
`candidatePoolSize`	number	20	Candidates to rerank before final filter
`recencyHalfLifeDays`	number	14	Freshness decay half-life
`recencyWeight`	number	0.1	Weight of recency in scoring
`timeDecayHalfLifeDays`	number	60	Memory age decay factor
`reinforcementFactor`	number	0.5	Access-based half-life multiplier (0–2, set 0 to disable)
`maxHalfLifeMultiplier`	number	3	Hard cap on reinforcement boost
`filterNoise`	boolean	true	Filter refusals, greetings, etc.
`lengthNormAnchor`	number	500	Reference length for normalization (chars)

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:

Value	Behavior
`"systemSessionMemory"` (default)	Built-in session memory (simpler)
`"memoryReflection"`	Advanced LLM-powered reflection with inheritance/derived injection
`"none"`	Session summaries disabled

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

Field	Type	Default	Description
`storeToLanceDB`	boolean	true	Persist reflections to LanceDB
`writeLegacyCombined`	boolean	true	Also write legacy combined row
`injectMode`	string	`inheritance+derived`	`inheritance-only` / `inheritance+derived`
`agentId`	string	—	Dedicated reflection agent (e.g. `"memory-distiller"`)
`messageCount`	number	120	Messages to include in reflection
`maxInputChars`	number	24000	Max chars sent to reflection LLM
`timeoutMs`	number	20000	Reflection LLM timeout (ms)
`thinkLevel`	string	`medium`	Reasoning depth: `off` / `minimal` / `low` / `medium` / `high`
`errorReminderMaxEntries`	number	3	Max error entries injected into reflection
`dedupeErrorSignals`	boolean	true	Deduplicate error signals before injection

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

Field	Type	Default	Description
`mode`	string	`fixed`	Recall mode: `fixed` / `dynamic`
`topK`	number	6	Max reflection entries retrieved (max 20)
`includeKinds`	array	`["invariant"]`	Which kinds to include: `invariant` / `derived`
`maxAgeDays`	number	45	Max age of reflections to retrieve
`maxEntriesPerKey`	number	10	Max entries per scope key
`minRepeated`	number	2	Min times an entry must appear to be included
`minScore`	number	0.18	Minimum relevance score (range 0–5)
`minPromptLength`	number	8	Min prompt length to trigger recall

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"

Field	Type	Default	Description
`sessionMemory.enabled`	boolean	false	Legacy: enable session summaries on `/new`
`sessionMemory.messageCount`	number	15	Legacy: maps to `memoryReflection.messageCount`

Self-Improvement Governance

Field	Type	Default	Description
`selfImprovement.enabled`	boolean	true	Enable self-improvement tools (`self_improvement_log` etc.) — on by default
`selfImprovement.beforeResetNote`	boolean	true	Inject learning reminder before session reset
`selfImprovement.skipSubagentBootstrap`	boolean	true	Skip bootstrap for sub-agents
`selfImprovement.ensureLearningFiles`	boolean	true	Auto-create `LEARNINGS.md` / `ERRORS.md` if missing

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

Field	Type	Default	Description
`mdMirror.enabled`	boolean	false	Mirror memory entries as `.md` files
`mdMirror.dir`	string	—	Directory for markdown mirror files

Decay

Field	Type	Default	Description
`decay.recencyHalfLifeDays`	number	30	Base Weibull decay half-life
`decay.recencyWeight`	number	0.4	Weight of recency in lifecycle score (distinct from `retrieval.recencyWeight`)
`decay.frequencyWeight`	number	0.3	Weight of access frequency
`decay.intrinsicWeight`	number	0.3	Weight of importance × confidence
`decay.betaCore`	number	0.8	Weibull shape for core memories
`decay.betaWorking`	number	1.0	Weibull shape for working memories
`decay.betaPeripheral`	number	1.3	Weibull shape for peripheral memories
`decay.coreDecayFloor`	number	0.9	Minimum lifecycle score for core tier
`decay.workingDecayFloor`	number	0.7	Minimum lifecycle score for working tier
`decay.peripheralDecayFloor`	number	0.5	Minimum lifecycle score for peripheral tier
`decay.staleThreshold`	number	0.3	Score below which a memory is considered stale
`decay.searchBoostMin`	number	0.3	Minimum search boost applied to lifecycle score
`decay.importanceModulation`	number	1.5	Multiplier for importance in lifecycle score

Tier Management

Field	Type	Default	Description
`tier.coreAccessThreshold`	number	10	Access count for core promotion
`tier.coreCompositeThreshold`	number	0.7	Lifecycle score for core promotion
`tier.coreImportanceThreshold`	number	0.8	Minimum importance for core promotion
`tier.workingAccessThreshold`	number	3	Access count for working promotion
`tier.workingCompositeThreshold`	number	0.4	Lifecycle score for working promotion
`tier.peripheralCompositeThreshold`	number	0.15	Score below which demotion occurs
`tier.peripheralAgeDays`	number	60	Age threshold for stale memory demotion

MCP Tools

Core Tools (auto-registered)

memory_recall — Search long-term memory via hybrid retrieval

Parameter	Type	Required	Default	Notes
`query`	string	yes	—	Search query
`limit`	number	no	5	Max 20
`scope`	string	no	—	Specific scope to search
`category`	enum	no	—	`preference\|fact\|decision\|entity\|reflection\|other`

memory_store — Save information to long-term memory

Parameter	Type	Required	Default	Notes
`text`	string	yes	—	Information to remember
`importance`	number	no	0.7	Range 0–1
`category`	enum	no	—	Memory classification
`scope`	string	no	`agent:<id>`	Target scope

memory_forget — Delete memories by search or direct ID

Parameter	Type	Required	Notes
`query`	string	one of	Search query to locate memory
`memoryId`	string	one of	Full UUID or 8+ char prefix
`scope`	string	no	Scope for search/deletion

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

Parameter	Type	Required	Notes
`memoryId`	string	yes	Full UUID or 8+ char prefix
`text`	string	no	New content (triggers re-embedding; `preference`/`entity` creates supersede version)
`importance`	number	no	New score 0–1
`category`	enum	no	New classification

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

Parameter	Type	Required	Notes
`type`	enum	yes	`"learning"` or `"error"`
`summary`	string	yes	One-line summary
`details`	string	no	Detailed context
`suggestedAction`	string	no	Action to prevent recurrence
`category`	string	no	Learning: `correction\|best_practice\|knowledge_gap`; Error: `correction\|bug_fix\|integration_issue`
`area`	string	no	`frontend\|backend\|infra\|tests\|docs\|config`
`priority`	string	no	`low\|medium\|high\|critical`

self_improvement_extract_skill — Create skill scaffold from a learning entry

Parameter	Type	Required	Default	Notes
`learningId`	string	yes	—	Format `LRN-YYYYMMDD-001` or `ERR-*`
`skillName`	string	yes	—	Lowercase with hyphens
`sourceFile`	enum	no	`LEARNINGS.md`	`LEARNINGS.md\|ERRORS.md`
`outputDir`	string	no	`"skills"`	Relative output directory

self_improvement_review — Summarize governance backlog (no parameters)

Smart Extraction

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

Enable

json

{
  "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):

json

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

Disable: { "smartExtraction": false }

6-Category Classification

Input Category	Stored As	Dedup Behavior
Profile	`fact`	Always merge (auto-consolidates)
Preferences	`preference`	Conditional merge
Entities	`entity`	Conditional merge
Events	`decision`	Append-only (no merge)
Cases	`fact`	Append-only (no merge)
Patterns	`other`	Conditional merge

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

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

Embedding Providers

Provider	Model	Base URL	Dimensions	Notes
Jina (recommended)	`jina-embeddings-v5-text-small`	`https://api.jina.ai/v1`	1024	Latest (Feb 2026), task-aware LoRA, 32K ctx
Jina (multimodal)	`jina-embeddings-v4`	`https://api.jina.ai/v1`	1024	Text + image, Qwen2.5-VL backbone
OpenAI	`text-embedding-3-large`	`https://api.openai.com/v1`	3072	Best OpenAI quality (MTEB 64.6%)
OpenAI	`text-embedding-3-small`	`https://api.openai.com/v1`	1536	Cost-efficient
DashScope (Alibaba)	`text-embedding-v4`	`https://dashscope.aliyuncs.com/compatible-mode/v1`	1024	Recommended for Chinese users; also supports rerank (see note below)
Google Gemini	`gemini-embedding-2-preview`	`https://generativelanguage.googleapis.com/v1beta/openai/`	3072	Latest (Mar 2026), multimodal, 100+ languages
Google Gemini	`gemini-embedding-001`	`https://generativelanguage.googleapis.com/v1beta/openai/`	3072	Stable text-only
Ollama (local)	`mxbai-embed-large`	`http://localhost:11434/v1`	1024	Recommended local — beats text-embedding-3-large
Ollama (local)	`snowflake-arctic-embed2`	`http://localhost:11434/v1`	1024	Best multilingual local option
Ollama (local)	`nomic-embed-text:v1.5`	`http://localhost:11434/v1`	768	Lightweight classic, 270MB

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:

json

"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

Provider	`rerankProvider`	Endpoint	Model	Notes
Jina (default)	`jina`	`https://api.jina.ai/v1/rerank`	`jina-reranker-v3`	Latest text reranker (2025, Qwen3 backbone, 131K ctx)
Jina (multimodal)	`jina`	`https://api.jina.ai/v1/rerank`	`jina-reranker-m0`	Multimodal (text+images), use when docs contain images
SiliconFlow	`siliconflow`	`https://api.siliconflow.com/v1/rerank`	`BAAI/bge-reranker-v2-m3`	Free tier available
Voyage AI	`voyage`	`https://api.voyageai.com/v1/rerank`	`rerank-2.5`	Sends `{model, query, documents}`, no `top_n`
Pinecone	`pinecone`	`https://api.pinecone.io/rerank`	`bge-reranker-v2-m3`	Pinecone customers only
vLLM / Docker Model Runner	`vllm`	Custom endpoint	any compatible model	Self-hosted via Docker Model Runner

Jina key can be reused for both embedding and reranking.

Multi-Scope Isolation

Scope Format	Description
`global`	Shared across all agents
`agent:<id>`	Agent-specific memories
`custom:<name>`	Custom-named scopes
`project:<id>`	Project-specific memories
`user:<id>`	User-specific memories

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):

json

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

Memory Lifecycle (Weibull Decay)

Three Tiers

Tier	Decay Floor	Beta	Behavior
Core	0.9	0.8	Gentle sub-exponential decline
Working	0.7	1.0	Standard exponential (default)
Peripheral	0.5	1.3	Rapid super-exponential fade

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

bash

# 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)

markdown

## 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)

markdown

## /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

Memory Lancedb Pro Skill

Install

memory-lancedb-pro

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

Step 1 — Present configuration plans and let user choose

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

Step 3 — Find openclaw.json

Step 4 — Read current config

Step 5 — Build the merged config based on chosen plan

Step 6 — Apply the config

Step 7 — Validate and restart

Step 8 — Verify

Installation

Quick Install (Beginner-Friendly)

Requirements

Install Method 1 — via OpenClaw plugin manager (recommended)

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

Install Method 3 — Existing deployments (Path B)

New User First-Install Checklist

LanceDB Version Compatibility

Upgrading plugin code vs. data

Plugin management commands

Easy-to-Miss Setup Steps

Post-Installation Verification

Troubleshooting — Error Message Quick Reference

Configuration

Minimal Quick-Start

Optimal Production Config (recommended)

Full Config (all options)

Configuration Field Reference

Embedding

Top-Level

LLM (for Smart Extraction)

Retrieval

Session Strategy (v1.1.0+)

Session Memory (deprecated — legacy compat only)

Self-Improvement Governance

Markdown Mirror

Decay

Tier Management

MCP Tools

Core Tools (auto-registered)

Management Tools (enable with enableManagementTools: true)

Self-Improvement Tools

Smart Extraction

Enable

6-Category Classification

L0/L1/L2 Layered Content per Memory

Two-Stage Deduplication

Embedding Providers

Reranker Providers

Multi-Scope Isolation

Memory Lifecycle (Weibull Decay)

Three Tiers

Promotion/Demotion Rules

Hybrid Retrieval

Adaptive Retrieval Triggering

Noise Filtering

CLI Commands

Auto-Capture & Auto-Recall

Self-Improvement Governance

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

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

Related skills

Management Tools (enable with `enableManagementTools: true`)