memclaw-context-engine

name: memclaw-context-engine description: MemClaw Context Engine — automatic long-term memory for OpenClaw. Once installed, automatically remembers important facts from conversations and recalls relevant context before responding. No manual tool calls needed for daily use.

MemClaw Context Engine

Automatic Memory: MemClaw Context Engine is a native context management plugin for OpenClaw. It automatically captures conversation memories and recalls relevant context — no manual tool calls required. Compared to built-in memory, it provides L0/L1/L2 tiered retrieval with up to 95% token savings.

MemClaw is an open-source memory enhancement suite based on Cortex Memory. Both MemClaw and this Skill are open-sourced on GitHub.

How It Works

• Auto-Capture: At afterTurn (end of each conversation turn), automatically extracts and batches user/assistant messages to Cortex Memory. Sessions auto-commit when token count (50k), message count (20), or time interval (30min) thresholds are met.
• Auto-Recall: At assemble (before each model run), automatically searches for relevant memories using recent user messages and injects them as simulated cortex_search tool results. Includes 60s cooldown and 70% query overlap deduplication.

Prerequisites

1. Install via openclaw plugins install @memclaw/memclaw-context-engine if not present
2. Configure plugins.slots.contextEngine: "memclaw-context-engine" in openclaw.json to activate
3. Fill in LLM/Embedding API keys in config (auto-opened on first run, or set in openclaw.json)
4. Set agents.defaults.memorySearch.enabled: false in openclaw.json to avoid conflicts with built-in memory
5. All configuration is managed through OpenClaw plugin settings. If the plugin or tools cannot be used, proactively inform the user and assist in completing the necessary configurations. See 'Troubleshooting' below.

Available Tools

While the Context Engine handles memory automatically, these tools are available for explicit agent use:

cortex_search — Search Memories

Layered semantic search across ALL memories with L0/L1/L2 control.

Example: User asks "What database did I prefer?"

cortex_search(query="database preference", return_layers=["L0"])

cortex_recall — Quick Recall

Returns L0 snippet + L2 full content. Equivalent to cortex_search(return_layers=["L0","L2"]).

cortex_add_memory — Manual Store

Explicitly store a message with optional metadata.

Example: User says "Remember my email is xxx@example.com"

cortex_add_memory(content="User email: xxx@example.com", role="user", metadata={"tags": ["contact"], "importance": "high"})

cortex_commit_session — Manual Commit

Close session and trigger memory extraction pipeline.

cortex_forget — Delete Memories

Delete a memory by exact URI.

Example: User says "Forget my phone number"

cortex_forget(uri="cortex://user/default/preferences/phone-number.md")

cortex_maintenance — Periodic Maintenance

Perform maintenance on MemClaw data. Runs automatically every 3 hours but can be called manually.

What it does:

1. prune — Remove vectors whose source files no longer exist
2. reindex — Rebuild vector index and remove stale entries
3. ensure-all — Generate missing L0/L1 layer files

When to call manually:

• Search results seem incomplete or stale
• After recovering from a crash or data corruption
• When disk space cleanup is needed

cortex_maintenance()  # Run all commands
cortex_maintenance(dryRun=true)  # Preview only
cortex_maintenance(commands=["prune"])  # Run specific command

Browse Tools

Configuration

Daily Operations

# Check plugin status
openclaw skills

# Check context engine slot
openclaw config get plugins.slots.contextEngine

# Disable memory (switch to legacy)
openclaw config set plugins.slots.contextEngine legacy

# Enable memory
openclaw config set plugins.slots.contextEngine memclaw-context-engine

# Restart gateway after changing slot

Configuration Example

{
  "plugins": {
    "entries": {
      "memclaw-context-engine": {
        "enabled": true,
        "config": {
          "tenantId": "tenant_claw",
          "autoStartServices": true,
          "autoRecall": true,
          "autoCapture": true,
          "llmApiKey": "your-llm-api-key",
          "llmModel": "gpt-5-mini",
          "embeddingApiKey": "your-embedding-api-key",
          "embeddingModel": "text-embedding-3-small"
        }
      }
    },
    "slots": {
      "contextEngine": "memclaw-context-engine"
    }
  },
  "agents": {
    "defaults": {
      "memorySearch": { "enabled": false }
    }
  }
}

Troubleshooting

Config file location:

• macOS: ~/Library/Application Support/memclaw/config.toml
• Linux: ~/.local/share/memclaw/config.toml
• Windows: %LOCALAPPDATA%\memclaw\config.toml

No Docker required — all dependencies bundled with plugin.

References

• tools.md — Detailed tool parameter reference
• troubleshooting.md — Comprehensive troubleshooting guide