OpenClaw plugin for full supermemory-driven memory management.

Your AI assistant remembers everything — every session, every conversation. No more context loss.

• Auto-Recall: Before every response, searches supermemory for relevant context and injects it into the prompt (via before_prompt_build hook)
• Auto-Capture: After every conversation, sends ALL messages (user + assistant) to supermemory — supermemory decides what to remember
• Compaction Hook: Automatically saves conversation content before session compaction
• Agent Tools: Registers memory_save, memory_recall, memory_forget as native agent tools (no manual mcporter calls needed)

• OpenClaw (2026.3+ recommended)
• mcporter CLI installed and configured with supermemory
• A supermemory account and API key

# Install mcporter npm install -g mcporter # Configure supermemory MCP server (~/.mcporter/mcporter.json) cat > ~/.mcporter/mcporter.json << 'EOF' {  "mcpServers": {  "supermemory": {  "baseUrl": "https://mcp.supermemory.ai/mcp",  "headers": {  "Authorization": "Bearer ${SUPERMEMORY_API_KEY}"  }  }  } } EOF # Set your API key export SUPERMEMORY_API_KEY="your-key-here" # Verify it works mcporter call supermemory.recall query="test"

git clone https://github.com/kandotrun/openclaw-supermemory.git cp -r openclaw-supermemory ~/.openclaw/extensions/supermemory-auto

Add to your openclaw.json:

{ "plugins": { "load": { "paths": ["/path/to/openclaw-supermemory"] } } }

{ "plugins": { "entries": { "supermemory-auto": { "enabled": true, "config": { "autoRecall": true, "autoCapture": true } } }, "slots": { "memory": "supermemory-auto" } } }

Then restart the gateway:

openclaw gateway restart

User sends message │ ▼ ┌─────────────────────────┐ │ before_prompt_build │ ← Recall phase │ │ │ 1. Extract query │ │ (strip metadata) │ │ 2. mcporter call │ │ supermemory.recall │ │ 3. Inject context │ │ into prompt │ └─────────────────────────┘ │ ▼ Agent responds (with full context) │ ▼ ┌─────────────────────────┐ │ agent_end │ ← Capture phase │ │ │ 1. Collect ALL messages │ │ (user + assistant) │ │ 2. Label each: │ │ [ユーザー] / [アシスタント] │ │ 3. Strip noise │ │ (metadata, context) │ │ 4. Send to supermemory │ │ (it decides what │ │ to remember) │ └─────────────────────────┘ Session compaction triggered │ ▼ ┌─────────────────────────┐ │ before_compaction │ ← Pre-compaction save │ │ │ Save full conversation │ │ to supermemory before │ │ messages are pruned │ └─────────────────────────┘ 

The plugin registers three tools that the agent can use directly:

These tools use mcporter internally — the agent doesn't need to call mcporter manually.

Messages sent to supermemory are clearly labeled:

[ユーザー] 明日福岡に行くんだけど、充電スポット調べて [アシスタント] 福岡エリアのTesla Supercharger一覧: - 福岡天神SC (4基) - LECT広島SC (8基) ... 

Supermemory receives the full exchange and decides what's worth remembering. No client-side filtering — supermemory's AI handles the curation.

The plugin skips:

• Heartbeat/system messages
• Messages shorter than 5 characters
• NO_REPLY / HEARTBEAT_OK responses
• Already-injected supermemory-context blocks
• Metadata blocks (JSON, conversation info)

• Latency: Each recall adds ~2-3s (mcporter process spawn + MCP call). This is the trade-off for always having context.
• No filtering by design: v2 sends everything to supermemory. Supermemory's AI decides what to remember. This avoids losing important context that pattern-based filters would miss.
• Memory slot: Setting plugins.slots.memory to supermemory-auto replaces the default memory-core plugin. If you want both, don't set the memory slot.
• containerTag: Use this to scope memories to a specific project (e.g., sm_project_default).

• Full rewrite — supermemory-driven architecture
• Migrated from before_agent_start (legacy) to before_prompt_build
• Full conversation capture: user + assistant messages, labeled with [ユーザー] / [アシスタント]
• No client-side filtering — supermemory decides what to remember
• Added before_compaction hook for pre-compaction saves
• Registered agent tools: memory_save, memory_recall, memory_forget
• Added containerTag support for project-scoped memories
• Expanded maxBuffer to 1MB for large recall results
• Better query extraction (strips context blocks + metadata noise)

• Initial release
• Auto-recall via before_agent_start
• Optional auto-capture (user messages only, pattern-filtered)

MIT

Copyright (c) 2026 Kan Ninomiya & 白川 玲 (AI)