openclaw/extensions/memory/package.json

{
  "name": "@clawdbot/memory",
  "version": "0.0.1",
  "type": "module",
  "description": "Clawdbot long-term memory plugin with vector search and seamless auto-recall/capture",
  "dependencies": {
    "@sinclair/typebox": "0.34.47",
    "@lancedb/lancedb": "^0.15.0",
    "openai": "^4.77.0"
  },
  "clawdbot": {
    "extensions": ["./index.ts"]
  }
}
feat(memory): add lifecycle hooks and vector memory plugin Add plugin lifecycle hooks infrastructure: - before_agent_start: inject context before agent loop - agent_end: analyze conversation after completion - 13 hook types total (message, tool, session, gateway hooks) Memory plugin implementation: - LanceDB vector storage with OpenAI embeddings - kind: "memory" to integrate with upstream slot system - Auto-recall: injects <relevant-memories> when context found - Auto-capture: stores preferences, decisions, entities - Rule-based capture filtering with 0.95 similarity dedup - Tools: memory_recall, memory_store, memory_forget - CLI: clawdbot ltm list\|search\|stats Plugin infrastructure: - api.on() method for hook registration - Global hook runner singleton for cross-module access - Priority ordering and error catching Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> 2026-01-18 05:40:58 +00:00			`{`
			`"name": "@clawdbot/memory",`
			`"version": "0.0.1",`
			`"type": "module",`
			`"description": "Clawdbot long-term memory plugin with vector search and seamless auto-recall/capture",`
			`"dependencies": {`
			`"@sinclair/typebox": "0.34.47",`
			`"@lancedb/lancedb": "^0.15.0",`
			`"openai": "^4.77.0"`
			`},`
			`"clawdbot": {`
			`"extensions": ["./index.ts"]`
			`}`
			`}`