openclaw/extensions/memory-lancedb/package.json

{
  "name": "@clawdbot/memory-lancedb",
  "version": "2026.1.23",
  "type": "module",
  "description": "Clawdbot LanceDB-backed long-term memory plugin with auto-recall/capture",
  "dependencies": {
    "@lancedb/lancedb": "^0.23.0",
    "@sinclair/typebox": "0.34.47",
    "openai": "^6.16.0"
  },
  "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			`{`
refactor: rename lancedb memory plugin 2026-01-18 15:47:56 +00:00			`"name": "@clawdbot/memory-lancedb",`
chore: bump version to 2026.1.23 2026-01-23 22:14:56 +00:00			`"version": "2026.1.23",`
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			`"type": "module",`
refactor: rename lancedb memory plugin 2026-01-18 15:47:56 +00:00			`"description": "Clawdbot LanceDB-backed long-term memory plugin with auto-recall/capture",`
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			`"dependencies": {`
chore: update deps 2026-01-20 12:58:49 +00:00			`"@lancedb/lancedb": "^0.23.0",`
chore: update dependencies 2026-01-18 14:15:32 +00:00			`"@sinclair/typebox": "0.34.47",`
chore: update deps 2026-01-20 12:58:49 +00:00			`"openai": "^6.16.0"`
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			`},`
			`"clawdbot": {`
chore: update dependencies 2026-01-18 14:15:32 +00:00			`"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			`}`
			`}`