Lily Memory

Persistent memory plugin for OpenClaw agents. Gives your agent long-term memory that survives session resets, compaction, and restarts.

What It Does

• Auto-recall: Injects relevant memories as context before each LLM turn
• Auto-capture: Extracts facts from conversations and stores them automatically
• Hybrid search: SQLite FTS5 keyword search + Ollama vector cosine similarity
• Stuck detection: Detects topic repetition and nudges the agent to break loops
• Memory consolidation: Deduplicates entries on startup
• Dynamic entities: Config-driven allowlist + runtime tool to add entities
• Graceful degradation: Works without Ollama (keyword-only mode)
• Minimal dependencies: Uses better-sqlite3 for secure parameterized queries + native fetch

Requirements

• Node.js 18+ (for native fetch)
• better-sqlite3 npm package (installed via npm install)
• Optional: Ollama with nomic-embed-text model for semantic search

Quick Start

1. Install the plugin to your extensions directory
2. Add to your openclaw.json:

{
  "plugins": {
    "slots": { "memory": "lily-memory" },
    "entries": {
      "lily-memory": {
        "enabled": true,
        "config": {
          "dbPath": "~/.openclaw/memory/decisions.db",
          "entities": ["config", "system"]
        }
      }
    }
  }
}

1. Restart the gateway: openclaw gateway restart

Tools

| Tool | Description |

|------|-------------|

| memory_search | FTS5 keyword search across all facts |

| memory_entity | Look up all facts for a specific entity |

| memory_store | Save a fact to persistent memory |

| memory_semantic_search | Vector similarity search via Ollama |

| memory_add_entity | Register a new entity at runtime |

Configuration

| Option | Type | Default | Description |

|--------|------|---------|-------------|

| dbPath | string | ~/.openclaw/memory/decisions.db | SQLite database path |

| autoRecall | boolean | true | Inject memories before each turn |

| autoCapture | boolean | true | Extract facts from responses |

| maxRecallResults | number | 10 | Max memories per turn |

| maxCapturePerTurn | number | 5 | Max facts per response |

| stuckDetection | boolean | true | Topic repetition detection |

| vectorSearch | boolean | true | Ollama semantic search |

| ollamaUrl | string | http://localhost:11434 | Ollama endpoint |

| embeddingModel | string | nomic-embed-text | Embedding model |

| consolidation | boolean | true | Dedup on startup |

| vectorSimilarityThreshold | number | 0.5 | Min cosine similarity |

| entities | array | [] | Additional entity names |

Architecture

Recall flow: Extract keywords from message -> FTS5 + vector search -> merge and deduplicate -> inject as context

Capture flow: Regex scan for entity: key = value patterns -> validate entity against allowlist -> store to SQLite -> async embed via Ollama

Stuck detection: Track top 5 content words per response -> Jaccard similarity -> if 3+ consecutive >60% overlap, inject Reflexion nudge

License

MIT