lily-memory

name	lily-memory
description	Persistent memory plugin for OpenClaw agents. Hybrid SQLite FTS5 keyword + Ollama vector semantic search with auto-capture, auto-recall, stuck-detection, and memory consolidation. Zero npm dependencies.
metadata	{"openclaw":{"requires":{"bins":["node","sqlite3"]},"primaryEnv":""}}

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)
• Zero npm dependencies: Uses sqlite3 CLI + native fetch

Requirements

• Node.js 18+ (for native fetch)
• SQLite 3.33+ with FTS5 (ships with macOS; apt install sqlite3 on Linux)
• 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"]
        }
      }
    }
  }
}

3. 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