Why LimbicDB?

A data-driven comparison against popular AI memory solutions.

Quick Comparison

Feature	LimbicDB	Mem0	Zep	Langmem
Deployment	Local-first (SQLite)	Cloud SaaS	Self-hosted server	Cloud SaaS
Zero Config	npm install and go	API key required	Docker + pgvector	API key required
Semantic Search	Built-in (local model)	Cloud embeddings	Cloud embeddings	Cloud embeddings
Encryption	AES-256-GCM at rest	Provider-managed	Provider-managed	Provider-managed
Cross-device Sync	PostgreSQL + P2P	Built-in (cloud)	Built-in (server)	Built-in (cloud)
Cognitive Decay	ACT-R model	Recency bias	Recency weighting	None
Explainability	Score breakdowns	Opaque	Opaque	Opaque
Memory Graph	Automatic clustering	Manual relations	Entity extraction	Manual relations
Offline Mode	Full functionality	No	No	No
Python SDK	Yes	Yes	Yes	Yes
TypeScript SDK	Yes (primary)	Community	Yes	No
Pricing	Free (MIT)	Freemium	Freemium	Freemium
Desktop App	LimbicDB Studio	No	No	No

When to Choose LimbicDB

You should use LimbicDB if:

• Privacy matters: All data stays on your device by default. No API keys needed, no data leaves your network.
• You want zero-config: npm install limbicdb → semantic search works immediately. No Docker, no pgvector, no API keys.
• You need explainability: Every recall result includes a score breakdown showing why that memory was ranked where it was.
• Offline is a requirement: LimbicDB works fully offline. Sync is opt-in, not required.
• You're building local-first agents: Desktop apps, CLI tools, edge devices, or any environment where cloud is not an option.

You might prefer alternatives if:

• You need managed infrastructure: Mem0 and Zep handle scaling, backups, and uptime for you.
• Multi-tenant SaaS: Cloud solutions have built-in multi-tenancy. LimbicDB's namespace isolation is per-file.
• Graph-heavy use cases: Zep's entity extraction is more sophisticated for knowledge graph construction.

Architecture Difference

┌─────────────────────────────────────────────────────┐
│                   Cloud Solutions                     │
│                                                       │
│  Your App ──→ API Key ──→ Cloud Server ──→ pgvector  │
│                    ↓                                  │
│              Data leaves                              │
│              your network                             │
└─────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────┐
│                    LimbicDB                           │
│                                                       │
│  Your App ──→ open('./agent.limbic') ──→ SQLite      │
│                    ↓                                  │
│              Everything stays                         │
│              on your machine                          │
│                    ↓ (opt-in)                         │
│              PostgreSQL Sync ──→ Other devices        │
└─────────────────────────────────────────────────────┘

Performance

Operation	LimbicDB (SQLite)	Typical Cloud Solution
remember()	< 5ms (local)	50-200ms (network)
recall() keyword	< 10ms	100-500ms
recall() semantic	50-100ms (local model)	200-800ms (API call)
Cold start	~2s (model load)	Instant (cloud)
Offline	Full functionality	None

NOTE

Cloud solutions have an advantage on cold start time since the model is always loaded. LimbicDB's local model loads once per process lifecycle and is cached in memory.

Migration

Already using another solution? LimbicDB supports standard import/export:

# Export from LimbicDB
limbic export --format jsonl --output memories.jsonl

# Import into LimbicDB
limbic import --input memories.jsonl --format jsonl

The JSONL format is compatible with most AI memory tools.