Performance Benchmarks ​
LimbicDB is designed to handle thousands of memories efficiently within AI agent environments. The following benchmarks represent a baseline for the current implementation, demonstrating performance across different search mechanisms.
Generated: April 2026
Hardware Context
Results are environment-specific and should be interpreted as relative comparisons rather than absolute limits. They were compiled on a standard developer machine (macOS/Windows, typical multi-core CPU).
SQLite Backend (Recommended) ​
When using persistent storage via ./agent.limbic (the default mode), memory retrieval is exceptionally fast for keyword searches, and scales linearly for semantic searches.
| Memories | Keyword (ms) | Semantic (ms) | Hybrid (ms) | Notes |
|---|---|---|---|---|
| 100 | 0.2 | 2.6 | 2.0 | - |
| 1000 | 0.4 | 21.0 | 18.4 | - |
| 5000 | 1.5 | 113.0 | 95.0 | - |
Memory Backend (Ephemeral) ​
The pure :memory: backend avoids all disk I/O, providing instantaneous keyword lookups. Currently, it does not compute vector embeddings by default.
| Memories | Keyword (ms) | Semantic (ms) | Hybrid (ms) | Notes |
|---|---|---|---|---|
| 100 | 0.2 | N/A | N/A | No embeddings; |
| 1000 | 0.2 | N/A | N/A | No embeddings; |
| 5000 | 1.0 | N/A | N/A | No embeddings; |
Interpretation & Next Steps ​
- Keyword Search: Leverages SQLite's native FTS5 engine or fast in-memory matching. Extremely performant and suitable for large datasets.
- Semantic Search: Utilizes exact cosine similarity matching. While this guarantees 100% accurate recall against the provided vectors, the O(N) complexity causes latency to scale linearly.
- Hybrid Search: Merges keyword indexing speed (30% weight) and semantic accuracy (70% weight), carrying a similar computation cost to pure semantic search.
Future Optimizations ​
As LimbicDB evolves beyond the alpha stage, the following performance enhancements are planned:
- HNSW (Hierarchical Navigable Small World) Indexing: To transition semantic search scaling from O(N) to O(logN) using vector quantization extensions like
sqlite-vss. - WebAssembly Backends: Improving cross-platform embedding computation speed within edge environments.