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Performance Benchmarks
Measured performance of SuperLocalMemory on real hardware. Results reflect real-world use cases.
How fast SuperLocalMemory finds your memories.
| Database Size | Performance |
|---|---|
| Under 500 memories | Sub-100ms search β faster than you can blink |
| Around 1,000 memories | Sub-200ms β fully interactive response |
| 5,000 memories | Seconds range β optional indexes provide acceleration |
What this means: For typical personal use (under 500 memories), search is effectively instant. Beyond 1,000 memories, optional acceleration indexes are available.
Multiple AI tools writing to memory simultaneously β the "database locked" problem, solved.
| Scenario | Result |
|---|---|
| 1-2 agents writing simultaneously | High throughput, sub-15ms latency, zero errors |
| 5 agents writing simultaneously | Moderate throughput, low latency, zero errors |
| 10 agents writing simultaneously | Stable throughput, zero errors |
What this means: Zero "database is locked" errors, even with 10 AI tools writing at the same time.
How much disk space your memories use.
| Scale | Approximate Size |
|---|---|
| 1,000 memories | ~1.5 MB |
| 10,000 memories | ~14 MB |
What this means: Your entire AI memory history takes less space than a single high-res photo.
Building the relationship graph from your memories.
| Scale | Build Time |
|---|---|
| Under 100 memories | Under 1 second |
| Around 1,000 memories | Several seconds |
| 5,000 memories | Several minutes |
What this means: Graph builds quickly for most users. The system consistently discovers natural topic communities across your memories. At 5,000 memories (the design limit), a full rebuild is an explicit design choice balancing graph utility against compute cost.
Graph Scaling: Knowledge graph features work best with up to 10,000 memories. For larger databases, the system uses intelligent sampling (most recent + highest importance memories) for graph construction. Core search and memory storage have no upper limit.
Bayesian trust scoring detects malicious agents attempting to corrupt your memory.
The trust system achieves strong separation between honest and malicious agents. Even a sophisticated "sleeper" attack β where an agent behaves well to build trust, then turns hostile β is detected with a significant trust drop. Zero false positives on benign agents.
The core retrieval system achieves high precision β the first relevant result is at position 1 for the vast majority of queries. The graph and pattern layers provide structural enrichment (clustering, relationship navigation, coding preferences) rather than modifying search ranking directly.
We are currently running the LoCoMo benchmark (Snap Research, ACL 2024) β a standardized evaluation for long-conversation memory systems with multi-hop, temporal, and adversarial question types. Results will be published here when complete.
- All benchmarks run on local hardware with no cloud dependencies
- Each measurement repeated multiple times with statistical aggregation
- Database populated with realistic synthetic memories across diverse topics
- Tests run on clean database instances for each benchmark scenario
See also: Architecture-V2.5 | Home