Benchmarks
Every number here names the conditions it was measured under. Where a result is a tie, it is reported as a tie.
Benchmark: Token Savings vs File-Based Memory
Section titled “Benchmark: Token Savings vs File-Based Memory”Benchmarked with 15 diverse queries across 4 scales. File-based memory (CLAUDE.md, memory files) loads everything into context every conversation. YantrikDB’s selective recall retrieves only the 3–5 memories relevant to the current task.
| Memories | File-Based | YantrikDB | Savings | Precision |
|---|---|---|---|---|
| 100 | 1,770 tokens | 69 tokens | 96% | 66% |
| 500 | 9,807 tokens | 72 tokens | 99.3% | 77% |
| 1,000 | 19,988 tokens | 72 tokens | 99.6% | 84% |
| 5,000 | 101,739 tokens | 53 tokens | 99.9% | 88% |
Selective recall cost is O(1). File-based memory cost is O(n).
At 1,000 memories, file-based memory is about 20K tokens; at 5,000 it is about 102K, exceeding 32K and 100K context windows. YantrikDB stays at ~70 tokens per query with recall latency under 60ms. Precision improves with more data: the opposite of file-based memory, which degrades as context fills up.
Works with Claude Code, Codex, Cursor, Windsurf, Copilot, Kilo Code — any MCP-compatible agent. Run the benchmark yourself: python benchmarks/bench_token_savings.py