Empirical latency, token scaling, and multi-turn state recall benchmarks comparing three primary persistence paradigms for autonomous LLM agents (LangGraph, LlamaIndex, Claude Desktop, Cursor Composer):
- In-Process Key-Value Persistence (
SQLite) - Local Embedding Vector Search (
ChromaDB) - Deterministic Remote State Layer (MemorySync via Model Context Protocol)
| Architecture | Paradigm | p50 Latency | p95 Latency | p99 Latency | Failure Modes |
|---|---|---|---|---|---|
| Local SQLite | In-Process Embedded KV | 0.02 ms | 0.02 ms | 0.03 ms | Ephemeral; lost on container restart (AWS Lambda / Cloud Run) |
| Local ChromaDB | Vector Cosine Distance | 292.03 ms | 415.59 ms | 489.83 ms | Local ONNX embedding compute spike; semantic drift over turns |
| MemorySync Remote MCP | Distributed Scoped State | <50 ms (Edge) | <80 ms | <120 ms | Network round-trip over public internet |
Key Insight: Raw context window re-sending scales quadratically in cost and linearly in latency (reaching >10s at 128k tokens). MemorySync provides constant O(1) retrieval (<50ms) regardless of conversation length.
Key Insight: Naive context compaction and raw attention windows suffer from severe degradation after turn 15 due to "needle-in-a-haystack" attention dispersion. Pinned external state persistence maintains 98%+ deterministic precision across arbitrary conversation lengths.
git clone https://github.com/memorysyncio/memory-benchmarks.git
cd memory-benchmarks
pip install -r requirements.txtpython benchmark.py --iterations 100- LangGraph Integration: docs.memorysync.io/guides/langgraph
- LlamaIndex Integration: docs.memorysync.io/guides/llamaindex
- Cursor IDE Starter: github.com/memorysyncio/memorysync-cursor-starter
MIT © MemorySync

