The problem: AI agents today have goldfish memory. They forget after each session, can't track preference changes, and don't learn from past conversations. Current solutions fail: Vector search alone: 40% failure rate on complex queries Full-context approaches: Expensive, slow, less accurate

MemoryStack solved this with multi-stage memory retrieval.

Results on LongMemEval : MemoryStack: 92.8% Best commercial solution: 71.2% Full-context: 60.2% Biggest improvements: Preference tracking: 93.3% vs 56.7% Knowledge updates: 97.4% vs 83.3% Multi-session: 89.5% vs 64%

Try it : https://memorystack.app

Fully Open Source I'm releasing everything today: Complete source code (MIT license) Full research methodology and paper SDKs (Python, TypeScript) MCP integration for Claude,cursor,kiro Benchmark suite and evaluation tools

Plugin for OpenClaw/Clawdbot Enhance OpenClaw's memory with MemoryStack

give : https://github.com/memorystack-labs/Memorystack Research Paper : https://memorystack.app/research