TencentCloud/TencentDB-Agent-Memory

项目介绍

TencentDB Agent Memory = symbolic short-term memory + layered long-term memory. > - Symbolic short-term memory offloads heavy tool logs and condenses them into compact Mermaid symbols, cutting token usage and improving task success. - Layered long-term memory distills fragmented conversations into structured personas and scenes, instead of flat vector piles.

When integrated with OpenClaw, it cuts token usage by up to 61.38%, improves pass rate by 51.52% (relative), and raises PersonaMem accuracy from 48% to 76%.

These results are measured over continuous long-horizon sessions, not isolated turns. For example, SWE-bench runs 50 consecutive tasks per session to simulate the…

摘自 github.com/TencentCloud/TencentDB-Agent-Memory