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TencentDB-Agent-Memory

Observation pool · not formally ranked

This project does not currently meet the main index requirement of two current dimensions across two source types.

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MomentumCurrent · 2026-10-26
79
AttentionCurrent · 2026-10-26
62
Signal confidence Based on how many independent score dimensions currently have data.
Medium2/3 · 1 source types

Methodology v2.0 · snapshot 2026-10-27 · stale after 2 days

About

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…

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