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Tencent open-sourced TencentDB Agent Memory (published 2026-06-23), a fully local…

2026-06-23 · 11:07 UTC ·@hasantoxr ·1 min read

Brief

TencentDB Agent Memory is an open-source, locally runnable long-term memory for agents that structures memory into four tiers (L0–L3), compresses heavy tool logs into Mermaid symbol graphs, and retrieves raw text only when needed. Published 2026-06-23, it reports a 61.38% token cut, +51.52% task success on WideSearch, and PersonaMem accuracy improving 48%→76% over 50-task continuous sessions.

Why it matters

Tencent open-sourced TencentDB Agent Memory (published 2026-06-23), a fully local long-term memory system for AI agents that claims a 61.38% reduction in token consumption and requires zero external API dependencies.

Key details

  • The system uses a 4-tier semantic pyramid (L0 raw logs, L1 atomic facts, L2 scene blocks, L3 distilled user persona) so agents read persona first and access raw logs only for verification.
  • Benchmarks on continuous long-horizon sessions (50 consecutive tasks each) report WideSearch task success rate up 51.52%, token usage down 61.38%, and PersonaMem accuracy rising from 48% to 76%; short-term logs are compressed into Mermaid symbol graphs navigated by node IDs.
Source evidence

Tencent 🔥: They open-sourced a long term memory system for AI agents that cuts token usage by 61.38% and it runs fully local with zero external API dependencies.

TencentDB Agent Memory builds a 4-tier semantic pyramid:

> L0: Raw conversation logs
> L1: Atomic facts extracted from those logs
> L2: Scene blocks grouped from related facts
> L3: Full user persona distilled from everything above

The agent reads the persona first and drills down to raw logs only when it needs to verify a specific detail.

  • Upper layers carry judgment.
  • Lower layers carry evidence.

For short-term memory, it compresses heavy tool logs into Mermaid symbol graphs. Instead of thousands of tokens of verbose output sitting in context, you get a lightweight node map. The agent navigates it using node IDs, pulling the full raw text only when an error occurs.

The benchmark numbers from the WideSearch test: task success rate up 51.52%, token consumption down 61.38%. On PersonaMem, accuracy jumped from 48% to 76%.

These were measured over continuous long-horizon sessions running 50 consecutive tasks each not isolated single-turn evals.

github.com/TencentCloud/Tenc…