TencentDB Agent Memory
TencentDB Agent Memory is a fully local, open-source long-term memory system for AI agents built by Tencent. Using a 4-tier progressive pipeline with symbolic short-term memory (Mermaid canvas) and layered long-term memory (L0→L3 persona hierarchy), it cuts token usage by 61% and improves agent task success by 51% when integrated with OpenClaw — with zero external API dependencies.
on the blogTencentDB Agent Memory: Layered Long-Term Memory for AI Agents With 61% Token Reduction 5 min
TencentDB Agent Memory is a fully local, open-source long-term memory system for AI agents built by Tencent. Using a 4-tier progressive pipeline with symbolic short-term memory (Mermaid canvas) and layered long-term memory (L0→L3 persona hierarchy), it cuts token usage by 61% and improves agent task success by 51% when integrated with OpenClaw — with zero external API dependencies.
- 01Symbolic short-term memory — offloads verbose tool logs to external files and condenses in-context state into compact Mermaid symbol graphs with node_id tracing; cuts WideSearch token usage by 61.38% and improves pass rate from 33% to 50% (relative +51.52%)
- 024-tier long-term memory pipeline — L0 raw conversation → L1 atomic facts → L2 scenario blocks → L3 user persona; layered storage with full drill-down traceability (no irreversible compression); raises PersonaMem accuracy from 48% to 76%
- 03Fully local, zero external API dependencies — dual-layer storage (database for facts/logs + Markdown for personas/scenes); integrates as an OpenClaw plugin and Hermes skillpack; SWE-bench improvement +9.93% with 33% token savings