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TencentCloud/TencentDB-Agent-Memory

⭐ 27693 TypeScript repository created 2026-04-07

TencentDB Agent Memory provides a sophisticated memory architecture for AI agents, designed to optimize their operational efficiency and reasoning capabilities. It addresses the common issues of context overload and repetitive information by employing a hierarchical memory system. The core technology involves two pillars: memory layering and symbolic memory. Memory layering organizes agent experiences into a semantic pyramid, from raw conversations to atomic facts, scenarios, and personalized personas. This progressive disclosure mechanism ensures that agents access information hierarchically, drastically reducing the need for blind searches in flat vector stores. Simultaneously, symbolic memory, implemented using Mermaid graphs, condenses verbose intermediate logs and tool outputs into high-density symbols. This offloads heavy logs to external files while maintaining a lightweight symbolic representation in the agent's context, thus cutting token usage significantly. The system guarantees full traceability and lossless recovery, allowing agents and humans to drill down from high-level abstractions to raw evidence. Benchmarks show notable improvements in token usage and task success rates, particularly in long-horizon tasks.

https://github.com/TencentCloud/TencentDB-Agent-Memory

agentai-agentembeddingllmlocal-firstlong-term-memorymemoryopenclaw-pluginvector-searchmemory managementsymbolic memorymemory layeringcontext managementtoken optimization

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