I Tried TencentDB Agent Memory — Here's What the Token Reduction Looks Like The context window problem in long-running agents is familiar: by turn 20, you are paying for tool logs the agent does not need anymore. Truncation loses detail. Summarization compresses but also forgets. Tencent Cloud open-sourced TencentDB Agent Memory (MIT license, May 2026), and it takes a different approach: offload the verbose stuff to local files, keep a Mermaid task graph in context, let the agent drill back in when it needs specifics. The Architecture Four memory layers, each traceable back to raw data: L0 Conversation : raw dialogue + tool logs L1 Atom : structured facts extracted every N conversations L2 Scenario : aggregated solution patterns L3 Persona : user behavior profiles built over time The short-term trick: verbose tool output gets offloaded to refs/*.md files. In context, only a lightweight Mermaid graph remains. When the agent needs a specific output, it retrieves by node_id .…