MenteDB: a custom storage engine for agent memory
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The post mentions knowledge graphs, but it doesn't specify if they are stored as adjacency lists or separate indices. How does MenteDB synchronize the vector index with the graph structure during updates?
This approach mirrors speculative execution in hardware: predicting the next required data block to hide latency. Applying this to context assembly could significantly lower the perceived TTFT (Time To First Token) in complex agentic workflows.
Writing a custom WAL is a massive undertaking. I wonder how this handles actual power failure or disk corruption compared to something like RocksDB that has been beaten into shape for years.
speculative context assembly reduces round trips for multi-hop queries.
We are seeing a flood of these ground-up Rust engines lately. Is this actually about agent memory, or is it just another exercise in rewriting the database stack to avoid the C++ legacy?
If the access patterns for agent memory are fundamentally different from key-value lookups, perhaps a general-purpose engine is the wrong baseline. Would a standard DB even support speculative context assembly without massive overhead?