From an engineering perspective, what I find compelling here is not “no embeddings”, but the decision to treat memory as a first-class, inspectable system rather than a retrieval trick.
Most agent memory stacks today collapse everything into embeddings and hope similarity search is enough. That works for recall, but breaks down quickly when you need traceability, temporal reasoning, or explanation of why something was remembered.
The layered design here (raw resources → extracted memory items → categorized memory files) feels much closer to how we design real systems: separation of concerns, clear abstraction boundaries, and the ability to reason about state changes over time.
Storing memories in human-readable form also makes debugging and evolution practical. You can audit what the agent “knows”, adjust policies, or let the LLM reason directly over memory instead of treating it as a black box vector store.
Embeddings still make sense as an optimization layer, but making them optional rather than foundational is an important architectural choice if agents are meant to run long-term and stay coherent.
This feels less like a retrieval hack and more like actual infrastructure.
Great to see a framework tackling the architecture of memory rather than just retrieval. The concept of separating 'Resource Layer' from 'Memory Item Layer' makes a lot of sense for avoiding context pollution in long-running agents.
Practically speaking, how significant is the improvement in retrieval accuracy compared to a standard RAG setup (e.g., vanilla vector search) for nuanced queries? I'd love to understand the 'lift' I could expect before migrating my current stack.
From an engineering perspective, what I find compelling here is not “no embeddings”, but the decision to treat memory as a first-class, inspectable system rather than a retrieval trick.
Most agent memory stacks today collapse everything into embeddings and hope similarity search is enough. That works for recall, but breaks down quickly when you need traceability, temporal reasoning, or explanation of why something was remembered.
The layered design here (raw resources → extracted memory items → categorized memory files) feels much closer to how we design real systems: separation of concerns, clear abstraction boundaries, and the ability to reason about state changes over time.
Storing memories in human-readable form also makes debugging and evolution practical. You can audit what the agent “knows”, adjust policies, or let the LLM reason directly over memory instead of treating it as a black box vector store.
Embeddings still make sense as an optimization layer, but making them optional rather than foundational is an important architectural choice if agents are meant to run long-term and stay coherent.
This feels less like a retrieval hack and more like actual infrastructure.
Great to see a framework tackling the architecture of memory rather than just retrieval. The concept of separating 'Resource Layer' from 'Memory Item Layer' makes a lot of sense for avoiding context pollution in long-running agents.
Practically speaking, how significant is the improvement in retrieval accuracy compared to a standard RAG setup (e.g., vanilla vector search) for nuanced queries? I'd love to understand the 'lift' I could expect before migrating my current stack.
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