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Nice work. This is one of the more coherent “local OS for intelligence” setups I have seen, especially given the GTX 1650 constraint.

A few targeted thoughts from the memory and tools angle.

Memory architecture

You already have the right ingredients. Judge model, semantic store, periodic injection. I would lean harder into typed memories. Episodic, goals, preferences, skills. Each type should use slightly different retrieval heuristics. Add a background consolidation job that periodically merges or decays low importance items instead of appending forever. That turns the system into a controllable long term memory rather than a growing vector heap.

Tool layer

Since you already have real hardware access, web search, and system tools, you are most of the way to explicit planning instead of single tool hops. A small planner that emits { steps: [...] } would fit cleanly, with your current tool orchestrator executing and logging each step. Your JSON harness is already a good place to replay and compare different planning strategies.

Data layer (subtle but important)

Right now the separation makes sense. Chroma for vectors, JSON logs, separate embedding server. If you ever want to sync ATOM across devices, analyze runs at scale, or unify what the agent thought, what it did, and what it remembers, it helps to back this with a general document store that supports time ordered event logs and vector search in the same system. Document databases with built in vector indexes, for example MongoDB Atlas Vector Search, make memories, traces, and tool calls just different document types you can query, aggregate, and replay together instead of siloed JSON plus embeddings.

Overall, this is the kind of architecture people describe in papers but rarely wire end to end. The repo and UI make it inspectable, which matters a lot when iterating on memory and tool policies.