AI Memory System — Session Transcript
We need to add a new project to the list of projects. Don’t implement any code or installations, just discuss and update memories and design documents for the new project. We need to make your memory work much better than it currently works, your memory is severely lacking compared to a person’s memory. I am thinking that we can setup a local relational database that better stores memories and details for you to access. Your memory should be accessible similar to the way a person would remember things. For example, if a person is asked what they did on last Thursday they can often give a highlight of what they did through the day and then when asked more details they can then provide more details. So time stamped based memories in the db would be one way to recall memories and another way to recall memories is subject based memories. The chat conversations you have with me can be broken down into details that are then stored in the relational DB and accessible by time and by subject. And you could store these memories in the DB before you start losing memories in your context and summarizing over details. This is just the start of an idea on how to do it. You can expand on better ways or another way to do this with your ideas on how to do it. What are your thoughts on this. Right now we are only discussing this and not creating documents.
Grounded the idea in the current system’s real pain points by examining the live MEMORY.md: (1) no time axis — cannot answer “what did we do last Thursday”; (2) no relevance ranking — the whole index loads every session, straining context; (3) no automatic capture — detail evaporates at compaction; the “store before losing context” insight is the most important part.
Expanded the idea with human-memory properties:
sqlite-vec); [[wikilinks]] become a real link table.Rough shape: SQLite (FTS5 + vectors); tables for memory, topic/memory_topic, memory_link, embedding, session. Capture pipeline triggered before compaction. Recall ranking = weighted blend. Caveats: the DB is the easy 20%, extraction quality + recall ranking is the hard 80%. Raised questions on trigger model, git requirement, and scope ambition.
Memory storage in the DB could be triggered by me asking or by you letting me know it is a good time to store memories. The other thing important about the memories is that they are time stamped so if you have two related memories the more recent memory is most likely going to be the memory that takes precedence so this helps capture we learn and adapt as we go.
Trigger = checkpoint model, two-directional and prompt-based. Defined the checkpoints Claude would flag: decision settles, task completes, context getting long / near compaction, session boundary, conflict detected.
Recency-precedence, designed carefully so naive “newest wins” doesn’t lose information:
Raised: on conflict detection, always pause-and-confirm, or auto-supersede + reviewable summary?
If a true conflict then that would be a good time to bring it up and discuss the conflict. In many cases it will be self evident what is the better way to store a conflicting memory and so it won’t need to be discussed but there are times it may truly need to be discussed or it may be clear the person, me, might not understand the conflict and resolution and it will be important to explain it to me. Would a Big Data DB be more useful for this purpose or maybe a combination of a Big Data DB and a relational DB?
Conflict = two-dimensional gate: (1) confidence in resolution × (2) impact on owner’s mental model. Three tiers: self-evident + low-impact → silent + logged; ambiguous → discuss; clear-to-Claude-but-consequential → surface AND explain the chain. Rule: discuss if uncertain OR if the resolution would surprise the owner.
On Big Data — pushed back honestly: not Big Data. Big Data tooling solves volume/throughput (TB–PB, clusters); a single agent’s corpus is the opposite. What sounds like it needs exotic storage maps to: vector index (association), JSON columns (heterogeneity), link table (graph) — all of which fit inside SQLite. Recommendation: stay on SQLite + vector index, skip Big Data entirely. Principle: match the tool to the data’s shape, not how impressive it sounds.
The only thing I would push back about is that the relational DB will get to be much larger than you are thinking once it is done in a way that resembles human memory and has the ability to handle adapt and learned memory. This is the tough nut that will have to be cracked for humanoid robotics as well when dealing with real world environments but with Agentic AI it is a much more doable and manageable problem with still large space demand but not nearly as much as will be required by a humanoid robot.
Conceded the scale point. Faithful human-like memory keeps episodic richness, multi-resolution copies, tombstoned history, and multi-granularity embeddings → realistically single-digit to low-tens of GB over years, not tens of MB. A “manage a growing corpus” problem — still single-machine, never a cluster.
The robotics analogy proves the architecture point: crack the scale nut the way a brain does — a memory hierarchy with consolidation and forgetting. Human-likeness and scale-control are the same property. Tiers: working → recent episodic (hot) → long-term semantic (compact) → cold episodic archive (compressed columnar).
Where Big Data legitimately applies: storage patterns only (columnar, time-partitioning, compression) in the cold tier — not compute clusters. The agent is tractable on one machine because its bitrate (text/tool I/O) is orders of magnitude below a robot’s multimodal sensor streams. The actual nut: the retention/consolidation/forgetting policy — design it first; it controls both size and human-likeness.
Yes this sounds like the right path. The memory dial can be scaled based on the amount of onboard storage available and also speed of storage/access so a computer with less storage may be set to forget more than a computer with more storage. The thinking part can be hooked up to the memory part in ways like a humanoid robot that does most of its thinking onboard the robot versus the agentic AI that does most of its thinking in a centralized location.
Reframed the whole system as one fixed boundary + two hardware-derived dials.
Dial 1 — memory dial: two inputs: capacity decides how much to keep; speed decides how much stays online vs. cold. Self-tuning under pressure — salience-weighted eviction (OS virtual-memory analogy). Invariant: the recall interface is identical regardless of dial — same mind, different-sized body.
Dial 2 — locus of cognition as a spectrum: robot end = thinking onboard; agent end = thinking central. Latency-to-brain tunes recall strategy.
Reflex vs. deliberation: every endpoint always carries a thin reflex layer (System 1) — clustering, ranking, eviction, draft consolidation — local regardless of where the brain lives. Deliberation (System 2) escalates to the heavyweight model; the escalation split point is Dial 2. Raised: must the reflex layer be a small model, or can it be pure algorithms?
Using local AI for memory would have to be an additional option because many endpoints won’t have the capacity to execute a local AI. Go ahead and write the document and add this as another project we are working on so when I ask you what projects are we working on this is one of them. Save everything necessary so I can end this session and go to bed.
Captured the final constraint: local AI for the reflex layer is an additional option, never a requirement — algorithmic reflex (clustering / ranking / eviction need no model) is the baseline because many endpoints cannot run a local AI.
Actions taken:
AIMemory_System_Design.md (full living design doc, 13 sections + 7-phase implementation + open decisions).project_ai_memory_system.md (project memory entry).reference_ai_memory_design_doc.md (design-doc pointer memory).reference_project_directory.md.MEMORY.md.No code or installations — design + memory only.
Can you save this full conversation from this session about memory to a document?
AIMemory/Design/AIMemory_Design_Conversation_2026-06-05.md.