How a durable substrate turns ephemeral context into knowledge that compounds.
This is the canonical deep-dive on how Living Memory is built: the vocabulary, the composition model, and an honest account of what runs today and what is still being built. It is written to be accurate before it is impressive.
Architecture reviewThe work has a primary purpose: to add to the leading wave of AI and help steer it for the better, driven by knowledge, learning, and individual sovereignty. This is the north star. Everything in the architecture below is an attempt to make it real.
Who it serves, in order:
This is a mission statement, written to be aspirational. What is actually built is described in the appendix.
Three structural gaps. None of them is solved by a bigger model alone.
The terms below were sharpened in recent design work. They are deliberately distinct: each names one thing and only that thing. Two glosses anchor the whole model: lenses are about what data exists for me; frames are about what data is relevant right now.
render(corpus, lens, frame) → slice. The corpus is the authority; the slice is what the agent holds.
A second design split runs underneath the model: files are the agent's engagement surface; the database is the navigation index.
Agents traverse curated markdown by following wiki-links, the way a person reads, one connected note to the next. This is where the agent actually works.
The database answers cross-cutting queries and points back to the files. Embeddings are the discovery complement, a way to surface what to read.
SLF (Substrate · Lens · Frame) is the protocol and architectural layer. Alexandria is Lexenne's reference implementation of it. SLF is a governance protocol for AI memory: it decides who sees what fact, in what role, with what audit trail.
SQLite is to SQL as Alexandria is to SLF: the reference implementation, free and exemplary. Any conformant implementation works.
At a high, conceptual level, SLF is built around a few commitments:
Every fact carries its own access rules and regulatory metadata. Governance travels with the data itself.
Actions emit a verifiable receipt as a first-class output. The receipt is part of the action itself.
The reference implementation is Apache-2.0. The design is survivable without Lexenne: if the company disappears, a conformant implementation still works and the principal still owns their corpus.
SLF is designed to fill the "contextualization gap" that large-memory-language-model research acknowledges but does not solve: deciding who sees what, in what role, with what trail.
This page covers what SLF is and why it matters. The wire-level operations, the gate and receipt mechanics, the grant and identity machinery, and the governance specifics live in the design documents, deliberately out of scope here.
Status: SLF is a v0.4 draft design. Reference implementation is about to begin.
Storing and retrieving is the easy part. The hard part, the part that is the moat, is deciding what to keep: reweaving new knowledge into old, resolving contradictions when two facts disagree, and forgetting what has gone stale.
Conflict resolution is a frontier problem the broader field has not solved. It is the moat. The mission and the hardest technical problem turn out to be the same problem.
Lens-filtered cross-session retrieval is live: an agent starting a session can pull in relevant prior context. The compounding loop itself, reweaving, conflict resolution, and forgetting, is being built now.
The common approach is per-tool memory: each product ships its own memory feature, each one starts over, and none of them talk to each other. The knowledge fragments across silos that each begin from zero.
The alternative is one shared substrate that remembers state-changes across systems, read through role-shaped lenses. One memory, many lenses.
The durable position is the unified substrate. Anyone can add memory to a single tool. The hard, defensible thing is one corpus that compounds across all of them.
Figures grow over time. Three status levels: REAL (running today), IN BUILD, DESIGNED. "Running" here means running on a personal development fleet.