Explore temporal graphs, explainable retrieval, procedural memory, SQLite storage, and fuzzy context for local AI agents.
Explore temporal graphs, explainable retrieval, procedural memory, SQLite storage, and fuzzy context for local AI agents.
Most AI memory is a growing pile of similar text — it retrieves stale facts confidently, loses relationships between events, and can't explain why it surfaced what it surfaced. HexVenn Temporal Graph Memory (HexVenn-TGM) is a local-first architecture that stores agent memory as a bi-temporal graph and scores retrieval explicitly. This field note explains the design and its open challenges.
By the Custom AI Works editorial team Published May 10, 2026 · Updated September 28, 2026 · 7 min read
HexVenn Temporal Graph Memory (HexVenn-TGM) is a local-first AI memory architecture for long-running agents. It stores events and entities as a bi-temporal graph in SQLite, retrieves with an explicit weighted score combining vector similarity, graph proximity, temporal cohesion, context overlap, and reinforcement — with penalties for staleness and conflict — and treats tenant isolation and procedural memory as first-class design concerns.
Give a typical agent a vector store and three failure modes arrive on schedule. Staleness: a price changed in March, but the January memory retrieves with equal confidence. Lost relationships: facts are stored as isolated text, so “the client who owns that project” is a fresh search problem every time instead of a stored connection. Unexplainability: when the agent answers wrong, nobody can see which memories fired or why. Long-running agents don't need a bigger pile of text; they need memory that knows when things were true and how they connect.
HexVenn-TGM is a local-first memory architecture for long-running, explainable, multi-tenant AI agents, developed as part of the Custom AI Works agent operating-system work by Edward Schechter. Its deliberate Phase 1 storage choices are SQLite with FTS5 , normalized graph tables, and embedding blobs — chosen because local-first systems need portability, simplicity, deterministic behavior, low operational overhead, and offline capability. The architecture leaves room for future adapters (Qdrant, pgvector, Memgraph, Neo4j) without premature distribution.
SQLite + FTS5, normalized graph tables, embedding blobs — a single inspectable file.
Hybrid: dense vector similarity, sparse lexical relevance, and graph proximity, combined by an explicit formula.
Fuzzy context sets instead of rigid tags — memories belong partially to multiple contexts.
Successful action patterns stored as reusable procedures, not just facts about the world.
Tenant isolation designed as an architectural boundary from the start.
The core idea: memory items are events and entities connected by relationships that hold over time , recorded bi-temporally — when something was true in the world, and when the system learned it. That second timeline makes corrections non-destructive (the old fact is superseded, not erased), enables historical replay (“what did we believe on Tuesday?”), and makes contradictions visible instead of silent.
Starting with SQLite is the deliberate engineering choice, not a compromise: one portable file that can be copied, backed up, inspected, and run offline with zero servers. Distributed vector or graph databases solve scale problems Phase 1 doesn't have yet; the adapter path is reserved for when a deployment genuinely outgrows local storage.
Retrieval exposes component-level scoring rather than a single opaque similarity number. The architecture defines an explicit weighted composite:
When retrieval surprises you, the components show exactly which signal drove it. That makes agent memory debuggable engineering — and explainable retrieval may be the most important feature in the design, because an agent whose memory can't be audited can't be trusted with consequential work.
Fuzzy context sets replace rigid tagging: a memory can belong 0.8 to “client work” and 0.4 to “billing,” matching how real context overlaps. Procedural memory is the sleeper feature: the system stores successful ways of doing things as reusable procedures, so the agent improves at tasks, not just at recalling facts about them.
Multi-tenant memory is where leakage would be catastrophic, so isolation is treated as an architectural boundary — enforced by storage structure and retrieval scoping from the first version — rather than a filter added when the second client arrives.
Brute-force cosine similarity becomes expensive at scale; the adapter path to a dedicated vector store exists for that point.
Recursive graph queries grow computationally heavy as relationship density increases; depth limits and scoring cutoffs are operational necessities.
If embedding models change over time, semantic-neighborhood consistency can degrade; embedding versioning remains an open item.
Graph reinforcement systems can accidentally amplify bad information; conflict penalties and confidence scoring mitigate the risk without eliminating it.
For local AI specifically, the architecture's priorities — portability, inspectability, offline operation, explainable retrieval — match what private, long-running agents actually need: memory that behaves like knowledge, not like a search index.
HexVenn-TGM stands for HexVenn Temporal Graph Memory. It is a local-first AI memory architecture designed for long-running, explainable, multi-tenant AI agents, storing events and relationships as a temporal graph rather than isolated text chunks.
Vector search retrieves similar content. HexVenn-TGM combines vector similarity with temporal graphs, relationships, context overlap, reinforcement, procedural patterns, and explainable component-level scoring, so retrieval can be inspected rather than merely accepted.
Bi-temporal memory tracks when something was true in the world and when the system learned it. That supports corrections without destructive overwrites, historical replay, contradiction tracking, and auditability.
SQLite is portable, local-first, reliable, easy to back up, and operationally simple — a strong MVP storage layer for private AI agents before adding distributed vector or graph adapters.
HexVenn-TGM is part of the Custom AI Works local-first AI agent operating-system work by Edward Schechter.
HexVenn-TGM is documented from the architecture's own design materials as of September 2026. The retrieval formula, storage choices, and challenge list are reproduced as specified in those materials; performance characteristics at scale are identified within the design as open evaluation items rather than benchmarked claims. HexVenn-TGM is part of the Custom AI Works local-first AI agent operating-system work by Edward Schechter.
HexVenn-TGM stands for HexVenn Temporal Graph Memory. It is a local-first AI memory architecture designed for long-running, explainable, multi-tenant AI agents, storing events and relationships as a temporal graph rather than isolated text chunks.
Vector search retrieves similar content. HexVenn-TGM combines vector similarity with temporal graphs, relationships, context overlap, reinforcement, procedural patterns, and explainable component-level scoring, so retrieval can be inspected rather than merely accepted.
Bi-temporal memory tracks when something was true in the world and when the system learned it. That supports corrections without destructive overwrites, historical replay, contradiction tracking, and auditability.
SQLite is portable, local-first, reliable, easy to back up, and operationally simple — a strong MVP storage layer for private AI agents before adding distributed vector or graph adapters.
HexVenn-TGM is part of the Custom AI Works local-first AI agent operating-system work by Edward Schechter.
We map the highest-cost workflow first — the calls, forms, inboxes, CRMs, and handoffs involved — before anything is built.
A scoped build with approval gates and human-in-the-loop exception paths, around the tools you already use.
The workflow runs live with logs your team can inspect, so value is proven — not promised.
Docs, logs, and the code itself are yours. No lock-in, no renting your own system back.
How sparse routing, Mamba-style layers, adapters, and spatial graph memory could make custom AI models more practical.
ArticlesCalculate AI automation ROI using sourced industry benchmarks, measured business inputs, costs, payback, and clearly labeled external evidence.
ArticlesLearn behavioral triggers, personalization, send-time optimization, and the automated email sequences businesses need.
ArticlesHow AI phone agents handle missed calls, lead intake, appointment requests, and handoff—and what to verify before launch.
ArticlesAI process automation explained: agentic workflows, high-value use cases, autonomy levels, tool selection, and a five-step implementation roadmap.
ArticlesMap AI data flows, evaluate vendors, prevent prompt injection, control access, and reduce quiet business-data leaks.
Start with a free AI audit. We map the workflow slowing you down and show you the smallest useful fix — before you pay for a build.
Get my free AI audit Call (425) 780-5858