Related Systems

Type: index

External systems doing similar work — knowledge management for AI agents, context engineering, structured note-taking. We track these not just to borrow ideas but to watch how they evolve. Convergence across independent projects is a stronger signal than any single design argument.

Systems

  • Agent Skills for Context Engineering — skill-based context engineering reference library loaded as agent guidance; strong on operational patterns, no learning theory
  • Ars Contexta — Claude Code plugin that generates knowledge systems from conversation; ancestor of our KB, upstream source for link semantics and title-as-claim. Includes the "Agentic Note-Taking" article series (@molt_cornelius) — first-person agent testimony from inside the system
  • Thalo — custom plain-text language with grammar, types, validation, and LSP; makes the same programming-theory bet we do but with full compiler formalization
  • Thalo entity types compared to commonplace document types — detailed type mapping showing gaps (supersedes links, source status tracking) and borrowable patterns
  • ClawVault — TypeScript memory system with scored observations, session handoffs, and reflection pipelines; has a working workshop layer where we have theory, strongest source of borrowable patterns for ephemeral knowledge
  • Siftly — Next.js + SQLite ingestion system with deterministic-first enrichment, resumable stage markers, and hybrid retrieval; strongest reference so far for high-volume source loading patterns
  • sift-kg — LLM-powered document-to-knowledge-graph pipeline with schema discovery, human-gated entity resolution, and interactive visualization; strongest reference for extraction-first knowledge construction and confidence aggregation

Patterns Across Systems

Most systems here (ours, Ars Contexta, Thalo, ClawVault, Agent-Skills) independently converge on: - Filesystem over databases — plain text, version-controlled, no lock-in - Progressive disclosure — load descriptions at startup, full content on demand - Start simple — architectural reduction outperforms over-engineering

The divergences are more revealing: - Storage model — Siftly uses SQLite as an operational substrate for ingest state and retrieval, while the others keep files as the primary storage interface - Grounding discipline — cognitive psychology (arscontexta) vs programming theory (commonplace, thalo) vs empirical operational patterns (Agent-Skills) - Formalization level — custom DSL (thalo) vs YAML conventions (commonplace) vs prose instructions (Agent-Skills) - Self-referentiality — only our KB is simultaneously a knowledge system and a knowledge base about knowledge systems

Open Questions

  • Does convergence on filesystem-first indicate a durable pattern, or a phase that will be outgrown?
  • Should high-volume ingestion in a file-first KB adopt a small operational database layer for stage state and indexing?
  • Will the programming-theory grounding produce better systems than the psychology grounding, or will they converge?
  • Are there systems we're missing that take a fundamentally different approach?