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Vault-LD

Brain spec

First committed by dfreytag

Published in The-Knowledge-Graph-Guys/vault-ld. Publisher page

246 GitHub stars

What it is

Vault-LD: an open spec for Markdown vaults as linked data. YAML-LD frontmatter + a shared `@context` = an RDF knowledge graph. Prose for humans and LLMs, triples for machines.
— the description by dfreytag

AutoGineer overview, from the brain’s own files

Vault-LD provides a standardized way to bridge human-readable Markdown notes with machine-readable RDF knowledge graphs. By utilizing YAML-LD in the frontmatter of Markdown files and a shared context, it allows for a seamless roundtrip between prose and structured data. This enables AI agents and other systems to reason over a vault's contents as a graph while maintaining a format that remains easy for humans to edit and version control in Git. The specification includes reference tools for exporting vaults to RDF and ingesting RDF into vault structures, ensuring full fidelity in both directions.

How it works

Memory model: LLM wiki

AutoGineer overview, from the brain’s own files

Vault-LD organizes information in a folder-based structure of Markdown files. Each file uses YAML-LD frontmatter to define its properties and relationships, which are resolved against a shared context file. The system uses reference scripts to convert these Markdown files into RDF triples (schema and data layers) and vice versa, allowing the vault to function as both a human-editable wiki and a structured knowledge graph.

Folders
FolderRoleFiles
.githubagent-config11
docsdocs39
imagesattachments3
scriptsscripts7
Vault-LD Examplefolder14

The agent writes

Requirements

Works with: OpenAI Codex, GitHub Copilot, Cursor, Kiro, OpenCode, Amp, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. Clone the repository at the commit AutoGineer scanned:

    git clone https://github.com/The-Knowledge-Graph-Guys/vault-ld.git && cd vault-ld && git checkout 025e71be8d810e387dc451c920e05472d1c47170
  2. Point Claude Code at AGENTS.md: it does not read that file on its own.

  3. cd vault-ld && claude

  4. AutoGineer scanned these files at commit 025e71b.

  5. First committed by dfreytag. Published in The-Knowledge-Graph-Guys/vault-ld. Licence: Apache License 2.0.

Usage

AutoGineer overview, from the brain’s own files

  • Defining business semantics and ontologies within a Markdown-based wiki.
  • Enabling AI agents to perform graph-based reasoning over local note collections.
  • Synchronizing external RDF data sources with a human-friendly Markdown vault.
  • Maintaining a version-controlled knowledge base that is simultaneously readable by humans and machines.
“List all recipes in the vault that require chickpeas.”
The agent queries the RDF graph derived from the Markdown frontmatter to identify all notes with type 'Recipe' and 'requiresIngredient' set to 'Chickpeas'.
“Update the prep time for the Hummus recipe to 30 minutes.”
The agent modifies the 'prepTimeMinutes' field in the YAML frontmatter of the Hummus.md file, ensuring the change is reflected in both the prose and the underlying knowledge graph.

Documentation by dfreytag

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<picture> <source media="(prefers-color-scheme: dark)" srcset="images/logo-dark.svg"> <img src="images/logo-light.svg" alt="Vault-LD logo" width="140"> </picture>

Vault-LD

An open format for knowledge that two very different readers can share at once:<br>a person editing notes, and a machine reasoning over a graph.

Image: CI Image: Release Image: Docs Image: License Image: GitHub stars Image: Issues Image: Contributions welcome

Specification · Docs site · Example vault · Contributing

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RDF in the Vault

Karpathy's "LLM wiki" idea points at something real: a directory of plain Markdown notes is one of the most powerful ways to work with LLMs. It is readable by you, chunkable by a model, and diffable in git, with no database required. The YAML frontmatter at the centre of this approach carries a hidden pattern: it can map onto YAML-LD (JSON-LD with a YAML serialization). Resolve that frontmatter through one shared context and your notes stop being tagged text. They become linked data.

This matters for anyone already invested in RDF-based ontologies. Your business semantics (your classes, properties, and controlled vocabularies) drop straight into the wiki your team already uses, much as schema.org gave web pages a shared vocabulary without asking authors to leave HTML. The benefit runs both ways: business semantics arrive inside the wiki, and the ontology gains a human-friendly surface, where classes and properties can be read and edited as notes rather than as raw Turtle.

Both directions work because of the roundtrip. Since the frontmatter is YAML-LD, a note and an ontology definition are the same kind of object, and either can be projected losslessly to RDF and back. An ontology can enter as Turtle, be edited as Markdown, and leave as Turtle again, or the reverse, with no canonical "real" form privileged over the other.

Image: Vault-LD

What's here

  • SPEC.md: the normative reference. It defines how frontmatter becomes a knowledge graph (§4) and how any RDF graph round-trips through the vault format with full fidelity (§5), along with terminology, directory structure, conformance criteria, and a compatibility profile for lifting OKF-style Markdown bundles into linked data (Appendix B).

  • Vault-LD Example/: a complete, copyable vault that demonstrates every rule in the spec. It holds one ontology (Culinary), one controlled vocabulary (Difficulty Levels), and one instance (hummus). Its root context.jsonld composes the Culinary ontology's own context, showing how multiple contexts compose (SPEC §4.2).

  • scripts/vault_to_rdf.py: a reference exporter that projects a vault to RDF — see Exporting to RDF below.

  • scripts/rdf_to_vault.py: the reference ingester running the other way, RDF → vault — see Ingesting RDF below. Together the two tools close the roundtrip.

Exporting to RDF

The repo ships scripts/vault_to_rdf.py, a small Python tool that reads a vault and emits Turtle, split by layer into two files:

  • schema.ttl — the schema layer (classes, properties, concepts), each ontology in its own namespace (cul:, diff:, …),

  • data.ttl — the instance layer (typed notes) under the vault base, each IRI minted from the note's vault-relative path.

It classifies each note by its folder, mints each subject under the @base of its governing context (schema notes from their file name, instances from their vault-relative path — SPEC §4.5), resolves frontmatter through the vault's composed @context, and flags anything it can't map rather than dropping it. Export the example vault to build/ in one line:

pip install rdflib pyyaml && python scripts/vault_to_rdf.py "Vault-LD Example" --out-dir build

See EXPORT.md for the full usage guide, flags, and how layer classification and context composition work.

Ingesting RDF

The reverse direction, scripts/rdf_to_vault.py, projects any RDF graph into the vault format (SPEC §5.5) — one note per subject, hierarchy in frontmatter, IRI objects as wiki links. Point it at an existing vault and it updates notes in place, preserving bodies, tags:, and folder structure, and leaving unchanged notes untouched; point it at an empty directory and it builds the whole vault, synthesizing a composed context.jsonld when none exists. Predicates the context doesn't map get a short name coined and added to the right context document; anything the format can't express (blank nodes, language tags) is flagged, never dropped (SPEC §5.6). Import a foreign ontology in one line:

python scripts/rdf_to_vault.py MyVault foreign-ontology.ttl

See INGEST.md for the full usage guide and the fidelity rules. Because the two tools are inverses, vault → RDF → vault is a no-op and RDF → vault → RDF is graph-isomorphic.

A taste

Read this note:

---
type: "[[Recipe]]"
requiresIngredient: "[[Chickpeas]]"
prepTimeMinutes: 25
---

# Hummus
A smooth purée of chickpeas, tahini, lemon, and garlic.

You have just read these triples:

@prefix : <https://example.org/> .

:Hummus a :Recipe ;
            :requiresIngredient :Chickpeas ;
            :prepTimeMinutes 25 .

The frontmatter is the graph; the body is prose for human and machine readers. One shared context.jsonld supplies the IRIs, datatypes, and prefixes, so authors write short, clean names while the semantics are supplied for them. See the Vault-LD Specification for the full rules.

Contributing

Vault-LD is an open standard and contributions are welcome: see CONTRIBUTING.md. Spec changes start life as an issue using the Spec change proposal template; fixes to the reference tools, example vault, and docs can go straight to a pull request.

License

Apache License 2.0.


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If Vault-LD is useful to you, a ⭐ on GitHub helps others find it.

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LICENSE

Shown as plain text.


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85 files · 5 not served here

Advantages

  • Enables lossless roundtripping between Markdown notes and RDF graphs. evidence: README.md
  • Allows business semantics to be edited as human-friendly Markdown rather than raw Turtle. evidence: README.md
  • Supports composition of multiple contexts for complex knowledge modeling. evidence: README.md

Limitations

  • Requires adherence to specific YAML-LD frontmatter and context structures. evidence: README.md
  • Certain RDF features like blank nodes or language tags may require specific handling or flagging. evidence: README.md

Security

Risk level: Low risk

This brain runs

Agent instructions that run or fetch something (3)

  • AGENTS.md: This is the repo's institutional memory, and it is load-bearing: contributors are expected to point their own agent at HISTORY.md (plus `git log`) to produce commits and PRs that match this repo's standards.
  • AGENTS.md: **Security guarantees are pinned as tests** (`scripts/test_security.py`, run via `make test` in CI).
  • AGENTS.md: **`make test` and `make roundtrip` must be green** before proposing a change.
  • Low risk: network-instructions (Makefile:38)

Attribution and licence

First committed by
GitHub avatar of dfreytagdfreytag
Publisher
The Knowledge Graph Guys (Community)
Source
The-Knowledge-Graph-Guys/vault-ld · commit 025e71b
Licence
Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
C-HORNB, dfreytag, roelofsmn, tonyseale

AutoGineer is not affiliated with or endorsed by dfreytag or The Knowledge Graph Guys.

Install from source

5 of its files are not served here, so there is no ZIP. The-Knowledge-Graph-Guys/vault-ld at 025e71b