Markdown to Knowledge Graph: The 2026 Toolkit

What people actually mean by 'markdown knowledge graph'

Two very different things go by the name. The first is a page-link graph: a vault of .md files containing [[wikilinks]] and #tags, with each file rendered as a node and each wikilink rendered as an edge. Obsidian's graph view, Logseq's graph, and most of the prior art on this topic produce this kind of graph. It is real, it is useful for backlink visualisation, and it tells you essentially nothing about what is inside the files.

The second is an entity graph. A note titled 'Marie Curie' containing the sentence 'Marie discovered polonium with her husband Pierre at the University of Paris' is, structurally, one node and zero edges in a page-link graph. As an entity graph it is four typed nodes (Marie Curie as Person, Pierre Curie as Person, polonium as Element, University of Paris as Organisation) and three typed edges (DISCOVERED, COLLABORATED_WITH, AFFILIATED_WITH). The note has the same Markdown either way — the difference is whether the extraction step looked at link syntax or at prose.

Almost every tool that calls itself a 'markdown knowledge graph' does the first. A small and growing group does the second. The choice between them determines what your graph can actually do for you, and most teams discover the distinction the hard way after a year of building a vault that looks beautiful and answers no real questions.

Why Obsidian and Logseq graph views aren't really knowledge graphs

Obsidian's graph view does one thing well: it shows you which notes link to which other notes, and you can colour the nodes by tag or folder. Logseq's graph is structurally identical with a slightly different rendering. Both are excellent backlink visualisers, and both have inspired a generation of PKM enthusiasts to actually link their notes. Credit where due — the page-link graph is real value, just not the value of a knowledge graph.

The honest gap is in what the graph contains. There are no entity types: every node is 'a note'. There are no edge types: every edge is 'this note links to that note'. The graph is read-only at the canvas level — you cannot drag a new relationship into existence by drawing it. There is no way to filter by 'show me all the People' because the graph has no concept of People. Past about 500 notes the rendering becomes visually busy without becoming more informative.

None of this is a criticism of Obsidian or Logseq. They are not pretending to do entity extraction; they are doing exactly what their documentation says they do. The mistake is at the user end: assuming that 'graph view' in a Markdown editor means the same thing as 'knowledge graph' in the database sense. It does not. The two share the word 'graph' and very little else.

The two approaches: wikilink-extraction vs prose-extraction

Wikilink-extraction is deterministic. You parse each .md file, find every [[Target]] or [Target](Target.md) form, and emit an edge from the current note to the target. The same input always produces the same output. It runs in milliseconds, makes zero API calls, and is lossless on the structure you explicitly authored. It misses everything you did not explicitly link — which, for most vaults, is the majority of the actual information.

Prose-extraction reads the body text and uses either rule-based named-entity recognition or a large language model to identify entities and the relationships between them. It catches the entities you mentioned in passing without linking, the relationships implicit in a verb phrase, and the cross-document patterns that no human ever bothers to wikilink. It is noisier — you need a human-in-the-loop review step for the ambiguous spans — and it is more expensive, both in compute and (for the LLM variants) in API spend.

Hybrid pipelines do both. Wikilinks become deterministic edges with high confidence; prose-extracted entities and relationships become staged edges that need confirmation before they hit the live graph. The combination is what most production systems converge on, because the wikilink edges anchor the obvious structure and the prose edges fill in everything the author did not bother to make explicit.

Tool landscape in 2026

Graphify, hosted at github.com/safishamsi/graphify, is an open-source AI-assistant skill that turns folders of code, documentation, or research papers into queryable graphs using LLM-driven extraction. The licence is permissive (verify at the repo at draft time) and the project is the closest open-source analogue to what KnodeGraph does on the hosted side. It is the right starting point for anyone who wants a code-first, self-hosted entity extraction pipeline they can wire into their own scripts.

obsidian-graph, at github.com/drewburchfield/obsidian-graph, takes a semantic-search angle: it embeds each note using Voyage Context-3 embeddings, stores the vectors in a PostgreSQL+pgvector backend, and exposes a KG-style navigation UI over the embedding space. It is more 'graph over notes by semantic similarity' than 'typed entity graph extracted from prose', but for an Obsidian user who wants a smarter way to navigate a large vault it is the most polished open option in 2026.

InfraNodus, at infranodus.com, is a paid Obsidian plugin focused on text-network analysis. It builds a co-occurrence graph between terms in your notes — useful for discovering conceptual gaps and thematic clusters, less useful as a typed knowledge graph in the database sense. Dataview, the long-standing Obsidian plugin, is a query DSL over front-matter and wikilinks; it is a query layer, not an extraction layer, and it does not change the shape of your graph.

Tana represents a fourth pattern: typed nodes via 'supertags' that you apply manually. Every supertagged item becomes a typed node in Tana's graph, but the typing is human-driven rather than extracted. obsidian-llm-wiki, available on PyPI, runs a local Ollama-based LLM over a vault to extract a wiki-style structure — useful when API spend or data-residency forbid sending notes to a cloud LLM, at the cost of slower throughput and lower extraction quality than frontier models.

KnodeGraph rounds out the picture: a proprietary SaaS with a free tier and a $14.99-per-month Pro tier that extracts typed entities and relationships from prose using Claude, then surfaces the result in a staging area for human-in-the-loop review before commit. It is the no-code option in this list and is honest about its position: it is paid, it is hosted, and the workflow is upload-and-review rather than in-place edit. Open-source self-hosters should start with Graphify; Obsidian-native users should look at obsidian-graph; vault-curators who want managed extraction with review can use KnodeGraph.

No-code workflow: from vault to graph in 60 seconds

The most honest no-code path in 2026 is export-then-upload. From Obsidian: select the notes you want extracted, drag the files into a hosted extraction tool (KnodeGraph or equivalent), wait for the LLM pass to finish (typically 10-60 seconds for a few dozen notes), and review the staged entity and relationship suggestions before they commit to the live graph. The same flow works for Logseq, plain .md folders, and Notion-exported Markdown. There is no native Obsidian plugin integration in this flow — the friction is real, and pretending otherwise damages credibility.

What you get on the other end is a typed graph: Person, Organisation, Concept, Event, Document, and a small set of typed relationships, with each edge annotated by the source paragraph it came from. The provenance is what makes the result useful: any disagreement with an extracted edge resolves to clicking back to the exact sentence that justified it. That is the property that turns 'a graph of my notes' into 'a graph I can actually query and trust'.

The honest constraint: this is one-shot extraction, not continuous sync. Re-extracting changed notes requires re-uploading them. KnodeGraph's staging area handles the diff — new entities and relationships are surfaced for review against the existing graph — but the round-trip is still 'edit notes locally, export, upload, review'. For a one-time extraction of a research vault or a project backlog, that is fine; for a daily-edited journal, the friction adds up.

Programmatic workflow: Python + LLM

The programmatic version of the same workflow stays inside Python. The shape is: walk the vault with pathlib or a Markdown library like markdown-it-py, strip front-matter and wikilink markup, ship the cleaned plain text to a structured-output LLM call (Claude with a tool-use schema, or OpenAI with response_format=json_schema), parse the returned entities and relationships, and MERGE the result into a graph database — FalkorDB, Neo4j, or whatever you prefer. The prompt should request a fixed schema like {entities: [{type, name, source_span}], relations: [{type, source_entity, target_entity, source_span}]} so the LLM output is structured and machine-actionable.

Two pieces are worth getting right. The chunking strategy: most useful extractions happen on paragraph or section granularity, not whole-document, because LLMs get more accurate on shorter contexts and you keep per-call cost bounded. The entity-resolution layer: the same person mentioned across ten notes should produce one graph node, not ten — a simple normalisation pass keyed on canonical name plus a deterministic ID (DOI, ORCID, email) closes most of that gap before you need anything fancier.

For the wikilink layer specifically, KnodeGraph's existing how-to post 'From notes to knowledge graph' walks through the regex-and-rules version of the same pipeline. The two layers compose cleanly: wikilink edges from the regex pass become high-confidence skeleton, prose edges from the LLM pass become medium-confidence enrichment, and a review queue handles the long tail of disagreements between the two.

When to use which approach

Wikilink-extraction wins when your vault discipline is high — you link everything that matters, you maintain an MOC (Map of Content) hierarchy, and most of the entities you care about already have their own page. In that world the prose is mostly elaboration on a structure you already authored, and a deterministic wikilink parse captures essentially everything. Obsidian's native graph view, plus a Dataview query or two, is genuinely enough.

Prose-extraction wins when your vault is mostly free-writing — meeting notes, journal entries, research summaries, draft documents — and the entities are buried in sentences rather than promoted into [[wikilinks]]. That is most vaults in practice. An LLM pass over the prose surfaces the people, projects, dates, and decisions that would never have been wikilinked by hand, and the resulting graph answers questions that no page-link graph ever could.

Hybrid wins everywhere else, which is to say: in production. The deterministic wikilink layer anchors the obvious structure, the LLM layer fills in the implicit structure, and a review queue keeps the human in the loop for the spans where the model is unsure. The accuracy honest-take: AI entity extraction lands somewhere in the 88-94 F1 band on typical English prose per the NER literature; lower on technical jargon, lower still on non-English. Human-in-the-loop review is not a luxury, it is the practical answer to those numbers.

Related reading

• From notes to knowledge graph (programmatic) — The deeper Python-and-regex version of the workflow this post overviews.
• KnodeGraph vs Obsidian — BOFU comparison for Obsidian users who want typed extraction on top of their vault.
• KnodeGraph vs Logseq — BOFU comparison for Logseq vaults — same wikilink-graph limitation as Obsidian.
• Knowledge graphs for PKM — The broader PKM context for why typed entity graphs beat page-link graphs at scale.

Frequently Asked Questions

Source

Niklas Luhmann, 'Kommunikation mit Zettelkästen' (1981) — the original 90,000-card paper knowledge graph that every modern Markdown linking tool descends from. [link]