Onya is a knowledge graph data model and expression format, with a Python implementation. This repository combines the data model and format spec with the Python implementation. Onya grew out of a long-standing observation from the RDF community: the triple is too expressively weak in practice. Quads and named graphs are insufficient RDF-based workarounds; property graphs solve it for relationships but abandon the web: no IRIs, no shared vocabularies, no dereferenceability.

Onya takes annotatable, first-class relationships from the property graph world and keeps what RDF got right: IRIs throughout, and vocabulary reuse (schema.org works out of the box). Its key structural idea is recursive assertion: every edge and property can itself carry edges and properties, uniformly, with no RDF-style reification ceremony. Qualified values, relationship metadata, and n-ary relations work well through the same mechanism.

The Onya Literate format is Markdown-native, so easy for humans to write, and easy for LLMs to emit and consume, which makes Onya a natural interchange for LLMOps (extracted knowledge, agent context and more).

Note: Using our provided onya-graph.SKILL.md with your favorite AI coding agent is a good way to automate Onya authoring and explore the format. Try providing a text document and asking it to generate a graph therefrom.

The model in thirty seconds

Here's a complete, valid knowledge graph, first visualized:

Then in Onya Literate format:

# @docheader

* @document: http://example.org/friendship-graph
* @nodebase: http://example.org/people/
* @schema: https://schema.org/

# Chuks [Person]

* name: Chukwuemeka Okafor
* knows -> Ify
  * startDate: 2018-03-15
  * description: Met at university

# Ify [Person]

* name: Ifeoma Obasi
* jobTitle: Software Engineer

Notice how the knows edge carries its own assertions: a start date and a description of the relationship itself. No reification ceremony, no separate "edge properties" mechanism with its own rules. Every edge and every property can carry further edges and properties, recursively, through one uniform mechanism. Node IDs resolve against @nodebase; bare labels and types resolve against @schema, so schema.org (or any vocabulary) works out of the box.

How Onya relates to its conceptual neighbors

Two rows carry the heart of the distinction. Onya assertions are occurrences by construction — each edge or property is an instance, sidestepping the type-versus-occurrence ambiguity that complicated RDF-star standardization. And the mechanism is uniform all the way down: a property value can carry edges (a temperature with a measurementMethod link), which has no clean analogue in either the RDF or property-graph lineage.

Values are strings, with data contracts layered above

Every Onya value is a string, and the string layer is unconditionally valid: birthDate: spring 1958 is welcome exactly as written. Onya deliberately has no built-in type system — in the XSD/OWL lineage, types describe how computers store things, not what the things are, and data models built on them end up rejecting true statements that don't fit the machinery.

Instead, an author may attach an interpretation: a recorded promise about how a value is meant to be read, declared inline with @as or once per property label in the docheader. Contracts are honored at boundaries, on demand — never enforced ambiently by the model:

from onya.graph import graph
from onya.serial.literate import LiterateParser
from onya import interp

onya_text = '''
# @docheader

* @document: http://example.org/people-graph
* @nodebase: http://example.org/people/
* @schema: https://schema.org/
* @interpretations:
    * age: number

# Chuks [Person]

* age: 28
* birthDate: spring 1958
  * @as: datetime
'''

g = graph()
LiterateParser().parse(onya_text, g)
chuks = g['http://example.org/people/Chuks']

age = next(chuks.getprop('https://schema.org/age'))
age.value               # '28' — the model always stores the string
interp.value_of(age)    # 28 — the contract, honored where you ask

report = interp.validate(g)
report.ok               # False: 'spring 1958' fails its datetime contract —
print(report)           # a reported finding, never a parse error or rejection

An interpretation your software doesn't recognize is data, not an error: it parses, merges, and round-trips untouched. The built-in starter set (number, datetime, boolean, iri, text) is deliberately modest, and the plugin registry lets any community define its own. If you're arriving from data engineering, note that this is the value-level slice of "data contract" — shape, ownership, and SLAs would be further layers, deliberately not this one. The design rationale is in doc/design-interpretations-strings-vs-typing.md.

Python quick start

Requires Python 3.12 or later. The package is still in active development, so you can install directly from source:

git clone https://github.com/OoriData/Onya.git
cd Onya
pip install -U .

The base install is dependency-light. Optional extras pull in peripherals only when you need them: pip install "onya[nx]" for the networkx projection (analytics) and pip install "onya[postgres]" for the PostgreSQL store backend.

Parse a small graph in Onya Literate format, then work with it, including adding assertions on an edge:

from onya.graph import graph
from onya.serial.literate import LiterateParser, write

onya_text = '''
# @docheader

* @document: http://example.org/friendship-graph
* @nodebase: http://example.org/people/
* @schema: https://schema.org/

# Chuks [Person]

* name: Chukwuemeka Okafor

# Ify [Person]

* name: Ifeoma Obasi
'''

g = graph()
LiterateParser().parse(onya_text, g)

chuks = g['http://example.org/people/Chuks']
ify = g['http://example.org/people/Ify']

# Edges are first-class: they can carry their own assertions
friendship = chuks.add_edge('https://schema.org/knows', ify)
friendship.add_property('https://schema.org/startDate', '2018-03-15')

# Traverse, reading nested assertions along the way
for edge in chuks.traverse('https://schema.org/knows'):
    for name in edge.target.getprop('https://schema.org/name'):
        print(f'Chuks knows: {name.value}')
    for since in edge.getprop('https://schema.org/startDate'):
        print(f'  Friends since: {since.value}')

# Round-trip back to Onya Literate
write(g)

Finding the node a user means

Label arguments take a full IRI, a CURIE, or a bare @schema name, resolved against the prefixes the parsed document declared (g.prefixes; an undeclared prefix raises rather than guessing). g.search() is a ranked, typo-tolerant lookup by property value, and g.inbound() answers "what points here?" from a reverse index instead of a scan:

chuks.any_prop_value('name')             # -> 'Chukwuemeka Okafor' (same as 'schema:name')

hits = g.search('chukwuemka', types=['Person'])   # tiers: exact > prefix > word > fuzzy
hits[0].node_id, hits[0].tier, hits[0].score      # ranked list; ambiguity is the caller's call

[e.origin.id for e in g.inbound(ify, label='knows')]   # -> ['http://example.org/people/Chuks']

Once you've found the node, onya.view turns "show me this thing" into data — which fields, in what order, which links to follow (outbound, inbound, or edges on the link itself), and what to show of each — leaving the rendering to you:

from onya import view

specs = view.load([{'type': 'Person', 'fields': ['name'],
                    'follow': [{'edge': 'knows', 'show': ['name']}]}])
view.project(g, chuks, specs)
# {'id': '.../Chuks', 'type': 'Person', 'label': 'Chukwuemeka Okafor',
#  'fields': [('name', 'Chukwuemeka Okafor')],
#  'knows': [{'id': '.../Ify', 'label': 'Ifeoma Obasi', 'fields': [('name', 'Ifeoma Obasi')]}]}

Stores offer the same lookup without loading a graph (await store.search(name, 'chukwuemka', labels=[...]), store.nodes_by_type(name, type_iri)); PostgreSQL serves it from a pg_trgm trigram index. await view.project_from_store(store, name, node_id, specs) fetches just the neighborhood a view needs, so "search → project" is a handful of small queries.

Persistence

onya.store keeps graphs across sessions and processes. It is a peripheral, not an organ: the core model never imports it, and a store is correct exactly when a round trip through it is indistinguishable from an in-memory graph union — put(merge=True) unions with any stored graph under the SPEC merge rules. Three backends ship, all behind one async protocol and selected by URL scheme:

import asyncio
from onya.store import connect
from onya.serial.literate import read

r = read(open('test/resource/schemaorg/thingsfallapart.onya'))
g, name = r.graph, r.doc_iri

async def main():
    # Filesystem — one Onya Literate file per graph; the default, and the testing fake.
    async with await connect('file:/tmp/onya-graphs') as store:
        await store.put(name, g)                 # merge=True by default
        again = await store.get(name)            # -> onya.graph.graph
        print([str(n) async for n in store.names()])

    # SQLite — stdlib, zero added dependencies; also an AssertionStore.
    async with await connect('sqlite:/tmp/onya.db') as store:
        await store.put(name, g)
        async for origin, rel, target, ann in store.match(name, 'http://example.org/classics/CAchebe'):
            print(origin, rel, target)

    # PostgreSQL — pip install "onya[postgres]"; SQL/PGQ property graphs on PG >= 19.
    async with await connect('postgresql://user:pass@localhost/onya') as store:
        await store.put(name, g)

asyncio.run(main())

A blocking facade (from onya.store.sync import connect) mirrors the API with with/plain calls for scripts and REPL use. Design and schema details are in doc/design-persistence-architecture.md.

Visualize / export

The CLI converts Onya Literate (.onya) files to diagram formats:

onya convert test/resource/schemaorg/thingsfallapart.onya > out.mmd        # Mermaid (default)
onya convert test/resource/schemaorg/thingsfallapart.onya --dot > out.dot  # Graphviz DOT

View Mermaid output instantly at mermaid.live, producing e.g.:

networkx projection + analytics round trip

For graph analytics, onya.viz.nx (extras-gated: pip install "onya[nx]") projects a graph into a networkx.MultiDiGraph, and write_back records results — centrality, community, any networkx output — back into the Onya graph as typed, merge-safe assertions. The projection is lossy by design (first-level structure) and reflects the graph as-is; call g.merge() first for a normalized view.

import networkx
from onya.viz import nx
from onya.terms import ONYA_INTERP

mg = nx.to_networkx(g)                                          # -> networkx.MultiDiGraph
scores = networkx.betweenness_centrality(mg)
nx.write_back(g, 'https://example.org/betweenness', scores, interp=ONYA_INTERP('number'))
# scores are now first-class Onya assertions: queryable via g.select, merge-safe on store.put

See demo/nx_analytics/ for a runnable end-to-end example.

For the full API walkthrough — modifying and removing properties, querying by type, data contracts and validation, explicit graph merge, the serializer modules and their options — see doc/python-tutorial.md.

Acknowledgments

Onya is primarily developed by the crew at Oori Data. We offer LLMOps, data pipelines and software engineering services around AI/LLM applications.

Background

Onya is based on experience from developing Versa and also working on the MicroXML spec and implementations thereof.

The URL used for metavocabulary is managed via purl.org.

The name is from Igbo "ọ́nyà", web, snare, trap, and by extension, network. The expanded sense is ọ́nyà úchè, web of knowledge.

Contributing

Contributions welcome! We're interested in feedback from the community about what works and what doesn't in real-world usage. To get help with the code implementation, read CONTRIBUTING.md.

License

• Code (Python library): Apache 2.0 - See LICENSE
• Specification (SPEC.md): Creative Commons Attribution 4.0 International (CC BY 4.0) - See LICENSE-spec

The specification is under CC BY 4.0 to encourage broad adoption and derivative work while ensuring attribution. We want the format itself to be as open and reusable as possible, allowing anyone to create implementations in any language or adapt the format for their specific needs.

Related Work

• networkx: Network Analysis in Python — Onya bridges to it directly via onya.viz.nx (projection + analytics write-back)
• Apache AGE: PostgreSQL Extension for graphs. ANSI SQL & openCypher over the same DB.