PurRDF for Python

PurRDF is a from-scratch, dependency-light RDF 1.2 engine — parsers and serializers, SPARQL, SHACL, ShEx, RDFC-1.0 canonicalization, and the GTS graph-transport container — written in Rust and carried verbatim into Python, JavaScript, and C. The purrdf package is the Python surface of that one engine: the same byte-identical semantics in every language, including triple terms, reifiers, and base-direction literals that most incumbent libraries do not carry.

Install

pip install purrdf

Requires Python 3.13+. Wheels bundle the native extension; no Rust toolchain needed.

Parse RDF

import purrdf

quads = purrdf.parse(
    '<https://example.org/alice> <http://xmlns.com/foaf/0.1/name> "Alice" .',
    purrdf.RdfFormat.TURTLE,
)

purrdf.parse accepts Turtle, TriG, N-Triples, N-Quads, TriX, and HexTuples (purrdf.RdfFormat); JSON-LD and RDF/XML travel through the dedicated purrdf.from_json_ld / purrdf.to_json_ld and purrdf.from_rdf_xml / purrdf.to_rdf_xml converters. All codecs are first-party with byte-deterministic output.

Project graph, tabular, and research-object carriers

purrdf.project and purrdf.lift are thin calls into the same Rust projection engine used by every other surface. Configuration is mandatory, strict JSON: PurRDF supplies no vocabulary, identity IRI, or resource-limit default.

import json
import purrdf

config = json.dumps({
    "profile": "lpg-csv",
    "config": {
        "rdf_type": "https://example.org/type",
        "limits": {
            "max_artifacts": 16,
            "max_artifact_bytes": 1_000_000,
            "max_total_bytes": 4_000_000,
            "max_archive_bytes": 5_000_000,
            "max_term_depth": 16,
        },
        "max_records": 1_000,
    },
})
package = purrdf.project(
    "@prefix ex: <https://example.org/> . ex:alice ex:knows ex:bob .",
    format=purrdf.RdfFormat.TURTLE,
    profile="lpg-csv",
    config=config,
)
lifted = purrdf.lift(package.archive, profile="lpg-csv", config=config)
assert lifted.dataset.quad_count() == 1
print([(loss.code, loss.location) for loss in package.losses])

Project profiles are lpg-csv, neo4j-csv, open-cypher, graphml, csvw-exact, obo-graphs, skos, croissant-1.1, ro-crate-1.3, datacite-4.6, dcat-3, and frictionless-data-package-1. Only OBO Graphs and SKOS are deliberately write-only, ledgered views. Returned archives are canonical deterministic USTAR bytes and every result carries its always-computed structured loss records. Research-object contexts, vocabularies, identities, and profiles are all mandatory caller configuration. See the runnable projection_roundtrip.py file-producing example.

Validate with SHACL

The SHACL engine lives at purrdf.shapes (mirroring the Rust crate; purrdf.shacl is a back-compat alias):

from purrdf import shapes

report = shapes.validate(shapes_ttl=my_shapes, data_nt=my_data)
print(report["conforms"])

Complete SHACL Core, SHACL-SPARQL constraints/targets, and SHACL-AF sh:rule entailment via shapes.entail(...). Reusable parsed shapes are available as shapes.Shapes(shapes_ttl).validate_nt(data_nt).

Validate with ShEx

from purrdf import shex

results = shex.validate(
    my_schema_shexc,
    my_data_ttl,
    [("https://example.org/alice", "https://example.org/PersonShape")],
)
print(all(entry["conformant"] for entry in results))

The ShEx 2.1 validator passes 1,051/1,051 attempted validation tests of the official shexTest suite (see the repo's docs/CONFORMANCE.md).

rdflib compatibility layer

The package ships an rdflib-shaped API over the native engine:

from purrdf.compat.rdflib import Graph, URIRef

g = Graph()
g.parse(data=my_ntriples, format="nt")
print(len(g), g.serialize(format="turtle"))

For a literal, zero-change import rdflib, install the opt-in extra:

pip install purrdf[rdflib]

This pulls in the separate purrdf-rdflib distribution, whose top-level rdflib package re-exports the compat surface, so existing third-party code doing import rdflib / from rdflib.namespace import RDF transparently runs on purrdf. Caveat: that shadow claims the rdflib import name and must never be installed alongside the genuine rdflib — the two cannot co-inhabit one environment. It is a separate distribution (never bundled into the main purrdf wheel) precisely so environments that need the real rdflib simply omit it.

GTS graph transport and relational exports

GTS is PurRDF's single-file, content-addressed, append-only container for RDF 1.2 graphs. Build one from quads and export it straight to relational stores:

import purrdf

gts_bytes = purrdf.gts_from_quads(my_nquads_bytes, format=purrdf.RdfFormat.N_QUADS)

purrdf.gts_to_sqlite(gts_bytes, "graph.db")
purrdf.gts_to_duckdb(gts_bytes, "graph.duckdb")
files = purrdf.gts_to_parquet(gts_bytes, "out/")

The same entry points are grouped under purrdf.gts for discoverability.

Learn more

• Repository: https://github.com/Blackcat-Informatics/purrdf
• Project site: https://blackcatinformatics.ca/purrdf/
• GTS specification, conformance matrix, and full docs live under docs/ in the repo.

Licensed under MIT OR Apache-2.0, at your option.