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RDFLib is a pure Python package for working with RDF. RDFLib contains most things you need to work with RDF, including:

  • parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, Trig and JSON-LD
  • a Graph interface which can be backed by any one of a number of Store implementations
  • store implementations for in-memory, persistent on disk (Berkeley DB) and remote SPARQL endpoints
  • a SPARQL 1.1 implementation - supporting SPARQL 1.1 Queries and Update statements
  • SPARQL function extension mechanisms

RDFlib Family of packages

The RDFlib community maintains many RDF-related Python code repositories with different purposes. For example:

  • rdflib - the RDFLib core
  • sparqlwrapper - a simple Python wrapper around a SPARQL service to remotely execute your queries
  • pyLODE - An OWL ontology documentation tool using Python and templating, based on LODE.

Please see the list for all packages/repositories here:

Versions & Releases

See for the release overview.


See for our documentation built from the code. Note that there are latest, stable 5.0.0 and 4.2.2 documentation versions, matching releases.


The stable release of RDFLib may be installed with Python's package management tool pip:

$ pip install rdflib

Alternatively manually download the package from the Python Package Index (PyPI) at

The current version of RDFLib is 6.1.1, see the file for what's new in this release.

Installation of the current master branch (for developers)

With pip you can also install rdflib from the git repository with one of the following options:

$ pip install git+


$ pip install -e git+

or from your locally cloned repository you can install it with one of the following options:

$ python install


$ pip install -e .

Getting Started

RDFLib aims to be a pythonic RDF API. RDFLib's main data object is a Graph which is a Python collection of RDF Subject, Predicate, Object Triples:

To create graph and load it with RDF data from DBPedia then print the results:

from rdflib import Graph
g = Graph()

for s, p, o in g:
    print(s, p, o)

The components of the triples are URIs (resources) or Literals (values).

URIs are grouped together by namespace, common namespaces are included in RDFLib:

from rdflib.namespace import DC, DCTERMS, DOAP, FOAF, SKOS, OWL, RDF, RDFS, VOID, XMLNS, XSD

You can use them like this:

from rdflib import Graph, URIRef, Literal
from rdflib.namespace import RDFS, XSD

g = Graph()
semweb = URIRef('')
type = g.value(semweb, RDFS.label)

Where RDFS is the RDFS namespace, XSD the XML Schema Datatypes namespace and g.value returns an object of the triple-pattern given (or an arbitrary one if multiple exist).

Or like this, adding a triple to a graph g:

    Literal("Nick", datatype=XSD.string)

The triple (in n-triples notation) <> <> "Nick"^^<> . is created where the property FOAF.givenName is the URI <> and XSD.string is the URI <>.

You can bind namespaces to prefixes to shorten the URIs for RDF/XML, Turtle, N3, TriG, TriX & JSON-LD serializations:

g.bind("foaf", FOAF)
g.bind("xsd", XSD)

This will allow the n-triples triple above to be serialised like this:


With these results:

PREFIX foaf: <>
PREFIX xsd: <>

<> foaf:givenName "Nick"^^xsd:string .

New Namespaces can also be defined:

dbpedia = Namespace('')

abstracts = list(x for x in g.objects(semweb, dbpedia['abstract']) if x.language=='en')

See also ./examples


The library contains parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, JSON-LD, RDFa and Microdata.

The library presents a Graph interface which can be backed by any one of a number of Store implementations.

This core RDFLib package includes store implementations for in-memory storage and persistent storage on top of the Berkeley DB.

A SPARQL 1.1 implementation is included - supporting SPARQL 1.1 Queries and Update statements.

RDFLib is open source and is maintained on GitHub. RDFLib releases, current and previous are listed on PyPI

Multiple other projects are contained within the RDFlib "family", see

Running tests

Running the tests on the host

Run the test suite with pytest.


Running test coverage on the host with coverage report

Run the test suite and generate a HTML coverage report with pytest and pytest-cov.

pytest --cov

Running the tests in a Docker container

Run the test suite inside a Docker container for cross-platform support. This resolves issues such as installing BerkeleyDB on Windows and avoids the host and port issues on macOS.

make tests

Tip: If the underlying Dockerfile for the test runner changes, use make build.

Running the tests in a Docker container with coverage report

Run the test suite inside a Docker container with HTML coverage report.

make coverage

Viewing test coverage

Once tests have produced HTML output of the coverage report, view it by running:

pytest --cov --cov-report term --cov-report html
python -m http.server --directory=htmlcov


RDFLib survives and grows via user contributions! Please read our contributing guide to get started. Please consider lodging Pull Requests here:

You can also raise issues here:

Support & Contacts

For general "how do I..." queries, please use and tag your question with rdflib. Existing questions:

If you want to contact the rdflib maintainers, please do so via: