GraFlo — Graph Schema & Transformation Language (GSTL)

GraFlo is a manifest-driven schema and ingestion layer for labeled property graphs (LPGs). Write a GraphManifest (YAML or Python) once — it defines vertices, edges, typed properties, identities, and DB profile — then infer, validate, migrate, and load into any supported graph engine.

What you get

• One pipeline, several graph databases — The same manifest targets ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, or NebulaGraph; DatabaseProfile and DB-aware types absorb naming, defaults, and indexing differences.
• Explicit identities — Vertex identity fields and indexes back upserts so reloads merge on keys instead of blindly duplicating nodes.
• Reusable ingestion — Resource actor pipelines (including vertex_router / edge steps) bind to files, SQL, SPARQL/RDF, APIs, or in-memory batches via Bindings and the DataSourceRegistry.
• Schema as the contract — GraphManifest is the single source of truth: vertex/edge definitions, typed properties, identity fields, and DB profile are validated at finish_init time, not at write time. Schema migrations are first-class (graflo migrate_schema).
• Manifest as linked data — The GraFlo ontology (gf: at ontology.growgraph.dev) lets you export manifests to RDF and round-trip them for tooling, provenance, and SPARQL-facing catalogs.

What’s in the manifest

• schema — Schema: metadata, core_schema (vertices, edges, typed properties, identities), and db_profile (DatabaseProfile: target flavor, storage names, secondary indexes, TigerGraph default_property_values, …).
• ingestion_model — IngestionModel: named resources (actor sequences: descend, transform, vertex, edge, …) and a registry of reusable transforms.
• bindings — Connectors (e.g. FileConnector, TableConnector, SparqlConnector, APIConnector) plus resource_connector wiring. Optional connector_connection maps connectors to conn_proxy labels so YAML stays secret-free; a runtime ConnectionProvider supplies credentials. See API connector and pagination for REST pagination strategies.

Runtime path

1. Source instance — Batches from a DataSourceType adapter (FileDataSource, SQLDataSource, SparqlEndpointDataSource, APIDataSource, …).
2. Resource (actors) — Maps records to graph elements against the logical schema (validated during IngestionModel.finish_init / pipeline execution).
3. GraphContainer — Intermediate, database-agnostic vertex/edge batches.
4. DB-aware projection — Schema.resolve_db_aware() plus VertexConfigDBAware / EdgeConfigDBAware for the active DBType.
5. Graph DB — DBWriter + ConnectionManager and the backend-specific Connection implementation.

Supported source types (DataSourceType)

Supported targets

The graph engines listed in What you get are the supported output DBType values in graflo.onto. Each backend uses its own Connection implementation under the shared ConnectionManager / DBWriter / GraphEngine flow.

More capabilities

• GraFlo ontology (manifest RDF) — Serialize any GraphManifest to RDF (Turtle, JSON-LD) using the published vocabulary at https://ontology.growgraph.dev/graflo (owl:versionInfo 1.0.0). Covers schema, ingestion (resources, transforms, pipeline actors), and bindings. Round-trip via graflo.rdf or the manifest-to-rdf / rdf-to-manifest CLI. This is the meta-model of GraFlo itself — distinct from importing a domain OWL ontology into an LPG schema (RdfInferenceManager). Details: docs — GraFlo ontology.
• SPARQL & RDF — Endpoints and RDF files (.ttl, .rdf, .n3, …); optional OWL/RDFS domain schema inference (rdflib, SPARQLWrapper in the default install).
• Schema inference — From PostgreSQL-style 3NF layouts (PK/FK heuristics) or from OWL/RDFS (owl:Class → vertices, owl:ObjectProperty → edges, owl:DatatypeProperty → vertex fields).
• Schema migrations — Plan and apply guarded schema deltas (migrate_schema console script → graflo.cli.migrate_schema; library in graflo.migrate; see docs).
• Typed properties — Optional field types (INT, FLOAT, STRING, DATETIME, BOOL) on vertices and edges.
• Batching & concurrency — Configurable batch sizes, worker counts, and DB write concurrency on IngestionParams / DBWriter.
• GraphEngine — High-level orchestration for infer, define schema, and ingest (define_and_ingest, …); Caster stays available for lower-level control.

Documentation

Full documentation is available at: growgraph.github.io/graflo

Installation

pip install graflo

Optional extras (see pyproject.toml → [project.optional-dependencies]):

• dev — pytest, ty, pre-commit
• docs — MkDocs stack for building the documentation site
• plot — pygraphviz for the plot_manifest CLI (install system Graphviz first)

pip install "graflo[dev]"
pip install "graflo[dev,docs,plot]"

Usage Examples

Simple ingest

from suthing import FileHandle

from graflo import Bindings, GraphManifest
from graflo.db.connection.onto import ArangoConfig

manifest = GraphManifest.from_config(FileHandle.load("schema.yaml"))
manifest.finish_init()
schema = manifest.require_schema()
ingestion_model = manifest.require_ingestion_model()

# Option 1: Load config from docker/arango/.env (recommended)
conn_conf = ArangoConfig.from_docker_env()

# Option 2: Load from environment variables
# Set: ARANGO_URI, ARANGO_USERNAME, ARANGO_PASSWORD, ARANGO_DATABASE
conn_conf = ArangoConfig.from_env()

# Option 3: Load with custom prefix (for multiple configs)
# Set: USER_ARANGO_URI, USER_ARANGO_USERNAME, USER_ARANGO_PASSWORD, USER_ARANGO_DATABASE
user_conn_conf = ArangoConfig.from_env(prefix="USER")

# Option 4: Create config directly
# conn_conf = ArangoConfig(
#     uri="http://localhost:8535",
#     username="root",
#     password="123",
#     database="mygraph",  # For ArangoDB, 'database' maps to schema/graph
# )
# Note: If 'database' (or 'schema_name' for TigerGraph) is not set,
# Caster will automatically use Schema.metadata.name as fallback

from graflo.architecture.contract.bindings import FileConnector
import pathlib

# Create Bindings with file connectors
bindings = Bindings()
work_connector = FileConnector(regex="\Sjson$", sub_path=pathlib.Path("./data"))
bindings.add_connector(
    work_connector,
)
bindings.bind_resource("work", work_connector)

# Or initialize via connectors + resource_connector
# bindings = Bindings(
#     connectors=[
#         FileConnector(
#             name="work_files",
#             regex="^work\\.json$",
#             sub_path=pathlib.Path("./data"),
#         )
#     ],
#     resource_connector=[{"resource": "work", "connector": "work_files"}],
#     # Optional: for SQL/SPARQL connectors, name a proxy; register secrets via ConnectionProvider.
#     # connector_connection=[{"connector": "work_files", "conn_proxy": "files_readonly"}],
# )

from graflo.hq.caster import IngestionParams
from graflo.hq import GraphEngine

# Option 1: Use GraphEngine for schema definition and ingestion (recommended)
engine = GraphEngine()
ingestion_params = IngestionParams(
    clear_data=False,
    # max_items=1000,  # Optional: limit number of items to process
    # batch_size=10000,  # Optional: customize batch size
)

ingest_manifest = manifest.model_copy(update={"bindings": bindings})
ingest_manifest.finish_init()

engine.define_and_ingest(
    manifest=ingest_manifest,
    target_db_config=conn_conf,  # Target database config
    ingestion_params=ingestion_params,
    recreate_schema=False,  # Set to True to drop and redefine schema (script halts if schema exists)
)

# Option 2: Use Caster directly (schema must be defined separately)
# from graflo.hq import GraphEngine
# engine = GraphEngine()
# engine.define_schema(manifest=manifest, target_db_config=conn_conf, recreate_schema=False)
# 
# caster = Caster(schema=schema, ingestion_model=ingestion_model)
# caster.ingest(
#     target_db_config=conn_conf,
#     bindings=bindings,
#     ingestion_params=ingestion_params,
# )

PostgreSQL Schema Inference

from graflo.hq import GraphEngine
from graflo.db.connection.onto import PostgresConfig, ArangoConfig
from graflo import Caster
from graflo.onto import DBType

# Connect to PostgreSQL
postgres_config = PostgresConfig.from_docker_env()  # or PostgresConfig.from_env()

# Create GraphEngine and infer schema from PostgreSQL 3NF database
# Connection is automatically managed inside infer_schema()
engine = GraphEngine(target_db_flavor=DBType.ARANGO)
manifest = engine.infer_manifest(
    postgres_config,
    schema_name="public",  # PostgreSQL schema name
)
schema = manifest.require_schema()
ingestion_model = manifest.require_ingestion_model()

# Define schema in target database (optional, can also use define_and_ingest)
target_config = ArangoConfig.from_docker_env()
engine.define_schema(
    manifest=manifest,
    target_db_config=target_config,
    recreate_schema=False,
)

# Use the inferred schema with Caster for ingestion
caster = Caster(schema=schema, ingestion_model=ingestion_model)
# ... continue with ingestion

Manifest ↔ RDF (GraFlo ontology)

# Serialize manifest YAML to Turtle (embeds gf: vocabulary when --include-ontology is default)
uv run manifest-to-rdf manifest.yaml \
  --base-uri https://growgraph.dev/manifests/mygraph/v1 \
  --format turtle \
  --output mygraph.ttl

# Restore YAML from RDF
uv run rdf-to-manifest mygraph.ttl \
  --manifest-uri https://growgraph.dev/manifests/mygraph/v1 \
  --output manifest.restored.yaml

from graflo import GraphManifest
from graflo.rdf import ManifestRdfDeserializer, ManifestRdfSerializer

manifest = GraphManifest.from_yaml("manifest.yaml")
base = "https://growgraph.dev/manifests/mygraph/v1"

ttl = ManifestRdfSerializer().to_turtle(manifest, base)
restored = ManifestRdfDeserializer().from_turtle(ttl, base.rstrip("/"))

Ontology source: graflo/rdf/ontology/graflo.ttl. See GraFlo ontology.

RDF / SPARQL Ingestion (domain ontology → LPG)

from pathlib import Path
from graflo.hq import GraphEngine
from graflo.db.connection.onto import ArangoConfig
from graflo.architecture.manifest import GraphManifest

engine = GraphEngine()

# Infer schema from an OWL/RDFS ontology file
ontology = Path("ontology.ttl")
schema, ingestion_model = engine.infer_schema_from_rdf(source=ontology)

# Create source bindings (reads a local .ttl file per rdf:Class)
bindings = engine.create_bindings_from_rdf(source=ontology)

# Or point at a SPARQL endpoint instead:
# from graflo.db.connection.onto import SparqlEndpointConfig
# sparql_cfg = SparqlEndpointConfig(uri="http://localhost:3030", dataset="mydata")
# bindings = engine.create_bindings_from_rdf(
#     source=ontology,
#     endpoint_url=sparql_cfg.query_endpoint,
# )

target = ArangoConfig.from_docker_env()
engine.define_and_ingest(
    manifest=GraphManifest(
        graph_schema=schema,
        ingestion_model=ingestion_model,
        bindings=bindings,
    ),
    target_db_config=target,
)

Development

To install requirements

git clone [email protected]:growgraph/graflo.git && cd graflo
uv sync --extra dev

Tests

Test databases

Quick Start: To start all test databases at once, use the convenience scripts from the docker folder:

cd docker
./start-all.sh    # Start all services
./stop-all.sh      # Stop all services
./cleanup-all.sh   # Remove containers and volumes

Individual Services: To start individual databases, navigate to each database folder and run:

Spin up Arango from arango docker folder by

docker-compose --env-file .env up arango

Neo4j from neo4j docker folder by

docker-compose --env-file .env up neo4j

TigerGraph from tigergraph docker folder by

docker-compose --env-file .env up tigergraph

FalkorDB from falkordb docker folder by

docker-compose --env-file .env up falkordb

Memgraph from memgraph docker folder by

docker-compose --env-file .env up memgraph

NebulaGraph from nebula docker folder by

docker-compose --env-file .env up

and Apache Fuseki from fuseki docker folder by

docker-compose --env-file .env up fuseki

To run unit tests

uv run pytest test

Note: Tests require external database containers (ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph, Fuseki) to be running. CI builds intentionally skip test execution. Tests must be run locally with the required database images started (see Test databases section above). NebulaGraph tests are gated behind pytest --run-nebula.

Requirements

• Python 3.11+ (Python 3.11 and 3.12 are officially supported)
• python-arango
• nebula3-python>=3.8.3 (NebulaGraph v3.x support)
• nebula5-python>=5.2.1 (NebulaGraph v5.x support)
• sqlalchemy>=2.0.0 (for PostgreSQL and SQL data sources)
• rdflib>=7.0.0 + SPARQLWrapper>=2.0.0 (included in the default install)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.