Load Neo4j Graph Data Science data in Python using dltHub

Neo4j Graph Data Science provides analytics functionality that operates within the Neo4j database environment and is typically accessed via official drivers or REST-based HTTP APIs. The REST API base URL is http://<host>:<port> and all requests require an Authorization header using Basic or Bearer authentication.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Neo4j Graph Data Science data in under 10 minutes.

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What data can I load from Neo4j Graph Data Science?

Here are some of the endpoints you can load from Neo4j Graph Data Science:

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How do I authenticate with the Neo4j Graph Data Science API?

All requests must include an Authorization header using either basic (username

) or bearer authentication, base64-encoded as specified in RFC 7617.

1. Get your credentials

To obtain Neo4j Aura API credentials: 1. Log in to your Neo4j Aura Console. 2. Navigate to the API credentials or Client credentials section (typically under your account or project settings). 3. Select 'Create client credential' (or similar button). 4. Enter a name for the credential. 5. Copy and save the provided Client ID and Client Secret immediately; they will not be visible again after you close the modal. If your account manages multiple projects, ensure you note the relevant Project ID.

2. Add them to .dlt/secrets.toml

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[sources.neo4j_graph_data_science_source]
auth = "REPLACE_ME"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Neo4j Graph Data Science API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python neo4j_graph_data_science_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline neo4j_graph_data_science_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset neo4j_graph_data_science_data
The duckdb destination used duckdb:/neo4j_graph_data_science.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads gds.v2.graph and gds.v2.page_rank from the Neo4j Graph Data Science API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def neo4j_graph_data_science_source(auth=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<host>:<port>",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": auth},
        },
        "resources": [
            {"name": "graph_list", "endpoint": {"path": "gds.graph.list"}},
            {"name": "graph_stream_relationships", "endpoint": {"path": "gds.graph.relationships.stream"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="neo4j_graph_data_science_pipeline",
        destination="duckdb",
        dataset_name="neo4j_graph_data_science_data",
    )
    load_info = pipeline.run(neo4j_graph_data_science_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("neo4j_graph_data_science_pipeline").dataset()
sessions_df = data.graph_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM neo4j_graph_data_science_data.graph_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("neo4j_graph_data_science_pipeline").dataset()
data.graph_list.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Neo4j Graph Data Science data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack