3D Verse Python API Docs | dltHub

3D Verse is a REST API platform that converts text or images into 3D models and manages 3D assets and sessions. The REST API base URL is https://api.3dverse.com and All requests require an API token (Bearer) for 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 3D Verse data in under 10 minutes.

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What data can I load from 3D Verse?

Here are some of the endpoints you can load from 3D Verse:

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How do I authenticate with the 3D Verse API?

Authentication uses a bearer token passed in the Authorization header: Authorization: Bearer . Tokens are generated from the 3dverse/ModelsLab console (user or service tokens).

1. Get your credentials

1. Sign in or register at the 3dverse/ModelsLab Console (https://console.3dverse.com or https://modelslab.com/register).
2. Open the Console or Account -> API keys / Tokens section.
3. Create a new API token (user token or service token) and copy the token value.
4. Store the token securely; use it in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.three_d_verse_source]
api_token = "your_api_token_here"

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 Workbench:

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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 3D Verse 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 three_d_verse_pipeline.py

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

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Pipeline three_d_verse_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset three_d_verse_data
The duckdb destination used duckdb:/three_d_verse.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 sessions and assets from the 3D Verse 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 three_d_verse_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.3dverse.com",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "sessions", "endpoint": {"path": "sessions", "data_selector": "data"}},
            {"name": "assets", "endpoint": {"path": "folders/{folder_id}/assets", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="three_d_verse_pipeline",
        destination="duckdb",
        dataset_name="three_d_verse_data",
    )
    load_info = pipeline.run(three_d_verse_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("three_d_verse_pipeline").dataset()
sessions_df = data.sessions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM three_d_verse_data.sessions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("three_d_verse_pipeline").dataset()
data.sessions.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 3D Verse 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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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify the Authorization header contains a valid bearer token: Authorization: Bearer . Ensure the token has not expired and that you copied it exactly from the Console.

Rate limits and throttling

The API enforces rate limits; on 429 Too Many Requests responses, implement exponential backoff and retry. Check response headers for X-RateLimit-* where provided.

Pagination

List endpoints return paginated results. Check the response for keys like data, items, and a pagination object (next, page, limit) and follow the next link or page token when present.

Common errors

400 Bad Request — malformed request or invalid parameters. 401 Unauthorized — invalid or missing token. 403 Forbidden — insufficient permissions. 404 Not Found — resource missing. 429 Too Many Requests — rate limit exceeded. 500/502/503 — server error; retry with exponential backoff.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — 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-runtime

• 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