Weights & Biases Python API Docs | dltHub

Weights & Biases is a platform for experiment tracking, model evaluation, and dataset versioning that provides programmatic access to resources via its REST APIs. The REST API base URL is https://api.wandb.ai and all requests require a Bearer token.

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 Weights & Biases data in under 10 minutes.

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What data can I load from Weights & Biases?

Here are some of the endpoints you can load from Weights & Biases:

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How do I authenticate with the Weights & Biases API?

All API requests require authentication using a W&B API key, which must be provided in the request headers as an Authorization: Bearer [API-KEY] header.

1. Get your credentials

To obtain a Weights & Biases (W&B) API key, log in to your W&B account, click on your user profile icon in the upper right corner, and select User Settings. Scroll down to the API Keys section. Click the 'Create new API key' button, provide a descriptive name for the key, and click 'Create'. Copy the generated API key immediately and store it securely, as it will not be displayed again after you close the dialog. If you are an organization or team administrator, you can also manage and create keys for service accounts via the 'API Keys' tab in your organization or team settings.

2. Add them to .dlt/secrets.toml

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[sources.weights_biases_source]
wandb_api_key = "your_api_key_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 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 Weights & Biases 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 weights_biases_pipeline.py

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

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Pipeline weights_biases_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset weights_biases_data
The duckdb destination used duckdb:/weights_biases.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 /v1/models and /v1/chat/completions (commonly used for inference) or the Weave service endpoints /call/start and /call/end (commonly used for observability). from the Weights & Biases 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 weights_biases_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.wandb.ai",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "runs", "endpoint": {"path": "graphql", "data_selector": "project.runs.edges"}},
            {"name": "models", "endpoint": {"path": "v2/{entity}/{project}/models"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="weights_biases_pipeline",
        destination="duckdb",
        dataset_name="weights_biases_data",
    )
    load_info = pipeline.run(weights_biases_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("weights_biases_pipeline").dataset()
sessions_df = data.runs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM weights_biases_data.runs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("weights_biases_pipeline").dataset()
data.runs.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 Weights & Biases 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