Factbird Python API Docs | dltHub

Factbird is a cloud platform for manufacturing data collection and analytics; its API exposes the same data via a GraphQL endpoint. The REST API base URL is https://api.cloud.factbird.com and All requests require an OAuth2 access token presented in the Authorization header..

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 pip install "dlt[workspace]" and start loading Factbird data in under 10 minutes.

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What data can I load from Factbird?

Here are some of the endpoints you can load from Factbird:

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How do I authenticate with the Factbird API?

Factbird uses OAuth2 client‑credentials style authentication; every request must include headers Accept: application/json, Content-Type: application/json and Authorization: <api‑token> where <api‑token> is a short‑lived access token.

1. Get your credentials

1. Use the GraphQL mutation createAppClient (or contact Factbird support) to create an OAuth2 app client.
2. Save the returned clientSecret and appClient.id (clientSecret is only shown once).
3. Exchange the client credentials for a short‑lived access token according to the OAuth2 token flow described in the docs.
4. Include the token in the Authorization header for all API calls.

2. Add them to .dlt/secrets.toml

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[sources.factbird_source]
api_token = "your_short_lived_access_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 venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt 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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dlt ai toolkit rest-api-pipeline install

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 Factbird 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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python factbird_pipeline.py

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

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Pipeline factbird_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset factbird_data
The duckdb destination used duckdb:/factbird.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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dlt pipeline factbird_pipeline 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 graph_ql and docs from the Factbird 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 factbird_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cloud.factbird.com",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "graph_ql", "endpoint": {"path": "v1", "data_selector": "data.<queryName>"}},
            {"name": "docs", "endpoint": {"path": "v1/docs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="factbird_pipeline",
        destination="duckdb",
        dataset_name="factbird_data",
    )
    load_info = pipeline.run(factbird_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("factbird_pipeline").dataset()
sessions_df = data.graph_ql.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM factbird_data.graph_ql LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("factbird_pipeline").dataset()
data.graph_ql.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 Factbird 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 401/403 responses, confirm you are sending Authorization: <token> header and that the token is valid and not expired. If you created an app client via createAppClient, ensure you stored the clientSecret at creation time and successfully exchanged it for an access token.

GraphQL response wrapper & selectors

All GraphQL responses are wrapped in the top‑level data field. The records for any query appear under data.<queryName> (for example, data.machines). Use that nested key as the data selector when extracting lists. Errors are returned in the top‑level errors field.

Rate limits and permissions

API access is subject to rate limits and endpoint‑specific permissions. Some queries/mutations require the app client to be in specific groups (groupIds). If encountering 429 or permission errors, contact support to request higher limits or adjust app client group memberships.

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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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• 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