Bluetally Python API Docs | dltHub

BlueTally is an asset and field‑service operations platform providing an API to manage assets, components, activity logs, statuses and related resources. The REST API base URL is https://app.bluetallyapp.com/api/v1 and All requests require OAuth2 Bearer token 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 pip install "dlt[workspace]" and start loading Bluetally data in under 10 minutes.

---

What data can I load from Bluetally?

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

---

How do I authenticate with the Bluetally API?

The API uses OAuth2/Bearer authentication. Include an Authorization header: Authorization: Bearer <access_token> on all requests.

1. Get your credentials

1. Sign in to your BlueTally account at https://bluetally.com or the BlueTally dashboard. 2) Open the Developer / API section (Developer docs at https://developer.bluetally.com). 3) Create or register an API client/application to obtain OAuth2 credentials or generate an access token. 4) Use the access token in the Authorization: Bearer header for API requests.

2. Add them to .dlt/secrets.toml

Copy
[sources.bluetally_instance_source]
api_token = "your_bluetally_bearer_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Bluetally 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:

Copy
python bluetally_instance_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline bluetally_instance_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.

---

Python pipeline example

This example loads components and activity from the Bluetally API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def bluetally_instance_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.bluetallyapp.com/api/v1",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "components", "endpoint": {"path": "components", "data_selector": "components"}},
            {"name": "activity", "endpoint": {"path": "activity", "data_selector": "activity"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bluetally_instance_pipeline",
        destination="duckdb",
        dataset_name="bluetally_instance_data",
    )
    load_info = pipeline.run(bluetally_instance_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.

---

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):

Copy
import dlt

data = dlt.pipeline("bluetally_instance_pipeline").dataset()
sessions_df = data.components.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM bluetally_instance_data.components LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("bluetally_instance_pipeline").dataset()
data.components.df().head()

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

---

What destinations can I load Bluetally 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.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify your Authorization header is present and the Bearer token is valid and unexpired. Regenerate or refresh the OAuth2 token from the BlueTally developer dashboard.

Rate limits

The documentation does not publish explicit rate limits. If you encounter 429 responses, implement exponential backoff and retry.

Pagination

List endpoints return paginated results. Check response for pagination fields (limit, offset or next links) in the response body and follow those to retrieve additional pages.

Common error responses

The API uses standard HTTP status codes. 400 for bad requests, 401 for auth errors, 403 for forbidden, 404 for not found, 429 for rate limits, 500 for server errors. Handle them accordingly.

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

---

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.

Copy
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