Fishbowl Python API Docs | dltHub

Fishbowl is an on‑premise inventory and order‑management REST API for Fishbowl Advanced that exposes inventory, parts, products, orders, users and related resources via JSON endpoints. The REST API base URL is http://{FISHBOWL_SERVER}:{PORT}/api and all requests require a Bearer token obtained from POST /api/session.

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 Fishbowl data in under 10 minutes.

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What data can I load from Fishbowl?

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

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How do I authenticate with the Fishbowl API?

Create a user session by POSTing credentials to /api/session; the endpoint returns a token which must be sent on subsequent requests in the Authorization header as a Bearer token (Authorization: Bearer ).

1. Get your credentials

1. On your Fishbowl Server create or use an existing user with appropriate API rights.
2. Ensure the Fishbowl Server web port (default 2456) is accessible.
3. POST username/password to http://{SERVER}:{PORT}/api/session to receive a token.
4. Use that token in the Authorization header for all API requests.

2. Add them to .dlt/secrets.toml

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[sources.fishbowl_source]
token = "your_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.

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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 Fishbowl 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 fishbowl_pipeline.py

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

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Pipeline fishbowl_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fishbowl_data
The duckdb destination used duckdb:/fishbowl.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 fishbowl_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 parts and products from the Fishbowl 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 fishbowl_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://{FISHBOWL_SERVER}:{PORT}/api",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "parts", "endpoint": {"path": "api/parts", "data_selector": "results"}},
            {"name": "products", "endpoint": {"path": "api/products", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fishbowl_pipeline",
        destination="duckdb",
        dataset_name="fishbowl_data",
    )
    load_info = pipeline.run(fishbowl_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("fishbowl_pipeline").dataset()
sessions_df = data.parts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fishbowl_data.parts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fishbowl_pipeline").dataset()
data.parts.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 Fishbowl 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 POST /api/session returns 401 or no token, verify username/password, that the user has API rights, and that you are posting to the correct server and port. Ensure the returned token is included exactly as: Authorization: Bearer .

Server accessibility and TLS

Fishbowl is on-premise; confirm the server's web port (default 2456) is reachable from your network. If using HTTPS, install a valid certificate on the Fishbowl Server; otherwise use HTTP for local networks.

Pagination and selectors

Most list endpoints return pagination fields (totalCount, totalPages, pageNumber, pageSize) and the records under the results key. Respect pageNumber and pageSize parameters when iterating pages.

Rate limits and errors

The documentation does not define public rate limits. Handle common HTTP error codes (401 Unauthorized, 403 Forbidden, 404 Not Found, 500 Server Error) and implement retries/backoff for transient 5xx errors.

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