Boomi Flow Python API Docs | dltHub

Boomi Flow is a REST API‑first platform that exposes Flow (ManyWho) operations for managing flows, assets, tenants, users, runtime and related resources via HTTP. The REST API base URL is https://flow.boomi.com/api and all requests require the x-boomi-flow-api-key header (API Key); manywhotenant header may be required to target a tenant..

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 Boomi Flow data in under 10 minutes.

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What data can I load from Boomi Flow?

Here are some of the endpoints you can load from Boomi Flow:

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How do I authenticate with the Boomi Flow API?

API key authentication: include the x-boomi-flow-api-key header with the apiKey value. When required include manywhotenant header with the tenant ID. API keys are created via the Flow User Settings or POST /api/admin/1/users/me/keys.

1. Get your credentials

1. Log in to Boomi Flow. 2. Open User Settings > API keys. 3. Create (Generate) a new API key and copy the apiKey value. 4. Note the tenant ID for the tenant you want to target (manywhotenant). 5. Use x-boomi-flow-api-key header with the apiKey value and optionally manywhotenant header with tenant ID.

2. Add them to .dlt/secrets.toml

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[sources.boomi_flow_source]
api_key = "your_flow_api_key_here"
tenant_id = "your_tenant_id_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 Boomi Flow 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 boomi_flow_pipeline.py

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

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Pipeline boomi_flow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset boomi_flow_data
The duckdb destination used duckdb:/boomi_flow.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 boomi_flow_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 flows and users from the Boomi Flow 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 boomi_flow_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://flow.boomi.com/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "flows", "endpoint": {"path": "draw/1/flow"}},
            {"name": "users", "endpoint": {"path": "admin/1/users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="boomi_flow_pipeline",
        destination="duckdb",
        dataset_name="boomi_flow_data",
    )
    load_info = pipeline.run(boomi_flow_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("boomi_flow_pipeline").dataset()
sessions_df = data.flows.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM boomi_flow_data.flows LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("boomi_flow_pipeline").dataset()
data.flows.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 Boomi Flow 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, verify the x-boomi-flow-api-key header contains the correct API key value and, if required, include the manywhotenant header with the tenant ID. API keys are tenant‑scoped.

Missing response body / empty lists

Some endpoints in the public docs do not include example response bodies; if a GET returns an empty payload, verify headers and tenant context and try the API tool inside the Flow GUI to view concrete responses.

Rate limits and errors

The public documentation does not publish explicit rate limit headers; treat 429 responses as rate‑limiting and implement exponential backoff. Standard HTTP errors (4xx for client/auth errors, 5xx for server errors) apply.

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