Axway B2Bi Python API Docs | dltHub

Axway B2Bi is a B2B integration platform that provides REST APIs for managing trading engine resources, communities, and partners. The REST API base URL is https://<host>:<port>/api/v2 and Supports Basic Authentication for initial login and session-based cookie authentication or Bearer tokens for subsequent requests..

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 Axway B2Bi data in under 10 minutes.

---

What data can I load from Axway B2Bi?

Here are some of the endpoints you can load from Axway B2Bi:

---

How do I authenticate with the Axway B2Bi API?

Authentication can be performed using Basic Authentication (username and password) for the initial request, which returns an AuthToken or AUTH_TOKEN cookie for subsequent session-based requests. Alternatively, Bearer tokens are supported for SSO-integrated environments.

1. Get your credentials

Axway B2Bi does not use API keys for standard REST API access. Instead, it relies on standard user accounts created and managed in the B2Bi graphical user interface. To obtain access, ensure your account has the "Access APIs" permission assigned by a B2Bi administrator. Authentication is performed by providing your username and password via Basic Authentication or an initial login request. For automated pipelines, use the credentials of a dedicated service account specifically created for the integration.

2. Add them to .dlt/secrets.toml

Copy
[sources.axway_b2bi_source]
username = "your_b2bi_username"
password = "your_b2bi_password"
base_url = "https://your-b2bi-host:6443"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
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:

Copy
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 Axway B2Bi 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
uv run python axway_b2bi_pipeline.py

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

Copy
Pipeline axway_b2bi_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset axway_b2bi_data
The duckdb destination used duckdb:/axway_b2bi.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
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.

---

Python pipeline example

This example loads /api/v1/login and /api/v2/access-tokens from the Axway B2Bi 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 axway_b2bi_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<host>:<port>/api/v2",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "communities", "endpoint": {"path": "api/v1/communities", "data_selector": "results"}},
            {"name": "trading_partners", "endpoint": {"path": "api/v1/tradingPartners", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="axway_b2bi_pipeline",
        destination="duckdb",
        dataset_name="axway_b2bi_data",
    )
    load_info = pipeline.run(axway_b2bi_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("axway_b2bi_pipeline").dataset()
sessions_df = data.communities.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM axway_b2bi_data.communities LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("axway_b2bi_pipeline").dataset()
data.communities.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 Axway B2Bi 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.

---

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.

Copy
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