Arc XP Python API Docs | dltHub

Arc XP is a RESTful platform that exposes publishing and content-management APIs to retrieve and manage content, sites, authors, and related resources for Arc XP organizations. The REST API base URL is https://api.{org-id}.arcpublishing.com and All requests require a Bearer access token issued from the Arc XP Developer Center..

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 Arc XP data in under 10 minutes.

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What data can I load from Arc XP?

Here are some of the endpoints you can load from Arc XP:

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How do I authenticate with the Arc XP API?

Generate an access token in your Arc XP Developer Center and include it in requests using the Authorization header: 'Authorization: Bearer ACCESS_TOKEN'. Content-Type: application/json is typically required for JSON requests.

1. Get your credentials

1. Sign in to your Arc XP instance at https://{org-id}.arcpublishing.com using Okta credentials.
2. Open the Developer Center from the Administration section.
3. Go to Access Tokens and click "New access token".
4. Choose token type (Read‑only, Restricted‑access, or All‑access), generate, copy and store the token securely (you cannot view it again).

2. Add them to .dlt/secrets.toml

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[sources.arc_xp_source]
access_token = "your_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 Arc XP 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 arc_xp_pipeline.py

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

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Pipeline arc_xp_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset arc_xp_data
The duckdb destination used duckdb:/arc_xp.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 arc_xp_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 site_website and author_list from the Arc XP 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 arc_xp_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.{org-id}.arcpublishing.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "site_website", "endpoint": {"path": "site/v3/website/"}},
            {"name": "author_list", "endpoint": {"path": "v2/author-service"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="arc_xp_pipeline",
        destination="duckdb",
        dataset_name="arc_xp_data",
    )
    load_info = pipeline.run(arc_xp_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("arc_xp_pipeline").dataset()
sessions_df = data.site_website.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM arc_xp_data.site_website LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("arc_xp_pipeline").dataset()
data.site_website.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 Arc XP 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 Unauthorized or 403 Forbidden, verify your access token is valid, unexpired, and has sufficient scope. Ensure the Authorization header uses 'Bearer '.

Rate limiting (429)

The Content API defaults to 30 requests per minute. If you exceed your quota you will receive 429 responses. Implement exponential backoff and caching; contact Arc XP support for quota increases.

Request size limits (413)

Author API requests must not exceed 20KB in payload size; larger payloads will return 413 Request Entity Too Large.

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