Cisco DNA Center Python API Docs | dltHub

Cisco DNA Center is a network management and automation platform providing APIs to discover, configure, monitor, and manage network devices and services. The REST API base URL is https://<dnac-host> and all requests require an X-Auth-Token header obtained from the auth token endpoint..

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 Cisco DNA Center data in under 10 minutes.

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What data can I load from Cisco DNA Center?

Here are some of the endpoints you can load from Cisco DNA Center:

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How do I authenticate with the Cisco DNA Center API?

DNA Center uses a short-lived session token obtained by POSTing Basic Auth credentials to /dna/system/api/v1/auth/token (or /api/system/v1/auth/token on older versions). Include the returned token in the X-Auth-Token header for subsequent requests.

1. Get your credentials

1. Log in to Cisco DNA Center web UI as an administrative user. 2) Use the same username/password credentials for API access. 3) Call POST https:///dna/system/api/v1/auth/token with HTTP Basic Auth (username/password). 4) Extract token from JSON key 'Token' or 'token' (endpoint returns token string) and store for X-Auth-Token header.

2. Add them to .dlt/secrets.toml

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[sources.cisco_dna_center_source]
username = "your_dnac_username"
password = "your_dnac_password"

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 Cisco DNA Center 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 cisco_dna_center_pipeline.py

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

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Pipeline cisco_dna_center_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cisco_dna_center_data
The duckdb destination used duckdb:/cisco_dna_center.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 cisco_dna_center_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 network_device and interface from the Cisco DNA Center 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 cisco_dna_center_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<dnac-host>",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "network_device", "endpoint": {"path": "dna/intent/api/v1/network-device", "data_selector": "response"}},
            {"name": "interface", "endpoint": {"path": "api/v1/interface", "data_selector": "response"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cisco_dna_center_pipeline",
        destination="duckdb",
        dataset_name="cisco_dna_center_data",
    )
    load_info = pipeline.run(cisco_dna_center_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("cisco_dna_center_pipeline").dataset()
sessions_df = data.network_device.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cisco_dna_center_data.network_device LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cisco_dna_center_pipeline").dataset()
data.network_device.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 Cisco DNA Center 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 to /dna/system/api/v1/auth/token returns 401, verify username/password and use Basic Auth; ensure correct endpoint path for your version (/dna/system/... vs /api/system/...). Tokens expire (typically 1 hour) — re-request token and retry.

Rate limiting and timeouts

DNA Center may throttle high-frequency calls; implement retries with exponential backoff and use pagination parameters where applicable.

Pagination and large result sets

Many GET endpoints return results in a top-level 'response' object (array) and may support query params (offset/limit or startIndex/page) — use provided query params in docs and iterate until full dataset retrieved.

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