Open Policy Agent Python API Docs | dltHub

Open Policy Agent (OPA) is a general-purpose policy engine that exposes a REST API to manage policies, query decisions, and read/write data. The REST API base URL is http://<OPA_HOST>:8181 and OPA supports token-based authentication via the Authorization header using Bearer tokens when enabled..

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 Open Policy Agent data in under 10 minutes.

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What data can I load from Open Policy Agent?

Here are some of the endpoints you can load from Open Policy Agent:

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How do I authenticate with the Open Policy Agent API?

When OPA is configured with the --authentication=token flag, clients must include the 'Authorization' header with a value of 'Bearer '.

1. Get your credentials

Open Policy Agent (OPA) does not provide a graphical dashboard for managing API credentials. Authentication is handled by starting the OPA daemon with specific flags. To use token-based authentication: 1. Start OPA with the --authentication=token flag. 2. Define an authorization policy (in Rego) that validates the bearer token provided in requests. 3. When communicating with the OPA REST API, include the token in the HTTP Authorization header as 'Authorization: Bearer '. For TLS-based authentication, start OPA with --authentication=tls and provide the necessary certificate files (--tls-cert-file, --tls-private-key-file, and --tls-ca-cert-file).

2. Add them to .dlt/secrets.toml

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[sources.open_policy_agent_source]
opa_auth_token = "your_secret_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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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:

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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 Open Policy Agent 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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uv run python open_policy_agent_pipeline.py

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

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Pipeline open_policy_agent_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset open_policy_agent_data
The duckdb destination used duckdb:/open_policy_agent.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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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.

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Python pipeline example

This example loads /v1/data and /v1/policies from the Open Policy Agent 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 open_policy_agent_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<OPA_HOST>:8181",
            "auth": {"type": "bearer", "token": auth_token},
        },
        "resources": [
            {"name": "bundles", "endpoint": {"path": "v1/bundles", "data_selector": "result"}},
            {"name": "policies", "endpoint": {"path": "v1/policies", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="open_policy_agent_pipeline",
        destination="duckdb",
        dataset_name="open_policy_agent_data",
    )
    load_info = pipeline.run(open_policy_agent_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("open_policy_agent_pipeline").dataset()
sessions_df = data.bundles.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM open_policy_agent_data.bundles LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("open_policy_agent_pipeline").dataset()
data.bundles.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 Open Policy Agent 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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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.

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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