1password Python API Docs | dltHub

1Password Connect is a private REST API that lets you manage vaults, items, and retrieve API activity. The REST API base URL is http://localhost:8080 and All requests require a Bearer access token supplied in the Authorization header..

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 1password data in under 10 minutes.

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What data can I load from 1password?

Here are some of the endpoints you can load from 1password:

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How do I authenticate with the 1password API?

Provide a Bearer token in the Authorization: Bearer <access_token> header for every request.

1. Get your credentials

1. Log into the 1Password Developer Portal (https://developer.1password.com).
2. Navigate to Service Accounts or API Keys section.
3. Create a new Service Account or API key for Connect.
4. Copy the generated access token; it will be used as the Bearer token for API calls.
5. Store the token securely (e.g., in secrets.toml).

2. Add them to .dlt/secrets.toml

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[sources._1password_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 1password 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 _1password_pipeline.py

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

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Pipeline _1password_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _1password_data
The duckdb destination used duckdb:/_1password.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 _1password_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 vaults and vault_items from the 1password 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 _1password_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8080",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "vaults", "endpoint": {"path": "v1/vaults"}},
            {"name": "vault_items", "endpoint": {"path": "v1/vaults/{vault_id}/items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_1password_pipeline",
        destination="duckdb",
        dataset_name="_1password_data",
    )
    load_info = pipeline.run(_1password_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("_1password_pipeline").dataset()
sessions_df = data.vaults.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM _1password_data.vaults LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("_1password_pipeline").dataset()
data.vaults.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 1password 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 Errors

• 401 Unauthorized – Returned when the access token is missing, malformed, or expired. Ensure the Authorization: Bearer <token> header contains a valid token.

Resource Not Found

• 404 Not Found – Returned when a requested vault ID does not exist. Verify the vault identifier in the request path.

Rate Limits

• 1Password Service Accounts are subject to hourly and daily rate limits that vary by subscription tier. Exceeding these limits results in HTTP 429 Too Many Requests responses. Review the rate‑limit headers and back‑off accordingly.

Pagination

• List endpoints return full collections without built‑in pagination. For large datasets, implement client‑side batching or use cursor‑based pagination if introduced in future API versions.

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