Frase Python API Docs | dltHub

Frase is a content intelligence platform that provides a REST API to programmatically manage content, briefs, SERP analysis, AI-visibility, audits, research and webhooks. The REST API base URL is Primary documented API hosts observed: https://next.frase.io/api/v1 and https://api.frase.io/api/v1 and API uses API keys sent in request headers (X-API-KEY or token header depending on endpoint/docs)..

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 Frase data in under 10 minutes.

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What data can I load from Frase?

Here are some of the endpoints you can load from Frase:

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How do I authenticate with the Frase API?

The API requires an API key header. Examples in Frase docs show sending 'X-API-KEY: sk_live_your_api_key_here' and the Help Center get_document page references a 'token' header field; include Content-Type: application/json for JSON requests.

1. Get your credentials

1. Sign in to your Frase account (or sign up) 2) Go to Account / Settings or API keys section 3) Create or copy an API key (sk_live_... style) 4) Store it securely; use it in requests via X-API-KEY or as the 'token' header per endpoint docs.

2. Add them to .dlt/secrets.toml

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[sources.frase_source]
api_key = "sk_live_your_api_key_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 Frase 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 frase_pipeline.py

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

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Pipeline frase_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset frase_data
The duckdb destination used duckdb:/frase.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 frase_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 serp_analyze and get_document from the Frase 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 frase_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Primary documented API hosts observed: https://next.frase.io/api/v1 and https://api.frase.io/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "serp_analyze", "endpoint": {"path": "serp/analyze", "data_selector": "ai_overview"}},
            {"name": "get_document", "endpoint": {"path": "get_document_id"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="frase_pipeline",
        destination="duckdb",
        dataset_name="frase_data",
    )
    load_info = pipeline.run(frase_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("frase_pipeline").dataset()
sessions_df = data.get_document.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM frase_data.get_document LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("frase_pipeline").dataset()
data.get_document.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 Frase 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 401/403 responses confirm you are sending a valid API key in headers. Frase examples show using X-API-KEY or a 'token' header. Verify the key hasn't expired, and that your account plan includes API access.

Rate limits and plan limits

Marketing/docs indicate enterprise customers may have dedicated rate limits. If you encounter 429 responses, implement exponential backoff and contact Frase support or your account rep to raise limits.

Async jobs and webhook handling

Many Frase operations (research, audits, content generation) are async; poll job status endpoints or subscribe to webhooks (content.ready, research.completed, audit.completed) to receive completion notifications.

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