Winston-ai Python API Docs | dltHub

Winston AI is a platform that provides AI-powered content and image detection, plagiarism and fact-checking APIs. The REST API base URL is https://api.gowinston.ai/v2 and all requests require a Bearer token for authentication.

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 Winston-ai data in under 10 minutes.

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What data can I load from Winston-ai?

Here are some of the endpoints you can load from Winston-ai:

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How do I authenticate with the Winston-ai API?

All API endpoints are authenticated using Bearer tokens. Include the token in the Authorization header as: Authorization: Bearer .

1. Get your credentials

1. Register or sign in to the Winston AI developer dashboard at https://dev.gowinston.ai. 2) In the dashboard, open the API / Tokens section. 3) Create or copy an API token (Bearer token) to use in requests. 4) Store this token securely for use in your dlt source configuration.

2. Add them to .dlt/secrets.toml

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[sources.winston_ai_source]
api_token = "your_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 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 Winston-ai 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 winston_ai_pipeline.py

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

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Pipeline winston_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset winston_ai_data
The duckdb destination used duckdb:/winston_ai.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 winston_ai_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 ai_content_detection and image_detection from the Winston-ai 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 winston_ai_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.gowinston.ai/v2",
            "auth": {
                "type": "bearer",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "ai_content_detection", "endpoint": {"path": "v2/ai-content-detection"}},
            {"name": "image_detection", "endpoint": {"path": "v2/image-detection"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="winston_ai_pipeline",
        destination="duckdb",
        dataset_name="winston_ai_data",
    )
    load_info = pipeline.run(winston_ai_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("winston_ai_pipeline").dataset()
sessions_df = data.ai_content_detection.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM winston_ai_data.ai_content_detection LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("winston_ai_pipeline").dataset()
data.ai_content_detection.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 Winston-ai 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, verify your Authorization header is exactly: Authorization: Bearer . Ensure the token is active and has not been rotated or revoked in the developer dashboard (https://dev.gowinston.ai).

Rate limits and credits exhausted

The API returns 429 Too Many Requests for rate limiting and 402 Payment Required when credits are insufficient. Monitor credits_remaining returned in responses and refill/purchase credits in the dashboard.

Request validation and content errors

400 Bad Request indicates malformed payloads. 415 Unsupported Media Type is returned for unsupported file types. Check the endpoint-specific request body and Content-Type header.

Server errors

500/503 can occur for transient server issues; retry with exponential backoff.

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