Dezgo Python API Docs | dltHub

Dezgo is an AI image generation API platform providing text-to-image, image-to-image, inpainting and related image-generation services. The REST API base URL is https://api.dezgo.com/ and All requests require an API key provided in the X-Dezgo-Key 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 Dezgo data in under 10 minutes.

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What data can I load from Dezgo?

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

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How do I authenticate with the Dezgo API?

Dezgo uses a single API key header. Include header "X-Dezgo-Key: YOUR_API_KEY" on every request. Set Content-Type appropriately for JSON or multipart/form-data.

1. Get your credentials

1. Sign in at https://dezgo.com/ (or create an account). 2) Open your account dashboard and go to "API Keys". 3) Click "New" to create an API key (complete captcha if prompted). 4) Copy and store the generated key; it is shown in the keys list and can be revoked.

2. Add them to .dlt/secrets.toml

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[sources.dezgo_api_source]
api_key = "your_dezgo_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 Dezgo 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 dezgo_api_pipeline.py

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

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Pipeline dezgo_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dezgo_api_data
The duckdb destination used duckdb:/dezgo_api.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 dezgo_api_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 models and account_tx_last from the Dezgo 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 dezgo_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dezgo.com/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "models", "data_selector": "data"}},
            {"name": "account_tx_last", "endpoint": {"path": "account/tx/last", "data_selector": "(response object; returns a single transaction object with balance field)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dezgo_api_pipeline",
        destination="duckdb",
        dataset_name="dezgo_api_data",
    )
    load_info = pipeline.run(dezgo_api_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("dezgo_api_pipeline").dataset()
sessions_df = data.models.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dezgo_api_data.models LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dezgo_api_pipeline").dataset()
data.models.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 Dezgo 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 verify the X-Dezgo-Key header contains a valid API key and that the key has not been revoked. The docs state all requests must include X-Dezgo-Key.

Rate limits and billing

The API is prepaid Pay-As-You-Go. Job responses include cost headers (x-dezgo-job-amount-usd) and balance headers (x-dezgo-balance-total-usd). Monitor account balance and the endpoint /account/tx/last to check current balance.

Image response quirks

Image generation endpoints may return raw PNG binary in the response body for direct image requests; some job endpoints return JSON job metadata. Check response Content-Type: image/png vs application/json and handle both cases.

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