Imagga Python API Docs | dltHub

Imagga is an image recognition and processing tool that allows users to analyze and tag images using advanced artificial intelligence technology, offering solutions for tagging, categorization, color extraction, and visual search. The REST API base URL is https://api.imagga.com/v2 and All requests require HTTP Basic 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 Imagga data in under 10 minutes.

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What data can I load from Imagga?

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

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How do I authenticate with the Imagga API?

Authentication uses HTTP Basic auth with an API key and API secret, which are Base64-encoded as 'api_key:api_secret' and included in the 'Authorization' header.

1. Get your credentials

Please refer to the Imagga documentation or your Imagga account dashboard for instructions on how to obtain your API key and API secret.

2. Add them to .dlt/secrets.toml

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[sources.imagga_source]
api_key = "your_api_key_here"
api_secret = "your_api_secret_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 Imagga 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 imagga_pipeline.py

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

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Pipeline imagga_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset imagga_data
The duckdb destination used duckdb:/imagga.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 imagga_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 tagging and categories from the Imagga 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 imagga_source(api_key, api_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.imagga.com/v2",
            "auth": {
                "type": "http_basic",
                "api_key, api_secret": api_key, api_secret,
            },
        },
        "resources": [
            {"name": "tagging", "endpoint": {"path": "v2/tags", "data_selector": "result.tags"}},
            {"name": "categories", "endpoint": {"path": "v2/categories", "data_selector": "result.categories"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="imagga_pipeline",
        destination="duckdb",
        dataset_name="imagga_data",
    )
    load_info = pipeline.run(imagga_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("imagga_pipeline").dataset()
sessions_df = data.tagging.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM imagga_data.tagging LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("imagga_pipeline").dataset()
data.tagging.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 Imagga 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 encounter authentication errors, ensure your API key and API secret are correctly Base64-encoded and included in the 'Authorization: Basic' header. Double-check for typos or incorrect credentials.

Rate Limiting

Imagga API responses use HTTP status codes to indicate rate limits. If you receive a rate limit error, you may need to implement a retry mechanism with exponential backoff or adjust your request frequency.

Response Structure and Pagination

Responses consistently include a 'status' object and a 'result' object. Data arrays like 'tags', 'colors', or 'categories' are typically nested within the 'result' object (e.g., result.tags). Be aware of this structure when parsing responses. Pagination details were not explicitly found in the provided documentation, so assume standard API pagination practices or consult the official Imagga documentation for specific guidance.

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