Eden AI Python API Docs | dltHub

Eden AI is a unified AI API platform that provides access to multiple providers and features such as LLMs, image analysis, OCR, moderation, and detection via a single V3 API. The REST API base URL is https://api.edenai.run/v3 and All requests require a Bearer token 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 Eden AI data in under 10 minutes.

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What data can I load from Eden AI?

Here are some of the endpoints you can load from Eden AI:

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How do I authenticate with the Eden AI API?

Authentication uses a Bearer token passed in the Authorization header; include Content-Type: application/json for JSON requests.

1. Get your credentials

1. Sign in to the Eden AI dashboard at https://app.edenai.run/. 2) Navigate to the account or API settings section. 3) Create a new API token or copy the existing token shown there. 4) Use that token as a Bearer token in the Authorization header for all API calls.

2. Add them to .dlt/secrets.toml

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[sources.eden_ai_source]
api_key = "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 Eden 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 eden_ai_pipeline.py

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

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Pipeline eden_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset eden_ai_data
The duckdb destination used duckdb:/eden_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 eden_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 universal_ai and llm_chat_completions from the Eden 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 eden_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.edenai.run/v3",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "universal_ai", "endpoint": {"path": "v3/universal-ai", "data_selector": "output"}},
            {"name": "llm_chat_completions", "endpoint": {"path": "v3/llm/chat/completions", "data_selector": "choices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="eden_ai_pipeline",
        destination="duckdb",
        dataset_name="eden_ai_data",
    )
    load_info = pipeline.run(eden_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("eden_ai_pipeline").dataset()
sessions_df = data.universal_ai.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM eden_ai_data.universal_ai LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("eden_ai_pipeline").dataset()
data.universal_ai.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 Eden 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

All requests must include a valid Bearer token. The API returns 401 Unauthorized when the token is missing or invalid.

Rate limits and billing

When the request quota is exceeded the service responds with 429 Too Many Requests. Insufficient credits trigger a 402 Payment Required. Implement exponential back‑off and monitor usage.

Invalid model string / input validation

V3 expects model strings in the format feature/subfeature/provider[/model]. A malformed string results in a 400/422 response with an error payload such as:

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{
  "status": "error",
  "error": {
    "code": "invalid_model_string",
    "message": "Model string format must be feature/subfeature/provider[/model]"
  }
}

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