Enjin Python API Docs | dltHub

Enjin is a platform providing a GraphQL API to interact with the Enjin blockchain and Platform services (collections, tokens, marketplace, beams, and fuel-tanks). The REST API base URL is Mainnet: https://platform.enjin.io/graphql Testnet (Canary): https://platform.canary.enjin.io/graphql and all requests require an API token provided via 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 Enjin data in under 10 minutes.

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What data can I load from Enjin?

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

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How do I authenticate with the Enjin API?

Enjin uses bearer-style API tokens. Include the API token in the Authorization HTTP header for GraphQL requests, e.g. "Authorization: ". All GraphQL requests are sent over HTTPS to the GraphQL endpoints and are executed via POST with a JSON body containing "query" and optional "variables".

1. Get your credentials

1. Create/login to an Enjin Platform account (Testnet: https://platform.canary.enjin.io or Mainnet: https://platform.enjin.io). 2) In the Platform dashboard create an API token (API key) under account/API settings. 3) Copy the API token and use it in the Authorization header for requests.

2. Add them to .dlt/secrets.toml

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[sources.enjin_source]
api_token = "your_api_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 Enjin 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 enjin_pipeline.py

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

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Pipeline enjin_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset enjin_data
The duckdb destination used duckdb:/enjin.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 enjin_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 core_operations and marketplace from the Enjin 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 enjin_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Mainnet: https://platform.enjin.io/graphql Testnet (Canary): https://platform.canary.enjin.io/graphql",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "core_operations", "endpoint": {"path": "graphql", "data_selector": "data.<queryName>"}},
            {"name": "marketplace", "endpoint": {"path": "graphql/marketplace", "data_selector": "data.<queryName>"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="enjin_pipeline",
        destination="duckdb",
        dataset_name="enjin_data",
    )
    load_info = pipeline.run(enjin_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("enjin_pipeline").dataset()
sessions_df = data.core_operations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM enjin_data.core_operations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("enjin_pipeline").dataset()
data.core_operations.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 Enjin 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 your API token is valid, has not expired or been revoked, and that you supply it in the Authorization header exactly as provided. Tokens are created in the Platform dashboard.

Incorrect request/content-type

All GraphQL calls must be POST with Content-Type: application/json and a JSON body containing "query" (and "variables" if needed). Requests missing correct headers or malformed JSON will fail.

Pagination and data selectors

GraphQL responses return top-level JSON with a "data" key. The requested field name inside "data" contains the records (for example, a collections query will return data.collections). Use the exact field name you requested in your query as the data selector (e.g. data.collections).

Rate limits and errors

The documentation does not publish explicit rate-limit numbers. Expect standard API rate limiting; on rate-limit you may receive 429 or error objects in the GraphQL response. Inspect the GraphQL "errors" array for operation-level issues.

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