Immutable X Python API Docs | dltHub

Immutable X is a layer-2 blockchain data and NFT marketplace platform providing REST/SDK APIs and blockchain-data endpoints for querying NFTs, collections, activities and user inventories. The REST API base URL is Production: https://api.immutable.com Sandbox: https://api.sandbox.immutable.com and OAuth / Bearer access token (platform API) used in 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 Immutable X data in under 10 minutes.

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What data can I load from Immutable X?

Here are some of the endpoints you can load from Immutable X:

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How do I authenticate with the Immutable X API?

Immutable uses OAuth‑based client credentials (Passport) to obtain an access token. Authenticated requests include an Authorization: Bearer <access_token> header.

1. Get your credentials

1. Create or obtain a Passport client in Immutable Hub (Client ID). 2) Configure OAuth scopes/audience (default audience: platform_api). 3) Use SDK auth helpers or OAuth flow to exchange client credentials / user login for an access token. 4) Use the access token as a Bearer token in Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.immutable_x_source]
token = "your_access_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 Immutable X 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 immutable_x_pipeline.py

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

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Pipeline immutable_x_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset immutable_x_data
The duckdb destination used duckdb:/immutable_x.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 immutable_x_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 chains_accounts_nfts and activities from the Immutable X 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 immutable_x_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Production: https://api.immutable.com  Sandbox: https://api.sandbox.immutable.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "chains_accounts_nfts", "endpoint": {"path": "v1/chains/{chainName}/accounts/{address}/nfts"}},
            {"name": "activities", "endpoint": {"path": "v1/activities"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="immutable_x_pipeline",
        destination="duckdb",
        dataset_name="immutable_x_data",
    )
    load_info = pipeline.run(immutable_x_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("immutable_x_pipeline").dataset()
sessions_df = data.chains_accounts_nfts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM immutable_x_data.chains_accounts_nfts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("immutable_x_pipeline").dataset()
data.chains_accounts_nfts.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 Immutable X 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 responses, ensure you obtained a valid OAuth access token via the Immutable auth SDK (Passport client) and include it as Authorization: Bearer <token>. Sandbox uses a separate sandbox client and base URL.

Rate limits

Immutable enforces rate limits on API endpoints; if you receive 429, back off and retry with exponential backoff. Use pagination parameters to reduce page sizes.

Pagination quirks

Many Blockchain Data API endpoints are paginated. Use the SDK helper methods or follow the documented query parameters (page, limit, cursor) to iterate pages; ensure you handle empty pages and termination conditions.

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