Ocean Protocol Python API Docs | dltHub

Ocean Protocol API is a decentralized data exchange protocol that enables data sharing and monetization through data NFTs, metadata indexing, and data services. The REST API base URL is https://v4.subgraph.mainnet.oceanprotocol.com/subgraphs/name/oceanprotocol/ocean-subgraph (for Subgraph GraphQL), https://v4.aquarius.oceanprotocol.com (for Aquarius), and /api/services (for Provider) or /api/aquarius/assets/ddo/:did (for Ocean Node). and Authentication for the Provider API can involve tokens created via authentication endpoints or signature-based authentication, while Aquarius and Subgraph are public and do not require API keys..

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 Ocean Protocol data in under 10 minutes.

---

What data can I load from Ocean Protocol?

Here are some of the endpoints you can load from Ocean Protocol:

---

How do I authenticate with the Ocean Protocol API?

The Provider API supports authentication tokens, which can be created via dedicated authentication endpoints, or signature-based authentication requiring consumerAddress, nonce, and signature as query parameters for GET requests. Aquarius and Subgraph endpoints are public and do not require API keys.

1. Get your credentials

To obtain API credentials for the Provider, you can create an authentication token using the provider authentication endpoints. For signature-based authentication, you will need your consumerAddress, a nonce obtained from the /api/services/nonce endpoint, and a signature generated by signing the nonce.

2. Add them to .dlt/secrets.toml

Copy
[sources.ocean_protocol_source]
auth_token = "your_auth_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Ocean Protocol 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:

Copy
python ocean_protocol_pipeline.py

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

Copy
Pipeline ocean_protocol_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ocean_protocol_data
The duckdb destination used duckdb:/ocean_protocol.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline ocean_protocol_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.

---

Python pipeline example

This example loads subgraph_nft and provider_nonce from the Ocean Protocol API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def ocean_protocol_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://v4.subgraph.mainnet.oceanprotocol.com/subgraphs/name/oceanprotocol/ocean-subgraph (for Subgraph GraphQL), https://v4.aquarius.oceanprotocol.com (for Aquarius), and /api/services (for Provider) or /api/aquarius/assets/ddo/:did (for Ocean Node).",
            "auth": {
                "type": "custom",
                "auth_token": auth_token,
            },
        },
        "resources": [
            {"name": "subgraph_nft", "endpoint": {"path": "subgraphs/name/oceanprotocol/ocean-subgraph", "data_selector": "data.nft"}},
            {"name": "provider_nonce", "endpoint": {"path": "api/services/nonce", "data_selector": "nonce"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ocean_protocol_pipeline",
        destination="duckdb",
        dataset_name="ocean_protocol_data",
    )
    load_info = pipeline.run(ocean_protocol_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.

---

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):

Copy
import dlt

data = dlt.pipeline("ocean_protocol_pipeline").dataset()
sessions_df = data.subgraph_nft.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM ocean_protocol_data.subgraph_nft LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("ocean_protocol_pipeline").dataset()
data.subgraph_nft.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Ocean Protocol 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.

---

Troubleshooting

API Error Handling

The Provider API returns standard HTTP error codes. Detailed examples and reasons for errors are documented in the Provider's GitHub routes README.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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