GoPlus Labs Python API Docs | dltHub

GoPlus Security API is an open, license-free Web3.0 security data API service provided by GoPlus for the Web3.0 developers and users. The REST API base URL is https://api.gopluslabs.io and API key (and optional secret) based authentication obtained from GoPlus console..

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 GoPlus Labs data in under 10 minutes.

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What data can I load from GoPlus Labs?

Here are some of the endpoints you can load from GoPlus Labs:

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How do I authenticate with the GoPlus Labs API?

GoPlus issues API credentials via the GoPlus console. Integrations present in community repositories reference an API key and secret pair. Use the issued credentials in request headers or as configured by the provider (confirm exact header names with provider). The docs point to the console at https://console.gopluslabs.io to obtain keys.

1. Get your credentials

1. Open https://console.gopluslabs.io and sign in or sign up. 2) Navigate to API or Security API credentials/keys section. 3) Create a new API key (and secret if applicable). 4) Copy the key (and secret) into your application secrets store. 5) If unsure, contact GoPlus support or check the console UI for header usage examples.

2. Add them to .dlt/secrets.toml

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[sources.goplus_labs_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 GoPlus Labs 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 goplus_labs_pipeline.py

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

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Pipeline goplus_labs_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset goplus_labs_data
The duckdb destination used duckdb:/goplus_labs.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 goplus_labs_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 token_security and malicious_address from the GoPlus Labs 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 goplus_labs_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.gopluslabs.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "token_security", "endpoint": {"path": "reference/token-security-api"}},
            {"name": "malicious_address", "endpoint": {"path": "reference/malicious-address-api"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="goplus_labs_pipeline",
        destination="duckdb",
        dataset_name="goplus_labs_data",
    )
    load_info = pipeline.run(goplus_labs_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("goplus_labs_pipeline").dataset()
sessions_df = data.token_security.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM goplus_labs_data.token_security LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("goplus_labs_pipeline").dataset()
data.token_security.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 GoPlus Labs 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 requests return 401/403, verify you placed the API key (and secret if required) into the correct header or auth parameter. Confirm the key is active in https://console.gopluslabs.io and not expired or revoked.

Rate limiting and throttling

The public docs do not specify rate limits. If you receive 429 responses, implement exponential backoff and contact GoPlus for your plan limits.

Missing or unexpected response schema

The docs index lists endpoints but does not include concrete JSON examples in the scraped pages. If field names differ from expectations, fetch a small sample response and update the dlt data_selector to the exact JSON key that contains the records array.

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