Trust Python API Docs | dltHub

Trust API is a GraphQL-based wallet management and transaction API for Trust/TrustVault (Trustology) that exposes subwallets, balances, addresses, transactions and wallet management operations. The REST API base URL is https://tapi-sandbox.trustology-test.com/graphql and all requests require an x-api-key header for authentication (API key).

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 Trust data in under 10 minutes.

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What data can I load from Trust?

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

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How do I authenticate with the Trust API?

Authentication uses a single API key passed in the x-api-key HTTP header. Requests are JSON GraphQL POSTs to the GraphQL endpoint and must include Content-Type: application/json and x-api-key: <your_api_key>.

1. Get your credentials

1. Register / contact Trust/Trustology to request API access for TrustVault/Trust API; 2) Receive an API key for the environment (sandbox/test or production); 3) Store the key and send it as x-api-key in all requests; 4) For production access follow the provider onboarding / account steps on Trust developer portal or Trustology support (docs refer to using x-api-key header).

2. Add them to .dlt/secrets.toml

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[sources.trust_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 Trust 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 trust_pipeline.py

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

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Pipeline trust_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset trust_data
The duckdb destination used duckdb:/trust.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 trust_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 user_subwallets_transactions and btc_receive_addresses from the Trust 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 trust_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://tapi-sandbox.trustology-test.com/graphql",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "user_subwallets_transactions", "endpoint": {"path": "tapi-sandbox.trustology-test.com/graphql (GraphQL query named getTransactions)", "data_selector": "data.user.subWallet.transactions.items"}},
            {"name": "btc_receive_addresses", "endpoint": {"path": "tapi-sandbox.trustology-test.com/graphql (GraphQL query named getAllBTCReceiveAddresses)", "data_selector": "data.user.subWallet.addresses.items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="trust_pipeline",
        destination="duckdb",
        dataset_name="trust_data",
    )
    load_info = pipeline.run(trust_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("trust_pipeline").dataset()
sessions_df = data.user_subwallets_transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM trust_data.user_subwallets_transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("trust_pipeline").dataset()
data.user_subwallets_transactions.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 Trust 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 the API key is missing or invalid the GraphQL endpoint will return an error (HTTP 401/403 or GraphQL error). Ensure x-api-key header is provided and the key is for the correct environment (sandbox vs production). Rotate and confirm keys with Trust/Trustology if needed.

Rate limits and performance

The docs note some queries are expensive (e.g., balances across many blockchains). Use pagination (limit/nextToken) for large result sets and avoid broad queries that request deep nested data to reduce latency.

Pagination quirks

GraphQL responses use a limit + nextToken pagination pattern. For lists the array of records is under .items and a nextToken field is provided to fetch subsequent pages. Some queries return addresses (addresses.items) where each address contains transactions (transactions.items) which may themselves be paginated; to paginate transactions for a specific address use the dedicated Get Transactions query and its nextToken.

REST GET endpoints status

A community GitHub issue proposes REST-style GET endpoints (e.g., GET /transactions/:wallet_address and GET /tokens/:wallet_address) but they are not in the official docs — do not rely on these until the provider publishes them.

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