Theta Token Python API Docs | dltHub

Theta Token is a blockchain platform providing Theta and TFuel tokens and a JSON‑RPC API (Theta RPC) to interact with nodes, query chain state, and submit transactions. The REST API base URL is http://localhost:16888/rpc and No authentication is required for the local JSON‑RPC endpoints; wallet operations require local access and unlocking the 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 Theta Token data in under 10 minutes.

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What data can I load from Theta Token?

Here are some of the endpoints you can load from Theta Token:

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How do I authenticate with the Theta Token API?

The Theta node RPC is a JSON‑RPC HTTP API with no Bearer or API‑key headers. Sensitive wallet calls require the thetacli daemon and an unlocked local key.

1. Get your credentials

1. Start the thetacli daemon (e.g., thetacli daemon start --port=16889).
2. Create or import an account with thetacli.
3. Unlock the account using the thetacli.UnlockKey JSON‑RPC method, providing the address and password.
4. Keep the daemon accessible only on a trusted host/network.

2. Add them to .dlt/secrets.toml

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[sources.theta_token_source]

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 Theta Token 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 theta_token_pipeline.py

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

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Pipeline theta_token_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset theta_token_data
The duckdb destination used duckdb:/theta_token.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 theta_token_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 theta_getblock and theta_gettransaction from the Theta Token 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 theta_token_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:16888/rpc",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "theta_getblock", "endpoint": {"path": "rpc", "data_selector": "result.transactions"}},
            {"name": "theta_gettransaction", "endpoint": {"path": "rpc", "data_selector": "result.transaction"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="theta_token_pipeline",
        destination="duckdb",
        dataset_name="theta_token_data",
    )
    load_info = pipeline.run(theta_token_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("theta_token_pipeline").dataset()
sessions_df = data.theta_getblock.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM theta_token_data.theta_getblock LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("theta_token_pipeline").dataset()
data.theta_getblock.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 Theta Token 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 / access denied

The node RPC has no HTTP auth by default; if RPC is bound to non‑local interfaces, restrict access. Wallet operations require unlocking accounts via thetacli.UnlockKey; an incorrect password or locked key returns result.unlocked=false or a JSON‑RPC error.

JSON‑RPC errors and malformed request

Requests must be valid JSON‑RPC 2.0 POST bodies (Content-Type: application/json). Malformed JSON or missing fields returns standard JSON‑RPC error objects (e.g., -32700 Parse error, -32600 Invalid Request).

Pagination / large responses

Block and transaction responses are returned fully in result; transaction lists (e.g., pending tx_hashes or block transactions) are plain arrays. No cursor‑based pagination is documented.

Rate limits and network exposure

No public rate limits are documented for a locally run node. For public/explorer APIs, follow provider‑specific limits and avoid exposing the node directly without a proxy that can enforce rate limiting and access control.

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