Load Algorand blockchain data to DuckDB

Algorand provides a set of REST APIs for its daemon (Algod), Indexer, and Key Management Daemon (KMD) services to interface with the Algorand blockchain and node components. Everything needed to build a working Algorand blockchain → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Algorand blockchain to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Algorand blockchain to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Algorand blockchain API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Algorand blockchain API at a glance

These values come from the Algorand blockchain API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Algorand blockchain API?

Algorand nodes secure their REST APIs using an API token generated at node startup (found in the algod.token file). Requests must include this token in the 'X-Algo-API-Token' header.

1. Get your credentials

Algorand REST API credentials are not managed via a traditional web dashboard. Instead, they are generated locally by the Algorand node daemons (algod and kmd) when they start. 1. Locate your node's data directory (default depends on your installation). 2. Find the algod.token file (for algod REST API) or kmd.token file (for kmd REST API) within the data folder. 3. The content of these files is your API token. 4. If tokens are missing or need regeneration, run the command goal node generatetoken in your terminal.

2. Add them to .dlt/secrets.toml

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[sources.algorand_blockchain_source]
algod_api_token = "your_token_from_algod_token_file"
kmd_api_token = "your_token_from_kmd_token_file"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Algorand blockchain data can I load into DuckDB?

These are the Algorand blockchain endpoints dlt can load into DuckDB:

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How do I load only new Algorand blockchain records?

Algorand blockchain exposes next-token on v2/transactions, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "transactions", "endpoint": {
    "path": "v2/transactions",
    "data_selector": "transactions",
    "incremental": {"cursor_path": "next-token", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Algorand blockchain pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading algod and kmd from the Algorand blockchain API into DuckDB:

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

@dlt.source
def algorand_blockchain_source(x_algo_api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL varies depending on the specific node and network (e.g., https://mainnet-api.4160.nodely.dev for public Algod nodes). Local node URLs are typically found in the algod.net file.",
            "auth": {"type": "api_key", "api_key": x_algo_api_token, "name": "X-Algo-API-Token (algod), X-KMD-API-Token (kmd), or X-Indexer-API-Token (indexer)", "location": "header"},
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "v2/transactions", "data_selector": "transactions"}},
            {"name": "accounts", "endpoint": {"path": "v2/accounts", "data_selector": "accounts"}}
        ],
    }
    yield from rest_api_resources(config)


def load_algorand_blockchain_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="algorand_blockchain_pipeline",
        destination="duckdb",
        dataset_name="algorand_blockchain_data",
    )
    load_info = pipeline.run(algorand_blockchain_source())
    print(load_info)


if __name__ == "__main__":
    load_algorand_blockchain_to_duckdb()

Run it with python algorand_blockchain_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Algorand blockchain data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("algorand_blockchain_pipeline").dataset()
df = data.transactions.df()
print(df.head())

SQL:

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SELECT * FROM algorand_blockchain_data.transactions LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Algorand blockchain to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Algorand blockchain loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Algorand blockchain data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack