e-conomic Python API Docs | dltHub

e-conomic is an online accounting platform that provides a REST API for accessing accounting data such as customers, invoices, and orders. The REST API base URL is https://restapi.e-conomic.com and All requests require X‑AppSecretToken, X‑AgreementGrantToken headers and Content‑Type: application/json..

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 e-conomic data in under 10 minutes.

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What data can I load from e-conomic?

Here are some of the endpoints you can load from e-conomic:

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How do I authenticate with the e-conomic API?

Authentication uses two token headers: X‑AppSecretToken (your private app token) and X‑AgreementGrantToken (granted by an accounting user). All requests also include Content‑Type: application/json.

1. Get your credentials

1. Log in to your e‑conomic account.
2. Navigate to Apps under the developer portal.
3. Click Create new app and give it a name.
4. After creation, copy the AppSecretToken shown on the app details page and store it securely.
5. Open the Installation URL for the app; an accounting user must approve the app.
6. After approval, the URL will contain a query parameter grant_token – copy this value as the AgreementGrantToken.
7. Use both tokens as the values for the X‑AppSecretToken and X‑AgreementGrantToken headers in every API call.

2. Add them to .dlt/secrets.toml

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[sources.economic_migration_source]
app_secret_token = "your_app_secret_token"
agreement_grant_token = "your_agreement_grant_token"

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 e-conomic 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 economic_migration_pipeline.py

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

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Pipeline economic_migration_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset economic_migration_data
The duckdb destination used duckdb:/economic_migration.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 economic_migration_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 customers and invoices from the e-conomic 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 economic_migration_source(app_secret_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://restapi.e-conomic.com",
            "auth": {
                "type": "api_key",
                "api_key": app_secret_token,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "customers"}},
            {"name": "invoices", "endpoint": {"path": "invoices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="economic_migration_pipeline",
        destination="duckdb",
        dataset_name="economic_migration_data",
    )
    load_info = pipeline.run(economic_migration_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("economic_migration_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM economic_migration_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("economic_migration_pipeline").dataset()
data.customers.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 e-conomic 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 errors

• 401 Unauthorized – occurs when X‑AppSecretToken or X‑AgreementGrantToken is missing or invalid. Verify both tokens are correct and not expired.
• 403 Forbidden – indicates the accounting user has not granted access to the app. Re‑run the installation URL and obtain a fresh AgreementGrantToken.

Pagination limits

• The API returns a default page size of 20 items. Use the pageSize query parameter (max 1000) to increase the number of records per request.
• Use skip to offset pages when iterating over large collections.

Common HTTP status codes

• 200 OK – successful GET.
• 400 Bad Request – malformed request; see error object for details.
• 404 Not Found – resource does not exist.
• 429 Too Many Requests – rate limit exceeded; back‑off and retry after a short delay.
• 500/501 – server errors; contact e‑conomic support if persistent.

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