Razorpay Python API Docs | dltHub

Razorpay is a payment gateway platform that provides APIs for processing payments, managing invoices, subscriptions, customers, orders, refunds and transfers. The REST API base URL is https://api.razorpay.com/v1 and All requests require HTTP Basic authentication with key_id and key_secret..

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 Razorpay data in under 10 minutes.

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What data can I load from Razorpay?

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

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How do I authenticate with the Razorpay API?

Razorpay uses HTTP Basic authentication where the API Key ID is the username and the API Key Secret is the password, sent in an Authorization header as a Base64‑encoded token.

1. Get your credentials

1. Log in to the Razorpay Dashboard.
2. Navigate to Settings → API Keys.
3. Click Generate Key.
4. Copy the generated Key ID and Key Secret and store them securely.

2. Add them to .dlt/secrets.toml

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[sources.razorpay_source]
api_key_id = "your_key_id"
api_key_secret = "your_key_secret"

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 Razorpay 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 razorpay_pipeline.py

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

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Pipeline razorpay_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset razorpay_data
The duckdb destination used duckdb:/razorpay.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 razorpay_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 payments and orders from the Razorpay 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 razorpay_source(api_key_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.razorpay.com/v1",
            "auth": {
                "type": "http_basic",
                "username": api_key_id,
            },
        },
        "resources": [
            {"name": "payments", "endpoint": {"path": "payments", "data_selector": "items"}},
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="razorpay_pipeline",
        destination="duckdb",
        dataset_name="razorpay_data",
    )
    load_info = pipeline.run(razorpay_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("razorpay_pipeline").dataset()
sessions_df = data.payments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM razorpay_data.payments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("razorpay_pipeline").dataset()
data.payments.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 Razorpay 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 the Basic Auth credentials are missing, malformed, or invalid. Verify that the Authorization header contains the correct Base64‑encoded key_id:key_secret.

Rate Limiting

• 429 Too Many Requests – Razorpay imposes a limit on the number of API calls per minute. If you receive this response, back off for a few seconds before retrying. Implement exponential back‑off in your pipeline.

Pagination Quirks

• Razorpay uses skip and count query parameters for pagination. skip specifies the number of records to omit, while count defines the page size. Ensure your requests include both parameters to retrieve the full dataset without missing records.

General API Errors

• 400 Bad Request – Indicates missing or invalid request parameters.
• 404 Not Found – The requested resource does not exist. Check the endpoint path and identifiers.
• 500 Internal Server Error – Unexpected server-side error; retry after a short delay.

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