SprintVerify Python API Docs | dltHub

SprintVerify is an AI-powered verification and validation API platform that provides real-time document, identity and financial checks. The REST API base URL is https://uat.paysprint.in/sprintverify-uat/api/v1 and all requests require a JWT token in the Authorization header.

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 SprintVerify data in under 10 minutes.

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What data can I load from SprintVerify?

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

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How do I authenticate with the SprintVerify API?

SprintVerify uses JSON Web Tokens (JWT). Generate a JWT signed with your partner secret (HS256 shown in docs) containing timestamp, partnerId and reqid; send the token in the Authorization header for every request.

1. Get your credentials

1. Sign up / contact SprintVerify to get a partnerId and partner secret. 2) Generate a JWT (HS256) using the partner secret with payload {"timestamp": <epoch_seconds>, "partnerId":"<your_partner_id>", "reqid":"<unique_reqid>"}. 3) Use the resulting token in the Authorization header for API calls. 4) Ensure your client IP/server is whitelisted in SprintVerify dashboard (docs note Indian IP/server location restriction).

2. Add them to .dlt/secrets.toml

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[sources.sprint_verify_source]
token = "your_jwt_token_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 SprintVerify 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 sprint_verify_pipeline.py

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

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Pipeline sprint_verify_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sprint_verify_data
The duckdb destination used duckdb:/sprint_verify.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 sprint_verify_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 verification_fetch_personal_profile and status_check from the SprintVerify 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 sprint_verify_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://uat.paysprint.in/sprintverify-uat/api/v1",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "verification_fetch_personal_profile", "endpoint": {"path": "verification/fetch_personal_profile"}},
            {"name": "status_check", "endpoint": {"path": "verification/check"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sprint_verify_pipeline",
        destination="duckdb",
        dataset_name="sprint_verify_data",
    )
    load_info = pipeline.run(sprint_verify_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("sprint_verify_pipeline").dataset()
sessions_df = data.verification_fetch_personal_profile.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sprint_verify_data.verification_fetch_personal_profile LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sprint_verify_pipeline").dataset()
data.verification_fetch_personal_profile.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 SprintVerify 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 you receive 401/403 responses, verify that your JWT is correctly generated (HS256), includes timestamp (valid <=5 minutes), partnerId and a unique reqid, and is sent in Authorization header. Also ensure your partner secret and partnerId are correct and that the calling IP is whitelisted.

Rate limits and IP restrictions

Docs state IP address restrictions and that servers must be located in India; if requests are blocked, confirm IP whitelist with SprintVerify support. If you encounter 429 responses, throttle requests and contact support to request higher limits.

Request/response errors

For malformed requests, the API returns standard HTTP error codes (400 for bad request, 404 for not found). For signature or token issues you will see 401/403. For service errors expect 5xx responses — retry with exponential backoff and contact SprintVerify 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