React Router Python API Docs | dltHub

React Router is a client-side routing library for React apps that provides declarative routing, loaders, actions, progressive enhancement of forms, and server-aware data loading. The REST API base URL is https://api.reactrouter.com/ and no authentication—React Router is a client library, not a hosted REST API.

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 React Router data in under 10 minutes.

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What data can I load from React Router?

Here are some of the endpoints you can load from React Router:

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How do I authenticate with the React Router API?

React Router is distributed as a library; there are no API keys or HTTP authentication headers for the library docs at api.reactrouter.com. Use npm/yarn to install the package and import APIs in your code.

1. Get your credentials

Not applicable—React Router has no credential issuance. Install via npm: npm install react-router or npm install react-router-dom and import the library in your project.

2. Add them to .dlt/secrets.toml

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[sources.react_router_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 React Router 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 react_router_pipeline.py

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

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Pipeline react_router_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset react_router_data
The duckdb destination used duckdb:/react_router.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 react_router_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 form and useNavigation from the React Router 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 react_router_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.reactrouter.com/",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="react_router_pipeline",
        destination="duckdb",
        dataset_name="react_router_data",
    )
    load_info = pipeline.run(react_router_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("react_router_pipeline").dataset()
sessions_df = data..df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM react_router_data. LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("react_router_pipeline").dataset()
data..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 React Router 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 & Authorization

React Router does not perform auth; ensure your app's loaders/actions include proper auth checks and propagate auth tokens to backend HTTP requests. Typical symptoms: redirects to login, 401 responses from backend.

404s and Not Found Responses

Loaders can throw Response(..., {status:404}) to render route-level error UI. When fetching backend resources in loaders, map 404 responses to thrown Responses to surface error routes.

Form Submissions and Method Mismatch (405)

Submitting a Form to a route without a corresponding action handler will result in a 405. Ensure route modules export matching action functions for POST/PUT/DELETE mutations.

Pagination and URLSearchParams

Use GET forms or Link navigation to encode pagination and filters in URLSearchParams; loaders should read request.url searchParams to retrieve them.

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