Grants.gov Python API Docs | dltHub

Grants.gov is the official U.S. government platform that provides a REST API to search and retrieve federal grant opportunity metadata and related resources. The REST API base URL is https://api.grants.gov (production) | https://api.staging.grants.gov (staging) | https://api.simpler.grants.gov (Simpler gateway) and Public search endpoints are unrestricted; other APIs require an API key supplied in the X-API-Key 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 Grants.gov data in under 10 minutes.

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What data can I load from Grants.gov?

Here are some of the endpoints you can load from Grants.gov:

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How do I authenticate with the Grants.gov API?

Grants.gov notes that search2 and fetchOpportunity do not require authentication; other system‑to‑system APIs require an API key supplied in the X-API-Key header, obtained from the Grants.gov Help Desk or the Simpler.Grants.gov developer dashboard.

1. Get your credentials

1. For Grants.gov production API: open a ticket with the Grants.gov Help Desk requesting an API key for system‑to‑system access. 2) For the Simpler.Grants.gov gateway: sign in to https://simpler.grants.gov, navigate to Developer → Manage API Keys, click “Create API Key”, and copy the generated key.

2. Add them to .dlt/secrets.toml

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[sources.grantsgov_source]
api_key = "YOUR_API_KEY_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 Grants.gov 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 grantsgov_pipeline.py

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

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Pipeline grantsgov_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset grantsgov_data
The duckdb destination used duckdb:/grantsgov.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 grantsgov_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 search_opportunities and fetch_opportunity from the Grants.gov 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 grantsgov_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.grants.gov (production) | https://api.staging.grants.gov (staging) | https://api.simpler.grants.gov (Simpler gateway)",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "search_opportunities", "endpoint": {"path": "v1/api/search2", "data_selector": "data.oppHits"}},
            {"name": "fetch_opportunity", "endpoint": {"path": "v1/api/fetchOpportunity", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="grantsgov_pipeline",
        destination="duckdb",
        dataset_name="grantsgov_data",
    )
    load_info = pipeline.run(grantsgov_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("grantsgov_pipeline").dataset()
sessions_df = data.search_opportunities.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM grantsgov_data.search_opportunities LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("grantsgov_pipeline").dataset()
data.search_opportunities.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 Grants.gov 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 or an "Invalid API Key" error when using endpoints that require credentials, verify you are sending the key in the X-API-Key header, that the key is active, and that you requested a key from the correct Grants.gov environment (production vs staging). For Grants.gov native APIs open a Help Desk ticket to request/verify keys; for Simpler gateway regenerate keys in the developer dashboard.

Rate limiting and 429 responses

The documentation indicates rate limiting is enforced; on 429 implement exponential backoff and respect recommended fair‑use policies. Cache results and paginate to reduce calls.

Pagination and selectors

search2 returns paging fields (hitCount, startRecord) and the records array under data.oppHits; set page rows/startRecord in the POST body. Some endpoints return nested objects (e.g., data.searchParams, data.oppHits). Verify the response shape when calling the specific environment (production vs simpler gateway) because key names for arrays may vary (oppHits, items, opportunities).

Request format errors

Ensure Content-Type: application/json and valid JSON bodies for POST search calls; parameter formats are enforced (page_size ranges, date formats YYYY-MM-DD, enum case‑sensitivity).

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