Firehydrant Python API Docs | dltHub

FireHydrant is an incident management platform that offers a REST API for programmatic access to incident, postmortem, and operational data. The REST API base URL is https://api.firehydrant.io/v1 and All requests require a Bearer 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 Firehydrant data in under 10 minutes.

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What data can I load from Firehydrant?

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

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How do I authenticate with the Firehydrant API?

Authentication uses token‑based Bearer authentication; include the header Authorization: Bearer <your_api_key> with every request.

1. Get your credentials

1. Log in to the FireHydrant web console.
2. Navigate to Settings → API Keys (or directly visit https://app.firehydrant.io/settings/api_keys).
3. Click Create API Key, give it a descriptive name, and assign the required Owner permissions.
4. Save the newly generated key; copy the token value.
5. Use this token as the Bearer token in the Authorization header for all API calls.

2. Add them to .dlt/secrets.toml

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[sources.firehydrant_api_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 Firehydrant 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 firehydrant_api_pipeline.py

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

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Pipeline firehydrant_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset firehydrant_api_data
The duckdb destination used duckdb:/firehydrant_api.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 firehydrant_api_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 incidents and postmortems from the Firehydrant 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 firehydrant_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.firehydrant.io/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "incidents", "endpoint": {"path": "incidents", "data_selector": "data"}},
            {"name": "postmortems", "endpoint": {"path": "postmortems", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="firehydrant_api_pipeline",
        destination="duckdb",
        dataset_name="firehydrant_api_data",
    )
    load_info = pipeline.run(firehydrant_api_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("firehydrant_api_pipeline").dataset()
sessions_df = data.incidents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM firehydrant_api_data.incidents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("firehydrant_api_pipeline").dataset()
data.incidents.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 Firehydrant 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 a 401 Unauthorized response, verify that your Authorization header contains a valid Bearer token and that the token has not expired.

Rate limiting

FireHydrant enforces a limit of 50 requests per account every 10 seconds (300 per minute). When the limit is exceeded the API returns a 429 status code with a JSON body { "error": "rate limit exceeded" } and includes RateLimit-Limit and Retry-After headers. Respect the Retry-After value before retrying.

Pagination

Paginated responses contain two top‑level keys: data (the records array) and pagination (metadata such as next_page). Continue fetching subsequent pages by using the next_page URL provided in the pagination object.

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