New Relic Python API Docs | dltHub

New Relic is a cloud observability platform that collects, stores, and queries application and infrastructure telemetry (metrics, events, logs, traces) via REST and ingest APIs. The REST API base URL is https://api.newrelic.com/v2 and All REST v2 requests require an API key (user/API key) in the Api-Key header; Telemetry ingest SDKs require a license key..

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 New Relic data in under 10 minutes.

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What data can I load from New Relic?

Here are some of the endpoints you can load from New Relic:

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How do I authenticate with the New Relic API?

REST v2: include Api-Key: <YOUR_USER_API_KEY> in request headers. Telemetry SDK / ingest APIs: provide your New Relic license key (often via NEW_RELIC_LICENSE_KEY environment variable) to the Telemetry SDK or include the appropriate insert/license key header.

1. Get your credentials

1. Log in to New Relic One (one.newrelic.com or one.eu.newrelic.com for EU).
2. Open the API keys page (Launcher → API keys or direct: https://one.newrelic.com/launcher/api-keys-ui.api-keys-launcher).
3. Create or copy a User API key (for REST v2/NerdGraph) or a License/Insert key (for telemetry ingestion).
4. Use the key in requests: Api-Key header for REST v2, or set NEW_RELIC_LICENSE_KEY for Telemetry SDKs.

2. Add them to .dlt/secrets.toml

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[sources.new_relic_telemetry_sdk_source]
api_key = "your_user_api_key_here"
license_key = "your_license_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 New Relic 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 new_relic_telemetry_sdk_pipeline.py

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

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Pipeline new_relic_telemetry_sdk_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset new_relic_telemetry_sdk_data
The duckdb destination used duckdb:/new_relic_telemetry_sdk.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 new_relic_telemetry_sdk_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 applications and metrics_data from the New Relic 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 new_relic_telemetry_sdk_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.newrelic.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "applications", "endpoint": {"path": "v2/applications.json", "data_selector": "applications"}},
            {"name": "metrics_data", "endpoint": {"path": "v2/applications/{application_id}/metrics/data.json", "data_selector": "metric_data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="new_relic_telemetry_sdk_pipeline",
        destination="duckdb",
        dataset_name="new_relic_telemetry_sdk_data",
    )
    load_info = pipeline.run(new_relic_telemetry_sdk_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("new_relic_telemetry_sdk_pipeline").dataset()
sessions_df = data.applications.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM new_relic_telemetry_sdk_data.applications LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("new_relic_telemetry_sdk_pipeline").dataset()
data.applications.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 New Relic 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 Unauthorized or 403 Forbidden, verify you are using the correct key type: REST v2 endpoints expect a user Api-Key in the "Api-Key" header; telemetry ingestion requires a license/insert key (or via Telemetry SDK environment variable NEW_RELIC_LICENSE_KEY). Ensure keys are not expired or rotated and that you are using the correct region (api.eu.newrelic.com for EU).

Rate limits

Telemetry ingest APIs and REST endpoints are rate limited. 429 responses indicate rate limiting. For ingest APIs, batch your data or implement backoff and retry. Consult specific Metric/Trace/Log API docs for precise limits.

Pagination and response selectors

List endpoints in REST v2 return wrapped JSON objects; the array of records is usually under a top-level key named for the resource (for example, applications → "applications", accounts → "accounts", servers → "servers"). Use those keys as the dlt data selectors. Some metric endpoints return nested structures (for example, metric data under "metric_data").

Common errors: 400 Bad Request, 401 Unauthorized, 403 Forbidden, 404 Not Found, 422 Unprocessable Entity (for bad payloads), 429 Too Many Requests, 500/502/503 server errors.

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