Prompts

The Netra SDK exposes a prompts API that lets you:

Fetch managed prompts – Retrieve prompt versions by name and label from the Netra backend
Use labels for versioning – Target specific prompt versions such as "production", "staging", or any custom label
Integrate seamlessly – Drop fetched prompts directly into your LLM calls with zero boilerplate

This page shows how to use the get_prompt utility in Netra to fetch and use your managed prompts.

Getting Started

The prompts client is initialized automatically when you call Netra.init. No additional flags are required as long as a valid OTLP endpoint and API key are configured, prompts are ready to use.

from netra import Netra

Netra.init(
	app_name="sample-app",
)

get_prompt

Fetch a prompt version by name and label from the Netra backend.

from netra import Netra

Netra.init(
	app_name="sample-app",
)

# Fetch the production version of a prompt
prompt = Netra.prompts.get_prompt(name="welcome-message")

# Fetch a specific label
staging_prompt = Netra.prompts.get_prompt(
	name="welcome-message",
	label="staging",
)

Parameters

Parameter	Type	Description
`name`	`str`	Name of the prompt to fetch. This is required — passing an empty string returns `None`.
`label`	`str`	Label of the prompt version to retrieve (e.g. `"production"`, `"staging"`). Defaults to `"production"`.

Return Value

Scenario	Return
Prompt found	`dict` — the prompt version data returned by the Netra backend
`name` is empty or `None`	`None`
Network / server error	`{}` (empty dict)
Client not initialized	`{}` (empty dict)

Configuration

Required

get_prompt relies on the OTLP endpoint and API key configured via Netra.init or environment variables.

Setting	Environment Variable	Description
OTLP endpoint	`NETRA_OTLP_ENDPOINT`	Base URL for the Netra backend. The prompts client strips a trailing `/telemetry` suffix automatically.
API key	`NETRA_API_KEY`	API key sent as the `x-api-key` header on every request.

Error Handling

get_prompt is designed to never throw. Errors are logged and a safe fallback value is returned so your application keeps running.

from netra import Netra

Netra.init(app_name="sample-app")

prompt = Netra.prompts.get_prompt(name="onboarding-flow")

if not prompt:
    # Handle missing prompt — use a hardcoded fallback
    prompt_text = "Welcome! How can I help you today?"
else:
    prompt_text = prompt.get("messages", "")

Common error scenarios and their log messages:

Scenario	Log Level	Message
Empty `name` argument	`ERROR`	`netra.prompts: name is required to fetch a prompt`
Client not initialized	`ERROR`	`netra.prompts: Prompts client is not initialized; cannot fetch prompt version for '<name>'`
Network / HTTP error	`ERROR`	`netra.prompts: Failed to fetch prompt version for '<name>' (label=<label>): <error>`

Complete Example

from netra import Netra

# 1. Initialize Netra
Netra.init(
    app_name="chatbot-service",
    environment="production",
)

# 2. Fetch a managed prompt
system_prompt = Netra.prompts.get_prompt(name="chatbot-system")

if not system_prompt:
    raise RuntimeError("Required prompt 'chatbot-system' not found in Netra")

# 3. Use the prompt in your LLM call
from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=system_prompt.get("messages", ""),
)

print(response.choices[0].message.content)

# 4. Shut down cleanly
Netra.shutdown()

Next Steps

Traces Overview - Understand trace collection
Evaluation - Evaluate AI outputs
Simulation Overview - Learn about simulation testing

Python SDK

TypeScript SDK

Getting Started

get_prompt

Parameters

Return Value

Configuration

Required

Error Handling

Complete Example

Next Steps

​Getting Started

​get_prompt

​Parameters

​Return Value

​Configuration

​Required

​Error Handling

​Complete Example

​Next Steps

Getting Started

get_prompt

Parameters

Return Value

Configuration

Required

Error Handling

Complete Example

Next Steps