> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getnetra.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompts

> Fetch prompts from Netra into your codebase

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.

```python theme={null}
from netra import Netra

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

## **get\_prompt**

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

```python theme={null}
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.

```python theme={null}
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**

```python theme={null}
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](/sdk-reference/traces-overview) - Understand trace collection
* [Evaluation](/sdk-reference/evaluation/python) - Evaluate AI outputs
* [Simulation Overview](/Simulation/Simulation-overview) - Learn about simulation testing