Using the LLM client¶

The LLM Client library in YARF provides Robot Framework keywords for interacting with Large Language Model servers that support the OpenAI Chat Completions API format. This enables you to integrate AI capabilities into your test automation workflows.

In this guide, we will cover:

Setting up an LLM server
Basic text prompting
Using images in prompts
Configuring the client

Setting up an LLM server¶

The LLM Client is designed to work with any server that implements the OpenAI Chat Completions API.

Option 1: Inference Snap¶

Inference snaps are Canonical’s way of packaging AI models as snaps that are tuned for efficient local inference. Each snap automatically detects your machine’s available hardware (CPU/GPU/NPU) and selects a compatible runtime and model optimizations.

They are an easy way to run an OpenAI-compatible LLM endpoint locally, which makes them a great fit for YARF’s LLM Client. For this guide, you’ll install the Qwen VL snap, and configure it to be used with YARF.

For more details on available snaps and management commands, see the official Inference Snaps docs: https://documentation.ubuntu.com/inference-snaps/.

Steps¶

Install an inference snap. For this example, we will use the Qwen VL snap, which provides a vision-capable model:
Install the Qwen VL inference snap¶
```
sudo snap install qwen-vl --channel "2.5/beta"
```
Inference snaps start their API service automatically. Check the active API URL:
Get the OpenAI-compatible endpoint¶
```
qwen-vl status
# engine: cpu-avx512
# endpoints:
#     openai: http://localhost:8326/v1
```

Get the exact model name from the snap:

List model IDs exposed by the inference snap¶

curl -s http://localhost:8326/v1/models | jq -r '.data[].id'
# /snap/qwen-vl/components/248/model-qwen2-5-vl-7b-instruct-q4-k-m/Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf

Configure the LLM Client in your Robot Framework tests to use the snap’s endpoint and model:

Configure LLM Client for an inference snap endpoint¶

*** Test Cases ***
Use Inference Snap
    Configure Llm Client
    ...    server_url=http://localhost:8326/v1
    ...    endpoint=/chat/completions
    ...    model=/snap/qwen-vl/components/248/model-qwen2-5-vl-7b-instruct-q4-k-m/Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf

Note

The inference snap only exposes a single model, so you can also omit the model parameter and it will use that one by default.

Option 2: Ollama¶

Ollama is a local LLM server that allows you to run various language models on your machine. It provides an OpenAI-compatible API, making it easy to integrate with the LLM Client.

Steps¶

Install Ollama:

Install Ollama on Linux¶

curl -fsSL https://ollama.com/install.sh | sh

Run a vision-capable model (example: qwen3-vl:2b-instruct):
Download and run a vision-capable model¶
```
ollama run qwen3-vl:2b-instruct
```
By default, Ollama uses the same default values as the LLM Client ( http://localhost:11434/v1 and /chat/completions), so you only need to configure the model:
Configure LLM Client for an Ollama model¶
```
*** Test Cases ***
Use Ollama Model
    Configure Llm Client
    ...    model=qwen3-vl:2b-instruct
```

Basic text prompting¶

To use the LLM Client in your Robot Framework tests, first import the library:

Import LLM Client library¶

*** Settings ***
Library    yarf.rf_libraries.libraries.llm_client.LlmClient

Simple text prompt¶

Basic text prompting example¶

*** Test Cases ***
Ask LLM a Question
    ${response}=    Prompt Llm    What is the capital of France?
    Log             ${response}
    Should Contain  ${response}   Paris

Using system prompts¶

System prompts help guide the LLM’s behavior and responses:

Using system prompts to guide behavior¶

*** Test Cases ***
Structured Response
    ${response}=    Prompt Llm    
    ...             prompt=Analyze this test result
    ...             system_prompt=You are a test automation expert. Provide concise, actionable feedback.
    Log             ${response}

Using images in prompts¶

The LLM Client supports multi-modal prompts that include both text and images. This is particularly useful for visual testing scenarios.

Image from file path¶

Analyze a screenshot with the LLM¶

*** Test Cases ***
Analyze Interface Screenshot
    # Take a screenshot first (example using YARF's screenshot capabilities)
    ${image} =              Grab Screenshot
    
    # Prompt the LLM to analyze the image
    ${analysis}=    Prompt Llm    
    ...             prompt=Describe what you see in this user interface
    ...             image=${image}
    
    Log             ${analysis}
    # The screenshot shows an image of a simple calculator
    Should Contain  ${analysis}    calculator

Image validation workflows¶

Use LLM for visual validation¶

*** Test Cases ***
Validate Installation Screen
    ${image} =          Grab Screenshot
    ${validation}=      Prompt Llm
    ...                 prompt=Does this screen show the ubuntu installation on the "choose your language" step? Answer with YES or NO.
    ...                 image=${image}
    ...                 system_prompt=You are a UI testing assistant. Be very precise in your answers.
    
    Should Start With   ${validation}    YES

Configuring the client¶

The LLM Client can be configured to work with different servers, models, and parameters.

Changing the model¶

Switch to a different model¶

*** Test Cases ***
Use Different Model
    Configure Llm Client    model=phi4-mini:3.8b
    ${response}=            Prompt Llm    Hello, what model are you?
    Log                     ${response}

Using a different server¶

Connect to a remote LLM server¶

*** Test Cases ***
Remote Server Setup
    Configure Llm Client    
    ...    server_url=http://192.168.1.100:11434/v1
    ...    model=llama3.2-vision:11b
    
    ${response}=    Prompt Llm    Test connection
    Log             ${response}

Adjusting token limits¶

Configure maximum tokens for responses¶

*** Test Cases ***
Short Response
    Configure Llm Client    max_tokens=100
    ${response}=            Prompt Llm    Write a brief summary of automated testing
    Log                     ${response}

Complete configuration example¶

Full client configuration¶

*** Test Cases ***
Custom Configuration
    Configure Llm Client    
    ...    model=qwen3-vl:7b-instruct
    ...    server_url=http://llm-server:11434/v1
    ...    endpoint=/chat/completions
    ...    max_tokens=2048