---
title: Llama Stack Playground
description: Interactive interface to explore and experiment with Llama Stack capabilities
sidebar_label: Playground
sidebar_position: 10
---

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Llama Stack Playground

:::note[Experimental Feature]
The Llama Stack Playground is currently experimental and subject to change. We welcome feedback and contributions to help improve it.
:::

The Llama Stack Playground is a simple interface that aims to:
- **Showcase capabilities and concepts** of Llama Stack in an interactive environment
- **Demo end-to-end application code** to help users get started building their own applications
- **Provide a UI** to help users inspect and understand Llama Stack API providers and resources

## Key Features

### Interactive Playground Pages

The playground provides interactive pages for users to explore Llama Stack API capabilities:

#### Chatbot Interface

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<Tabs>
<TabItem value="chat" label="Chat">

**Simple Chat Interface**
- Chat directly with Llama models through an intuitive interface
- Uses the `/chat/completions` streaming API under the hood
- Real-time message streaming for responsive interactions
- Perfect for testing model capabilities and prompt engineering

</TabItem>
<TabItem value="rag" label="RAG Chat">

**Document-Aware Conversations**
- Upload documents to create memory banks
- Chat with a RAG-enabled agent that can query your documents
- Uses Llama Stack's `/agents` API to create and manage RAG sessions
- Ideal for exploring knowledge-enhanced AI applications

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#### Evaluation Interface

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<Tabs>
<TabItem value="scoring" label="Scoring Evaluations">

**Custom Dataset Evaluation**
- Upload your own evaluation datasets
- Run evaluations using available scoring functions
- Uses Llama Stack's `/scoring` API for flexible evaluation workflows
- Great for testing application performance on custom metrics

</TabItem>
<TabItem value="benchmarks" label="Benchmark Evaluations">

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**Pre-registered Evaluation Tasks**
- Evaluate models or agents on pre-defined tasks
- Uses Llama Stack's `/eval` API for comprehensive evaluation
- Combines datasets and scoring functions for standardized testing

**Setup Requirements:**
Register evaluation datasets and benchmarks first:

```bash
# Register evaluation dataset
llama-stack-client datasets register \
  --dataset-id "mmlu" \
  --provider-id "huggingface" \
  --url "https://huggingface.co/datasets/llamastack/evals" \
  --metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
  --schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string"}, "chat_completion_input": {"type": "string"}}'

# Register benchmark task
llama-stack-client benchmarks register \
  --eval-task-id meta-reference-mmlu \
  --provider-id meta-reference \
  --dataset-id mmlu \
  --scoring-functions basic::regex_parser_multiple_choice_answer
```

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#### Inspection Interface

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<Tabs>
<TabItem value="providers" label="API Providers">

**Provider Management**
- Inspect available Llama Stack API providers
- View provider configurations and capabilities
- Uses the `/providers` API for real-time provider information
- Essential for understanding your deployment's capabilities

</TabItem>
<TabItem value="resources" label="API Resources">

**Resource Exploration**
- Inspect Llama Stack API resources including:
  - **Models**: Available language models
  - **Datasets**: Registered evaluation datasets
  - **Memory Banks**: Vector databases and knowledge stores
  - **Benchmarks**: Evaluation tasks and scoring functions
  - **Shields**: Safety and content moderation tools
- Uses `/<resources>/list` APIs for comprehensive resource visibility
- For detailed information about resources, see [Core Concepts](/docs/concepts)

</TabItem>
</Tabs>

## Getting Started

### Quick Start Guide

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<TabItem value="setup" label="Setup">

**1. Start the Llama Stack API Server**

```bash
# Build and run a distribution (example: together)
llama stack build --distro together --image-type venv
llama stack run together
```

**2. Start the Streamlit UI**

```bash
# Launch the playground interface
uv run --with ".[ui]" streamlit run llama_stack.core/ui/app.py
```

</TabItem>
<TabItem value="usage" label="Usage Tips">

**Making the Most of the Playground:**

- **Start with Chat**: Test basic model interactions and prompt engineering
- **Explore RAG**: Upload sample documents to see knowledge-enhanced responses
- **Try Evaluations**: Use the scoring interface to understand evaluation metrics
- **Inspect Resources**: Check what providers and resources are available
- **Experiment with Settings**: Adjust parameters to see how they affect results

</TabItem>
</Tabs>

### Available Distributions

The playground works with any Llama Stack distribution. Popular options include:

<Tabs>
<TabItem value="together" label="Together AI">

```bash
llama stack build --distro together --image-type venv
llama stack run together
```

**Features:**
- Cloud-hosted models
- Fast inference
- Multiple model options

</TabItem>
<TabItem value="ollama" label="Ollama (Local)">

```bash
llama stack build --distro ollama --image-type venv
llama stack run ollama
```

**Features:**
- Local model execution
- Privacy-focused
- No internet required

</TabItem>
<TabItem value="meta-reference" label="Meta Reference">

```bash
llama stack build --distro meta-reference --image-type venv
llama stack run meta-reference
```

**Features:**
- Reference implementation
- All API features available
- Best for development

</TabItem>
</Tabs>

## Use Cases & Examples

### Educational Use Cases
- **Learning Llama Stack**: Hands-on exploration of API capabilities
- **Prompt Engineering**: Interactive testing of different prompting strategies
- **RAG Experimentation**: Understanding how document retrieval affects responses
- **Evaluation Understanding**: See how different metrics evaluate model performance

### Development Use Cases
- **Prototype Testing**: Quick validation of application concepts
- **API Exploration**: Understanding available endpoints and parameters
- **Integration Planning**: Seeing how different components work together
- **Demo Creation**: Showcasing Llama Stack capabilities to stakeholders

### Research Use Cases
- **Model Comparison**: Side-by-side testing of different models
- **Evaluation Design**: Understanding how scoring functions work
- **Safety Testing**: Exploring shield effectiveness with different inputs
- **Performance Analysis**: Measuring model behavior across different scenarios

## Best Practices

### 🚀 **Getting Started**
- Begin with simple chat interactions to understand basic functionality
- Gradually explore more advanced features like RAG and evaluations
- Use the inspection tools to understand your deployment's capabilities

### 🔧 **Development Workflow**
- Use the playground to prototype before writing application code
- Test different parameter settings interactively
- Validate evaluation approaches before implementing them programmatically

### 📊 **Evaluation & Testing**
- Start with simple scoring functions before trying complex evaluations
- Use the playground to understand evaluation results before automation
- Test safety features with various input types

### 🎯 **Production Preparation**
- Use playground insights to inform your production API usage
- Test edge cases and error conditions interactively
- Validate resource configurations before deployment

## Related Resources

- **[Getting Started Guide](../getting_started/quickstart)** - Complete setup and introduction
- **[Core Concepts](/docs/concepts)** - Understanding Llama Stack fundamentals
- **[Agents](./agent)** - Building intelligent agents
- **[RAG (Retrieval Augmented Generation)](./rag)** - Knowledge-enhanced applications
- **[Evaluations](./evals)** - Comprehensive evaluation framework
- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation
