Llama Stack — Meta Official LLM App Framework

What it is

Llama Stack is the official Meta framework for building applications with Llama models. It provides standardized APIs for inference, safety guardrails, retrieval-augmented generation, agent orchestration, evaluations, and tool use. The framework is designed to work across different deployment environments.

AI engineers who build with Llama models and want a cohesive, officially supported development experience will find Llama Stack preferable to assembling individual components.

How it saves time or tokens

Llama Stack unifies what would otherwise require separate libraries for inference, safety, RAG, and evaluation. The standardized API surface means you write integration code once and swap providers (local, cloud, or custom) without changing application logic.

How to use

1. Install Llama Stack via pip.
2. Configure a provider for inference (local, Fireworks, Together, or custom).
3. Use the client SDK to call inference, safety, and agent APIs.

from llama_stack_client import LlamaStackClient

client = LlamaStackClient(base_url='http://localhost:5000')

response = client.inference.chat_completion(
    model_id='Llama3.3-70B-Instruct',
    messages=[{'role': 'user', 'content': 'Explain RAG in two sentences.'}],
)
print(response.completion_message.content)

Example

Running safety checks on model output:

# Check output with Llama Guard
safety_response = client.safety.run_shield(
    shield_id='llama_guard',
    messages=[{'role': 'assistant', 'content': response.completion_message.content}],
)
print(f'Safe: {safety_response.violation is None}')

Related on TokRepo

• Local LLM tools — Compare local inference options for Llama models
• AI tools for agents — Agent frameworks and orchestration

Common pitfalls

• Llama Stack is optimized for Meta's Llama models. Using non-Llama models may require custom provider implementations.
• Local inference with large Llama models requires significant GPU memory. Plan hardware accordingly.
• The framework is evolving rapidly; API stability may vary between releases.