Llama Stack: The Developer Framework for the Future of AI

Llama Stack, developed by Meta, is a powerful open-source framework designed to solve one of the most pressing challenges in generative AI: building, evaluating, and deploying production-grade LLM applications with flexibility and scalability.

Unlike fragmented toolchains or opinionated platforms, Llama Stack provides a unified, modular architecture that integrates all critical components needed to create real-world AI systems — inference, agents, RAG, telemetry, safety, and evaluation — into a single, pluggable stack.

It’s built with developers in mind, providing a consistent API across local, cloud, and edge environments. Whether you’re prototyping with CPU-only setups or deploying GPU-accelerated agents at scale, Llama Stack ensures portability and reproducibility across any infrastructure.

At a glance, here’s what Llama Stack enables you to do:

• Standardize the AI infrastructure layer: Run the same application locally with Ollama or remotely via vLLM or OpenAI without changing your code.

• Swap components on the fly: Use the plugin system to replace any provider or retrievers, inference engines, safety modules, etc.

• Build autonomous agents: Incorporate memory, tool usage, and multi-step planning directly into your applications.

• Enable Retrieval-Augmented Generation (RAG): Integrate your own documents, vector stores, and embedding strategies to enhance model accuracy.

• Monitor and evaluate everything: Built-in observability and benchmarking tools ensure your system is safe, optimized, and reliable.

Architecture Overview

Llama Stack is composed of several independent yet interoperable layers that make it possible to build, monitor, and scale sophisticated LLM-based applications:

• Llama Server: The central API layer that coordinates all operations. It abstracts over various providers and components.

• API Providers (Plugins): Swappable modules that define how specific functionalities are implemented. For example, OpenAI or Ollama can power inference; FAISS or pgvector can power retrieval.

• Distributions: Preconfigured environments that bundle working sets of providers for specific use cases (local development, mobile apps, cloud-scale inference).

• Client SDKs: Language-specific bindings that allow easy integration with Python, JavaScript, Swift, and Kotlin applications.

The power of this architecture is in its uniform interface. No matter what providers or models you’re using, your interaction as a developer stays consistent.

Llama Stack Distributions

Llama Stack distributions are pre-packaged environments that bundle all the necessary components — like inference engines, agents, safety policies, and telemetry tools — into a single, ready-to-deploy setup. They’re designed to streamline the development and deployment of generative AI applications across local machines, cloud platforms, and on-prem infrastructure.

Key benefits:

• Pre-configured environments — Start building immediately without manual setup.

• Environment flexibility — Develop locally with Ollama, then deploy to cloud (e.g., Fireworks, Together) without changing code.

• Customizable — Build your distribution to match specific needs by choosing providers and configurations.

• Unified API layer — No matter the distribution, the interface remains consistent for seamless integration.

Key Use Cases in Practice

Chat Completions (Simple LLM Inference)

Use any supported model for conversational interfaces, either locally or through APIs like OpenAI.

Agent-Based Reasoning

• Agents can invoke external APIs

• They can manage sessions

• They dynamically adapt responses based on history and goal setting

Retrieval-Augmented Generation (RAG)

• Integrates with vector stores like FAISS or pgvector

• Allows combining live retrieval with LLMs for factuality and context

• Custom chunking strategies are supported

Tool Use

• Code interpreters

• Web search APIs (Tavily, Bing, Brave)

• HTTP endpoints

• Database queries

Evaluation and Telemetry

• Full observability: tracing, metrics, logs

• Benchmarking agents or pipelines

• Regression testing with snapshots

Practical Example: Running an Agent with Llama Stack + Docker

Let’s walk through a real-world example of setting up Llama Stack using Docker and running a basic LLM-powered agent capable of streaming responses.

Step 0: Minimal Setup

1. Install Python 3.12

pyenv install 3.12.9
pyenv global 3.12.9

2. Install Llama-Stack

pip install llama-stack

3. Install Ollama

Download from: https://ollama.com/download. Then run:

ollama run llama3.2:3b-instruct-fp16 --keepalive 1m

4. Build and Run Llama Stack Server

INFERENCE_MODEL=llama3.2:3b-instruct-fp16 \
llama stack build --template ollama --image-type venv --image-name venv --run

This:

• Creates a virtual environment

• Installs all required dependencies to use Llama-Stack

Step 1: Set Environment Variables

We start by setting up a few environment variables and creating a working directory for the Llama Stack server:

export INFERENCE_MODEL="llama3.2:3b"
export LLAMA_STACK_PORT=8321
mkdir -p ~/.llama

Step 2: Launch the Llama Stack Server (Docker or Podman)

You can now launch the server using your container runtime of choice. Below is the example using Podman, but Docker works just the same:

podman run --privileged -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  --network=host \
  llamastack/distribution-ollama \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env OLLAMA_URL=http://localhost:11434

Step 3: Run a Simple LLM Agent

Once the server is running, let’s use the Python client to create an agent and interact with it.

from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger
import uuid

# Connect to the Llama Stack server
client = LlamaStackClient(base_url="http://localhost:8321")

# Get the first available LLM model
models = client.models.list()
llm = next(m for m in models if m.model_type == "llm")
model_id = llm.identifier

# Create an agent instance
agent = Agent(client, model=model_id, instructions="You are a helpful assistant.")

# Create a session for the agent
s_id = agent.create_session(session_name=f"s{uuid.uuid4().hex}")

# Send a message and stream the agent's response
print("Streaming response...")
stream = agent.create_turn(
    messages=[{"role": "user", "content": "Give me a short technical overview of LLM"}],
    session_id=s_id,
    stream=True
)

# Print each part of the streamed response
for event in AgentEventLogger().log(stream):
    event.print()

If everything is set up correctly, the agent will stream a technical explanation of large language models (LLMs), chunk by chunk, directly to your terminal.

This example demonstrates how easy it is to run a local agent with streaming capabilities using Llama Stack’s infrastructure — completely modular, fully local, and production-ready.

I’m sure this example is quite simple and small, so I recommend diving into the detailed Llama Stack tutorial to explore more about this powerful framework. You can also check out this repository, which covers the basics of Llama Stack.

Conclusion

Llama Stack is not just another orchestration tool — it’s a composable, production-focused infrastructure layer for modern AI applications. Whether you’re building a chatbot, a RAG-powered research assistant, or a system of autonomous agents, Llama Stack gives you:

• Flexibility to integrate any provider

• Safety, telemetry, and observability out of the box

• A unified interface across local and cloud

• Full control over your AI system architecture

It’s one of the most promising frameworks for developers looking to take generative AI applications seriously, without reinventing infrastructure or locking into one vendor.