Llama Stack — Meta AI Agent Development Platform

What is Llama Stack?

Llama Stack is Meta's official framework for building production AI applications with Llama models. It provides a unified API covering inference, tool calling, RAG, safety guardrails, memory, and evaluation — everything you need to go from prototype to production with Llama.

Answer-Ready: Llama Stack is Meta's official AI agent development platform providing inference, tool calling, RAG, safety guardrails, memory, and evaluation APIs for building production applications with Llama models.

Best for: Teams building AI agents with Llama models who need a complete, standardized stack. Works with: Llama 3.3 (8B, 70B), Llama 3.1 405B, any Llama variant. Setup time: Under 10 minutes.

Core Features

1. Unified API

# Inference
response = client.inference.chat_completion(
    model_id="meta-llama/Llama-3.3-70B-Instruct",
    messages=[...],
)

# Tool Calling
response = client.inference.chat_completion(
    model_id="meta-llama/Llama-3.3-70B-Instruct",
    messages=[...],
    tools=[weather_tool, search_tool],
)

# RAG
client.memory_banks.create(
    name="docs",
    config={"type": "vector", "embedding_model": "all-MiniLM-L6-v2"},
)
client.memory_banks.insert(bank_id="docs", documents=[...])

2. Agentic Workflows

from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.lib.agents.event_logger import EventLogger

agent = Agent(
    client,
    model="meta-llama/Llama-3.3-70B-Instruct",
    instructions="You are a helpful research assistant.",
    tools=["brave_search", "code_interpreter"],
    enable_session_persistence=True,
)

session = agent.create_session("research")
response = agent.create_turn(
    session_id=session.session_id,
    messages=[{"role": "user", "content": "Research quantum computing trends"}],
)
for event in EventLogger().log(response):
    print(event)

3. Safety & Guardrails

# Built-in Llama Guard for content safety
response = client.safety.run_shield(
    shield_id="llama-guard",
    messages=[{"role": "user", "content": "..."}],
)
if response.violation:
    print(f"Blocked: {response.violation.user_message}")

4. Multiple Providers

Run the same API against different backends:

5. Evaluation

# Evaluate model performance
results = client.eval.run_eval(
    model_id="meta-llama/Llama-3.3-70B-Instruct",
    benchmark="mmlu",
)

Architecture

Llama Stack Server (unified API)
  ├── Inference (chat, completion, embeddings)
  ├── Safety (Llama Guard, content shields)
  ├── Memory (vector stores, session persistence)
  ├── Agents (tool use, multi-step reasoning)
  └── Eval (benchmarks, custom evaluations)

FAQ

Q: Does it only work with Llama models? A: Primarily designed for Llama, but the API is model-agnostic. Community providers add support for other models.

Q: How does it compare to LangChain? A: Llama Stack is a unified server with standardized APIs. LangChain is a client-side framework. They can work together.

Q: Is it production ready? A: Yes, Meta uses it internally and supports production deployments through partner providers.