#Main Features

#Server-Based Architecture

• Centralized Server: Llama Stack Server hosts inference, agents, safety, tool runtime, vector I/O, and files
• Remote or Inline Providers: Support for remote APIs (e.g., OpenAI-compatible) and inline providers (e.g., meta-reference, sqlite-vec, localfs)
• Kubernetes Deployment: Deploy via Llama Stack Operator using LlamaStackDistribution custom resources

#AI Agents with Tools

• Agent Creation: Create agents with model, instructions, and a list of tools
• Client-Side Tools: Define tools with the @client_tool decorator; the client executes tool calls and returns results to the server
• Session Management: Create sessions and run multi-turn conversations with streaming responses
• Streaming: Support for streaming agent responses for real-time display

#Configuration and Extensibility

• Stack Configuration: YAML-based configuration for APIs, providers, persistence (e.g., kv_default, sql_default), and models
• Environment Fallbacks: Use ${env.VAR:~default} in config for flexible deployment
• Multiple Distributions: Starter, postgres-demo, meta-reference-gpu and other distribution options

#Integration

• Python Client: llama-stack-client for Python 3.12+ with full agent and model APIs
• Vector Store APIs: Create and query vector stores from the client, including PGVector-backed stores with provider_id="pgvector" and Milvus-backed stores with provider_id="milvus-remote"
• REST-Friendly: Server exposes APIs for inference, agents, and tool runtime; can be wrapped in FastAPI or other web frameworks for production use