OpenAI API Compatibility — llama-stack documentation (original) (raw)

Server path

Llama Stack exposes an OpenAI-compatible API endpoint at /v1/openai/v1. So, for a Llama Stack server running locally on port 8321, the full url to the OpenAI-compatible API endpoint is http://localhost:8321/v1/openai/v1.

Clients

You should be able to use any client that speaks OpenAI APIs with Llama Stack. We regularly test with the official Llama Stack clients as well as OpenAI’s official Python client.

Llama Stack Client

When using the Llama Stack client, set the base_url to the root of your Llama Stack server. It will automatically route OpenAI-compatible requests to the right server endpoint for you.

from llama_stack_client import LlamaStackClient

client = LlamaStackClient(base_url="http://localhost:8321")

OpenAI Client

When using an OpenAI client, set the base_url to the /v1/openai/v1 path on your Llama Stack server.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none")

Regardless of the client you choose, the following code examples should all work the same.

APIs implemented

Models

Many of the APIs require you to pass in a model parameter. To see the list of models available in your Llama Stack server:

models = client.models.list()

Responses

Note

The Responses API implementation is still in active development. While it is quite usable, there are still unimplemented parts of the API. We’d love feedback on any use-cases you try that do not work to help prioritize the pieces left to implement. Please open issues in the meta-llama/llama-stack GitHub repository with details of anything that does not work.

Simple inference

Request:

response = client.responses.create( model="meta-llama/Llama-3.2-3B-Instruct", input="Write a haiku about coding." )

print(response.output_text)

Example output:

Pixels dancing slow Syntax whispers secrets sweet Code's gentle silence

Structured Output

Request:

response = client.responses.create( model="meta-llama/Llama-3.2-3B-Instruct", input=[ { "role": "system", "content": "Extract the participants from the event information.", }, { "role": "user", "content": "Alice and Bob are going to a science fair on Friday.", }, ], text={ "format": { "type": "json_schema", "name": "participants", "schema": { "type": "object", "properties": { "participants": {"type": "array", "items": {"type": "string"}} }, "required": ["participants"], }, } }, ) print(response.output_text)

Example output:

{ "participants": ["Alice", "Bob"] }

Chat Completions

Simple inference

Request:

chat_completion = client.chat.completions.create( model="meta-llama/Llama-3.2-3B-Instruct", messages=[{"role": "user", "content": "Write a haiku about coding."}], )

print(chat_completion.choices[0].message.content)

Example output:

Lines of code unfold Logic flows like a river Code's gentle beauty

Structured Output

Request:

chat_completion = client.chat.completions.create( model="meta-llama/Llama-3.2-3B-Instruct", messages=[ { "role": "system", "content": "Extract the participants from the event information.", }, { "role": "user", "content": "Alice and Bob are going to a science fair on Friday.", }, ], response_format={ "type": "json_schema", "json_schema": { "name": "participants", "schema": { "type": "object", "properties": { "participants": {"type": "array", "items": {"type": "string"}} }, "required": ["participants"], }, }, }, )

print(chat_completion.choices[0].message.content)

Example output:

{ "participants": ["Alice", "Bob"] }

Completions

Simple inference

Request:

completion = client.completions.create( model="meta-llama/Llama-3.2-3B-Instruct", prompt="Write a haiku about coding." )

print(completion.choices[0].text)

Example output:

Lines of code unfurl Logic whispers in the dark Art in hidden form