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This guide shows how to set up a minimal deployment to use the TensorZero Gateway with the OpenAI API.

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Simple Setup

You can use the short-hand openai::model_name to use an OpenAI model with TensorZero, unless you need advanced features like fallbacks or custom credentials.

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Chat Completions API

You can use OpenAI models in your TensorZero variants by setting the model field to openai::model_name. For example: 
[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "openai::gpt-4o-mini-2024-07-18"
Additionally, you can set model_name in the inference request to use a specific OpenAI model, without having to configure a function and variant in TensorZero. 
curl -X POST http://localhost:3000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "openai::gpt-4o-mini-2024-07-18",
    "input": {
      "messages": [
        {
          "role": "user",
          "content": "What is the capital of Japan?"
        }
      ]
    }
  }'

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Responses API

For models that use the OpenAI Responses API (like gpt-5), use the openai::responses::model_name shorthand: 
[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "openai::responses::gpt-5-codex"
You can also use model_name in inference requests: 
curl -X POST http://localhost:3000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "openai::responses::gpt-5-codex",
    "input": {
      "messages": [
        {
          "role": "user",
          "content": "What is the capital of Japan?"
        }
      ]
    }
  }'
See the OpenAI Responses API guide for more details on using this API.

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Advanced Setup

For more complex scenarios (e.g. fallbacks, custom credentials), you can configure your own model and OpenAI provider in TensorZero. For this minimal setup, you’ll need just two files in your project directory: 
- config/
  - tensorzero.toml
- docker-compose.yml
You can also find the complete code for this example on GitHub.
For production deployments, see our Deployment Guide.

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Configuration

Create a minimal configuration file that defines a model and a simple chat function:
config/tensorzero.toml
[models.gpt_4o_mini_2024_07_18]
routing = ["openai"]

[models.gpt_4o_mini_2024_07_18.providers.openai]
type = "openai"
model_name = "gpt-4o-mini-2024-07-18"

[functions.my_function_name]
type = "chat"

[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "gpt_4o_mini_2024_07_18"
See the list of models available on OpenAI. See the Configuration Reference for optional fields (e.g. overwriting api_base).

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Credentials

You must set the OPENAI_API_KEY environment variable before running the gateway. You can customize the credential location by setting the api_key_location to env::YOUR_ENVIRONMENT_VARIABLE or dynamic::ARGUMENT_NAME. See the Credential Management guide and Configuration Reference for more information. Additionally, see the OpenAI-Compatible guide for more information on how to use other OpenAI-Compatible providers.

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Deployment (Docker Compose)

Create a minimal Docker Compose configuration:
docker-compose.yml
# This is a simplified example for learning purposes. Do not use this in production.
# For production-ready deployments, see: https://www.tensorzero.com/docs/deployment/tensorzero-gateway

services:
  gateway:
    image: tensorzero/gateway
    volumes:
      - ./config:/app/config:ro
    command: --config-file /app/config/tensorzero.toml
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY:?Environment variable OPENAI_API_KEY must be set.}
    ports:
      - "3000:3000"
    extra_hosts:
      - "host.docker.internal:host-gateway"
You can start the gateway with docker compose up.

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Inference

Make an inference request to the gateway: 
curl -X POST http://localhost:3000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "function_name": "my_function_name",
    "input": {
      "messages": [
        {
          "role": "user",
          "content": "What is the capital of Japan?"
        }
      ]
    }
  }'

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Other Features

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Generate embeddings

The OpenAI model provider supports generating embeddings. You can find a complete code example on GitHub.