OpenAI

OpenAI provides access to GPT models including GPT-5 and other cutting-edge language models. Braintrust integrates seamlessly with OpenAI through direct API access, wrapOpenAI wrapper functions for automatic tracing, and proxy support.

This guide covers manual instrumentation. For quicker setup, use auto-instrumentation.

For the GPT-5 family, whether the temperature parameter is configurable depends on the reasoning effort. GPT-5.1 and later accept temperature only when reasoning effort is set to none. At any higher reasoning effort, and for older GPT-5 models (gpt-5, gpt-5-mini, gpt-5-nano) and GPT-5 Pro, temperature isn’t configurable and is disabled in the Braintrust UI.

Setup

To use OpenAI models in the Braintrust playground, API, and gateway, connect OpenAI as a provider in your organization or project AI providers.

Go to Settings > AI providers.
Click Organization provider or Project provider, depending on whether you want the provider to be available across every project in the organization or just the current project.
Under Model providers, click OpenAI.
Choose your authentication method:
- API key: Visit OpenAI’s API platform, create a new API key, and paste it into the Secret field.
  API keys are stored as one-way cryptographic hashes, never in plaintext.
- Workload identity federation: Exchange a Braintrust-signed OIDC token for an OpenAI access token, instead of storing a long-lived OpenAI API key in Braintrust.
  Workload identity federation is available only for organization-level providers on Braintrust-hosted organizations with the Braintrust gateway enabled. Project-level providers and self-hosted deployments must use API key authentication.
If you chose Workload identity federation, use the setup values shown in Braintrust to configure OpenAI:
1. Create a workload identity provider in OpenAI. Enter a descriptive Name, use the OIDC issuer URL and Audience shown in Braintrust, and leave uploaded JWKS and attribute transformations disabled.
2. From the workload identity provider details page, create a mapping. Use sub as the Key and the subject pattern shown in Braintrust as the Value. Add a mapping attribute for each additional claim shown in Braintrust. Choose the OpenAI Project, Service account, and Permissions Braintrust should use.
3. Paste the OpenAI IDs back into Braintrust:
  - Identity provider ID: The workload identity provider ID configured for Braintrust.
  - Service account ID: The OpenAI service account ID Braintrust should use.
  - Subject suffix: A stable suffix for this OpenAI connection. It must match the final part of the subject pattern used in OpenAI.
For general OpenAI concepts and dashboard details, see OpenAI’s workload identity federation docs.
Click Save.

To call OpenAI directly from your application code rather than through the Braintrust gateway, set your OpenAI API key and Braintrust API key as environment variables:

.env

OPENAI_API_KEY=<your-openai-api-key>
BRAINTRUST_API_KEY=<your-braintrust-api-key>

# For organizations on the EU data plane, use https://api-eu.braintrust.dev
# For self-hosted deployments, use your data plane URL
# BRAINTRUST_API_URL=<your-braintrust-api-url>

Install the braintrust and openai packages.

# pnpm
pnpm add braintrust openai
# npm
npm install braintrust openai

pip install braintrust openai

go get github.com/braintrustdata/braintrust-sdk-go
go get github.com/braintrustdata/braintrust-sdk-go/trace/contrib/openai
go get github.com/openai/openai-go

gem install braintrust openai

# add to build.gradle dependencies{} block
implementation 'dev.braintrust:braintrust-sdk-java:<version-goes-here>'
implementation 'com.openai:openai-java:<version-goes-here>'

# add to .csproj file
dotnet add package Braintrust.Sdk
dotnet add package OpenAI

Trace with OpenAI

Trace your OpenAI LLM calls for observability and monitoring. Using the OpenAI Agents SDK? See the OpenAI Agents SDK framework docs.

Trace automatically

Braintrust provides automatic tracing for OpenAI API calls, handling streaming, metrics collection, and other details.

TypeScript & Python: Use wrapOpenAI / wrap_openai wrapper functions
Go: Use the tracing middleware with the OpenAI client
Ruby: Use Braintrust::Trace::OpenAI.wrap to wrap the OpenAI client
Java: Use the tracing interceptor with the OpenAI client
C#: Use BraintrustOpenAI.WrapOpenAI to wrap the OpenAI client

For more control over tracing, learn how to customize traces.

import OpenAI from "openai";

// Initialize the Braintrust logger
const logger = initLogger({
  projectName: "My Project", // Your project name
  apiKey: process.env.BRAINTRUST_API_KEY,
});

// Wrap the OpenAI client with wrapOpenAI
const client = wrapOpenAI(
  new OpenAI({
    apiKey: process.env.OPENAI_API_KEY,
  }),
);

// All API calls are automatically logged
const result = await client.chat.completions.create({
  model: "gpt-5-mini",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What is machine learning?" },
  ],
});

import os

from braintrust import init_logger, wrap_openai
from openai import OpenAI

logger = init_logger(project="My Project")
client = wrap_openai(OpenAI(api_key=os.environ["OPENAI_API_KEY"]))

# All API calls are automatically logged
result = client.chat.completions.create(
    model="gpt-5-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is machine learning?"},
    ],
)

package main

import (
	"context"
	"log"
	"os"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
	"go.opentelemetry.io/otel"
	"go.opentelemetry.io/otel/sdk/trace"

	"github.com/braintrustdata/braintrust-sdk-go"
	traceopenai "github.com/braintrustdata/braintrust-sdk-go/trace/contrib/openai"
)

func main() {
	// Set up OpenTelemetry TracerProvider
	tp := trace.NewTracerProvider()
	defer tp.Shutdown(context.Background())
	otel.SetTracerProvider(tp)

	// Initialize Braintrust client
	_, err := braintrust.New(tp,
		braintrust.WithProject("My Project"),
		braintrust.WithAPIKey(os.Getenv("BRAINTRUST_API_KEY")),
	)
	if err != nil {
		log.Fatal(err)
	}

	// Create OpenAI client with tracing middleware
	client := openai.NewClient(
		option.WithMiddleware(traceopenai.NewMiddleware()),
	)

	// All API calls are automatically logged
	result, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.SystemMessage("You are a helpful assistant."),
			openai.UserMessage("What is machine learning?"),
		},
		Model: openai.ChatModelGPT4o,
	})
	if err != nil {
		log.Fatal(err)
	}
	_ = result
}

require 'braintrust'
require 'openai'

# Initialize Braintrust
Braintrust.init(project: 'My Project')

# Create OpenAI client
client = OpenAI::Client.new(api_key: ENV.fetch('OPENAI_API_KEY', nil))

# Wrap the client with Braintrust tracing
Braintrust::Trace::OpenAI.wrap(client)

# All API calls are automatically logged
client.chat.completions.create(
  model: 'gpt-4o-mini',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is machine learning?' }
  ]
)

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import dev.braintrust.Braintrust;
import dev.braintrust.instrumentation.openai.BraintrustOpenAI;

class OpenAITracing {
    public static void main(String[] args) {
        var braintrust = Braintrust.get();
        var openTelemetry = braintrust.openTelemetryCreate();

        // Wrap the OpenAI client with Braintrust instrumentation
        OpenAIClient client = BraintrustOpenAI.wrapOpenAI(openTelemetry, OpenAIOkHttpClient.fromEnv());

        // All API calls are automatically logged
        var request = ChatCompletionCreateParams.builder()
            .model(ChatModel.GPT_4O_MINI)
            .addSystemMessage("You are a helpful assistant.")
            .addUserMessage("What is machine learning?")
            .temperature(0.0)
            .build();

        var result = client.chat().completions().create(request);
    }
}

using System;
using System.Threading.Tasks;
using Braintrust.Sdk;
using Braintrust.Sdk.OpenAI;
using OpenAI;
using OpenAI.Chat;

class OpenAITracing
{
    static async Task Main(string[] args)
    {
        var braintrust = Braintrust.Sdk.Braintrust.Get();
        var activitySource = braintrust.GetActivitySource();

        var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
        if (string.IsNullOrEmpty(apiKey))
        {
            Console.WriteLine("Error: OPENAI_API_KEY environment variable is not set.");
            return;
        }

        // Wrap the OpenAI client with Braintrust instrumentation
        var client = BraintrustOpenAI.WrapOpenAI(
            activitySource,
            apiKey
        );

        // All API calls are automatically logged
        var chatClient = client.GetChatClient("gpt-4o-mini");
        var messages = new ChatMessage[]
        {
            new SystemChatMessage("You are a helpful assistant."),
            new UserChatMessage("What is machine learning?")
        };

        var result = await chatClient.CompleteChatAsync(messages);
    }
}

Stream OpenAI responses

wrap_openai/wrapOpenAI can automatically log metrics like prompt_tokens, completion_tokens, and tokens for streaming LLM calls if the LLM API returns them. Set include_usage to true in the stream_options parameter to receive these metrics from OpenAI.

model: "gpt-5-mini",
  messages: [{ role: "user", content: "Count to 10" }],
  stream: true,
  stream_options: {
    include_usage: true, // Required for token metrics
  },
});

for await (const chunk of result) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

model="gpt-5-mini",
    messages=[{"role": "user", "content": "Count to 10"}],
    stream=True,
    stream_options={"include_usage": True},  # Required for token metrics
)

for chunk in result:
    print(chunk.choices[0].delta.content or "", end="")

Evaluate with OpenAI

Evaluations help you distill the non-deterministic outputs of OpenAI models into an effective feedback loop that enables you to ship more reliable, higher quality products. Braintrust Eval is a simple function composed of a dataset of user inputs, a task, and a set of scorers. To learn more about evaluations, see the Experiments guide.

Basic OpenAI eval setup

Evaluate the outputs of OpenAI models with Braintrust.

import { Eval } from "braintrust";
import { OpenAI } from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

Eval("OpenAI Evaluation", {
  // An array of user inputs and expected outputs
  data: () => [
    { input: "What is 2+2?", expected: "4" },
    { input: "What is the capital of France?", expected: "Paris" },
  ],
  task: async (input) => {
    // Your OpenAI LLM call
    const response = await client.chat.completions.create({
      model: "gpt-5-mini",
      messages: [{ role: "user", content: input }],
    });
    return response.choices[0].message.content;
  },
  scores: [
    {
      name: "accuracy",
      // A simple scorer that returns 1 if the output matches the expected output, 0 otherwise
      scorer: (args) => (args.output === args.expected ? 1 : 0),
    },
  ],
});

import os

from braintrust import Eval
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])


def task(input):
    response = client.chat.completions.create(
        model="gpt-5-mini",
        messages=[{"role": "user", "content": input}],
    )
    return response.choices[0].message.content


def accuracy_scorer(output, expected, **kwargs):
    return 1 if output == expected else 0


Eval(
    "OpenAI Evaluation",
    data=[
        {"input": "What is 2+2?", "expected": "4"},
        {"input": "What is the capital of France?", "expected": "Paris"},
    ],
    task=task,
    scores=[accuracy_scorer],
)

package main

import (
	"context"
	"log"
	"os"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
	"go.opentelemetry.io/otel"
	"go.opentelemetry.io/otel/sdk/trace"

	"github.com/braintrustdata/braintrust-sdk-go"
	"github.com/braintrustdata/braintrust-sdk-go/eval"
	traceopenai "github.com/braintrustdata/braintrust-sdk-go/trace/contrib/openai"
)

func main() {
	ctx := context.Background()

	// Set up OpenTelemetry TracerProvider
	tp := trace.NewTracerProvider()
	defer tp.Shutdown(ctx)
	otel.SetTracerProvider(tp)

	// Initialize Braintrust
	bt, err := braintrust.New(tp,
		braintrust.WithAPIKey(os.Getenv("BRAINTRUST_API_KEY")),
	)
	if err != nil {
		log.Fatal(err)
	}

	// Create OpenAI client with tracing
	client := openai.NewClient(
		option.WithMiddleware(traceopenai.NewMiddleware()),
	)

	// Create evaluator
	evaluator := braintrust.NewEvaluator[string, string](bt)

	// Run evaluation
	_, err = evaluator.Run(ctx, eval.Opts[string, string]{
		Experiment: "OpenAI Evaluation",
		// Dataset of user inputs and expected outputs
		Dataset: eval.NewDataset([]eval.Case[string, string]{
			{Input: "What is 2+2?", Expected: "4"},
			{Input: "What is the capital of France?", Expected: "Paris"},
		}),
		// Task function with OpenAI LLM call
		Task: eval.T(func(ctx context.Context, input string) (string, error) {
			response, err := client.Chat.Completions.New(ctx, openai.ChatCompletionNewParams{
				Model: openai.ChatModelGPT4oMini,
				Messages: []openai.ChatCompletionMessageParamUnion{
					openai.UserMessage(input),
				},
			})
			if err != nil {
				return "", err
			}
			return response.Choices[0].Message.Content, nil
		}),
		// Simple scorer that returns 1 if output matches expected, 0 otherwise
		Scorers: []eval.Scorer[string, string]{
			eval.NewScorer("accuracy", func(ctx context.Context, r eval.TaskResult[string, string]) (eval.Scores, error) {
				score := 0.0
				if r.Output == r.Expected {
					score = 1.0
				}
				return eval.S(score), nil
			}),
		},
	})
	if err != nil {
		log.Fatal(err)
	}
}

require 'braintrust'
require 'openai'

Braintrust.init

client = OpenAI::Client.new(api_key: ENV.fetch('OPENAI_API_KEY', nil))

Braintrust::Eval.run(
  project: 'OpenAI Evaluation',
  experiment: 'basic-eval',
  # An array of user inputs and expected outputs
  cases: [
    { input: 'What is 2+2?', expected: '4' },
    { input: 'What is the capital of France?', expected: 'Paris' }
  ],
  # Your OpenAI LLM call
  task: lambda do |input|
    response = client.chat.completions.create(
      model: 'gpt-4o-mini',
      messages: [{ role: 'user', content: input }]
    )
    response.choices[0].message.content
  end,
  # A simple scorer that returns 1 if the output matches the expected output, 0 otherwise
  scorers: [
    Braintrust::Eval.scorer('accuracy') do |_input, expected, output|
      output == expected ? 1.0 : 0.0
    end
  ]
)

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import dev.braintrust.Braintrust;
import dev.braintrust.eval.DatasetCase;
import dev.braintrust.eval.Scorer;
import dev.braintrust.instrumentation.openai.BraintrustOpenAI;
import java.util.function.Function;

class OpenAIEvaluation {
    public static void main(String[] args) {
        var braintrust = Braintrust.get();
        var openTelemetry = braintrust.openTelemetryCreate();
        OpenAIClient client = BraintrustOpenAI.wrapOpenAI(openTelemetry, OpenAIOkHttpClient.fromEnv());

        Function<String, String> taskFunction = (String input) -> {
            var request = ChatCompletionCreateParams.builder()
                .model(ChatModel.GPT_4O_MINI)
                .addUserMessage(input)
                .temperature(0.0)
                .build();
            var response = client.chat().completions().create(request);
            return response.choices().get(0).message().content().orElse("");
        };

        var eval = braintrust.<String, String>evalBuilder()
            .name("OpenAI Evaluation")
            .cases(
                DatasetCase.of("What is 2+2?", "4"),
                DatasetCase.of("What is the capital of France?", "Paris"))
            .taskFunction(taskFunction)
            .scorers(
                Scorer.of("contains_answer", (evalCase, output) ->
                    output.contains("4") || output.contains("Paris") ? 1.0 : 0.0))
            .build();

        var result = eval.run();
        System.out.println(result.createReportString());
    }
}

using System;
using System.Threading.Tasks;
using Braintrust.Sdk;
using Braintrust.Sdk.Eval;
using Braintrust.Sdk.OpenAI;
using OpenAI;
using OpenAI.Chat;

class OpenAIEvaluation
{
    static async Task Main(string[] args)
    {
        var braintrust = Braintrust.Sdk.Braintrust.Get();
        var activitySource = braintrust.GetActivitySource();

        var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
        if (string.IsNullOrEmpty(apiKey))
        {
            Console.WriteLine("Error: OPENAI_API_KEY environment variable is not set.");
            return;
        }

        var client = BraintrustOpenAI.WrapOpenAI(
            activitySource,
            apiKey
        );

        // Define the task function that uses OpenAI
        string TaskFunction(string input)
        {
            var chatClient = client.GetChatClient("gpt-4o-mini");
            var messages = new ChatMessage[]
            {
                new UserChatMessage(input)
            };
            var options = new ChatCompletionOptions
            {
                Temperature = 0.0f
            };
            var response = chatClient.CompleteChat(messages, options);
            return response.Value.Content[0].Text;
        }

        // Create and run the evaluation
        var eval = await braintrust
            .EvalBuilder<string, string>()
            .Name("OpenAI Evaluation")
            .Cases(
                new DatasetCase<string, string>("What is 2+2?", "4"),
                new DatasetCase<string, string>("What is the capital of France?", "Paris")
            )
            .TaskFunction(TaskFunction)
            .Scorers(
                new FunctionScorer<string, string>("accuracy", (expected, actual) =>
                    actual.Contains(expected) ? 1.0 : 0.0)
            )
            .BuildAsync();

        var result = await eval.RunAsync();
        Console.WriteLine(result.CreateReportString());
    }
}

Learn more about eval data and scorers.

Use OpenAI as an LLM judge

You can use OpenAI models to score the outputs of other AI systems. This example uses the LLMClassifierFromSpec scorer to score the relevance of the outputs of an AI system. Install the autoevals package to use the LLMClassifierFromSpec scorer.

# pnpm
pnpm add autoevals
# npm
npm install autoevals

pip install autoevals

Create a scorer that uses the LLMClassifierFromSpec scorer to score the relevance of the outputs of an AI system. You can then include relevanceScorer as a scorer in your Eval function (see above).

import { LLMClassifierFromSpec } from "autoevals";

const relevanceScorer = LLMClassifierFromSpec("Relevance", {
  choice_scores: { Relevant: 1, Irrelevant: 0 },
  model: "gpt-5-mini",
  use_cot: true,
});

from autoevals import LLMClassifierFromSpec

relevance_scorer = LLMClassifierFromSpec(
    "Relevance",
    choice_scores={"Relevant": 1, "Irrelevant": 0},
    model="gpt-5-mini",
    use_cot=True,
)

Additional features

Structured outputs

OpenAI’s structured outputs are supported with the wrapper functions.

import { z } from "zod";

// Define a Zod schema for the response
const ResponseSchema = z.object({
  name: z.string(),
  age: z.number(),
});

const completion = await client.beta.chat.completions.parse({
  model: "gpt-5-mini",
  messages: [
    { role: "system", content: "Extract the person's name and age." },
    { role: "user", content: "My name is John and I'm 30 years old." },
  ],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "person",
      // The Zod schema for the response
      schema: ResponseSchema,
    },
  },
});

from pydantic import BaseModel


class Person(BaseModel):
    name: str
    age: int


completion = client.beta.chat.completions.parse(
    model="gpt-5-mini",
    messages=[
        {"role": "system", "content": "Extract the person's name and age."},
        {"role": "user", "content": "My name is John and I'm 30 years old."},
    ],
    response_format=Person,
)

Function calling and tools

Braintrust supports OpenAI function calling for building AI agents with tools.

const tools = [
  {
    type: "function" as const,
    function: {
      name: "get_weather",
      description: "Get current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string" },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat.completions.create({
  model: "gpt-5-mini",
  messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
  tools,
});

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                },
                "required": ["location"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="gpt-5-mini",
    messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
    tools=tools,
)

Streaming audio transcriptions

Braintrust traces streaming audio transcription calls for sync and async OpenAI clients. Each span captures the audio file as an attachment and the final transcript as the span output.

with open("audio.m4a", "rb") as f:
    stream = client.audio.transcriptions.create(
        model="gpt-4o-transcribe",
        file=f,
        stream=True,
    )
    for event in stream:
        if event.type == "transcript.text.delta":
            print(event.delta, end="", flush=True)

Multimodal content, attachments, errors, and masking sensitive data

To learn more about these topics, check out the customize traces guide.

Use OpenAI with Braintrust gateway

You can also access OpenAI models through the Braintrust gateway, which provides a unified interface for multiple providers. Use any supported provider’s SDK to call OpenAI models.

const client = new OpenAI({
  baseURL: "https://gateway.braintrust.dev/v1",

  apiKey: process.env.BRAINTRUST_API_KEY,
});

const response = await client.chat.completions.create({
  model: "gpt-5-mini",
  messages: [{ role: "user", content: "What is a proxy?" }],
  seed: 1, // A seed activates the proxy's cache
});

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.braintrust.dev/v1",
    api_key=os.environ["BRAINTRUST_API_KEY"],
)

response = client.chat.completions.create(
    model="gpt-5-mini",
    messages=[{"role": "user", "content": "What is a proxy?"}],
    seed=1,  # A seed activates the proxy's cache
)

package main

import (
	"context"
	"log"
	"os"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
)

func main() {
	ctx := context.Background()

	client := openai.NewClient(
		option.WithBaseURL("https://gateway.braintrust.dev/v1"),
		option.WithAPIKey(os.Getenv("BRAINTRUST_API_KEY")),
	)

	response, err := client.Chat.Completions.New(ctx, openai.ChatCompletionNewParams{
		Model: openai.ChatModelGPT4oMini,
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("What is a proxy?"),
		},
		Seed: openai.Int(1), // A seed activates the proxy's cache
	})
	if err != nil {
		log.Fatal(err)
	}
	_ = response
}

require 'openai'

client = OpenAI::Client.new(
  base_url: 'https://gateway.braintrust.dev/v1',
  api_key: ENV.fetch('BRAINTRUST_API_KEY', nil)
)

client.chat.completions.create(
  model: 'gpt-4o-mini',
  messages: [{ role: 'user', content: 'What is a proxy?' }],
  seed: 1 # A seed activates the proxy's cache
)

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

class OpenAIProxy {
    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.builder()
            .apiKey(System.getenv("BRAINTRUST_API_KEY"))
            .baseUrl("https://gateway.braintrust.dev/v1")
            .build();

        var response = client.chat().completions().create(
            ChatCompletionCreateParams.builder()
                .model(ChatModel.GPT_4O_MINI)
                .addUserMessage("What is a proxy?")
                .seed(1L) // A seed activates the proxy's cache
                .build());
    }
}

using System;
using System.Threading.Tasks;
using OpenAI;
using OpenAI.Chat;

class OpenAIProxy
{
    static async Task Main(string[] args)
    {
        var apiKey = Environment.GetEnvironmentVariable("BRAINTRUST_API_KEY");
        if (string.IsNullOrEmpty(apiKey))
        {
            Console.WriteLine("Error: BRAINTRUST_API_KEY environment variable is not set.");
            return;
        }

        var client = new OpenAIClient(
            new System.ClientModel.ApiKeyCredential(apiKey),
            new OpenAIClientOptions
            {
                Endpoint = new Uri("https://gateway.braintrust.dev/v1")
            }
        );

        var chatClient = client.GetChatClient("gpt-4o-mini");
        var messages = new ChatMessage[]
        {
            new UserChatMessage("What is a proxy?")
        };

        var response = await chatClient.CompleteChatAsync(messages);
    }
}

Generate embeddings

The gateway also supports the OpenAI-compatible /embeddings endpoint for generating embeddings. See Generate embeddings for an example.

AI providers

SDKs

Developer tools

Setup

Trace with OpenAI

Trace automatically

Stream OpenAI responses

Evaluate with OpenAI

Basic OpenAI eval setup

Use OpenAI as an LLM judge

Additional features

Structured outputs

Function calling and tools

Streaming audio transcriptions

Multimodal content, attachments, errors, and masking sensitive data

Use OpenAI with Braintrust gateway

Generate embeddings

Cookbooks

​Setup

​Trace with OpenAI

​Trace automatically

​Stream OpenAI responses

​Evaluate with OpenAI

​Basic OpenAI eval setup

​Use OpenAI as an LLM judge

​Additional features

​Structured outputs

​Function calling and tools

​Streaming audio transcriptions

​Multimodal content, attachments, errors, and masking sensitive data

​Use OpenAI with Braintrust gateway

​Generate embeddings

​Cookbooks

Setup

Trace with OpenAI

Trace automatically

Stream OpenAI responses

Evaluate with OpenAI

Basic OpenAI eval setup

Use OpenAI as an LLM judge

Additional features

Structured outputs

Function calling and tools

Streaming audio transcriptions

Multimodal content, attachments, errors, and masking sensitive data

Use OpenAI with Braintrust gateway

Generate embeddings

Cookbooks