OpenAI

Integrate OpenAI models through the Adaline Proxy for automatic telemetry and observability.

OpenAI

Integrate OpenAI models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes.

Supported Models

Chat Models

Model	Description
gpt-5.3-codex	Latest Codex model
gpt-5.2-pro	Latest flagship model
gpt-5.2	High capability next-gen model
gpt-5.2-codex	GPT-5.2 Codex
chatgpt-5.2	ChatGPT 5.2
gpt-5.2-chat-latest	GPT-5.2 chat latest snapshot
gpt-5.1	Next-gen model
gpt-5	GPT-5 series base
gpt-5-mini	Compact GPT-5
gpt-5-nano	Ultra-compact GPT-5
gpt-5-chat-latest	GPT-5 chat latest snapshot
gpt-4.1	Improved GPT-4 with enhanced reasoning
gpt-4.1-mini	Compact GPT-4.1
gpt-4.1-nano	Ultra-compact GPT-4.1
gpt-4o	Multimodal with vision support
gpt-4o-2024-08-06	GPT-4o August 2024 snapshot
gpt-4o-2024-05-13	GPT-4o May 2024 snapshot
chatgpt-4o-latest	ChatGPT-4o latest snapshot
gpt-4o-mini	Fast and cost-effective multimodal
gpt-4o-mini-2024-07-18	GPT-4o Mini July 2024 snapshot
gpt-4-turbo	High capability with 128k context
gpt-4-turbo-2024-04-09	GPT-4 Turbo April 2024 snapshot
gpt-4-turbo-preview	GPT-4 Turbo preview
gpt-4	Original GPT-4 model
gpt-4-0613	GPT-4 June 2023 snapshot
gpt-4-0125-preview	GPT-4 January 2024 preview
gpt-4-1106-preview	GPT-4 November 2023 preview
gpt-3.5-turbo	Fast and cost-effective
gpt-3.5-turbo-0125	GPT-3.5 Turbo January 2024 snapshot
gpt-3.5-turbo-1106	GPT-3.5 Turbo November 2023 snapshot
o4-mini	Latest compact reasoning model
o4-mini-2025-04-16	O4 Mini April 2025 snapshot
o3	Advanced reasoning model
o3-2025-04-16	O3 April 2025 snapshot
o3-mini	Compact reasoning model
o3-mini-2025-01-31	O3 Mini January 2025 snapshot
o1	Reasoning model for complex tasks
o1-2024-12-17	O1 December 2024 snapshot

Embedding Models

Model	Description
text-embedding-3-large	Highest quality embeddings
text-embedding-3-small	Balanced quality and cost
text-embedding-ada-002	Legacy embedding model

Proxy Base URL

https://gateway.adaline.ai/v1/openai/

Prerequisites

1. An OpenAI API key
2. An Adaline API key, project ID, and prompt ID

Chat Completions

Complete Chat

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-openai-api-key",
    base_url="https://gateway.adaline.ai/v1/openai/"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id"
}

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is machine learning?"}
    ],
    extra_headers=headers
)

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

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-openai-api-key",
  baseURL: "https://gateway.adaline.ai/v1/openai/",
});

const response = await client.chat.completions.create(
  {
    model: "gpt-4o",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "What is machine learning?" },
    ],
  },
  {
    headers: {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id",
    },
  }
);

console.log(response.choices[0].message.content);

Stream Chat

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-openai-api-key",
    base_url="https://gateway.adaline.ai/v1/openai/"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id"
}

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    stream=True,
    stream_options={"include_usage": True},
    extra_headers=headers
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-openai-api-key",
  baseURL: "https://gateway.adaline.ai/v1/openai/",
});

const stream = await client.chat.completions.create(
  {
    model: "gpt-4o",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "Explain quantum computing in simple terms." },
    ],
    stream: true,
    stream_options: { include_usage: true },
  },
  {
    headers: {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id",
    },
  }
);

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}

Embeddings

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-openai-api-key",
    base_url="https://gateway.adaline.ai/v1/openai/"
)

headers = {
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id"
}

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The quick brown fox jumps over the lazy dog",
    extra_headers=headers
)

embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-openai-api-key",
  baseURL: "https://gateway.adaline.ai/v1/openai/",
});

const response = await client.embeddings.create(
  {
    model: "text-embedding-3-small",
    input: "The quick brown fox jumps over the lazy dog",
  },
  {
    headers: {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id",
    },
  }
);

console.log(`Embedding dimension: ${response.data[0].embedding.length}`);

Optional Headers

Customize tracing behavior with optional headers:

headers = {
    # Required
    "adaline-api-key": "your-adaline-api-key",
    "adaline-project-id": "your-project-id",
    "adaline-prompt-id": "your-prompt-id",
    # Optional
    "adaline-trace-name": "openai-chat-completion",
    "adaline-trace-session-id": "user-session-123",
    "adaline-span-name": "gpt-4o-call",
    "adaline-trace-tags": '["production", "chat"]',
    "adaline-trace-attributes": '{"create": {"userId": "user-123"}}',
}

See the full Headers Reference for all available options.

Next Steps

• Multi-Step Workflows — RAG pipelines, multi-step generation, and conversational agents
• Headers Reference — Complete header documentation

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