OpenAI

Learn to integrate OpenAI with Portkey, enabling seamless completions, prompt management, and advanced functionalities like streaming, function calling and fine-tuning.

Portkey has native integrations with OpenAI SDKs for Node.js, Python, and its REST APIs. For OpenAI integration using other frameworks, explore our partnerships, including Langchain, LlamaIndex, among others.

Provider Slug. openai

Using the Portkey Gateway

To integrate the Portkey gateway with OpenAI,

• Set the baseURL to the Portkey Gateway URL
• Include Portkey-specific headers such as provider, apiKey, ‘virtualKey’ and others.

Here’s how to apply it to a chat completion request:

NodeJS

Install the Portkey SDK with npm

npm install portkey-ai

Chat Completions

import Portkey from 'portkey-ai';

const client = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  virtualKey: 'PROVIDER_VIRTUAL_KEY'
});

async function main() {
  const response = await client.chat.completions.create({
    messages: [{ role: "user", content: "Bob the builder.." }],
    model: "gpt-4o",
  });

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

main();

This request will be automatically logged by Portkey. You can view this in your logs dashboard. Portkey logs the tokens utilized, execution time, and cost for each request. Additionally, you can delve into the details to review the precise request and response data.

Portkey supports OpenAI’s new “developer” role in chat completions. With o1 models and newer, the developer role replaces the previous system role.

Using the Responses API

OpenAI has released a new Responses API that combines the best of both Chat Completions and Assistants APIs. Portkey fully supports this new API, enabling you to use it with both the Portkey SDK and OpenAI SDK.

Python

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    virtual_key="OPENAI_VIRTUAL_KEY"
)

response = portkey.responses.create(
    model="gpt-4.1",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

The Responses API provides a more flexible foundation for building agentic applications with built-in tools that execute automatically.

Remote MCP support on Resposes API

Portkey supports Remote MCP support by OpenAI on it’s Responses API. Learn More

Track End-User IDs

Portkey allows you to track user IDs passed with the user parameter in OpenAI requests, enabling you to monitor user-level costs, requests, and more.

NodeJS

const chatCompletion = await portkey.chat.completions.create({
  messages: [{ role: "user", content: "Say this is a test" }],
  model: "gpt-4o",
  user: "user_12345",
});

When you include the user parameter in your requests, Portkey logs will display the associated user ID, as shown in the image below:

In addition to the user parameter, Portkey allows you to send arbitrary custom metadata with your requests. This powerful feature enables you to associate additional context or information with each request, which can be useful for analysis, debugging, or other custom use cases.

Metadata

• The same integration approach applies to APIs for completions, embeddings, vision, moderation, transcription, translation, speech and files.
• If you are looking for a way to add your Org ID & Project ID to the requests, head over to this section.

Using the Prompts API

Portkey also supports creating and managing prompt templates in the prompt library. This enables the collaborative development of prompts directly through the user interface.

1. Create a prompt template with variables and set the hyperparameters.

2. Use this prompt in your codebase using the Portkey SDK.

Node

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
})

// Make the prompt creation call with the variables

const promptCompletion = await portkey.prompts.completions.create({
    promptID: "Your Prompt ID",
    variables: {
       // The variables specified in the prompt
    }
})

// We can also override the hyperparameters

const promptCompletion = await portkey.prompts.completions.create({
    promptID: "Your Prompt ID",
    variables: {
       // The variables specified in the prompt
    },
    max_tokens: 250,
    presence_penalty: 0.2
})

Observe how this streamlines your code readability and simplifies prompt updates via the UI without altering the codebase.

Advanced Use Cases

Realtime API

Portkey supports OpenAI’s Realtime API with a seamless integration. This allows you to use Portkey’s logging, cost tracking, and guardrail features while using the Realtime API.

Realtime API

Streaming Responses

Portkey supports streaming responses using Server Sent Events (SSE).

OpenAI NodeJS

import OpenAI from 'openai';

import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'
const openai = new OpenAI({
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "openai",
    apiKey: "PORTKEY_API_KEY" // defaults to process.env["PORTKEY_API_KEY"]
  })
});

async function main() {
  const stream = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  });

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

main();

Streaming with the Responses API

You can also stream responses from the Responses API:

Python

response = portkey.responses.create(
    model="gpt-4.1",
    instructions="You are a helpful assistant.",
    input="Hello!",
    stream=True
)

for event in response:
    print(event)

Using Vision Models

Portkey’s multimodal Gateway fully supports OpenAI vision models as well. See this guide for more info:

Vision

Vision with the Responses API

You can also use the Responses API to process images alongside text:

Python

response = portkey.responses.create(
    model="gpt-4.1",
    input=[
        {
            "role": "user",
            "content": [
                { "type": "input_text", "text": "What is in this image?" },
                {
                    "type": "input_image",
                    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                }
            ]
        }
    ]
)

print(response)

Function Calling

Function calls within your OpenAI or Portkey SDK operations remain standard. These logs will appear in Portkey, highlighting the utilized functions and their outputs.

Additionally, you can define functions within your prompts and invoke the portkey.prompts.completions.create method as above.

Function Calling with the Responses API

The Responses API also supports function calling with the same powerful capabilities:

Python

tools = [
    {
        "type": "function",
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                },
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location", "unit"]
        }
    }
]

response = portkey.responses.create(
    model="gpt-4.1",
    tools=tools,
    input="What is the weather like in Boston today?",
    tool_choice="auto"
)

print(response)

Fine-Tuning

Please refer to our fine-tuning guides to take advantage of Portkey’s advanced continuous fine-tuning capabilities.

Image Generation

Portkey supports multiple modalities for OpenAI and you can make image generation requests through Portkey’s AI Gateway the same way as making completion calls.

OpenAI NodeJS

// Define the OpenAI client as shown above

const image = await openai.images.generate({
  model:"dall-e-3",
  prompt:"Lucy in the sky with diamonds",
  size:"1024x1024"
})

Portkey’s fast AI gateway captures the information about the request on your Portkey Dashboard. On your logs screen, you’d be able to see this request with the request and response.

Log view for an image generation request on OpenAI

More information on image generation is available in the API Reference.

Audio - Transcription, Translation, and Text-to-Speech

Portkey’s multimodal Gateway also supports the audio methods on OpenAI API. Check out the below guides for more info:

Check out the below guides for more info:

Text-to-Speech

Speech-to-Text

---

Intergrated Tools with Repsponses API

Web Search Tool

Web search delivers accurate and clearly-cited answers from the web, using the same tool as search in ChatGPT:

Python

response = portkey.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "web_search_preview",
        "search_context_size": "medium", # Options: "high", "medium" (default), or "low"
        "user_location": {  # Optional - for localized results
            "type": "approximate",
            "country": "US",
            "city": "San Francisco",
            "region": "California"
        }
    }],
    input="What was a positive news story from today?"
)

print(response)

Options for search_context_size:

• high: Most comprehensive context, higher cost, slower response
• medium: Balanced context, cost, and latency (default)
• low: Minimal context, lowest cost, fastest response

Responses include citations for URLs found in search results, with clickable references.

File Search Tool

File search enables quick retrieval from your knowledge base across multiple file types:

Python

response = portkey.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["vs_1234567890"],
        "max_num_results": 20,
        "filters": {  # Optional - filter by metadata
            "type": "eq",
            "key": "document_type",
            "value": "report"
        }
    }],
    input="What are the attributes of an ancient brown dragon?"
)

print(response)

This tool requires you to first create a vector store and upload files to it. Supports various file formats including PDFs, DOCXs, TXT, and more. Results include file citations in the response.

Enhanced Reasoning

Control the depth of model reasoning for more comprehensive analysis:

Python

response = portkey.responses.create(
    model="o3-mini",
    input="How much wood would a woodchuck chuck?",
    reasoning={
        "effort": "high"  # Options: "high", "medium", or "low"
    }
)

print(response)

Computer Use Assistant

Portkey also supports the Computer Use Assistant (CUA) tool, which helps agents control computers or virtual machines through screenshots and actions. This feature is available for select developers as a research preview on premium tiers.

Learn More about Computer use tool here

Managing OpenAI Projects & Organizations in Portkey

When integrating OpenAI with Portkey, you can specify your OpenAI organization and project IDs along with your API key. This is particularly useful if you belong to multiple organizations or are accessing projects through a legacy user API key.

Specifying the organization and project IDs helps you maintain better control over your access rules, usage, and costs.

In Portkey, you can add your Org & Project details by,

1. Creating your Virtual Key
2. Defining a Gateway Config
3. Passing Details in a Request

Let’s explore each method in more detail.

Using Virtual Keys

When selecting OpenAI from the dropdown menu while creating a virtual key, Portkey automatically displays optional fields for the organization ID and project ID alongside the API key field.

Get your OpenAI API key from here, then add it to Portkey to create the virtual key that can be used throughout Portkey.

Virtual Keys

Portkey takes budget management a step further than OpenAI. While OpenAI allows setting budget limits per project, Portkey enables you to set budget limits for each virtual key you create. For more information on budget limits, refer to this documentation:

Budget Limits

Using The Gateway Config

You can also specify the organization and project details in the gateway config, either at the root level or within a specific target.

{
	"provider": "openai",
	"api_key": "OPENAI_API_KEY",
	"openai_organization": "org-xxxxxx",
	"openai_project": "proj_xxxxxxxx"
}

While Making a Request

You can also pass your organization and project details directly when making a request using curl, the OpenAI SDK, or the Portkey SDK.

OpenAI Python SDK

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key="OPENAI_API_KEY",
    organization="org-xxxxxxxxxx",
    project="proj_xxxxxxxxx",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY"
    )
)

chat_complete = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Say this is a test"}],
)

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

---

Portkey Features

Portkey supports the complete host of it’s functionality via the OpenAI SDK so you don’t need to migrate away from it.

Please find more information in the relevant sections:

1. Add metadata to your requests
2. Add gateway configs to the OpenAI client or a single request
3. Tracing OpenAI requests
4. Setup a fallback to Azure OpenAI