OpenAI

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:

Install the Portkey SDK with npm

npm install portkey-ai

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();

Install the Portkey SDK with npm

npm install portkey-ai

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();

Install the Portkey SDK with pip

pip install portkey-ai

from portkey_ai import Portkey

client = Portkey(
  api_key = "PORTKEY_API_KEY",
  virtual_key = "PROVIDER_VIRTUAL_KEY"
)

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]
)

print(response.choices[0].message)

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      { "role": "user", "content": "Hello!" }
    ]
  }'

Install the OpenAI & Portkey SDKs with pip

pip install openai portkey-ai

from openai import OpenAI
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL

client = OpenAI(
    api_key="xx",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        api_key="PORTKEY_API_KEY",
        virtual_key="OPENAI_VIRTUAL_KEY"
    )
)

completion = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]
)

print(completion.choices[0].message)

Install the OpenAI & Portkey SDKs with npm

npm install openai portkey-ai

import OpenAI from 'openai';
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const openai = new OpenAI({
  apiKey: 'xx',
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY",
    virtualKey: "OPENAI_VIRTUAL_KEY"
  })
});

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

  console.log(chatCompletion.choices);
}

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.

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)

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)

import Portkey from 'portkey-ai';

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

async function main() {
  const response = await portkey.responses.create({
    model: "gpt-4.1",
    input: "Tell me a three sentence bedtime story about a unicorn."
  });

  console.log(response);
}

main();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key="OPENAI_API_KEY",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY",
        virtual_key="OPENAI_VIRTUAL_KEY"
    )
)

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

print(response)

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",
    virtualKey: "OPENAI_VIRTUAL_KEY"
  })
});

async function main() {
  const response = await openai.responses.create({
    model: "gpt-4.1",
    input: "Tell me a three sentence bedtime story about a unicorn."
  });

  console.log(response);
}

main();

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

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.

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

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

response = portkey.chat.completions.create(
  model="gpt-4o",
  messages=[{ role: "user", content: "Say this is a test" }]
  user="user_123456"
)

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.

Create a prompt template with variables and set the hyperparameters.

Use this prompt in your codebase using the Portkey SDK.

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
})

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
})

from portkey_ai import Portkey

client = Portkey(
    api_key="PORTKEY_API_KEY",  # defaults to os.environ.get("PORTKEY_API_KEY")
)

prompt_completion = client.prompts.completions.create(
    prompt_id="Your Prompt ID",
    variables={
       # The variables specified in the prompt
    }
)

print(prompt_completion)

# We can also override the hyperparameters

prompt_completion = client.prompts.completions.create(
    prompt_id="Your Prompt ID",
    variables={
       # The variables specified in the prompt
    },
    max_tokens=250,
    presence_penalty=0.2
)

print(prompt_completion)

curl -X POST "https://api.portkey.ai/v1/prompts/:PROMPT_ID/completions" \
-H "Content-Type: application/json" \
-H "x-portkey-api-key: $PORTKEY_API_KEY" \
-d '{
    "variables": {
        # The variables to use
    },
    "max_tokens": 250, # Optional
    "presence_penalty": 0.2 # Optional
}'

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).

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();

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();

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key="OPENAI_API_KEY",  # defaults to os.environ.get("OPENAI_API_KEY")
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY" # defaults to os.environ.get("PORTKEY_API_KEY")
    )
)

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

for chunk in chat_complete:
    print(chunk.choices[0].delta.content, end="", flush=True)

Streaming with the Responses API

You can also stream responses from the Responses API:

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

for event in response:
    print(event)

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

for event in response:
    print(event)

const response = await portkey.responses.create({
    model: "gpt-4.1",
    instructions: "You are a helpful assistant.",
    input: "Hello!",
    stream: true
});

for await (const event of response) {
    console.log(event);
}

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

for event in response:
    print(event)

const response = await openai.responses.create({
    model: "gpt-4.1",
    instructions: "You are a helpful assistant.",
    input: "Hello!",
    stream: true
});

for await (const event of response) {
    console.log(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:

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)

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)

const response = await 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"
                }
            ]
        }
    ]
});

console.log(response);

response = client.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)

const response = await openai.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"
                }
            ]
        }
    ]
});

console.log(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:

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)

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)

const 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"]
        }
    }
];

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

console.log(response);

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

print(response)

const 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"]
        }
    }
];

const response = await openai.responses.create({
    model: "gpt-4.1",
    tools: tools,
    input: "What is the weather like in Boston today?",
    tool_choice: "auto"
});

console.log(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.

// 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"
})

// 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"
})

# Define the OpenAI client as shown above

image = 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:

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)

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)

const response = await 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?"
});

console.log(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:

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)

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)

const response = await 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?"
});

console.log(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:

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)

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)

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

console.log(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,

Creating your Virtual Key
Defining a Gateway Config
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.

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)

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)

import OpenAI from "openai";
import { PORTKEY_GATEWAY_URL, createHeaders } from "portkey-ai";

const openai = new OpenAI({
  apiKey: "OPENAI_API_KEY",
  organization: "org-xxxxxx",
  project: "proj_xxxxxxx",
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "openai",
    apiKey: "PORTKEY_API_KEY",
  }),
});

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

  console.log(chatCompletion.choices);
}

main();

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "x-portkey-openai-organization: org-xxxxxxx" \
  -H "x-portkey-openai-project: proj_xxxxxxx" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: openai" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user","content": "Hello!"}]
  }'

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="openai",
    Authorization="Bearer OPENAI_API_KEY",
    openai_organization="org-xxxxxxxxx",
    openai_project="proj_xxxxxxxxx",
)

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

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

import Portkey from "portkey-ai";

const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY",
  provider: "openai",
  Authorization: "Bearer OPENAI_API_KEY",
  openaiOrganization: "org-xxxxxxxxxxx",
  openaiProject: "proj_xxxxxxxxxxxxx",
});

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

  console.log(chatCompletion.choices);
}

main();

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:

Overview Structured Outputs

On this page

Using the Portkey Gateway
Using the Responses API
Track End-User IDs
Using the Prompts API
Advanced Use Cases
Realtime API
Streaming Responses
Streaming with the Responses API
Using Vision Models
Vision with the Responses API
Function Calling
Function Calling with the Responses API
Fine-Tuning
Image Generation
Audio - Transcription, Translation, and Text-to-Speech
Intergrated Tools with Repsponses API
Web Search Tool
File Search Tool
Enhanced Reasoning
Computer Use Assistant
Managing OpenAI Projects & Organizations in Portkey
Using Virtual Keys
Using The Gateway Config
While Making a Request
Portkey Features

Ecosystem

LLMs

Tools

Vector Databases

Agents

Libraries

Using the Portkey Gateway

Using the Responses API

Track End-User IDs

Metadata

Using the Prompts API

Advanced Use Cases

Realtime API

Realtime API

Streaming Responses

Streaming with the Responses API

Using Vision Models

Vision with the Responses API

Function Calling

Function Calling with the Responses API

Fine-Tuning

Image Generation

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

Intergrated Tools with Repsponses API

Web Search Tool

File Search Tool

Enhanced Reasoning

Computer Use Assistant

Managing OpenAI Projects & Organizations in Portkey

Using Virtual Keys

Using The Gateway Config

While Making a Request

Portkey Features

Ecosystem

LLMs

Tools

Vector Databases

Agents

Libraries

​Using the Portkey Gateway

​Using the Responses API

​Track End-User IDs

Metadata

​Using the Prompts API

​Advanced Use Cases

​Realtime API

Realtime API

​Streaming Responses

​Streaming with the Responses API

​Using Vision Models

​Vision with the Responses API

​Function Calling

​Function Calling with the Responses API

​Fine-Tuning

​Image Generation

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

​Intergrated Tools with Repsponses API

​Web Search Tool

​File Search Tool

​Enhanced Reasoning

​Computer Use Assistant

​Managing OpenAI Projects & Organizations in Portkey

​Using Virtual Keys

​Using The Gateway Config

​While Making a Request

​Portkey Features

Using the Portkey Gateway

Using the Responses API

Track End-User IDs

Using the Prompts API

Advanced Use Cases

Realtime API

Streaming Responses

Streaming with the Responses API

Using Vision Models

Vision with the Responses API

Function Calling

Function Calling with the Responses API

Fine-Tuning

Image Generation

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

Intergrated Tools with Repsponses API

Web Search Tool

File Search Tool

Enhanced Reasoning

Computer Use Assistant

Managing OpenAI Projects & Organizations in Portkey

Using Virtual Keys

Using The Gateway Config

While Making a Request

Portkey Features