API quickstart

Use this quickstart if you want to call the API from your own code or product, or use third-party integrations such as coding assistants.

Before you start

• Sign in to the Privatemode portal.
• Create or select an organization. Rate limits, usage, and billing are managed at the organization level.
• Create an API key in the portal.

For more details, see Portal overview, Organizations, and API keys.

1. Create an API key

In the portal, go to API keys and create a new API key.

Save the key securely when it's shown to you. You will need it to authenticate the Privatemode SDK or proxy.

2. Connect to Privatemode

Choose where attestation and encryption happen:

For client-verified end-to-end encryption, follow the SDK or proxy instructions below. To evaluate Privatemode without setting up either component and learn about its security tradeoff, follow the proxyless API guide.

npm i privatemode-ai
export PRIVATEMODE_API_KEY="<your-api-key>"

import { PrivatemodeAI } from 'privatemode-ai';

const client = new PrivatemodeAI({
  apiKey: process.env.PRIVATEMODE_API_KEY,
});

const response = await client.chat.completions.create({
  model: 'glm-latest',
  reasoning_effort: 'low',
  messages: [{ role: 'user', content: 'Hello Privatemode!' }],
});

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

It's nice to meet you. Is there something I can help you with or would you like to chat?

docker run -p 127.0.0.1:8080:8080 ghcr.io/edgelesssys/privatemode/privatemode-proxy:latest --api-key <your-api-key>

#!/usr/bin/env bash

curl localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-latest",
    "reasoning_effort": "low",
    "messages": [
      {
        "role": "user",
        "content": "Hello Privatemode!"
      }
    ]
  }'

{
  "id": "chatcmpl-proxy_021e5a68-0346-432b-a0f0-7b516ab0c9a7_0",
  "object": "chat.completion",
  "created": 1776086356,
  "model": "glm-latest",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": " Hello! I'm GLM, ...",
        "refusal": null,
        "annotations": null,
        "audio": null,
        "function_call": null,
        "tool_calls": [],
        "reasoning": " The user greeted me ... ",
        "reasoning_content": " The user greeted me ... "
      },
      "logprobs": null,
      "finish_reason": "stop",
      "stop_reason": 163586,
      "token_ids": null
    }
  ],
  "service_tier": null,
  "system_fingerprint": null,
  "usage": {
    "prompt_tokens": 30,
    "total_tokens": 420,
    "completion_tokens": 390,
    "prompt_tokens_details": null
  },
  "prompt_logprobs": null,
  "prompt_token_ids": null,
  "kv_transfer_params": null
}

import os
import openai

client = openai.OpenAI(
    # A placeholder suffices when the proxy runs with --api-key.
    api_key=os.environ.get("PRIVATEMODE_API_KEY", "placeholder"),
    base_url="http://localhost:8080/v1",  # Adjust as necessary
)

response = client.chat.completions.create(
    model="glm-latest",
    reasoning_effort="low",
    messages=[
        {"role": "user", "content": "Hello Privatemode!"},
    ],
)

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

It's nice to meet you. Is there something I can help you with or would you like to chat?

The code performs the following steps:

1. Construct a prompt request following the OpenAI Chat API specification. The example sets reasoning_effort to low. Chat models reason before they answer, and the default effort produces a long reasoning trace even for a greeting. See Reasoning for the options per model.
2. Send the prompt request to the Privatemode proxy. The proxy handles end-to-end encryption and verifies the integrity of the Privatemode backend that serves the endpoint.
3. Receive and print the response.

Next steps

• Read the SDK documentation
• Learn more about the Privatemode proxy
• Explore available models
• Read the API overview
• Set up coding assistants
• Manage keys, usage, and billing in the portal