OpenAI Drop-In Mode

Use GetProfile as a drop-in replacement for OpenAI SDK

The easiest way to add persistent memory to your AI application. Simply point your existing OpenAI SDK to GetProfile’s base URL and everything else works automatically.

How It Works

┌─────────────────┐     ┌──────────────────────────────────┐     ┌─────────────────┐
│                 │     │         GetProfile Proxy          │     │                 │
│   Your App      │────▶│                                  │────▶│   LLM Provider  │
│                 │     │  1. Load user profile            │     │   (OpenAI, etc) │
│                 │     │  2. Retrieve relevant memories   │     │                 │
└─────────────────┘     │  3. Inject context into prompt   │     └─────────────────┘
                        │  4. Forward to LLM               │
                        │  5. Stream response back         │
                        │  6. Extract traits (background)  │
                        └──────────────────────────────────┘

Quick Start

TypeScript

import OpenAI from 'openai';

const client = new OpenAI({
apiKey: 'gp_your_getprofile_key',
baseURL: 'http://localhost:3100/v1', // Or your GetProfile instance
defaultHeaders: {
'X-GetProfile-Id': 'user-123', // Your app's user ID
'X-Upstream-Key': 'sk-your-openai-key',
},
});

// Use exactly like OpenAI - memory is automatic!
const response = await client.chat.completions.create({
model: 'gpt-5',
messages: [{ role: 'user', content: 'Hello!' }],
});

Required Headers

Header	Description	Required
Authorization	Your GetProfile API key (Bearer gp_...)	Yes
X-GetProfile-Id	Your app’s user identifier	Yes*
X-Upstream-Key	Your LLM provider API key	Yes

*Can also be provided via body.user or body.metadata.profile_id

What Gets Injected

GetProfile automatically adds a system message with user context:

## User Profile
Alex is an experienced software engineer who prefers concise, technical explanations.
They work primarily with Python and have been exploring distributed systems.

## User Attributes
- Communication style: technical
- Detail preference: brief
- Expertise level: advanced

## Relevant Context
- User mentioned working on a microservices migration last week
- User prefers async/await patterns over callbacks

Streaming Support

GetProfile fully supports streaming responses:

const stream = await client.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Hello!" }],
  stream: true,
});

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

Background trait extraction happens after the stream completes.

Per-Request Trait Overrides

You can override the trait schema for individual requests:

const response = await client.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Help me plan my trip" }],
  // @ts-ignore - GetProfile extension
  extra_body: {
    getprofile: {
      traits: [
        {
          key: "travel_preferences",
          valueType: "array",
          extraction: {
            enabled: true,
            promptSnippet: "Extract travel style preferences",
          },
          injection: {
            enabled: true,
            template: "User travel preferences: {{value}}",
          },
        },
      ],
    },
  },
});

Configuration

See Configuration Overview for full proxy configuration options.

Next Steps

Trait Schemas

Customize what GetProfile extracts

JavaScript SDK

Programmatic access from Node.js/TypeScript