> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getprofile.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> User profile and long-term memory for your AI agent

## The Problem

LLMs are stateless. Every conversation starts from scratch. Your AI assistant doesn't remember:

* User preferences ("I prefer concise answers")
* Past context ("We discussed this project last week")
* Personal details ("I'm a Python developer working at a startup")

This makes AI interactions feel impersonal and repetitive.

## The Solution

GetProfile is a **drop-in LLM proxy** that automatically:

<CardGroup cols={2}>
  <Card title="Captures" icon="inbox">
    Conversations between users and your AI
  </Card>

  <Card title="Extracts" icon="brain">
    Structured traits and memories using LLM analysis
  </Card>

  <Card title="Injects" icon="syringe">
    Relevant context into every prompt
  </Card>

  <Card title="Updates" icon="arrows-rotate">
    User profiles continuously in the background
  </Card>
</CardGroup>

## Multiple Integration Options

<CardGroup cols={2}>
  <Card title="LLM Proxy" icon="arrow-right-arrow-left">
    Change your OpenAI base URL for automatic memory injection
  </Card>

  <Card title="JavaScript SDK" icon="js">
    Programmatic access from Node.js/TypeScript
  </Card>
</CardGroup>

### Proxy Integration (Automatic)

Just change your OpenAI base URL. That's it.

```typescript theme={null}
// Before: Stateless AI
const client = new OpenAI({ apiKey: "sk-..." });

// After: AI with memory
const client = new OpenAI({
  apiKey: "gp_...", // Your GetProfile API key
  baseURL: "https://api.yourserver.com/v1", // Or your self-hosted instance
  defaultHeaders: {
    "X-GetProfile-Id": userId, // Your app's user ID
    "X-Upstream-Key": "sk-...", // Your OpenAI key
  },
});

// Same API, now with persistent memory
const response = await client.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "How should I refactor this?" }],
});
```

## Key Features

<AccordionGroup>
  <Accordion title="Structured User Profiles" icon="user">
    Unlike generic memory solutions that store blobs of text, GetProfile extracts **typed traits** with confidence scores:

    ```json theme={null}
    {
      "name": { "value": "Alex", "confidence": 0.95 },
      "expertise_level": { "value": "advanced", "confidence": 0.8 },
      "communication_style": { "value": "technical", "confidence": 0.7 }
    }
    ```
  </Accordion>

  <Accordion title="Zero-Friction Integration" icon="plug">
    * OpenAI-compatible proxy — works with any OpenAI SDK
    * No code changes — just update your base URL
    * Streaming support — full SSE streaming passthrough
  </Accordion>

  <Accordion title="Customizable Trait Schema" icon="sliders">
    Define what matters for your app with JSON configuration files.
  </Accordion>

  <Accordion title="Open Source & Self-Hostable" icon="code">
    * Apache 2.0 licensed — use it anywhere
    * Self-host with Docker — your data stays with you
    * Transparent — audit the code, understand what's happening
  </Accordion>
</AccordionGroup>

## What's Next?

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Get up and running in 5 minutes
  </Card>

  <Card title="How It Works" icon="gear" href="/how-it-works">
    Understand the architecture
  </Card>

  <Card title="Integration Options" icon="plug" href="/integrations/overview">
    Choose the right integration for your use case
  </Card>

  <Card title="JavaScript SDK" icon="js" href="/client-libraries/javascript">
    Programmatic access from Node.js/TypeScript
  </Card>
</CardGroup>