# API Documentation

Provider-native integration patterns for OpenAI, Anthropic, and Google clients with Mnexium routing, header conventions, and parity-safe request shapes.

## SDK Integration

### Choose Your Integration Style

Mnexium supports two integration approaches. Choose based on your needs:

#### OpenAI Connector (Recommended)

Use the OpenAI SDK for **all providers** (OpenAI, Claude, Gemini). Same code, same response format, just change the model name.

- Unified API across all providers
- Full `mnx` support in request body
- Consistent response format
- Lowest integration complexity

#### Native SDKs

Use each provider's official SDK with their native endpoints and response formats.

- Native SDK features and types
- Provider-specific response formats
- `mnx` via headers (SDKs strip body params)
- Different base URLs per provider

### Code Examples

#### OpenAI Connector

Use the OpenAI SDK to call **any provider** through Mnexium's unified endpoint. Just change the model name and pass the appropriate provider key.

| Provider | Header | Example Models |
| --- | --- | --- |
| OpenAI | `x-openai-key` | `gpt-4o`, `gpt-4o-mini` |
| Anthropic | `x-anthropic-key` | `claude-sonnet-4-20250514` |
| Google | `x-google-key` | `gemini-2.0-flash-lite` |

```javascript
import OpenAI from "openai";

const BASE_URL = "https://mnexium.com/api/v1";

// OpenAI client
const openai = new OpenAI({
  baseURL: BASE_URL,
  defaultHeaders: {
    "x-mnexium-key": process.env.MNX_KEY,
    "x-openai-key": process.env.OPENAI_API_KEY,
  },
});

// Claude client (via OpenAI SDK)
const claude = new OpenAI({
  baseURL: BASE_URL,
  defaultHeaders: {
    "x-mnexium-key": process.env.MNX_KEY,
    "x-anthropic-key": process.env.CLAUDE_API_KEY,
  },
});

// Gemini client (via OpenAI SDK)
const gemini = new OpenAI({
  baseURL: BASE_URL,
  defaultHeaders: {
    "x-mnexium-key": process.env.MNX_KEY,
    "x-google-key": process.env.GEMINI_KEY,
  },
});

// All calls use the same API!
const openaiResponse = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "What do you know about me?" }],
  mnx: { subject_id: "user_123", recall: true },
});

const claudeResponse = await claude.chat.completions.create({
  model: "claude-sonnet-4-20250514",
  messages: [{ role: "user", content: "What do you know about me?" }],
  mnx: { subject_id: "user_123", recall: true },
});

const geminiResponse = await gemini.chat.completions.create({
  model: "gemini-2.0-flash-lite",
  messages: [{ role: "user", content: "What do you know about me?" }],
  mnx: { subject_id: "user_123", recall: true },
});
```

### Cross-Provider Memory Sharing

Memories learned with one provider are automatically available to all others. Use the same `subject_id` across providers to share context.

```javascript
// Learn a fact with OpenAI
await openai.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "My favorite color is purple" }],
  mnx: { subject_id: "user_123", learn: "force" },
});

// Recall with Claude - it knows the color!
const claudeResponse = await claude.chat.completions.create({
  model: "claude-sonnet-4-20250514",
  messages: [{ role: "user", content: "What is my favorite color?" }],
  mnx: { subject_id: "user_123", recall: true },
});
// Claude responds: "Your favorite color is purple!"
```

This enables multi-model workflows where each task can use the most appropriate model while keeping user context consistent.