# Cencori
[Cencori](https://cencori.com) is the unified infrastructure layer for AI applications. It provides built-in security, observability, and multi-provider support through a single API.
With Cencori, you get:
- **Multi-Provider Gateway**: Access OpenAI, Anthropic, Google Gemini, Mistral, DeepSeek, and more through one API
- **Built-in Security**: Automatic PII detection, prompt injection protection, and content filtering
- **Full Observability**: Complete audit logs, analytics, and cost tracking for every request
- **Transparent Pricing**: Credits-based billing with real-time cost visibility
## Setup
The Cencori provider is available in the `cencori` module. You can install it with:
```bash pnpm add cencori ```
```bash npm install cencori ```
```bash yarn add cencori ```
## Provider Instance
To create a Cencori provider instance, use the `createCencori` function:
```ts
import { createCencori } from 'cencori/vercel';
const cencori = createCencori({
apiKey: 'YOUR_CENCORI_API_KEY',
});
```
You can obtain your Cencori API key from the [Cencori Dashboard](https://cencori.com/dashboard).
### Environment Variable
Alternatively, you can set the `CENCORI_API_KEY` environment variable and use the default provider:
```ts
import { cencori } from 'cencori/vercel';
// Uses CENCORI_API_KEY environment variable
const model = cencori('gpt-4o');
```
## Language Models
Cencori provides access to language models from multiple providers. Use the provider function directly to create a model:
```ts
// OpenAI models
const gpt4o = cencori('gpt-4o');
const gpt4oMini = cencori('gpt-4o-mini');
// Anthropic models
const claude = cencori('claude-3-5-sonnet');
const opus = cencori('claude-3-opus');
// Google Gemini models
const gemini = cencori('gemini-2.5-flash');
const geminiPro = cencori('gemini-3.1-pro-preview');
// Other providers
const mistral = cencori('mistral-large');
const deepseek = cencori('deepseek-v3.2');
const grok = cencori('grok-4');
const llama = cencori('llama-3-70b');
```
You can find the full list of available models in the [Cencori Documentation](https://cencori.com/docs).
## Examples
Here are examples of using Cencori with the AI SDK:
### generateText
```ts
import { cencori } from 'cencori/vercel';
import { generateText } from 'ai';
const { text } = await generateText({
model: cencori('gpt-4o'),
prompt: 'What is Cencori?',
});
console.log(text);
```
### streamText
```ts
import { cencori } from 'cencori/vercel';
import { streamText } from 'ai';
const result = streamText({
model: cencori('claude-3-5-sonnet'),
prompt: 'Write a short story about AI safety.',
});
for await (const chunk of result.textStream) {
console.log(chunk);
}
```
### Tool Calling
Cencori supports tool calling with all compatible models:
```ts
import { cencori } from 'cencori/vercel';
import { generateText, tool } from 'ai';
import { z } from 'zod';
const { text, toolCalls } = await generateText({
model: cencori('gpt-4o'),
prompt: 'What is the weather in San Francisco?',
tools: {
getWeather: tool({
description: 'Get the weather for a location',
parameters: z.object({
location: z.string().describe('The city and state'),
}),
execute: async ({ location }) => {
return { temperature: 72, condition: 'sunny', location };
},
}),
},
});
console.log(text, toolCalls);
```
### Next.js Route Handler
```ts
import { cencori } from 'cencori/vercel';
import { streamText } from 'ai';
export async function POST(req: Request) {
const { messages } = await req.json();
const result = streamText({
model: cencori('gemini-2.5-flash'),
messages,
});
return result.toDataStreamResponse();
}
```
### useChat Hook
```tsx
'use client';
import { useChat } from '@ai-sdk/react';
export default function Chat() {
const { messages, input, handleInputChange, handleSubmit } = useChat({
api: '/api/chat',
});
return (
{messages.map(m => (
{m.role}: {m.content}
))}