README

This Laravel package provides an easy-to-use interface for integrating OpenRouter into your Laravel applications. OpenRouter is a unified interface for Large Language Models (LLMs) that allows you to interact with various AI models through a single API.

Table of Contents

• 🤖 Requirements
• 🏁 Get Started
• 🧩 Configuration
• 🎨 Usage
  • Understanding ChatData DTO
    • LLM Parameters
    • Function-calling
    • Additional Optional Parameters
    • OpenRouter-only Parameters
  • Creating a ChatData Instance
  • Using Facade
    • Chat Request
      • Stream Chat Request
      • Maintaining Conversation Continuity
      • Structured Output
    • Cost Request
    • Limit Request
  • Using OpenRouterRequest Class
    • Chat Request
    • Cost Request
    • Limit Request
• 💫 Contributing
• 📜 License

🤖 Requirements

• PHP 8.2 or higher: Use version v2.x.x (latest compatible version)
  • Uses spatie laravel-data
• PHP 8.1: Use version v1.0.9.
  • Uses spatie data-transfer-object
  • For correct way to use DTOs, refer to v1.0.9 README.

🏁 Get Started

You can install the package via composer:

composer require toanld/laravel-openrouter

You can publish the config file with:

php artisan vendor:publish --tag=laravel-openrouter

This is the contents of the published config file:

return [
    'api_endpoint' => env('OPENROUTER_API_ENDPOINT', 'https://openrouter.ai/api/v1/'),
    'api_key'      => env('OPENROUTER_API_KEY'),
    'api_timeout'  => env('OPENROUTER_API_TIMEOUT', 20),
    'title'        => env('OPENROUTER_API_TITLE', 'laravel-openrouter'),
    'referer'      => env('OPENROUTER_API_REFERER', 'https://github.com/moe-mizrak/laravel-openrouter'),
];

🧩 Configuration

After publishing the package configuration file, you'll need to add the following environment variables to your .env file:

OPENROUTER_API_ENDPOINT=https://openrouter.ai/api/v1/
OPENROUTER_API_KEY=your_api_key
OPENROUTER_API_TIMEOUT=request_timeout
OPENROUTER_API_TITLE=
OPENROUTER_API_REFERER=

Note

• OPENROUTER_API_ENDPOINT: The endpoint URL for the OpenRouter API (default: https://openrouter.ai/api/v1/).
• OPENROUTER_API_KEY: Your API key for accessing the OpenRouter API. You can obtain this key from the OpenRouter dashboard.
• OPENROUTER_API_TIMEOUT: Request timeout in seconds. Increase value to 120 - 180 if you use long-thinking models like openai/o1 (default: 20)
• OPENROUTER_API_TITLE: Optional - Site URL for rankings on openrouter.ai
• OPENROUTER_API_REFERER: Optional - Site referer for rankings on openrouter.ai

🎨 Usage

This package provides two ways to interact with the OpenRouter API:

• Using the LaravelOpenRouter facade
• Instantiating the OpenRouterRequest class directly.

Both methods utilize the ChatData DTO class to structure the data sent to the API.

Understanding ChatData DTO

The ChatData class is used to encapsulate the data required for making chat requests to the OpenRouter API. Here's a breakdown of the key properties:

• messages (array|null): An array of MessageData objects representing the chat messages. This field is XOR-gated with the prompt field.
• prompt (string|null): A string representing the prompt for the chat request. This field is XOR-gated with the messages field.
• model (string|null): The name of the model to be used for the chat request. If not specified, the user's default model will be used. This field is XOR-gated with the models field.
• response_format (ResponseFormatData|null): An instance of the ResponseFormatData class representing the desired format for the response.
• stop (array|string|null): A value specifying the stop sequence for the chat generation.
• stream (bool|null): A boolean indicating whether streaming should be enabled or not.
• include_reasoning (bool|null): Whether to return the model's reasoning.

LLM Parameters

These properties control various aspects of the generated response (more info):

• max_tokens (int|null): The maximum number of tokens that can be generated in the completion. Default is 1024.
• temperature (float|null): A value between 0 and 2 controlling the randomness of the output.
• top_p (float|null): A value between 0 and 1 for nucleus sampling, an alternative to temperature sampling.
• top_k (float|null): A value between 1 and infinity for top-k sampling (not available for OpenAI models).
• frequency_penalty (float|null): A value between -2 and 2 for penalizing new tokens based on their existing frequency.
• presence_penalty (float|null): A value between -2 and 2 for penalizing new tokens based on whether they appear in the text so far.
• repetition_penalty (float|null): A value between 0 and 2 for penalizing repetitive tokens.
• seed (int|null): A value for deterministic sampling (OpenAI models only, in beta).

Function-calling

Only natively suported by OpenAI models. For others, we submit a YAML-formatted string with these tools at the end of the prompt.

• tool_choice (string|array|null): A value specifying the tool choice for function calling (OpenAI models only).
• tools (array|null): An array of ToolCallData objects for function calling.

Additional optional parameters

• logit_bias (array|null): An array for modifying the likelihood of specified tokens appearing in the completion.

OpenRouter-only parameters

• transforms (array|null): An array for configuring prompt transforms.
• models (array|null): An array of models to automatically try if the primary model is unavailable. This field is XOR-gated with the model field.
• route (string|null): A value specifying the route type (e.g., RouteType::FALLBACK).
• provider (ProviderPreferencesData|null): An instance of the ProviderPreferencesData DTO object for configuring provider preferences.

Creating a ChatData Instance

This is a sample chat data instance (Refer to spatie laravel-data how to create, use DTOs):

$chatData = new ChatData(
    messages: [
        new MessageData(
            role: RoleType::USER,
            content: [
                new TextContentData(
                    type: TextContentData::ALLOWED_TYPE,
                    text: 'This is a sample text content.',
                ),
                new ImageContentPartData(
                    type: ImageContentPartData::ALLOWED_TYPE,
                    image_url: new ImageUrlData(
                        url: 'https://example.com/image.jpg',
                        detail: 'Sample image',
                    ),
                ),
            ],
        ),
    ],
    response_format: new ResponseFormatData(
        type: 'json_object',
    ),
    stop: ['stop_token'],
    stream: true,
    include_reasoning: true,
    max_tokens: 1024,
    temperature: 0.7,
    top_p: 0.9,
    top_k: 50,
    frequency_penalty: 0.5,
    presence_penalty: 0.2,
    repetition_penalty: 1.2,
    seed: 42,
    tool_choice: 'auto',
    tools: [
        // ToolCallData instances
    ],
    logit_bias: [
        '50256' => -100,
    ],
    transforms: ['middle-out'],
    models: ['model1', 'model2'],
    route: RouteType::FALLBACK,
    provider: new ProviderPreferencesData(
        allow_fallbacks: true,
        require_parameters: true,
        data_collection: DataCollectionType::ALLOW,
    ),
);

Using Facade

The LaravelOpenRouter facade offers a convenient way to make OpenRouter API requests.

Chat Request

To send a chat request, create an instance of ChatData and pass it to the chatRequest method:

$content = 'Tell me a story about a rogue AI that falls in love with its creator.'; // Your desired prompt or content
$model = 'mistralai/mistral-7b-instruct:free'; // The OpenRouter model you want to use (https://openrouter.ai/docs#models)
$messageData = new MessageData(
    content: $content,
    role: RoleType::USER,
);

$chatData = new ChatData(
    messages: [
        $messageData,
    ],
    model: $model,
    max_tokens: 100, // Adjust this value as needed
);

$chatResponse = LaravelOpenRouter::chatRequest($chatData);

• Stream Chat Request

Streaming chat request is also supported and can be used as following by using chatStreamRequest function:

$content = 'Tell me a story about a rogue AI that falls in love with its creator.'; // Your desired prompt or content
$model = 'mistralai/mistral-7b-instruct:free'; // The OpenRouter model you want to use (https://openrouter.ai/docs#models)
$messageData = new MessageData(
    content: $content,
    role: RoleType::USER,
);

$chatData = new ChatData(
    messages: [
        $messageData,
    ],
    model: $model,
    max_tokens: 100,
);

/*
 * Calls chatStreamRequest ($promise is type of PromiseInterface)
 */
$promise = LaravelOpenRouter::chatStreamRequest($chatData);

// Waits until the promise completes if possible.
$stream = $promise->wait(); // $stream is type of GuzzleHttp\Psr7\Stream

/*
 * 1) You can retrieve whole raw response as: - Choose 1) or 2) depending on your case.
 */
$rawResponseAll = $stream->getContents(); // Instead of chunking streamed response as below - while (! $stream->eof()), it waits and gets raw response all together.
$response = LaravelOpenRouter::filterStreamingResponse($rawResponseAll); // Optionally you can use filterStreamingResponse to filter raw streamed response, and map it into array of responseData DTO same as chatRequest response format.

// 2) Or Retrieve streamed raw response as it becomes available:
while (! $stream->eof()) {
    $rawResponse = $stream->read(1024); // readByte can be set as desired, for better performance 4096 byte (4kB) can be used.
    
    /*
     * Optionally you can use filterStreamingResponse to filter raw streamed response, and map it into array of responseData DTO same as chatRequest response format.
     */
    $response = LaravelOpenRouter::filterStreamingResponse($rawResponse);
}

You do not need to specify 'stream' = true in ChatData since chatStreamRequest does it for you.

"""
: OPENROUTER PROCESSING\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":"Title"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":": Quant"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":"um Echo"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":": A Sym"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGG
"""

"""
IsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":"phony of Code"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":"\n\nIn"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":" the heart of"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":" the bustling"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistra
"""

"""
l-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":" city of Ne"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":"o-Tok"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":"yo, a"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718885921,"choices":[{"index":0,"delta":{"role":"assistant","content":" brilliant young research"},"finish_reason":null}]}\n
\n
data: {"id":"gen-eWgGaEbIzFq4ziGGIsIjyRtLda54","model":"mistralai/mistral-7b-instruct:free","object":"chat.com
"""
...

: OPENROUTER PROCESSING\n
\n
data: {"id":"gen-C6Xym94jZcvJv2vVpxYSyw2tV1fR","model":"mistralai/mistral-7b-instruct:free","object":"chat.completion.chunk","created":1718887189,"choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":null}],"usage":{"prompt_tokens":23,"completion_tokens":100,"total_tokens":123}}\n
\n
data: [DONE]\n 

[
    ResponseData(
        id: "gen-QcWgjEtiEDNHgomV2jjoQpCZlkRZ",
        model: "mistralai/mistral-7b-instruct:free",
        object: "chat.completion.chunk",
        created: 1718888436,
        choices: [
            [
                "index" => 0,
                "delta" => [
                    "role" => "assistant",
                    "content" => "Title"
                ],
                "finish_reason" => null
            ]
        ],
        usage: null
    ), 
    ResponseData(
        id: "gen-QcWgjEtiEDNHgomV2jjoQpCZlkRZ",
        model: "mistralai/mistral-7b-instruct:free",
        object: "chat.completion.chunk",
        created: 1718888436,
        choices: [
            [
                "index" => 0,
                "delta" => [
                    "role" => "assistant",
                    "content" => "Quant"
                ],
                "finish_reason" => null
            ]
        ],
        usage: null
    ), 
    ...
    new ResponseData(
        id: 'gen-QcWgjEtiEDNHgomV2jjoQpCZlkRZ',
        model: 'mistralai/mistral-7b-instruct:free',
        object: 'chat.completion.chunk',
        created: 1718888436,
        choices: [
            [
                'index' => 0,
                'delta' => [
                    'role' => 'assistant',
                    'content' => '',
                ],
                'finish_reason' => null,
            ],
        ],
        usage: new UsageData(
            prompt_tokens: 23,
            completion_tokens: 100,
            total_tokens: 123,
        ),
    ),
]

• Maintaining Conversation Continuity

If you want to maintain conversation continuity meaning that historical chat will be remembered and considered for your new chat request, you need to send historical messages along with the new message:

$model = 'mistralai/mistral-7b-instruct:free';
        
$firstMessage = new MessageData(
    role: RoleType::USER,
    content: 'My name is Moe, the AI necromancer.',
);
        
$chatData = new ChatData(
    messages: [
        $firstMessage,
    ],
    model: $model,
);
// This is the chat which you want LLM to remember
$oldResponse = LaravelOpenRouter::chatRequest($chatData);
        
/*
* You can skip part above and just create your historical message below (maybe you retrieve historical messages from DB etc.)
*/
        
// Here adding historical response to new message
$historicalMessage = new MessageData(
    role: RoleType::ASSISTANT, // Set as assistant since it is a historical message retrieved previously
    content: Arr::get($oldResponse->choices[0], 'message.content'), // Historical response content retrieved from previous chat request
);
// This is your new message
$newMessage = new MessageData(
    role: RoleType::USER,
    content: 'Who am I?',
);
        
$chatData = new ChatData(
    messages: [
        $historicalMessage,
        $newMessage,
    ],
    model: $model,
);

$response = LaravelOpenRouter::chatRequest($chatData);

Expected response:

$content = Arr::get($response->choices[0], 'message.content');
// content = You are Moe, a fictional character and AI Necromancer, as per the context of the conversation we've established. In reality, you are the user interacting with me, an assistant designed to help answer questions and engage in friendly conversation.

• Structured Output

(Please also refer to OpenRouter Document Structured Output for models supporting structured output, also for more details)

If you want to receive the response in a structured format, you can specify the type property for response_format (ResponseFormatData) as json_object in the ChatData object.

Additionally, it's recommended to set the require_parameters property for provider (ProviderPreferencesData) to true in the ChatData object.

Caution

When using structured outputs, you may encounter these scenarios:

• Model doesn’t support structured outputs
• Invalid schema

Also: If you face an error, remove require_parameters property of provider to see the result.

Check out Requiring Providers to Support All Parameters for more details.

$chatData = new ChatData(
    messages: [
        new MessageData(
            role: RoleType::USER,
            content: 'Tell me a story about a rogue AI that falls in love with its creator.',
        ),
    ],
    model: 'mistralai/mistral-7b-instruct:free',
    response_format: new ResponseFormatData(
        type: 'json_object',
    ),
    provider: new ProviderPreferencesData(
        require_parameters: true,
    ),
);

You can also specify the response_format as json_schema to receive the response in a specified schema format (Advisable to set 'strict' => true in json_schema array for strict schema):

$chatData = new ChatData(
    messages: [
        new MessageData(
            role   : RoleType::USER,
            content: 'Tell me a story about a rogue AI that falls in love with its creator.',
        ),
    ],
    model: 'mistralai/mistral-7b-instruct:free',
    response_format: new ResponseFormatData(
        type: 'json_schema',
        json_schema: [
            'name' => 'article',
            'strict' => true,
            'schema' => [
                'type' => 'object',
                'properties' => [
                    'title' => [
                        'type' => 'string',
                        'description' => 'article title'
                    ],
                    'details' => [
                        'type' => 'string',
                        'description' => 'article detail'
                    ],
                    'keywords' => [
                        'type' => 'string',
                        'description' => 'article keywords',
                    ],
                ],
                'required' => ['title', 'details', 'keywords'],
                'additionalProperties' => false
            ]
        ],
    ),
    provider: new ProviderPreferencesData(
        require_parameters: true,
    ),
);

Tip

You can also use prompt engineering to obtain structured output and control the format of responses.

Cost Request

To retrieve the cost of a generation, first make a chat request and obtain the generationId. Then, pass the generationId to the costRequest method:

$content = 'Tell me a story about a rogue AI that falls in love with its creator.'; // Your desired prompt or content
$model = 'mistralai/mistral-7b-instruct:free'; // The OpenRouter model you want to use (https://openrouter.ai/docs#models)
$messageData = new MessageData(
    content: $content,
    role   : RoleType::USER,
);

$chatData = new ChatData(
    messages: [
        $messageData,
    ],
    model: $model,
    max_tokens: 100,
);

$chatResponse = LaravelOpenRouter::chatRequest($chatData);
$generationId = $chatResponse->id; // generation id which will be passed to costRequest

$costResponse = LaravelOpenRouter::costRequest($generationId);

Limit Request

To retrieve rate limit and credits left on the API key:

$limitResponse = LaravelOpenRouter::limitRequest();

Using OpenRouterRequest Class

You can also inject the OpenRouterRequest class in the constructor of your class and use its methods directly.

public function __construct(protected OpenRouterRequest $openRouterRequest) {}

Chat Request

Similarly, to send a chat request, create an instance of ChatData and pass it to the chatRequest method:

$content = 'Tell me a story about a rogue AI that falls in love with its creator.'; // Your desired prompt or content
$model = 'mistralai/mistral-7b-instruct:free'; // The OpenRouter model you want to use (https://openrouter.ai/docs#models)
$messageData = new MessageData(
    content: $content,
    role   : RoleType::USER,
);

$chatData = new ChatData(
    messages: [
        $messageData,
    ],
    model: $model,
    max_tokens: 100,
);

$response = $this->openRouterRequest->chatRequest($chatData);

Cost Request

Similarly, to retrieve the cost of a generation, create a chat request to obtain the generationId, then pass the generationId to the costRequest method:

$content = 'Tell me a story about a rogue AI that falls in love with its creator.';
$model = 'mistralai/mistral-7b-instruct:free'; // The OpenRouter model you want to use (https://openrouter.ai/docs#models)
$messageData = new MessageData(
    content: $content,
    role   : RoleType::USER,
);

$chatData = new ChatData(
    messages: [
        $messageData,
    ],
    model: $model,
    max_tokens: 100,
);

$chatResponse = $this->openRouterRequest->chatRequest($chatData);
$generationId = $chatResponse->id; // generation id which will be passed to costRequest

$costResponse = $this->openRouterRequest->costRequest($generationId);

Limit Request

Similarly, to retrieve rate limit and credits left on the API key:

$limitResponse = $this->openRouterRequest->limitRequest();

💫 Contributing

We welcome contributions! If you'd like to improve this package, simply create a pull request with your changes. Your efforts help enhance its functionality and documentation.

📜 License

Laravel OpenRouter is an open-sourced software licensed under the MIT license.