@anolilab/ai-model-registry

A comprehensive, unified registry for AI model providers and their metadata. This package provides a tree-shakable interface to access model information from 50+ AI providers including OpenAI, Anthropic, Google, Meta, Groq, and many others.

✨ Features

• 🔄 Unified Interface: Access models from multiple providers through a single API
• 🛡️ Type Safety: Full TypeScript support with Zod schema validation
• 📦 Tree Shaking: Import only what you need to minimize bundle size
• 💰 Rich Metadata: Comprehensive model information including capabilities, pricing, and limits
• 🔍 Powerful Search: Advanced search and filtering capabilities across all models
• 🔄 Auto-Sync: Automatic data synchronization between models with the same ID
• 💸 Pricing Integration: Real-time pricing data from Helicone API (840+ models)
• 📊 Provider Stats: Detailed statistics and analytics

📦 Installation

# Using npm
npm install @anolilab/ai-model-registry

# Using yarn
yarn add @anolilab/ai-model-registry

# Using pnpm
pnpm add @anolilab/ai-model-registry

🚀 Quick Start

import { getProviders, getModelsByProvider, getModelById, searchModels, getAllModels } from "@anolilab/ai-model-registry";

// Get all available providers
const providers = getProviders();
console.log(providers);
// ['Anthropic', 'Google', 'Groq', 'Meta', 'OpenAI', 'DeepSeek', ...]

// Get all models from a specific provider
const anthropicModels = getModelsByProvider("Anthropic");
console.log(`Found ${anthropicModels.length} Anthropic models`);

// Get a specific model by ID
const model = getModelById("claude-3-opus-20240229");
if (model) {
    console.log(`Model: ${model.name}`);
    console.log(`Provider: ${model.provider}`);
    console.log(`Cost: $${model.cost.input}/1K input, $${model.cost.output}/1K output`);
    console.log(`Context: ${model.limit.context?.toLocaleString()} tokens`);
}

// Search for models with specific capabilities
const visionModels = searchModels({ vision: true });
const reasoningModels = searchModels({ reasoning: true });
const toolCallModels = searchModels({ tool_call: true });

// Get all models for advanced filtering
const allModels = getAllModels();

📚 API Reference

Core Functions

getProviders(): string[]

Returns an array of all available provider names.

const providers = getProviders();
// ['Anthropic', 'Google', 'Groq', 'Meta', 'OpenAI', ...]

getModelsByProvider(provider: string): Model[]

Returns all models for a specific provider.

const openAIModels = getModelsByProvider("OpenAI");
const anthropicModels = getModelsByProvider("Anthropic");

getModelById(id: string): Model | undefined

Returns a specific model by its ID, or undefined if not found.

const gpt4 = getModelById("gpt-4");
const claude = getModelById("claude-3-opus-20240229");

getAllModels(): Model[]

Returns all models (useful for advanced filtering and custom logic).

const allModels = getAllModels();
const expensiveModels = allModels.filter((model) => (model.cost.input || 0) > 0.1 || (model.cost.output || 0) > 0.1);

getProviderStats(): Record<string, number>

Returns provider statistics with model counts.

const stats = getProviderStats();
console.log(stats);
// {
//   'OpenAI': 15,
//   'Anthropic': 8,
//   'Google': 12,
//   'Meta': 25,
//   ...
// }

Advanced Search

searchModels(criteria: SearchCriteria): Model[]

Search models by various criteria with powerful filtering options.

interface SearchCriteria {
    // Capability filters
    vision?: boolean;
    reasoning?: boolean;
    tool_call?: boolean;
    streaming_supported?: boolean;
    preview?: boolean;

    // Provider filter
    provider?: string;

    // Modality filters
    modalities?: {
        input?: string[];
        output?: string[];
    };

    // Context window filters
    context_min?: number;
    context_max?: number;

    // Cost filters
    max_input_cost?: number;
    max_output_cost?: number;
}

Search Examples

// Find all vision-capable models
const visionModels = searchModels({ vision: true });

// Find models with reasoning capabilities
const reasoningModels = searchModels({ reasoning: true });

// Find models that support tool calling
const toolCallModels = searchModels({ tool_call: true });

// Find models from a specific provider
const openAIModels = searchModels({ provider: "OpenAI" });

// Find models with large context windows
const largeContextModels = searchModels({ context_min: 100000 });

// Find affordable models
const affordableModels = searchModels({
    max_input_cost: 0.01,
    max_output_cost: 0.02,
});

// Find models that accept text and image input
const multimodalModels = searchModels({
    modalities: {
        input: ["text", "image"],
    },
});

// Find models with streaming support
const streamingModels = searchModels({ streaming_supported: true });

// Find preview/beta models
const previewModels = searchModels({ preview: true });

🏗️ Model Schema

Each model follows a comprehensive schema with the following structure:

interface Model {
    // Core identification
    id: string;
    name: string | null;
    provider?: string;
    providerId?: string;

    // Provider metadata
    providerEnv?: string[];
    providerNpm?: string;
    providerDoc?: string;
    providerModelsDevId?: string;

    // Date information
    releaseDate?: string | null;
    lastUpdated?: string | null;
    launchDate?: string;
    trainingCutoff?: string | null;

    // Capabilities
    attachment: boolean;
    reasoning: boolean;
    temperature: boolean;
    toolCall: boolean;
    openWeights: boolean;
    vision?: boolean;
    extendedThinking?: boolean;
    preview?: boolean;

    // Knowledge and context
    knowledge?: string | null;

    // Pricing structure
    cost: {
        input: number | null; // per 1K tokens
        output: number | null; // per 1K tokens
        inputCacheHit: number | null; // cache hit pricing
        imageGeneration?: number | null;
        imageGenerationUltra?: number | null;
        videoGeneration?: number | null;
        videoGenerationWithAudio?: number | null;
        videoGenerationWithoutAudio?: number | null;
    };

    // Limits
    limit: {
        context: number | null; // max tokens
        output: number | null; // max tokens
    };

    // Modalities
    modalities: {
        input: string[]; // ['text', 'image', 'audio', ...]
        output: string[]; // ['text', 'image', 'audio', ...]
    };

    // Infrastructure
    regions?: string[];
    streamingSupported?: boolean | null;
    deploymentType?: string;
    version?: string | null;

    // Provider-specific capabilities
    cacheRead?: boolean;
    codeExecution?: boolean;
    searchGrounding?: boolean;
    structuredOutputs?: boolean;
    batchMode?: boolean;
    audioGeneration?: boolean;
    imageGeneration?: boolean;
    compoundSystem?: boolean;

    // Version management
    versions?: {
        stable?: string | null;
        preview?: string | null;
    };

    // Additional metadata
    description?: string;
    ownedBy?: string;
    originalModelId?: string;
    providerStatus?: string;
    supportsTools?: boolean;
    supportsStructuredOutput?: boolean;
}

🌳 Tree Shaking

The package supports tree shaking, so you can import only what you need:

// Only import specific functions
import { getProviders, getModelById } from "@anolilab/ai-model-registry";

// Import schema for validation
import { ModelSchema } from "@anolilab/ai-model-registry/schema";

// Import icons (if needed)
import { getIcon } from "@anolilab/ai-model-registry/icons";

🏢 Supported Providers

The registry includes models from 50+ providers:

Major Providers

• OpenAI (GPT-4, GPT-3.5, O1, O3, etc.)
• Anthropic (Claude 3.5, Claude 3, Claude 2.1, etc.)
• Google (Gemini 2.5, Gemini 1.5, PaLM, etc.)
• Meta (Llama 3, Llama 2, Code Llama, etc.)
• Groq (Various models with ultra-fast inference)
• DeepSeek (DeepSeek R1, DeepSeek V3, etc.)

Specialized Providers

• Mistral AI (Mistral Large, Mixtral, etc.)
• Cohere (Command R, Command A, etc.)
• Perplexity (Sonar, Sonar Pro, etc.)
• Together AI (Various open models)
• Fireworks AI (Various models)
• Vercel (v0 models)

Open Source & Research

• HuggingFace (Various hosted models)
• ModelScope (Chinese models)
• OpenRouter (Aggregated models)
• GitHub Copilot (Code models)
• Azure (OpenAI models)

And many more...

🛠️ Development

Prerequisites

• Node.js 22+
• pnpm (recommended) or npm

Setup

# Clone the repository
git clone https://github.com/anolilab/ai-models.git
cd ai-models

# Install dependencies
pnpm install

# Complete build process
pnpm run download    # Download provider data
pnpm run aggregate   # Aggregate and enrich data
pnpm run generate-icons  # Generate provider icons
pnpm run build       # Build the package

# Run tests
pnpm test

Build Process

The complete build process involves several steps to download, aggregate, and generate all necessary data:

# 1. Download provider data
pnpm run download

# 2. Aggregate and enrich provider data (includes pricing from Helicone API)
pnpm run aggregate

# 3. Generate provider icons
pnpm run generate-icons

# 4. Build the package
pnpm run build

What Each Step Does

1. Download (pnpm run download): Downloads model data from various AI providers using transformers in scripts/download/transformers/. This creates the raw provider data in data/providers/.

2. Aggregate (pnpm run aggregate):

   • Reads all provider data from data/providers/
   • Fetches pricing data from Helicone API
   • Enriches models with icon information
   • Synchronizes data between models with the same ID
   • Generates data/all-models.json and src/models-data.ts

3. Generate Icons (pnpm run generate-icons):

   • Creates SVG sprite sheet from LobeHub icons and custom icons
   • Generates src/icons-sprite.ts with icon mappings
   • Provides fallback icons for providers without official icons

4. Build (pnpm run build):

   • Compiles TypeScript to JavaScript
   • Generates type definitions
   • Creates the final distributable package

Available Scripts

# Download provider data from various sources
pnpm run download

# Download data for a specific provider
pnpm run download --provider openai
pnpm run download --provider anthropic

# Aggregate provider data (includes pricing enrichment and synchronization)
pnpm run aggregate

# Generate provider icons
pnpm run generate-icons

# Build the package
pnpm run build

# Build for production
pnpm run build:prod

# Run tests
pnpm test

# Run tests with coverage
pnpm run test:coverage

# Lint code
pnpm run lint:eslint

# Type check
pnpm run lint:types

Project Structure

packages/ai-model-registry/
├── src/
│   ├── index.ts          # Main exports
│   ├── schema.ts         # Model schema definitions
│   └── models-data.ts    # Generated model data
├── scripts/
│   ├── aggregate-providers.ts    # Data aggregation script
│   ├── generate-svg-sprite.ts    # Icon generation
│   └── download/                 # Provider data downloaders
├── data/
│   ├── all-models.json   # Generated model data
│   └── providers/        # Raw provider data
└── assets/
    └── icons/            # Provider icons

💰 Pricing Data Integration

This package automatically includes real-time pricing data from Helicone's LLM Cost API during the aggregation process.

Features

• 🔄 Automatic Enrichment: Pricing data is automatically added during aggregation
• 🎯 Smart Matching: Uses multiple strategies to match models with pricing data
• 🛡️ Non-Destructive: Preserves existing pricing data while filling in missing values
• 🔄 Cost Conversion: Automatically converts from per 1M tokens to per 1K tokens format
• 📊 840+ Models: Covers pricing for 840+ models across all major providers

Supported Pricing Providers

Helicone provides pricing data for models from:

• OpenAI (GPT-4, GPT-3.5, O1, O3, etc.)
• Anthropic (Claude models)
• Google (Gemini models)
• Meta (Llama models)
• Mistral (Mistral models)
• Groq (Various models)
• And many more...

Usage

# Aggregate all models with pricing data
pnpm run aggregate

# Build the package (includes aggregation with pricing)
pnpm run build

🔄 Model Data Synchronization

The provider registry includes a powerful data synchronization system that automatically merges missing data between models with the same ID across different providers.

How It Works

1. Groups models by ID: Finds all models with the same ID across different providers
2. Calculates completeness scores: Evaluates how complete each model's data is (excluding cost fields)
3. Uses the most complete model as base: Selects the model with the highest data completeness
4. Merges missing data: Fills in missing fields from other models with the same ID
5. Preserves cost data: Never overwrites existing cost information

Protected Fields

The following cost-related fields are never synchronized to preserve pricing accuracy:

• cost (entire cost object)
• input (input cost)
• output (output cost)
• inputCacheHit (cache hit pricing)
• imageGeneration (image generation pricing)
• videoGeneration (video generation pricing)

Example

If you have the same model (e.g., gpt-4) from multiple providers:

OpenAI Provider:

{
    "id": "gpt-4",
    "name": "GPT-4",
    "cost": { "input": 0.03, "output": 0.06 },
    "description": null,
    "releaseDate": "2023-03-14"
}

Azure Provider:

{
    "id": "gpt-4",
    "name": null,
    "cost": { "input": 0.03, "output": 0.06 },
    "description": "GPT-4 is a large multimodal model",
    "releaseDate": null
}

Result after synchronization:

{
    "id": "gpt-4",
    "name": "GPT-4",
    "cost": { "input": 0.03, "output": 0.06 },
    "description": "GPT-4 is a large multimodal model",
    "releaseDate": "2023-03-14"
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Adding New Providers

1. Create a transformer in scripts/download/transformers/
2. Add provider configuration to scripts/config.ts
3. Run pnpm run download --provider <provider-name>
4. Test with pnpm run aggregate

Reporting Issues

Please use our Issue Tracker to report bugs or request features.

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Helicone for providing pricing data
• OpenRouter for reference data
• All the AI providers for their amazing models
• The open source community for inspiration and tools

📞 Support

• Documentation: GitHub Wiki
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@anolilab.com

Made with ❤️ by AnoliLab