AI SDK for Crystal

A Crystal port of the Vercel AI SDK - providing a unified interface for interacting with AI providers.

Features

• Unified API - Single interface for multiple AI providers
• Type-safe - Full Crystal type safety with compile-time checks
• Streaming support - Real-time streaming responses via SSE
• Multiple model types - Language, embedding, and image models
• Provider tools - Google Search, Code Execution, Web Search, and more

Supported Providers

Installation

Add to your shard.yml:

dependencies:
  ai-sdk:
    github: krthr/ai-sdk
    version: ~> 0.1.0

Then run:

shards install

Quick Start

Google Generative AI

require "ai_sdk"

# Set environment variable: GOOGLE_GENERATIVE_AI_API_KEY

# Create a model
model = AI::Google.google.chat("gemini-2.0-flash")

# Generate text
result = model.do_generate(
  AI::Provider::LanguageModel::CallOptions.new(
    prompt: [
      AI::Provider::LanguageModel::UserMessage.new(
        content: [AI::Provider::LanguageModel::TextPart.new(text: "Hello, how are you?")]
      )
    ] of AI::Provider::LanguageModel::Message
  )
)

puts result.text

OpenAI

require "ai_sdk"

# Set environment variable: OPENAI_API_KEY

# Create a model
model = AI::OpenAI.openai.chat("gpt-4o")

# Generate text
result = model.do_generate(
  AI::Provider::LanguageModel::CallOptions.new(
    prompt: [
      AI::Provider::LanguageModel::UserMessage.new(
        content: [AI::Provider::LanguageModel::TextPart.new(text: "What is Crystal programming language?")]
      )
    ] of AI::Provider::LanguageModel::Message
  )
)

puts result.text

Streaming

# Stream text generation
stream_result = model.do_stream(options)

stream_result.stream.each do |part|
  case part
  when AI::Provider::LanguageModel::TextStartPart
    # Text generation started
  when AI::Provider::LanguageModel::TextDeltaPart
    print part.text_delta
  when AI::Provider::LanguageModel::FinishPart
    puts "\nFinished: #{part.finish_reason.unified}"
  end
end

Embeddings

# Google embeddings
embedding_model = AI::Google.google.embedding("text-embedding-004")

result = embedding_model.do_embed(
  AI::Provider::EmbeddingModel::CallOptions.new(
    values: ["Hello, world!"]
  )
)

puts result.embeddings.first # Array of Float64

# OpenAI embeddings
embedding_model = AI::OpenAI.openai.embedding("text-embedding-3-small")

Image Generation

# Google image generation
image_model = AI::Google.google.image("imagen-3.0-generate-002")

result = image_model.do_generate(
  AI::Provider::ImageModel::CallOptions.new(
    prompt: "A beautiful sunset over mountains",
    n: 1
  )
)

# OpenAI image generation
image_model = AI::OpenAI.openai.image("dall-e-3")

Tool Calling

# Define a function tool
weather_tool = AI::Provider::LanguageModel::FunctionTool.new(
  name: "get_weather",
  description: "Get the current weather for a location",
  input_schema: JSON::Any.new({
    "type" => JSON::Any.new("object"),
    "properties" => JSON::Any.new({
      "location" => JSON::Any.new({
        "type" => JSON::Any.new("string"),
        "description" => JSON::Any.new("City name")
      })
    }),
    "required" => JSON::Any.new(["location"])
  })
)

result = model.do_generate(
  AI::Provider::LanguageModel::CallOptions.new(
    prompt: messages,
    tools: [weather_tool] of AI::Provider::LanguageModel::FunctionTool | AI::Provider::LanguageModel::ProviderTool
  )
)

Provider-Specific Tools

# Google Search (Google)
google_search = AI::Google::Tools.google_search

# Web Search (OpenAI)
web_search = AI::OpenAI::Tools.web_search(
  search_context_size: "medium",
  user_location: AI::OpenAI::Tools::UserLocation.new(
    type: "approximate",
    country: "US"
  )
)

# Code Interpreter (OpenAI)
code_interpreter = AI::OpenAI::Tools.code_interpreter

Configuration

Custom API Keys

# Google
provider = AI::Google.create_google_generative_ai(
  api_key: "your-api-key",
  base_url: "https://custom-endpoint.example.com"
)

# OpenAI
provider = AI::OpenAI.create_openai(
  api_key: "your-api-key",
  base_url: "https://custom-endpoint.example.com",
  organization: "org-xxx",
  project: "proj-xxx"
)

Environment Variables

API Reference

Model Types

• LanguageModel::V3 - Text generation (chat completions)
• EmbeddingModel::V3 - Text embeddings
• ImageModel::V3 - Image generation

Core Types

• CallOptions - Request configuration
• GenerateResult - Generation response
• StreamResult - Streaming response with iterator
• Usage - Token usage information

Development

# Install dependencies
shards install

# Run tests
crystal spec

# Run linter
./bin/ameba

# Format code
crystal tool format

Architecture

This SDK follows the Vercel AI SDK Provider Specification v3:

• Synchronous API - Blocking calls (Crystal fibers for concurrency)
• JSON::Serializable - Type-safe JSON handling
• Iterator for Streaming - Crystal iterators for SSE streams
• Crest HTTP Client - All HTTP via Crest library

See context/KNOWLEDGE.md for detailed patterns and decisions.

Contributing

1. Fork it
2. Create your feature branch (git checkout -b feature/my-feature)
3. Run tests (crystal spec)
4. Run linter (./bin/ameba)
5. Commit your changes (git commit -am 'Add my feature')
6. Push to the branch (git push origin feature/my-feature)
7. Create a Pull Request

License

MIT License - see LICENSE for details.

Acknowledgments

• Vercel AI SDK - Original TypeScript implementation
• Crest - HTTP client for Crystal