MiniAI

A minimalist Python library for byte-sized AI tasks. No complex chains, no confusing abstractions, just AI that works.

pip install miniai

The Problem

Using AI models for simple tasks comes with too much overhead: Writing boilerplate API calls, managing prompts and message lists, configuring providers and models, learning complex frameworks like LangChain.

These barriers make it annoying to quickly prototype and integrate AI capabilities into your workflow.

The Solution

MiniAI provides a dead-simple interface for common AI tasks.

from miniai import ai

text = ai.ask("Write a haiku about Python")

ai.ask() is a powerful function that can do anything. But here are some helper functions for common tasks:

image_analysis = ai.ask("What's in this image?", images=["image.png", "https://example.com/image.png"])

category = ai.classify("I love this product!", ["positive", "negative", "neutral"])

entities = ai.extract("Apple was founded by Steve Jobs in 1976", ["people", "organizations", "dates"])

summary = ai.summarize("Very long text")

translated = ai.translate("Hello world", to="spanish")

audio = ai.text_to_speech("Hello world")

text = ai.speech_to_text(audio) # Transcription

See more examples in the examples directory.

Intuitive and Flexible

The only terminology you need to know is "provider" and "model". A provider is an AI service/company like OpenAI or Anthropic. A model is a specific AI model like GPT-4o or Claude 3.5.

The defaults will usually get your work done, but if you need more control, it's super intuitive. Need a different provider? A different model? A system prompt? Additional settings? just pass a parameter to ai.ask().

text = ai.ask("Write a haiku about Python", system_prompt="Respond in Spanish") # uses gpt-4o from openai by default

text = ai.ask("Write a haiku about Python", provider="anthropic")

text = ai.ask("Write a haiku about Python", provider="openai", model="gpt-3.5-turbo", temperature=0.5, max_tokens=100)

If you need the raw response from the provider, just pass raw_response=True.

response = ai.ask("Write a haiku about Python", raw_response=True)
print(response.content) # The text answer
print(response.raw_response) # Full provider response

Turn Any Function into an AI Function

The function decorator is a powerful way to turn any function into an AI function.

@ai.function
def generate_poem(topic, style):
    """Generate a poem about {topic} in the style of {style}."""

poem = generate_poem("autumn leaves", "haiku")
print(poem)

# With system prompt and model
@ai.function(system_prompt="You are a professional software engineer.", model="gpt-4o-mini")
def write_code(task, language):
    """Write {language} code to {task}. Include comments."""

code = write_code("sort a list", "python")

Why Choose MiniAI?

• 🚀 Simple API: Just one import, intuitive methods
• 🔧 Zero configuration: Works out of the box (with environment variables)
• 🧠 Smart defaults: Uses appropriate models for each task
• 🔄 Model agnostic: Works with OpenAI, Anthropic, and more coming soon
• 📦 Lightweight: No heavy dependencies
• 🧩 Extensible: Easy to add new providers and tasks
• 🛠️ Error handling: Clear and helpful error messages

API Reference

API Keys: MiniAI requires API keys for the desired providers. You can set these using environment variables (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY) or programmatically using ai.set_api_key(provider, key).

Core Function

Helper Functions

Accessing Raw Provider Output: By default, MiniAI functions return a directly usable result (e.g., a string for ai.ask, a list for ai.extract). To get the complete, unmodified response from the underlying AI provider's API, pass raw_response=True. This returns a Response object containing:

• content: The processed output (same as when raw_response=False).
• raw_response: The full, untouched response object from the provider.

Configuration

Note: Use ai.use('mock') to enable the mock provider for testing without API keys.

Decorator

License

MIT

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for step-by-step guidelines.