llm-kit-pro

llm-kit-pro is a unified, async-first Python toolkit for interacting with multiple Large Language Model (LLM) providers through a consistent, provider-agnostic API.

It is designed for developers who need to switch between providers (OpenAI, Gemini, Anthropic/Bedrock) without rewriting their core application logic, with a heavy emphasis on structured data and multimodal inputs.

✨ Features

• Unified API: One interface for OpenAI, Gemini, Anthropic, and AWS Bedrock.
• Pydantic-First Structured Output: Pass Pydantic models directly to get validated, type-safe dictionaries back.
• Native "Strict Mode": Automatically handles OpenAI's Structured Outputs requirements.
• Multimodal Inputs: First-class support for PDF, PNG, JPEG, and Text files across all supported providers.
• Async-First: Built from the ground up for high-performance asynchronous Python applications.
• Provider-Agnostic Inputs: Use LLMFile to handle different file types without worrying about provider-specific formatting.
• Universal File Loader: Load files from local paths or URLs with automatic MIME type detection.

📦 Installation

pip install llm-kit-pro

Install with specific provider support:

# For OpenAI
pip install "llm-kit-pro[openai]"

# For Google Gemini
pip install "llm-kit-pro[gemini]"

# For Anthropic
pip install "llm-kit-pro[anthropic]"

# For AWS Bedrock (Anthropic/Llama/etc)
pip install "llm-kit-pro[bedrock]"

🚀 Quick Start

1. Simple Text Generation

from llm_kit_pro.providers.openai import OpenAIClient
from llm_kit_pro.providers.openai.config import OpenAIConfig

client = OpenAIClient(OpenAIConfig(
    api_key="your-key",
    model="gpt-4o-mini"
))

text = await client.generate_text(
    prompt="Explain quantum entanglement like I'm five."
)
print(text)

2. Structured Data with Pydantic

Instead of messy regex or manual JSON parsing, define your schema as a Pydantic model. llm-kit-pro handles the schema injection, strict mode enforcement, and final validation.

from pydantic import BaseModel
from llm_kit_pro.providers.gemini import GeminiClient
from llm_kit_pro.providers.gemini.config import GeminiConfig

class MovieReview(BaseModel):
    title: str
    rating: int
    summary: str
    sentiment: str

client = GeminiClient(GeminiConfig(
    api_key="your-key",
    model="gemini-2.5-flash"
))

# Pass the class directly!
data = await client.generate_json(
    prompt="Review the movie 'Inception'",
    schema=MovieReview
)

print(data["rating"])

3. Multimodal: Extracting from a PDF

llm-kit-pro treats files as first-class citizens. You can pass images or PDFs directly to the model.

from llm_kit_pro.core.helpers import load_file
from pydantic import BaseModel

class Invoice(BaseModel):
    vendor: str
    amount: float
    due_date: str

# Load your file (from local path or URL)
pdf = load_file("invoice.pdf")
# Or from URL: pdf = await load_file_async("https://example.com/invoice.pdf")

# Extract structured data from the document
data = await client.generate_json(
    prompt="Extract the invoice details",
    schema=Invoice,
    files=[pdf]
)

🧠 Core Abstractions

BaseLLMClient

Every provider implements this interface, ensuring your code remains portable.

• generate_text(prompt, files=None, **kwargs) -> str
• generate_json(prompt, schema, files=None, **kwargs) -> dict

LLMFile

A simple container for file-based inputs.

• content: Raw bytes.
• mime_type: e.g., application/pdf, image/jpeg.
• filename: Optional metadata.

File Loading Helpers

Utilities to load files from local paths or URLs:

• load_file(source) -> LLMFile - Universal loader (auto-detects local vs URL)
• load_file_async(source) -> LLMFile - Async version
• load_file_from_path(path) -> LLMFile - Load from local filesystem
• load_file_from_url(url) -> LLMFile - Download from URL

See File Loader Guide for detailed documentation.

🔌 Supported Providers

📍 Status

🚧 Under active development

The public API is stabilizing. We are currently focusing on adding more Bedrock adapters (Llama 3, Titan).

📄 License

MIT License