mbxai 0.5.5

MBX AI SDK

MBX AI

A Python library for building AI applications with LLMs.

Features

• OpenRouter Integration: Connect to various LLM providers through OpenRouter
• Tool Integration: Easily integrate tools with LLMs using the Model Context Protocol (MCP)
• Structured Output: Get structured, typed responses from LLMs
• Chat Interface: Simple chat interface for interacting with LLMs
• FastAPI Server: Built-in FastAPI server for tool integration

Installation

pip install mbxai

Quick Start

Basic Usage

from mbxai import OpenRouterClient

# Initialize the client
client = OpenRouterClient(api_key="your-api-key")

# Chat with an LLM
response = await client.chat([
    {"role": "user", "content": "Hello, how are you?"}
])
print(response.choices[0].message.content)

Using Tools

from mbxai import OpenRouterClient, ToolClient
from pydantic import BaseModel

# Define your tool's input and output models
class CalculatorInput(BaseModel):
    a: float
    b: float

class CalculatorOutput(BaseModel):
    result: float

# Create a calculator tool
async def calculator(input: CalculatorInput) -> CalculatorOutput:
    return CalculatorOutput(result=input.a + input.b)

# Initialize the client with tools
client = ToolClient(OpenRouterClient(api_key="your-api-key"))
client.add_tool(calculator)

# Use the tool in a chat
response = await client.chat([
    {"role": "user", "content": "What is 2 + 3?"}
])
print(response.choices[0].message.content)

Using MCP (Model Context Protocol)

from mbxai import OpenRouterClient, MCPClient
from mbxai.mcp import MCPServer
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel

# Define your tool's input and output models
class CalculatorInput(BaseModel):
    a: float
    b: float

class CalculatorOutput(BaseModel):
    result: float

# Create a FastMCP instance
mcp = FastMCP("calculator-service")

# Create a calculator tool
@mcp.tool()
async def calculator(argument: CalculatorInput) -> CalculatorOutput:
    return CalculatorOutput(result=argument.a + argument.b)

# Start the MCP server
server = MCPServer("calculator-service")
await server.add_tool(calculator)
await server.start()

# Initialize the MCP client
client = MCPClient(OpenRouterClient(api_key="your-api-key"))
await client.register_mcp_server("calculator-service", "http://localhost:8000")

# Use the tool in a chat
response = await client.chat([
    {"role": "user", "content": "What is 2 + 3?"}
])
print(response.choices[0].message.content)

Development

Setup

1. Clone the repository:

git clone https://github.com/yourusername/mbxai.git
cd mbxai

2. Create a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -e ".[dev]"

Running Tests

pytest tests/

License

MIT License