semvec/pypi-packages huggingface dataset:
axcent 0.3.0
The easiest way to build AI agents in Python
Requires Python: >=3.8

# Axcent

**The easiest way to build AI agents in Python.**

Axcent is a lightweight framework designed to let you build powerful AI agents with tool calling, context caching, and multi-backend support in just a few lines of code.

## Installation

```bash
pip install axcent
```

To use Gemini models:
```bash
pip install axcent[gemini]
```

## Quick Start (OpenAI)

```python
import os
from axcent import Agent

# Set your API Key
os.environ["OPENAI_API_KEY"] = "sk-..."

# Initialize Agent
agent = Agent(system_prompt="You are a helpful assistant.")

# Register a Tool
@agent.tool
def get_weather(city: str) -> str:
    """Returns weather info for a city."""
    return f"The weather in {city} is sunny!"

# Ask away!
response = agent.ask("What is the weather in Tokyo?")
print(response)
```

## Features

- **Simple Tool Registration**: Just use `@agent.tool`.
- **Automatic Context Caching**: Optimizes token usage by enforcing stable prompt structures.
- **Token Monitoring**: Track prompt, completion, and cached tokens via `agent.get_total_usage()`.
- **Backend Agnostic**:
    - **OpenAI**: First-class support.
    - **Google Gemini**: Support for all of the latest models.
    - **OpenRouter**: Use any model via OpenRouter API compatibility.

## Multi-Backend Usage

### Google Gemini

```python
from axcent import Agent, GeminiBackend
import os

# Set API Key (or GOOGLE_API_KEY)
os.environ["GEMINI_API_KEY"] = "AIza..."

# Use Gemini Backend (uses google-genai V2 SDK)
backend = GeminiBackend(model="gemini-3-flash")
agent = Agent(system_prompt="You are a helper.", backend=backend)
```

### OpenRouter

```python
import os
from axcent import Agent

os.environ["OPENAI_API_KEY"] = "sk-or-..."
os.environ["OPENAI_BASE_URL"] = "https://openrouter.ai/api/v1"

agent = Agent(system_prompt="You are a helper.")
```

## License

MIT
nagoya-bus-mcp 0.7.0
Model Context Protocol (MCP) server providing tools to query Nagoya City bus timetables
Requires Python: >=3.11
Keywords: mcp, model context protocol, fastmcp, nagoya, bus, public transport, transit

# nagoya-bus-mcp
[![PyPI - Version](https://img.shields.io/pypi/v/nagoya-bus-mcp)](https://pypi.org/project/nagoya-bus-mcp/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nagoya-bus-mcp)](https://pypi.org/project/nagoya-bus-mcp/)
[![CI](https://github.com/ymyzk/nagoya-bus-mcp/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/ymyzk/nagoya-bus-mcp/actions/workflows/ci.yml)

## Getting started
The Nagoya Bus MCP server is published to PyPI.

### Claude Desktop
Add the following configuration to `claude_desktop_config.json`.
```json
{
  "mcpServers": {
    "nagoya-bus": {
      "command": "uvx",
      "args": ["nagoya-bus-mcp"]
    }
  }
}
```

### Visual Studio Code
Add the following configuration to `.vscode/mcp.json`.
```json
{
  "servers": {
    "nagoya-bus": {
      "type": "stdio",
      "command": "uvx",
      "args": ["nagoya-bus-mcp"],
      "env": {}
    }
  }
}
```

### Manual
```shell
# Using uvx
$ uvx nagoya-bus-mcp

# Using Docker
$ docker run -i --rm ghcr.io/ymyzk/nagoya-bus-mcp
```

## For developers
```
# Use MCP Inspector
$ npx @modelcontextprotocol/inspector uv run nagoya-bus-mcp

# Try API client
$ uv run python -m nagoya_bus_mcp.client
```