semvec/pypi-packages huggingface dataset:
contextflow-ai 0.1.3
Context optimization for AI agents
Requires Python: >=3.10

<p align="center">
  <p align="center">
	<img width="128" height="128" src="https://github.com/athaapa/contextflow/blob/74d4dffdb3c0c1777ccd3593b7d52f89f9406afc/images/logo.png" alt="Logo">
  </p>

  <h1 align="center"><b>ContextFlow</b></h1>
  <p align="center">
     Context optimization for AI agents.
    <br />
  </p>
</p>

**ContextFlow** is a fast, open-source Python library that compresses chat, agent, or RAG context before you send it to an expensive LLM. It finds the “needle in the haystack,” scoring every message by utility, keeping only critical facts, summarizing the rest, and dropping noise—preserving quality while saving money.

# Why ContextFlow?
Most LLM applications waste money by sending entire chat histories, long chains of document chunks, or endless boilerplate every API call. Most of it is redundant, repetitive, or irrelevant to the user’s actual goal.

ContextFlow solves this by:
- Ranking every message by utility using a fast LLM batch
- Keeping only high-signal content (order numbers, errors, decisions)
- Aggressively summarizing medium-utility content
- Dropping low-value fluff and polite filler
- Always preserving the last few recent messages for context

# Quickstart
## Installation
```bash
pip install contextflow
```
## Setup
Get your Gemini API Key (for message scoring and summarization):
```.env
export GEMINI_API_KEY="YOUR_KEY_HERE"
```
If you don't have one, you can get one for free [here](https://aistudio.google.com/api-keys).
## Example
```python
from contextflow import ContextFlow

# Chat history (list of {"role": str, "content": str})
messages = [
    # ... up to 50 messages ...
]

cf = ContextFlow()

result = cf.optimize(
    messages=messages,
    agent_goal="Resolve customer shipping inquiry",  # this guides scoring
    max_tokens=400  # your target for output
)

print("Tokens before:", result["analytics"]["tokens_before"])
print("Tokens after:", result["analytics"]["tokens_after"])
print("Reduction:", result["analytics"]["reduction_pct"], "%")
print("Messages ready for LLM:", result["messages"])

# Now use result.messages in your LLM query!
# response = openai.ChatCompletion.create(model="gpt-4", messages=result.messages)
```
# License
MIT License - see [LICENSE](https://github.com/athaapa/contextflow/blob/6b79c95d55c53c40bd8f500a6c921ea394c32666/LICENSE) file for details.
octoq 1.0.1
A quantum computation package
Requires Python: >=3.8
Keywords: quantum computation

# octoq

#### 介绍
quantum computation

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
kitty-maze 1.1.1
kitty maze game
Requires Python: >=3.10

# Kitty Maze

## Launch

`pip install kitty-maze && kitty-maze`

## Controls

* `ctrl-C`: quit current screen
* `F11`: fullscreen

## Gameplay

* Use the arrow keys to move the player character.
* Walk into objects to learn more about them.
* Click on objects to interact with them. Not all objects are interactive.
* The right side bar contains an inventory, a map, and a narration area. Click
  on filled inventory slots to use items.
blazeinfer 0.0.2
A high-performance, light-weight llm inference framework.
Requires Python: >=3.10

<div align="center">

![# BlazeInfer](assets/logo.png)
A high-performance, light-weight llm inference framework.

</div>

<div align="center">

[![PyPI version](https://img.shields.io/pypi/v/blazeinfer.svg)](https://pypi.org/project/blazeinfer/)

</div>

--------------------------------------------------------------------------------

## About

## Features
- Optimized Attention Kernels

## Getting Started

## Acknowledgement
- [lite_llama](https://github.com/harleyszhang/lite_llama/tree/main)
tingting 1.0.1
Python client for TingTing
Requires Python: >=3.7
Keywords: tingting, api, client, sdk

# TingTing Python Client

A lightweight Python client for interacting with the [TingTing API](https://app.tingting.io).  
Easily create and manage SMS/voice campaigns, contacts, and more — all from Python.

---

## 📦 Installation

```bash
pip install tingting
```

---

## 🚀 Quickstart Example

```python
import os
from dotenv import load_dotenv
from tingting import Campaign, Category, Contact, Services, TingTingClient

# Load environment variables (BASE_URL, TOKEN)
load_dotenv()

BASE_URL = os.environ.get("BASE_URL", "https://app.tingting.io")
TOKEN = os.environ.get("TOKEN", "your_api_token_here")

# Initialize client
client = TingTingClient(BASE_URL, TOKEN)

# Fetch available dialers & voices
dialers = client.get_dialers()
voices = client.get_voices()

# Create a campaign
campaign = Campaign(
    client=client,
    name="Phone Campaign Name",
    services=Services.PHONE,
    category=Category.TEXT,
    message="This is the phone message to be said",
    dialers=dialers,
    voice=voices[0],
    lengthFactor=1,  # Only works for premium voice
).create()

# Add contacts to the campaign
for i in range(4):
    contact = Contact(
        client=client,
        phoneNumber=str(9990000000 + i),
        attributes={"name": "John", "age": 100 + i},
    )
    campaign.add_contact(contact)

# Run the campaign
campaign.run()
```
syncraft 0.3.2
Parser combinator library
Requires Python: >=3.10
Keywords: parser, combinator, sql, sqlite, generator, printer

# Syncraft

Syncraft is a parser/generator combinator library for Python. It helps you

- Build grammars
- Parse SQL statement to AST
- Search AST by grammar
- Convert AST to dataclass
- Check constraints over the AST/dataclass
- Change dataclass and convert back to AST


## Installation

### pip
```bash
pip install syncraft
```

### uv
```bash
uv add syncraft
```

Python 3.10+ is required.

### With pip
```bash
pip install syncraft[dev]
```

### With uv
```bash
uv sync --group dev 
```

TODO
- [ ]  Static analysis tool based on Syntax.graph
- [ ]  Profile hook on Cache
- [ ]  try_parse based on Syntax.parallel
- [ ]  regex.py convert Regex to FABuilder
- [ ]  Semantic layer (datalog + unification).
rrdtool-bindings 0.5.0
Modern Python packaging for RRDtool Python bindings to C extension.
Requires Python: >=3.10
Keywords: rrdtool

# rrdtool-bindings

Python bindings from [RRDtool](https://oss.oetiker.ch/rrdtool),
packaged to work with modern Python packaging tools.

Spiritual successor to [python-rrdtool](https://pypi.org/project/rrdtool/),
which has not been updated since 2022 and the GitHub project was archived in 2023.
It failed to install/compile via `uv` or `pdm` on recent Fedora releases,
so this project was born.

Supported Python versions: 3.10+

## Installation

This package compiles the bindings, so it requires the following dependencies:

* Debian/Ubuntu: `apt-get install -y gcc librrd-dev python3-dev`
* Fedora/Red Hat: `dnf install -y gcc rrdtool-devel python3-devel`

Then run:

    pip install rrdtool-bindings

## Usage

Example from the RRDtool documentation.

```python
import rrdtool

# Create Round Robin Database
rrdtool.create('test.rrd', '--start', 'now', '--step', '300', 'RRA:AVERAGE:0.5:1:1200', 'DS:temp:GAUGE:600:-273:5000')

# Feed updates to the RRD
rrdtool.update('test.rrd', 'N:32')
```

See [rrdpython](https://oss.oetiker.ch/rrdtool/prog/rrdpython.en.html) and `tests/test_bindings.py` for more examples.

## History

### v0.5.0 - 2025-11-10

* Add support for Python 3.14.
* Drop support for end-of-life Python 3.9.
* Add Linux platform check prior to check for Debian 13.

### v0.4.0 - 2025-09-15

* Fix for Debian 13 (Trixie).

### v0.3.1 - 2025-04-07

* Update change history in README.

### v0.3.0 - 2025-04-07

* Update supported Python versions.

### v0.2.0 - 2024-05-31

Initial release.

Uses the C code from [5JAN24](https://github.com/oetiker/rrdtool-1.x/blob/b39df920f0ff31a49460d9872006a2579ee4c7ed/bindings/python/rrdtoolmodule.c).


## Contributing

Changes to the C code should be submitted [upstream](https://github.com/oetiker/rrdtool-1.x),
then copied to this repository.

This project uses [uv](https://docs.astral.sh/uv/),
so you will need that installed first.
Then fork/clone the repository and run `uv sync`.
Wheels can be compiled locally via `uv build`.

[`just`](https://github.com/casey/just) is used as a task runner for convenience,
but it is optional,
any of the commands in `Justfile` can be ran by hand.
atsphinx-audioplayer 0.2.1
Register directive to render audio element
Requires Python: >=3.9

====================
atsphinx-audioplayer
====================

Register directive to render audio element

Getting started
===============

.. code:: console

   pip install atsphinx-audioplayer

Usage
=====

Write ``audio`` directive into your document.

.. code:: rst

   .. audio:: talk.mp3

You can write as role too.

.. code:: rst

   :audio:`talk.mp3`
iflow-mcp_qbittorrent-mcp 0.1.0
Add your description here
Requires Python: >=3.11

# qBittorrent MCP 服务

qBittorrent MCP 是一个基于 FastMCP 的服务，提供了与 qBittorrent WebUI API 交互的功能接口。

## 功能列表

该服务提供了以下功能：

### 种子管理
- `add_torrent`: 添加种子文件到 qBittorrent
- `delete_torrent`: 删除指定种子（可选同时删除文件）
- `pause_torrent`: 暂停种子下载
- `resume_torrent`: 恢复种子下载
- `get_torrent_list`: 获取所有种子列表

### 跟踪器与标签
- `get_torrent_trackers`: 获取种子的跟踪器列表
- `add_trackers_to_torrent`: 向种子添加新的跟踪器
- `add_torrent_tags`: 为种子添加标签

### 速度与优先级控制
- `set_global_download_limit`: 设置全局下载速度限制
- `set_global_upload_limit`: 设置全局上传速度限制
- `set_torrent_download_limit`: 设置特定种子的下载速度限制
- `set_torrent_upload_limit`: 设置特定种子的上传速度限制
- `set_file_priority`: 设置特定文件的下载优先级

### 系统信息
- `get_application_version`: 获取qBittorrent应用程序版本

## 配置

服务使用以下配置参数：
- `DEFAULT_HOST`: qBittorrent WebUI的主机地址
- `DEFAULT_USERNAME`: qBittorrent WebUI用户名
- `DEFAULT_PASSWORD`: qBittorrent WebUI密码

## 使用方法

1. 确保已安装所需依赖：
   ```
   pip install httpx mcp
   ```

2. 运行MCP服务：
   ```
   python main.py
   ```

## 开发

服务分为两个主要文件：
- `main.py`: 定义MCP服务接口和配置参数
- `api.py`: 实现与qBittorrent WebUI的交互逻辑
```json
   "mcp_servers": [
        {
            "command": "uv",
            "args": [
                "--directory",
                "/workspace/PC-Canary/apps/qBittorrent/qbittorrent_mcp",
                "run",
                "qbittorrent.py"
            ]
        }
    ]
```
iflow-mcp_modal-mcp-toolbox 0.1.12
A collection of Model Context Protocol (MCP) tools for Modal
Requires Python: <3.13,>=3.10

# Modal MCP Toolbox 🛠️

[![smithery badge](https://smithery.ai/badge/@philipp-eisen/modal-mcp-toolbox)](https://smithery.ai/server/@philipp-eisen/modal-mcp-toolbox)

A collection of Model Context Protocol (MCP) tools that run on Modal.
This let's you extend the capabilities of your LLM in tools such as [Goose](https://block.github.io/goose/) or the [Claude Desktop App](https://claude.ai/download).

<a href="https://glama.ai/mcp/servers/ai78w0p5mc"><img width="380" height="200" src="https://glama.ai/mcp/servers/ai78w0p5mc/badge" alt="Modal Toolbox MCP server" /></a>

## Tools

- `run_python_code_in_sandbox`: Let's you run python code in a sandboxed environment.
- `generate_flux_image`: Generate an image using the FLUX model.

## Demo

### Flux Image Generation

![🎬Flux Image Generation](./assets/flux.gif)

### Python Code Execution

![🎬Python Code Execution](./assets/python-sandbox.gif)

## Prerequisites

- A [modal account](https://modal.com/signup) and a configured modal CLI.
- [UV](https://github.com/astral-sh/uv?tab=readme-ov-file#installation)
- A client that supports MCP. Such as the [Claude Desktop App](https://claude.ai/download) or [Goose](https://block.github.io/goose/)

This runs against your modal account, so you will need to have a modal account and be logged in.

## Installation

Installation depends on the client that uses the MCP. Here is instructions for Claude and Goose.

### Claude

Got to `Settings > Developer` in the Claude Desktop App. And click on Edit Config.
![🖼️Claude Settings](./assets/claude-settings.png)

Add the config for the mcp server. My config looks like this:

```json
{
  "mcpServers": {
    "modal-toolbox": {
      "command": "uvx",
      "args": ["modal-mcp-toolbox"]
    }
  }
}
```

### Goose

Go to `Settings` and Click on Add.

![🖼️Goose Settings](./assets/goose-settings-1.png)

Then add an extension like in the screenshot below.
The important part is to set command to:

```
uvx modal-mcp-toolbox
```

The rest you can fill in as you like.

![🖼️Goose MCP Settings](./assets/goose-settings-2.png)

### Installing via Smithery (not working currently)

To install Modal MCP Toolbox for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@philipp-eisen/modal-mcp-toolbox):

```bash
npx -y @smithery/cli install @philipp-eisen/modal-mcp-toolbox --client claude
```
iflow-mcp-vegalite-server 0.0.1
A simple Data Visualization MCP server using Vega-Lite
Requires Python: >=3.10

[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/isaacwasserman-mcp-vegalite-server-badge.png)](https://mseep.ai/app/isaacwasserman-mcp-vegalite-server)

# Data Visualization MCP Server
[![smithery badge](https://smithery.ai/badge/mcp-server-vegalite)](https://smithery.ai/server/mcp-server-vegalite)

## Overview
A Model Context Protocol (MCP) server implementation that provides the LLM an interface for visualizing data using Vega-Lite syntax.

## Components

### Tools
The server offers two core tools:

- `save_data`
   - Save a table of data agregations to the server for later visualization
   - Input:
     - `name` (string): Name of the data table to be saved
     - `data` (array): Array of objects representing the data table
   - Returns: success message
- `visualize_data`
   - Visualize a table of data using Vega-Lite syntax
   - Input:
     - `data_name` (string): Name of the data table to be visualized
     - `vegalite_specification` (string): JSON string representing the Vega-Lite specification
   - Returns: If the `--output_type` is set to `text`, returns a success message with an additional `artifact` key containing the complete Vega-Lite specification with data. If the `--output_type` is set to `png`, returns a base64 encoded PNG image of the visualization using the MPC `ImageContent` container.

## Usage with Claude Desktop

```python
# Add the server to your claude_desktop_config.json
{
  "mcpServers": {
    "datavis": {
        "command": "uv",
        "args": [
            "--directory",
            "/absolute/path/to/mcp-datavis-server",
            "run",
            "mcp_server_datavis",
            "--output_type",
            "png" # or "text"
        ]
    }
  }
}
```
types-tzdata 2025.0.0.20251111
Typing stubs for tzdata
Requires Python: >=3.9

## Typing stubs for tzdata

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`tzdata`](https://github.com/python/tzdata) package. It can be used by type checkers
to check code that uses `tzdata`. This version of
`types-tzdata` aims to provide accurate annotations for
`tzdata==2025.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/tzdata`](https://github.com/python/typeshed/tree/main/stubs/tzdata)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.407

It was generated from typeshed commit
[`017aed4dceade7e7dc9880b5182a516e01e135f6`](https://github.com/python/typeshed/commit/017aed4dceade7e7dc9880b5182a516e01e135f6).
iflow-mcp-xueqiu-mcp 0.1.0
Add your description here
Requires Python: >=3.11

# Xueqiu MCP

基于雪球API的MCP服务，让您通过Claude或其他AI助手轻松获取股票数据。

## 项目简介

本项目基于[pysnowball](https://github.com/uname-yang/pysnowball)封装了雪球API，并通过MCP协议提供服务，使您能够在Claude等AI助手中直接查询股票数据。

## 安装方法

本项目使用`uv`进行依赖管理。请按照以下步骤进行安装：

```bash
# 克隆仓库
git clone https://github.com/liqiongyu/xueqiu_mcp.git
cd xueqiu_mcp

# 使用uv安装依赖
uv venv && uv pip install -e .
```

## 配置

### 配置雪球Token

1. 在项目根目录创建`.env`文件
2. 添加以下内容：

```
XUEQIU_TOKEN=您的雪球token
```

* 快捷方式：

```bash
echo 'XUEQIU_TOKEN="xq_a_token=xxxxx;u=xxxx"' > .env
```

关于如何获取雪球token，请参考[pysnowball文档](https://github.com/uname-yang/pysnowball/blob/master/how_to_get_token.md)。

## 运行服务

使用以下命令启动MCP服务：

```bash
uv --directory /path/to/xueqiu_mcp run main.py
```

或者，如果您已经配置了Claude Desktop：

```json
"xueqiu-mcp": {
  "args": [
    "--directory",
    "/path/to/xueqiu_mcp",
    "run",
    "main.py"
  ],
  "command": "uv"
}
```

## 功能特性

- 获取股票实时行情
- 查询指数收益
- 获取深港通/沪港通北向数据
- 基金相关数据查询
- 关键词搜索股票代码

## 展示图

![image](./images/cursor_mcp.png)

![image](./images/claude_mcp.png)

## 致谢

- [pysnowball](https://github.com/uname-yang/pysnowball) - 雪球股票数据接口的Python版本
- [fastmcp](https://github.com/fastmcp) - MCP服务框架

## 许可证

[MIT License](./LICENSE)