semvec/pypi-packages huggingface dataset:
fastselenium 0.0.6
make it easy to use selenium

# fastselenium

#### 介绍
封装了一些selenium的用法，使其更加简单

#### 软件架构
软件架构说明


#### 安装教程

1.  pip安装
```shell script
pip install fastselenium
```
2.  pip安装（使用阿里镜像加速）
```shell script
pip install fastselenium -i https://mirrors.aliyun.com/pypi/simple
```

#### 使用说明

1.  demo
```python
import fastselenium
from selenium import webdriver
driver = webdriver.Chrome()
driver.get('www.gitee.com')
test_res = fastselenium.save_cookie(
            driver=driver
        )
```
agent-society-mcp-client-wrapper 0.0.3
A Python library which wraps around Anthropic's low-level MCP client
Requires Python: >=3.12.8

# MCP Wrapper for AgentSociety

A library that wraps low-level [MCP](https://github.com/agentsociety/mcp) client functionality, exposing it for easy usage as tools within a [LangChain](https://github.com/langchain-ai/langchain) workflow. This enables seamless integration of remote MCP tools for language model workflows and agent applications.

## Structure

### Library Code: `mcpwrap/`

- **`client.py`** — Basic utility to invoke a LangChain model with a set of tools.
- **`server_stub.py`** — Async abstraction (`ServerSession`) for connecting to and interfacing with an MCP server, exposing MCP tools for LangChain.
- **`multi_mcp_model.py`** — Orchestrates multiple MCP server sessions and integrates their toolsets for use by one `BaseChatModel`. Handles async invocation and batching of tool requests.
- **`llm_integration.py`** — Converts MCP tool schemas (JSON Schema) into dynamic, structured LangChain tools using Pydantic models.
- *(You may need to install [mcp](https://pypi.org/project/mcp/) and LangChain dependencies.)*

### Example App: `sample/`

- Provides a minimal, integration-tested example of how to use this library in practice. (See directory for details.)

## Usage Overview

### 1. Connect MCP servers as tool providers

```python
from mcpwrap.server_stub import ServerSession

session = ServerSession(name="example", url="http://localhost:8080")
await session.initialize()  # Async context
```

### 2. Compose Multi-server Tools for LangChain

```python
from langchain_core.language_models.chat_models import BaseChatModel
from mcpwrap.multi_mcp_model import MultiMcpModel

base_model = ... # Any supported BaseChatModel
mcp_model = MultiMcpModel(
    base_model=base_model,
    mcp_servers=[session]  # Add as many as you like
)
await mcp_model.initialize()
```

### 3. Use in a Chat Workflow

```python
results = await mcp_model.ainvoke(messages)  # messages: List[BaseMessage]
```

## Features

- **Wraps multiple MCP servers:** Maps their tools to a common namespace for use as LangChain tools.
- **Schema Conversion:** Converts arbitrary MCP tool JSON schemas into Pydantic models for argument validation.
- **Async, batched operations:** Tools are called concurrently when invoked by an LLM agent.
- **Error and retry handling:** Optionally retries failed server/tool executions.

## Requirements

- `pip install -r requirements.txt`

## Notes

- Ensure MCP servers are reachable at their configured URLs.
- Schema support currently assumes JSON Schema Draft 7 compatibility for tool arguments.

## Contributing

PRs, issues, and questions are welcome!
cv-frames 0.0.3
Read frames from OpenCV like humans

cv-frames
=========

.. image:: https://github.com/kqf/cv-frames/actions/workflows/tests.yml/badge.svg
   :target: https://github.com/kqf/cv-frames/actions
   :alt: Tests

.. image:: https://img.shields.io/pypi/dm/cv-frames.svg
   :target: https://pypi.org/project/cv-frames/
   :alt: PyPI Downloads

``cv-frames`` is a lightweight utility that simplifies working with video files frame-by-frame using OpenCV.
It includes both a Python API and a command-line interface (CLI) for interacting with video frames in an intuitive way.

----

Installation
------------

Install via pip:

.. code-block:: bash

    pip install cv-frames

----

Command Line Usage
------------------

After installation, you can use the CLI:

.. code-block:: bash

    cv-frames show path/to/video.mp4

Navigate through frames with any key. Press **q** to quit.

----

Python Usage
------------

Example: Read and show frames programmatically

.. code-block:: python

    from pathlib import Path
    from cvframes import iterate

    for i, (_, frame) in enumerate(iterate(Path("video.mp4"))):
        print(f"Frame {i} shape:", frame.shape)

Example: Split side-by-side video into left/right frames

.. code-block:: python

    from cvframes import iterate_sbs

    # Write the processed frames to disc
    for capture, (left, right) in iterate_sbs("sbs_video.mp4", oname="processed.mp4"):
        processed = do_something(left)
        capture.write(processed)