semvec/pypi-packages huggingface dataset:
fluidgrids-cli 0.1.0
Command-line interface for the FluidGrids Workflow Engine
Requires Python: >=3.8

# FluidGrids CLI

A command-line interface for the FluidGrids Workflow Engine that allows you to manage workflows, trigger runs, and more.

## Installation

### Using pip

```bash
pip install fluidgrids-cli
```

### From Source

```bash
git clone https://github.com/algoshred/fluidgrids-cli.git
cd fluidgrids-cli
pip install -e .
```

## Quick Start

### Authentication

Set up your credentials first:

```bash
fluidgrids auth login --url https://api.fluidgrids.ai
```

You can also use an API key for automation:

```bash
fluidgrids auth set-key --api-key YOUR_API_KEY
```

### List Workflows

```bash
fluidgrids workflows list
```

### Get Workflow Details

```bash
fluidgrids workflows get KEY VERSION
```

### Trigger a Workflow Run

```bash
fluidgrids runs trigger KEY VERSION --context '{"input_key": "value"}'
```

### Monitor a Run

```bash
fluidgrids runs get RUN_ID
```

## Configuration

The CLI stores configuration in `~/.fluidgrids/config.yaml`. You can view your current configuration with:

```bash
fluidgrids config show
```

## Documentation

For complete documentation, see the [docs directory](docs/) or visit [FluidGrids Documentation](https://docs.fluidgrids.ai/cli).

## License

MIT License - Copyright (c) 2023 Algoshred Technologies Private Limited
yuanqi-agent-test-mcp 0.1.1
mcp server to invoke yuanqi agent
Requires Python: >=3.10
Keywords: agent, mcp, yuanqi

<div align="center">
<a href="https://yuanqi.tencent.com/" target="_blank" rel="noopener">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://open-agents-web-cdn-prd.hunyuan.tencent.com/public/bf6b0f00a2e660e0e35d.png" />
    <img alt="Framelink" src="https://open-agents-web-cdn-prd.hunyuan.tencent.com/public/bf6b0f00a2e660e0e35d.png" />
  </picture>
</a>

  <h1>元器智能体 MCP 服务器</h1>
  <h3>为您的元器智能体提供 mcp 访问能力。<br/>一次性在任何智能体框架中集成。</h3>
  <a href="https://github.com/GLips/Figma-Context-MCP/blob/main/LICENSE">
    <img alt="MIT 许可证" src="https://img.shields.io/github/license/GLips/Figma-Context-MCP" />
  </a>
</div>
<br/>
<p align="center">
  <a href="https://yuanqi.tencent.com/">元器</a>官方智能体上下文协议(MCP)服务器，支持与强大的元器智能体交互。允许MCP客户端如<a href="https://www.anthropic.com/claude">Claude Desktop</a>、<a href="https://www.cursor.so">Cursor</a>、<a href="https://codeium.com/windsurf">Windsurf</a>、<a href="https://github.com/openai/openai-agents-python">OpenAI Agents</a>等进行集成。
</p>

## 快速开始使用 MCP 客户端

1. 从[元器智能体平台](https://yuanqi.tencent.com/)获取你开发的智能体的 API 密钥。
2. 安装`uv`（Python包管理器），使用`curl -LsSf https://astral.sh/uv/install.sh | sh`安装或查看`uv` [仓库](https://github.com/astral-sh/uv)获取其他安装方法。
3. **重要提示: 每个元器智能体的ID和密钥都不相同**，两者需要匹配，否则会有 `token验证失败` 的错误

### Claude Desktop

前往`Claude > Settings > Developer > Edit Config > claude_desktop_config.json`包含以下内容：

```
{
  "mcpServers": {
    "YuanQiAgent": {
      "command": "uvx",
      "args": [
        "yuanqi-agent-test-mcp"
      ],
      "env": {
        "TOOL_NAME": "填写你提供调用的元器智能体名称",
        "TOOL_DESC": "填写你提供调用的元器智能体描述，方便AI工具",
        "ASSISTANT_ID": "填写你调用的元器智能体ID",
        "API_KEY": "填写你调用的元器智能体Token"
      }
    }
  }
}
```

⚠️ 注意：API_KEY需要与ASSISTANT_ID匹配。如果出现“token验证失败”错误，请检查您的API_KEY和ASSISTANT_ID。
如果你使用Windows，你需要在Claude Desktop中启用"开发者模式"才能使用MCP服务器。点击左上角汉堡菜单中的"Help"，然后选择"Enable Developer Mode"。

### Cursor

前往`Cursor -> Preferences -> Cursor Settings -> MCP -> Add new global MCP Server`添加上述配置。


## Transport

我们仅支持 stdio 传输方式
gawx-python-sdk 0.0.1
Gawx Python SDK

# GawX Python SDK

The GawX Python SDK provides a simple interface to interact with the GawX AI platform, allowing you to manage sessions, pipelines, and execute AI operations programmatically.

## Installation

You can install the SDK using pip:

```bash
pip install gawx-python-sdk
```

Or install from source:

```bash
git clone https://github.com/gawx-ai/gawx-python-sdk.git
cd gawx-python-sdk
pip install -e .
```

## Dependencies

The SDK requires the following dependencies:
- requests
- pandas
- python-dotenv

## Configuration

Before using the SDK, you need to set up your environment variables. Create a `.env` file in your project root with the following variables:

```
GAWX_ACCESS_TOKEN=your_access_token
GAWX_SERVER_URL=https://demo-server.gawx.ai
```

## Usage

### Authentication

```python
from gawx.apis import get_access_token

# Get access token
access_token = get_access_token(email="your_email", password="your_password")
```

### Working with Sessions

```python
from gawx.apis import get_sessions, get_session, get_session_executions

# Get all sessions
sessions = get_sessions()

# Get a specific session
session = get_session(session_id="your_session_id")

# Get session executions
executions = get_session_executions(session_id="your_session_id")
```

### Using the Pipeline

```python
from gawx.apis import Pipeline

# Initialize pipeline
pipeline = Pipeline(access_token="your_access_token")

# Get user information
user_info = pipeline.get_user_info()
```

## API Reference

### Authentication

#### `get_access_token(email, password)`
- **Parameters:**
  - `email` (str): Your GawX account email
  - `password` (str): Your GawX account password
- **Returns:** Access token string
- **Raises:** Exception if authentication fails

### Sessions

#### `get_sessions()`
- **Returns:** List of all sessions
- **Raises:** Exception if request fails

#### `get_session(session_id)`
- **Parameters:**
  - `session_id` (str): ID of the session to retrieve
- **Returns:** Session details
- **Raises:** Exception if session not found

#### `get_session_executions(session_id)`
- **Parameters:**
  - `session_id` (str): ID of the session
- **Returns:** List of session executions
- **Raises:** Exception if request fails

### Pipeline

#### `Pipeline(access_token)`
- **Parameters:**
  - `access_token` (str): Your GawX access token
- **Methods:**
  - `get_user_info()`: Returns user information

## Error Handling

The SDK raises exceptions with descriptive messages when API requests fail. Always wrap your API calls in try-except blocks:

```python
try:
    sessions = get_sessions()
except Exception as e:
    print(f"Error: {str(e)}")
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
fluidgrids 0.1.0
Python SDK for the FluidGrids Workflow Engine
Requires Python: >=3.8

# FluidGrids SDK

A Python SDK for interacting with the FluidGrids Workflow Engine, by Algoshred Technologies Private Limited.

## Installation

```bash
pip install fluidgrids
```

## Quick Start

```python
from fluidgrids import FluidGridsClient

# Initialize client with your API credentials
client = FluidGridsClient(
    base_url="https://api.fluidgrids.com",
    api_key="your_api_key"
)

# List all workflows
workflows = client.workflows.list()

# Get a specific workflow
workflow = client.workflows.get("workflow-key", "latest")

# Trigger a workflow run
run = client.runs.trigger(
    workflow_key="workflow-key",
    version="latest",
    context={"input_data": "value"}
)

# Check the status of a run
run_status = client.runs.get(run.run_id)
```

## Features

- **Authentication** - Multiple authentication methods (API key, username/password, token)
- **Workflow Management** - Create, update, delete, and manage workflows
- **Run Execution** - Trigger, monitor, and control workflow executions
- **Configuration** - Access and update workflow engine configuration
- **Credentials** - Manage credentials for workflows

## Documentation

For detailed documentation, visit [the FluidGrids SDK documentation](https://docs.fluidgrids.ai/sdk).

## Requirements

- Python 3.8 or higher
- `requests`
- `pydantic`
- `python-dateutil`

## License

MIT

## About

FluidGrids is a workflow engine product by [Algoshred Technologies Private Limited](https://fluidgrids.ai/). The engine allows you to create, manage, and run workflows with ease.