semvec/pypi-packages huggingface dataset:
kimss 0.1.0
Kimss Python SDK for conversational AI agents
License: MIT
Requires Python: >=3.9

# Kimss Python SDK

Lightweight client for the [Kimss](https://kimss.ai) API. Use it to call your Kimss agents from Python scripts, backends, or notebooks.

## Install

```bash
pip install kimss
```

Or from this repo (editable):

```bash
cd kimss_sdk && pip install -e .
```

## Authentication

Use a **long-lived API key** (not a browser session token). Create keys in your Kimss app under **Developer Settings → API Keys**. The key is scoped to your tenant and user.

## Usage

```python
from kimss import KimssClient, Agent

client = KimssClient(
    api_key="kimss_xxxxxxxxxxxxxxxxxxxxxxxx",  # from Developer Settings
    base_url="https://api.kimss.ai",          # or your deployment URL
)

# Get an agent and send a message
agent = client.get_agent(agent_id="asst_xxxx")
result = agent.query("Hello")
# result is the API "res" payload (run_id, thread_id, messages, usage, etc.)

# Continue a thread
result2 = agent.query("What did I just say?", thread_id=result.get("thread_id"))

# Or use the client directly
result3 = client.chat(assistant_id="asst_xxxx", message="Hi", thread_id=result.get("thread_id"))
```

## API

- **`KimssClient(api_key, base_url="https://api.kimss.ai")`** – authenticated client.
- **`client.get_agent(agent_id)`** – returns an `Agent` for that assistant.
- **`agent.query(message, thread_id=None, chat_type="user_chat")`** – send a message; returns the `res` object from `POST /assistant_chat/`.
- **`client.chat(assistant_id, message, thread_id=None, chat_type="user_chat")`** – one-off chat without an Agent handle.

All requests use the `X-Kimss-Key` header. No streaming in this version; responses are full JSON.
alayaflow 0.1.17
A desktop platform for executing LangGraph workflows with uv-managed sandboxes.
License: MIT
Requires Python: >=3.10

# AlayaFlow

A desktop platform for executing LangGraph workflows with multiple executors.

## 简介

AlayaFlow 是一个桌面平台，用于执行 LangGraph 工作流。


## 安装

### 从 PyPI 安装（推荐）

```bash
pip install alayaflow
```

### 从源码安装

```bash
git clone git@github.com:AlayaDB-AI/AlayaFlow.git
cd alayaflow
pip install -e .
```

## 系统要求

- Python >= 3.10
- uv (Rust-based Python package installer)

## 使用示例

```python
from alayaflow.workflow import WorkflowManager, WorkflowLoader

# 创建工作流管理器
manager = WorkflowManager()

# 加载工作流
manager.load_workflow(workflow_id="my-workflow", version="1.0.0")

# 执行工作流...
```

## 配置

项目支持通过 `.env` 文件进行配置。主要配置项包括：

- `dev_mode`: 开发模式（dev/dev-uneditable/prod）
- `alayahub_url`: AlayaHub API 地址
- `langfuse_enabled`: 是否启用 Langfuse 追踪
- 各种存储路径配置

## 开发

### 安装开发依赖

```bash
pip install -e ".[dev]"
```

### 运行测试

```bash
pytest
```

## 许可证

本项目采用 MIT License。详情请参阅 [LICENSE](LICENSE) 文件。

## 贡献

欢迎提交 Issue 和 Pull Request！

## 相关链接

- [LangGraph](https://github.com/langchain-ai/langgraph)
- [uv](https://github.com/astral-sh/uv)