semvec/pypi-packages huggingface dataset:
agent-evolve 1.0.0
A comprehensive toolkit for evolving and tracking AI agents
License: MIT
Requires Python: >=3.8
Keywords: ai, agent, evolution, machine-learning, optimization

# Agent Evolve

A comprehensive toolkit for evolving and tracking AI agents through evolutionary programming and automated optimization.

## Features

- 🧬 **Evolutionary Optimization**: Automatically improve your AI agents using OpenEvolve
- 📊 **Interactive Dashboard**: Real-time tracking and visualization of evolution progress
- 🎯 **Smart Extraction**: Automatically find and extract functions marked for evolution
- 🔧 **Auto-Generation**: Generate evaluators, training data, and configurations automatically
- 📈 **Metrics Tracking**: Comprehensive metrics collection and analysis
- 🚀 **CLI Interface**: Easy-to-use command-line tools for all operations

## Quick Start

### Installation

```bash
pip install agent-evolve
```

### Basic Usage

1. **Mark functions for evolution** by adding the `@evolve` decorator:

```python
from agent_evolve import evolve

@evolve()
def my_function(x: int) -> str:
    return f"Hello {x}"
```

2. **Extract evolution targets**:

```bash
agent-evolve extract /path/to/your/code
```

3. **Run the evolution pipeline**:

```bash
agent-evolve pipeline my_function
```

4. **View results in the dashboard**:

```bash
agent-evolve dashboard
```

## CLI Commands

- `extract` - Extract functions marked with @evolve decorator
- `generate-training-data` - Generate training data for extracted functions
- `generate-evaluators` - Generate evaluation functions
- `generate-configs` - Generate OpenEvolve configuration files
- `evolve` - Run evolution optimization on a specific tool
- `pipeline` - Run the complete pipeline (training data → evaluator → config → evolution)
- `dashboard` - Launch the interactive Streamlit dashboard
- `analyze` - Analyze tracking data

## Dashboard Features

- **Evolution Pipeline**: Step-by-step progress tracking
- **Code Comparison**: Side-by-side diffs of original vs evolved code
- **Metrics & Timeline**: Performance improvements over time
- **Interactive Tools**: Generate evaluators, run evolution, and more

## Requirements

- Python 3.8+
- OpenAI API key (for evaluator generation)
- OpenEvolve package

## License

MIT License - see LICENSE file for details.

## Contributing

Contributions welcome! Please read our contributing guidelines and submit pull requests.
intsage 1.0.0
SAGE Framework Meta Package
License: MIT
Requires Python: >=3.10
Keywords: framework, intellistream, ai, data, sage, ml

# SAGE Framework Meta Package

SAGE Framework是一个统一的AI推理和数据流处理框架，提供完整的端到端解决方案。

## 简介

这是SAGE框架的元包(meta package)，它集成了以下核心组件：

- **sage-kernel**: 统一内核，包含核心运行时、工具和CLI
- **sage-middleware**: 中间件组件，包含LLM中间件服务
- **sage-userspace**: 用户空间组件，提供高级API和应用框架
- **sage-dev-toolkit**: 开发工具包，提供开发和调试工具

## 安装

```bash
pip install intellistream-sage
```

## 快速开始

```python
import sage

# 创建本地环境
env = sage.LocalEnvironment()

# 创建数据流
stream = env.from_collection([1, 2, 3, 4, 5])

# 应用转换
result = stream.map(lambda x: x * 2).collect()
print(result)  # [2, 4, 6, 8, 10]
```

## 文档

更多详细信息请参考：
- [官方文档](https://intellistream.github.io/SAGE-Pub/)
- [GitHub仓库](https://github.com/intellistream/SAGE)

## 许可证

MIT License