semvec/pypi-packages huggingface dataset:
task-manager-mcp 1.0.0
Intelligent Task Management and Assignment MCP Servers
License: MIT
Requires Python: >=3.9
Keywords: mcp, task-management, ai, productivity, project-management

# AI 任务管理系统

这个系统使用FastAPI后端和React前端，通过AI智能体帮助用户规划和管理项目任务。

## 系统功能

- 通过自然语言对话规划项目任务
- 自动分解复杂任务为子任务
- 识别并管理任务间的依赖关系
- 可视化任务流程图

## 快速开始

### 启动服务

```bash
# 给脚本添加执行权限
chmod +x start_services.sh stop_services.sh run_tests.sh

# 启动服务
./start_services.sh
```

首次启动时，系统会提示您输入API密钥和API基础URL。

### 停止服务

```bash
./stop_services.sh
```

### 测试系统

我们提供了两个测试脚本：

1. 综合测试后端API:
```bash
python3 test_backend_comprehensive.py
```

2. 专门测试RouterAgent:
```bash
python3 test_router_agent.py
```

## 环境变量配置

系统需要以下环境变量：

- `OPENAI_API_KEY`: 用于访问AI服务的API密钥
- `OPENAI_BASE_URL`: AI服务的基础URL，默认为 `https://api.nuwaapi.com`

这些变量可以通过以下方式设置：

1. 在系统环境变量中设置
2. 在 `backend/.env` 文件中设置
3. 通过启动脚本交互式设置

## 日志文件

所有日志文件保存在 `logs` 目录下：

- `logs/backend.log`: 后端服务日志

## 故障排除

如果遇到问题，请检查：

1. API密钥是否正确设置
2. API基础URL是否正确
3. 日志文件中的错误信息

## 开发者说明

### 目录结构

- `backend/`: 后端代码
  - `src/`: 源代码
  - `main.py`: 主入口点
- `logs/`: 日志文件
- `project_documents/`: 项目文档
- `*.sh`: 服务管理脚本
- `*.py`: 测试脚本
promptopt 0.1.0
Enterprise prompt optimization framework combining DSPy and GRPO approaches
License: MIT
Requires Python: >=3.8
Keywords: prompt optimization, dspy, grpo, llm, prompt engineering, enterprise, ai

# PromptOpt: Enterprise Prompt Optimization Framework

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A unified framework for testing, comparing, and hybridizing DSPy and GRPO approaches to prompt optimization, with special focus on enterprise deployment via synthetic data and Colab accessibility.

## 🌟 Key Features

- **🤖 Multiple Optimization Strategies**: DSPy, GRPO, and hybrid approaches
- **💰 Cost-Aware Optimization**: Built-in budget management and tracking
- **🏢 Enterprise-Ready**: Compliance support, ROI analysis, deployment tools
- **☁️ Colab-Optimized**: Works perfectly in Google Colab (no GPU needed)
- **📊 Tournament Evaluation**: Head-to-head prompt comparison system
- **🎯 Synthetic Data**: Generate realistic business scenarios
- **🔧 API-Based**: Works with OpenAI and Anthropic APIs

## Installation

### Using Conda (Recommended)

```bash
# Create a new conda environment
conda create -n promptopt python=3.9
conda activate promptopt

# Install the package
pip install -e .

# For development
pip install -r requirements-dev.txt
```

### Using pip only

```bash
pip install promptopt
```

For development:
```bash
pip install -e ".[dev]"
```

For Colab environments:
```bash
pip install promptopt[colab]
```

## Quick Start

```python
from promptopt import EnterprisePOC
from promptopt.colab import ColabManager

# Set up environment
manager = ColabManager()
manager.setup_enterprise_environment()

# Run a complete POC
poc = EnterprisePOC()
results = poc.run_complete_poc(
    business_scenario="customer_support",
    company_context={"industry": "tech", "size": "enterprise"},
    budget_limit=500.0
)

# View results
print(f"Optimization improvements: {results.optimization_improvements}")
print(f"Projected ROI: {results.roi_projections}")
```

## 📚 Documentation

See [DOCUMENTATION.md](DOCUMENTATION.md) for comprehensive guides and API reference.

## 🚀 Quick Start in Google Colab

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/promptopt/promptopt/blob/main/notebooks/Enterprise_Quickstart.ipynb)

## 🧪 Running Tests

```bash
conda activate promptopt
pytest tests/
```

## 🤝 Contributing

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

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- DSPy framework for few-shot optimization techniques
- GRPO methodology for tournament-based optimization
- OpenAI and Anthropic for LLM APIs

## 📊 Example Results

Using PromptOpt, teams typically see:
- **30-40% improvement** in response quality
- **85%+ consistency** across team members
- **$2000+/month savings** from optimized prompts
- **2-week ROI** for enterprise deployments