Project Manager AI Assistant

Your intelligent co-pilot for seamless project planning and execution

Project Manager AI Assistant - Clean and intuitive interface

📋 Table of Contents

English
中文

English

Project Manager AI Assistant leverages Large Language Models (LLMs) to transform simple project descriptions into comprehensive, actionable plans with tasks, dependencies, schedules, and risk assessments.

✨ Features

🤖 AI-Powered Task Generation: Automatically decomposes high-level project goals into detailed, granular tasks
🔗 Automated Dependency Analysis: Intelligently identifies and maps dependencies between tasks
📊 Dynamic Gantt Chart Visualization: Generates interactive Gantt charts for clear project timeline views
👥 Team-Aware Planning: Allocates tasks based on real team members' skills and profiles
⚠️ Proactive Risk Assessment: Identifies potential risks and suggests mitigation strategies
🔌 Pluggable Architecture: Easily extend with new integrations (Jira, Asana, Slack) without touching core logic
📈 Real-time Progress Tracking: Monitor project progress with live updates and notifications
🎯 Smart Resource Allocation: Optimize team workload distribution based on skills and availability

📸 Application Screenshots

主界面 / Main Interface 项目描述和团队信息输入界面	处理进度 / Processing Progress AI分析项目的实时进度显示
项目计划结果 / Project Plan Results 生成的详细项目计划和任务分解	甘特图 / Gantt Chart 可视化的项目时间线和依赖关系

🚀 Quick Start

Prerequisites

Python 3.9+
OpenAI API Key
Git

Installation

Clone the repository

git clone https://github.com/your-username/project-manager-assistant.git
cd project-manager-assistant

Set up virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```

Configure environment

cp .env.example .env
# Edit .env and add your OpenAI API key
# OPENAI_API_KEY="sk-..."

Run the application

# Start backend (FastAPI)
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# Start frontend (Streamlit) - in a new terminal
streamlit run streamlit_app/app.py --server.port 8501

Access the application
- Frontend: http://localhost:8501
- API Documentation: http://localhost:8000/docs

Project Input Example

Simply provide a project description and your team's profile

📖 Documentation

User Guide - Complete guide to using the application
API Reference - Detailed API documentation
Architecture Guide - System design and technical details
Plugin Development - How to create custom plugins
Deployment Guide - Production deployment instructions

🛠️ Architecture

This project is built on a powerful LangGraph-powered pluggable architecture:

project-manager-assistant/
├── app/                    # Backend API (FastAPI)
│   ├── main.py            # Application entry point
│   ├── models/            # Data models
│   ├── services/          # Business logic
│   └── plugins/           # Integration plugins
├── streamlit_app/         # Frontend UI (Streamlit)
│   ├── app.py            # Main application
│   ├── components/       # UI components
│   └── pages/           # Application pages
├── docs/                 # Documentation
├── tests/               # Test suite
└── requirements.txt     # Python dependencies

Core Components

Core Engine: Sophisticated agentic workflow for planning, analysis, and optimization
Plugin System: Independent integrations for external tools (Jira, Asana, Slack)
API Layer: RESTful API for programmatic access
UI Layer: Streamlit-based interactive interface

🤝 Contributing

We welcome contributions from the community! Here's how you can help:

Ways to Contribute

🐛 Report Bugs: Use our issue tracker
💡 Request Features: Submit feature requests via issues
🔧 Fix Issues: Pick up issues labeled "good first issue"
📝 Improve Documentation: Help make our docs better
🔌 Create Plugins: Develop new integrations
🧪 Write Tests: Improve our test coverage

Development Setup

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Make your changes and add tests
Run tests: pytest
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

Code Style

Follow PEP 8 for Python code
Use Black for code formatting
Write comprehensive tests for new features
Update documentation for any API changes

For more details, see our Contributing Guide.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

中文

项目管理AI助手 利用大型语言模型（LLM），将简单的项目描述转化为包含任务、依赖关系、时间安排和风险评估的全面可执行计划。

✨ 功能特性

🤖 AI智能任务生成: 自动将高层级项目目标分解为详细的具体任务
🔗 自动化依赖分析: 智能识别并映射任务间的依赖关系
📊 动态甘特图可视化: 生成交互式甘特图，清晰展示项目时间线
👥 感知团队的规划: 基于真实团队成员的技能画像进行任务分配
⚠️ 前瞻性风险评估: 识别潜在风险并提出缓解策略
🔌 插件化架构: 无需修改核心代码即可轻松扩展新集成（Jira、Asana、Slack）
📈 实时进度跟踪: 通过实时更新和通知监控项目进度
🎯 智能资源分配: 基于技能和可用性优化团队工作负载分配

📸 应用截图

主界面简洁直观的项目描述和团队信息输入界面	处理进度 AI智能分析项目的实时进度展示
项目计划结果 AI生成的详细项目计划和任务分解	甘特图可视化的项目时间线和任务依赖关系

🚀 快速开始

前置要求

Python 3.9+
OpenAI API 密钥
Git

安装步骤

克隆代码仓库

git clone https://github.com/your-username/project-manager-assistant.git
cd project-manager-assistant

设置虚拟环境

python -m venv venv
source venv/bin/activate  # Windows系统: venv\Scripts\activate

安装依赖
```
pip install -r requirements.txt
```

配置环境变量

cp .env.example .env
# 编辑 .env 文件并添加您的 OpenAI API 密钥
# OPENAI_API_KEY="sk-..."

运行应用

# 启动后端 (FastAPI)
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# 启动前端 (Streamlit) - 在新的终端中
streamlit run streamlit_app/app.py --server.port 8501

访问应用
- 前端界面: http://localhost:8501
- API文档: http://localhost:8000/docs

项目输入示例

您只需提供项目描述和团队成员简介

📖 文档

用户指南 - 完整的使用指南
API参考 - 详细的API文档
架构指南 - 系统设计和技术细节
插件开发 - 如何创建自定义插件
部署指南 - 生产环境部署说明

🛠️ 架构设计

本项目构建于强大的 LangGraph 驱动的插件化架构之上：

project-manager-assistant/
├── app/                    # 后端API (FastAPI)
│   ├── main.py            # 应用入口点
│   ├── models/            # 数据模型
│   ├── services/          # 业务逻辑
│   └── plugins/           # 集成插件
├── streamlit_app/         # 前端界面 (Streamlit)
│   ├── app.py            # 主应用
│   ├── components/       # UI组件
│   └── pages/           # 应用页面
├── docs/                 # 文档
├── tests/               # 测试套件
└── requirements.txt     # Python依赖

核心组件

核心引擎: 复杂的智能工作流，负责规划、分析和优化
插件系统: 外部工具的独立集成（Jira、Asana、Slack）
API层: 提供程序化访问的RESTful API
UI层: 基于Streamlit的交互式界面

🤝 贡献指南

我们欢迎社区贡献！以下是您可以帮助的方式：

贡献方式

🐛 报告Bug: 使用我们的问题跟踪器
💡 请求功能: 通过issues提交功能请求
🔧 修复问题: 处理标记为"good first issue"的问题
📝 改进文档: 帮助改进我们的文档
🔌 创建插件: 开发新的集成
🧪 编写测试: 提高测试覆盖率

开发环境设置

Fork代码仓库
创建功能分支: git checkout -b feature/amazing-feature
进行更改并添加测试
运行测试: pytest
提交更改: git commit -m 'Add amazing feature'
推送到分支: git push origin feature/amazing-feature
创建Pull Request

代码规范

Python代码遵循 PEP 8
使用 Black 进行代码格式化
为新功能编写全面的测试
任何API更改都要更新文档

更多详细信息，请参阅我们的 贡献指南。

📄 许可证

本项目采用MIT许可证 - 详情请参阅 LICENSE 文件。

Made with ❤️ by the Project Manager AI Assistant Team

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
app		app
data		data
docs		docs
streamlit_app		streamlit_app
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
ARCHITECTURE_ADAPTER_PATTERN.md		ARCHITECTURE_ADAPTER_PATTERN.md
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
FIXES_APPLIED.md		FIXES_APPLIED.md
LICENSE		LICENSE
LOCALIZATION_ARCHITECTURE.md		LOCALIZATION_ARCHITECTURE.md
README.md		README.md
fix_session_id.py		fix_session_id.py
main.py		main.py
pyproject.toml		pyproject.toml
requirements.lock		requirements.lock
restart_fixes.sh		restart_fixes.sh
run.sh		run.sh
team_test.json		team_test.json
test_real_sse.html		test_real_sse.html
test_sse.html		test_sse.html
test_sse_complete.html		test_sse_complete.html
uv.lock		uv.lock
worker.py		worker.py

License

zhangzhefang-github/project-manager-assistant

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