# GTPlanner
**Repository Path**: newchina/GTPlanner
## Basic Information
- **Project Name**: GTPlanner
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-02-14
- **Last Updated**: 2026-02-14
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# GTPlanner: AI-Powered PRD Generator
An intelligent Agent PRD generation tool that transforms natural language descriptions into structured technical documentation optimized for Vibe coding.
Overview •
Web UI •
Claude Code Skill •
MCP Integration •
Quick Start •
Configuration •
Project Structure •
Prefab Ecosystem •
Contributing •
License
Languages:
English •
简体中文 •
日本語
---
## Overview—How to Make Agents Work for You?
- First, define the task: What are the inputs? What are the specific steps? What are the outputs? How do you define success? These are typically called SOPs. **Work that can be SOPed can be automated by AI.**
- Second, provide the right tools for your agent. Humans use Office suites, browse the web, manage files and data, etc. If you want agents to work, they need these tools too.
- Finally, specify how the agent should organize its outputs.
In the Agent context, there's a more appropriate term for this: **context engineering**. Specifically, before starting Code Agents (Claude Code/Cursor/Augment/Devin/...), we want to clearly define through documentation:
- design.md: Define what the work is.
- prefab.md: Define available tools and how to use them. We call these **Prefabs**.
- starter-kit: Define development frameworks and available environments (this part is mostly consistent across projects).
This is what GTPlanner does—simplifying the process of building Agents.
## Why Choose GTPlanner
GTPlanner helps you easily generate an Agent PRD—a standard operating procedure (SOP) that AI can understand—to quickly automate your tasks. GTPlanner's design philosophy:
- **Determinism**: Eliminate AI ambiguity through clear SOPs (Agent PRDs), ensuring highly controllable and predictable execution paths and results.
- **Composability**: Break SOPs into reusable "Prefabs" and task modules, combining them like building blocks to create more complex workflows.
- **Freedom**: We don't lock you into execution platforms (like n8n), but instead use minimal AI frameworks and native Python code for maximum flexibility and freedom.
---
## Web UI (Recommended)
For the best experience, we strongly recommend using the Web UI. It provides a streamlined AI planning workflow tailored for modern developers.

**Key Advantages:**
- **Intelligent Planning Assistant**: AI-assisted rapid generation of system architecture and project plans
- **Instant Documentation**: Automatically create comprehensive technical documentation
- **Perfectly Suited for Vibe Coding**: Optimized output for Cursor, Windsurf, GitHub Copilot
- **Team Collaboration**: Multi-format export for easy sharing
[Try the Live Demo](https://the-agent-builder.com/)
---
## Claude Code Skill
GTPlanner is available as a Claude Code plugin skill, enabling seamless PRD generation directly within Claude Code.
**Trigger Words:** `PRD`, `项目规划`, `架构设计`, `技术方案`, `设计文档`
### Installation
1. Clone GTPlanner as a plugin:
```bash
cd ~/.claude/plugins
git clone https://github.com/OpenSQZ/GTPlanner.git
```
2. Configure environment:
```bash
cd GTPlanner
cp .env.example .env
# Edit .env with your LLM configuration
```
3. Initialize:
```bash
# In Claude Code, run:
/gtplanner-init
```
### Usage
Simply mention trigger words in Claude Code:
```
帮我设计一个自动化视频处理流程:从 YouTube 下载视频,提取字幕,生成摘要
```
Or in English:
```
Design a workflow that monitors GitHub issues, analyzes sentiment, and auto-assigns labels
```
### Available Tools
| Tool | Description |
|------|-------------|
| `short_planning` | Requirement analysis and scope definition |
| `tool_recommend` | Technology stack recommendations |
| `research` | Deep technical research (requires JINA_API_KEY) |
| `design` | Generate design documents (quick/deep mode) |
### Workflow
```
short_planning (initial) → tool_recommend → short_planning (technical) → design
```
Detailed skill documentation → [Skill Guide](./.claude/skills/gtplanner/SKILL.md)
---
## MCP Integration
Click to expand MCP integration instructions
GTPlanner supports the Model Context Protocol (MCP) for direct use in AI programming tools.
|
**Using in Cherry Studio**

|
**Using in Cursor**

|
Detailed configuration guide → [MCP Documentation](./mcp/README.md)
---
## Quick Start
### Online Quick Experience (No Installation)
[Try Web UI](https://the-agent-builder.com/) - WYSIWYG planning and documentation generation
### Local Setup
#### Prerequisites
- **Python ≥ 3.10** (3.11+ recommended)
- **Package Manager**: [uv](https://github.com/astral-sh/uv) (recommended)
- **LLM API Key**: OpenAI / Anthropic / Azure OpenAI / Self-hosted compatible endpoint
#### Installation
```bash
git clone https://github.com/OpenSQZ/GTPlanner.git
cd GTPlanner
# Install dependencies with uv
uv sync
```
#### Configuration
Copy the configuration template and set your API Key:
```bash
cp .env.example .env
# Edit .env file and set required environment variables
```
**Required Configuration:**
```bash
LLM_API_KEY="your-api-key-here"
LLM_BASE_URL="https://api.openai.com/v1"
LLM_MODEL="gpt-5"
```
Detailed configuration guide (including common providers, Langfuse, etc.) → [Configuration Documentation](./docs/configuration.md)
### CLI Usage
#### Interactive Mode
```bash
python gtplanner.py
```
After starting, simply input your requirements, for example:
```
Create a video analysis assistant that can automatically extract video subtitles, generate summaries and key points.
```
#### Direct Execution
```bash
python gtplanner.py "Design a document analysis assistant that supports PDF, Word document parsing and intelligent Q&A"
```
CLI detailed documentation (session management, parameter explanations, etc.) → [CLI Documentation](./gtplanner/agent/cli/README.md)
### API Usage
Start the FastAPI service:
```bash
uv run fastapi_main.py
# Runs on http://0.0.0.0:11211 by default
```
Visit `http://0.0.0.0:11211/docs` to view API documentation
API detailed documentation (endpoint descriptions, usage examples, etc.) → [API Documentation](./gtplanner/agent/api/README.md)
### MCP Integration
```bash
cd mcp
uv sync
uv run python mcp_service.py
```
MCP detailed documentation (client configuration, available tools, etc.) → [MCP Documentation](./mcp/README.md)
---
## Configuration
GTPlanner supports multiple configuration methods:
- **Environment Variables** (.env file): API Key, Base URL, Model, etc.
- **Configuration File** (settings.toml): Language, tracing, vector services, etc.
- **Langfuse Tracing** (optional): Execution process tracing and performance analysis
Complete configuration guide → [Configuration Documentation](./docs/configuration.md)
---
## Project Structure
```
GTPlanner/
├── README.md # Main documentation
├── gtplanner.py # CLI entry point
├── fastapi_main.py # API service entry
├── settings.toml # Configuration file
│
├── .claude/ # Claude Code plugin
│ ├── plugin.json # Plugin manifest
│ ├── skills/gtplanner/ # → [Skill Guide](./.claude/skills/gtplanner/SKILL.md)
│ └── commands/ # Slash commands
│
├── agent/ # Core agent code
│ ├── flows/ # Control flows
│ ├── subflows/ # Specialized subflows
│ ├── function_calling/ # Tool definitions
│ └── ...
│
├── utils/ # Utilities
│
├── prefabs/ # Prefab ecosystem
│ ├── README.md # → [Prefab Documentation](./prefabs/README.md)
│ └── releases/ # Release management
│ ├── community-prefabs.json # Prefab registry
│ └── CONTRIBUTING.md # → [Prefab Contributing Guide](./prefabs/releases/CONTRIBUTING.md)
│
├── mcp/ # MCP service
│ └── README.md # → [MCP Documentation](./mcp/README.md)
│
├── docs/ # Documentation
│ ├── zh/ # Chinese documentation
│ ├── ja/ # Japanese documentation
│ ├── configuration.md # Configuration guide
│ └── architecture/ # Architecture documentation
│
├── workspace/ # Runtime directory
│ ├── logs/ # Logs
│ └── output/ # Output documents
│
└── tests/ # Tests
```
System architecture documentation → [Architecture Documentation](./docs/architecture/README.md)
---
## Prefab Ecosystem
GTPlanner extends capabilities through the Prefab ecosystem. Each Prefab is a standardized, reusable AI functional component.
### What is a Prefab?
Prefabs are ready-to-use AI functional modules that can be:
- **Discovered**: GTPlanner automatically recognizes available Prefabs
- **Deployed**: Automatically deployed to the platform after PR merge
- **Integrated**: Called through standard APIs
- **Version Managed**: Semantic versioning
### How Do Prefabs Enhance GTPlanner?
When you contribute a Prefab to `community-prefabs.json`:
1. **Expand Planning Capabilities**: GTPlanner learns about a new solution
2. **Smart Recommendations**: GTPlanner recommends appropriate Prefabs when generating plans
3. **Automatic Integration**: Planning documents include Prefab usage instructions
**Example Prefabs:**
- **Media Processing**: [Video Processing Prefab](./Video-processing/) - Video to audio, subtitle extraction
- **Data Services**: [Amap Weather Prefab](./Amap-Weather/) - Weather queries
- **Document Processing**: PDF parsing, Excel processing
- **AI Services**: Speech recognition, image recognition
### Getting Started
**Using Prefabs:**
Call any published Prefab through the Prefab Gateway:
```bash
# 1. Create API Key on AgentBuilder platform
# 2. Call Prefab through gateway
curl -X POST "https://gateway.agentbuilder.com/v1/prefabs/{prefab-id}/execute" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"function": "function_name", "parameters": {...}}'
```
**Browse Available Prefabs:**
```bash
cat prefabs/releases/community-prefabs.json | jq '.'
```
**Create Your Own Prefab:**
```bash
git clone https://github.com/The-Agent-Builder/Prefab-Template.git my-prefab
cd my-prefab
uv sync --dev
# Develop, test, publish
```
Complete Prefab documentation → [Prefab Guide](./prefabs/README.md)
Gateway calling details → [Prefab Usage Guide](./prefabs/README.md#for-users-using-prefabs)
---
## Contributing
We believe that an excellent tool requires community wisdom and collaboration. GTPlanner welcomes your participation!
### Contributing Prefabs (Easiest Way to Contribute)
**Why Contribute Prefabs?**
Each Prefab will:
- Extend GTPlanner's planning capabilities
- Help other developers solve problems
- Be automatically included in the recommendation system
- Gain community recognition
**How to Contribute?**
1. Create a Prefab using the template → [Prefab-Template](https://github.com/The-Agent-Builder/Prefab-Template)
2. Develop and test your functionality
3. Publish a GitHub Release and upload the `.whl` file
4. Submit a PR to `prefabs/releases/community-prefabs.json`
**Prefab Impact:**
- **Enter Recommendation System**: Prefabs in `community-prefabs.json` are recognized by GTPlanner
- **Smart Matching**: Automatically recommended for appropriate scenarios during planning
- **Auto Deployment**: Automatically deployed to Prefab platform after PR merge
Detailed contribution guide → [Prefab Contributing Documentation](./prefabs/releases/CONTRIBUTING.md)
### Contributing Core Code
Improve GTPlanner's planning quality and system performance through evaluation-driven development.
Core code contribution → [Contributing Guide](./docs/zh/CONTRIBUTING.md)
### Sharing Case Studies
Share your experience to help the community discover GTPlanner's full potential:
- **Use Cases**: Applications in real projects
- **GTPlanner-Generated PRDs**: Showcase planning quality
- **Tutorials and Best Practices**: Help new users get started
Submit cases → Create PR in `docs/examples/community-cases/`
---
## License
This project is licensed under the MIT License. See [LICENSE](./LICENSE.md) for details.
---
## Acknowledgments
- Built on [PocketFlow](https://github.com/The-Pocket/PocketFlow) async workflow engine
- Configuration management powered by [Dynaconf](https://www.dynaconf.com/)
- Designed for seamless integration with AI assistants through MCP protocol
---
GTPlanner - Transform your ideas into structured blueprints with the power of AI.