LangGPT

The structured prompt framework proposed by Yunzhong Jiangshu is officially said to split prompts into modules such as Role, Profile, Goal, Skills, Rules, and Workflow. It is reusable and scalable, and has been open-sourced on GitHub.

AI Prompt Tools#Structured Prompt#Open-Source Frameworks#Prompt Template

Updated: 2026-10-30

What is LangGPT?

LangGPT is a structured prompt engineering framework proposed by Yun Zhong Jiangshu in 2023. The official description of it is "a structured and reusable prompt design framework", and it is also said to be understandable as "a programming language in the field of prompts". Its core proposition is to transform prompts from scattered techniques into a hierarchical and templatized methodology, enabling prompts to be written once like code modules and reused repeatedly.

Who is it for?

Product or operation personnel who need to maintain prompts for a long time: Manage multiple prompts with a unified template to avoid loss of control over style and structure.
Developers who want more stable outputs: Constrain model behavior through Rules and Workflow to reduce random outputs.
Leaders in charge of teams building AI applications: Treat structured prompt engineering as a delivery specification to reduce handover costs.
Prompt enthusiasts: Start transforming from the mature templates in the example library, which is faster than groping from scratch.
People who need to migrate prompts across multiple models: Structured writing reduces the migration cost across different models.

Key features

Structured Template

The official states that it is inspired by programming paradigms and organizes prompts in a hierarchical manner to form a unified writing structure.

Modular Design

The official framework divides prompt engineering into modules such as Role, Profile, Goal, Skills, Rules, Workflow, and Initialization, with each module undertaking clear responsibilities.

Variables and References

The official version states that it supports using variable syntax to implement self-referential prompts, maintain consistency in complex instructions, and reduce repeated revisions.

Commands and Conditional Logic

The official states that it allows defining reusable commands and supports conditional judgment structures, injecting execution logic into prompts.

Reusability and Scalability

The official description states "Created once, adapted infinitely", and prompts can be modified and combined like modules.

Open Source & Community

The official states that the project is open-source on GitHub, has a large number of stars and user practices, and has published relevant papers.

How to use LangGPT

Start by modifying the official example templates to understand the purpose of each module, and then try writing from scratch on your own.
The Rules module is the most critical section for differences in effectiveness.
It is more effective to clearly write down prohibited behaviors than to elaborate on capabilities.
In long conversations, the model may easily forget the settings, so you can repeat key constraints as reminders in the conversation.
For complex prompt words, it is recommended to use a format other than Markdown for hosting, and JSON or YAML can be used for structured data scenarios.
A template is just a skeleton.
you still need to fill in the business background and acceptance criteria yourself.
otherwise, the output will still be empty.
In addition, for tools of this infrastructure and community-oriented type, their value often only becomes apparent after a period of use: spending a little more time early on to familiarize yourself with its data structures and interface conventions will significantly reduce the cost of integrating your own workflows later on.