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kuafuai/devopsgpt

5,959HTMLAudience · vibe coderComplexity · 3/5Setup · moderate

TLDR

Multi-agent AI system that turns a plain-English software requirement into working, deployed code by connecting a large language model to automated testing and deployment tools.

Mindmap

mindmap
  root((devopsgpt))
    What it does
      Requirement to code
      Automated deployment
      CI/CD integration
    Workflow
      Clarify requirement
      Generate interface docs
      Write code
      Test and deploy
    Setup
      Python script
      Docker container
      Browser UI
    Use cases
      New feature generation
      Extend existing projects
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Code map

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Things people build with this

USE CASE 1

Describe a new software feature in plain English and let DevOpsGPT write, test, and deploy the code for you.

USE CASE 2

Add a new capability to an existing codebase without writing any code by describing the change in natural language.

USE CASE 3

Generate a working web app from a written specification and deploy it locally using the Docker-based setup.

Tech stack

PythonDockerHTML

Getting it running

Difficulty · moderate Time to first run · 1h+

Requires a paid GPT API token, a cloud-hosted version at kuafuai.net is available for those who prefer not to self-host.

In plain English

DevOpsGPT is a multi-agent system that takes a written software requirement in plain language and tries to turn it into working, deployed code. It connects a large language model to DevOps tooling so that the whole pipeline from requirement clarification through code generation, continuous integration, and deployment can run with less manual intervention. The general workflow starts when you describe what you want built. DevOpsGPT asks clarifying questions to refine the requirement, then generates interface documentation, writes pseudocode based on any existing project code, and produces working code from that pseudocode. From there it hooks into DevOps tools for automated testing and deployment to a target environment. The README notes two current limitations: generated requirements and interface documentation may not be precise enough in complex scenarios, and automatic understanding of existing project code is still incomplete, with a future version planned to address it. The tool works with any programming language and is intended to extend existing projects, not just generate greenfield code. An enterprise edition adds deeper existing-project analysis, support for language models beyond GPT, private deployment, and connections to more DevOps platforms. The open-source version requires a GPT API token and runs locally via a Python script or a Docker container. Once running, you interact through a browser interface at a local address. Setup for the self-hosted version takes a few steps: download the release or clone the repo, copy the template configuration file, add your API token, and run a startup script. Docker users mount a workspace directory and the config file, then start the container. Generated code lands in a local workspace folder. A cloud-hosted version is available at kuafuai.net for those who prefer not to self-host. The project is experimental and comes with a disclaimer that the developers accept no liability for issues arising from its use.

Copy-paste prompts

Prompt 1
I want to use DevOpsGPT to build a simple REST API that returns a list of users. How do I write the requirement, add my GPT API key to the config, and start the process?
Prompt 2
Walk me through running DevOpsGPT with Docker: what volumes do I mount, what environment variables do I set, and where does the generated code land?
Prompt 3
I have an existing Python Flask project and want DevOpsGPT to add a login endpoint. How do I point it at my existing codebase so it extends rather than replaces my code?
Prompt 4
What are DevOpsGPT's current limitations with existing projects, and what workarounds does the README suggest for complex scenarios?
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