Dify Local Deployment Guide: One-Click Setup with aaPanel

Deploy Dify locally using aaPanel + Docker on a virtual machine — no Linux experience required.
This beginner-friendly tutorial walks through deploying Dify — an open-source LLM app development platform combining BaaS and LLMOps — on a local machine using VMware, Ubuntu 22.04, aaPanel, and Docker. It covers the full process from VM creation and Ubuntu setup to aaPanel configuration and one-click Dify deployment, with detailed guidance on fixing common issues like DNS misconfiguration and Docker registry pull failures.
What Is Dify: An Open-Source LLM Application Development Platform
Dify is an open-source large language model (LLM) application development platform that combines the concepts of Backend-as-a-Service (BaaS) and LLMOps, enabling developers to build production-grade AI applications faster. More importantly, Dify isn't just for technical users — even those without a technical background can participate in defining AI applications and managing data operations.
As a development platform, Dify comes with the key technology stack needed to build LLM applications and supports calling hundreds of mainstream models — including domestic Chinese models like Wenxin Yiyan, Doubao, Zhipu, Baichuan, iFlytek Spark, and Tongyi Qianwen, as well as international ones like OpenAI, Gemini, and Claude. It provides an intuitive prompt orchestration interface and a high-quality RAG (Retrieval-Augmented Generation) engine for connecting external knowledge bases.

Dify vs. LangChain: Scaffolding vs. Toolbox
Many developers compare Dify with LangChain. If LangChain is like a "toolbox" (library) filled with hammers and nails, then Dify is more like a complete "scaffolding system" — it doesn't just provide tools, it also includes full engineering design and software testing. This means developers don't have to reinvent the wheel and can focus more energy on innovation and business logic.
Building Agents Based on Model Capabilities
In Dify's model support list, two icons are worth noting: a "hammer" icon indicates the model supports Tool Use, and a "glasses" icon indicates vision capabilities. If you want to build an Agent that can call tools, you must select a model with the "hammer" icon. OpenAI supports both tool calling and vision; among domestic models, Moonshot (Kimi) and DeepSeek also support tool calling, making them suitable for Agent development.
Environment Setup: Virtual Machine and Ubuntu System Configuration
This tutorial uses the most beginner-friendly Dify deployment method — local deployment via aaPanel. Before getting started, you'll need two things:
- Virtualization software: VMware Workstation 17 or VMware Player (for running Linux)
- Linux system image: Ubuntu 22.04
One important note when installing VMware: do not install it on the C drive, as it generates a large amount of cache files at runtime and can easily fill up your system partition. Install it on a different drive.

Creating and Installing the Ubuntu Virtual Machine
When creating a new virtual machine in VMware, select "I will install the operating system later", set the OS type to Linux, and the version to Ubuntu 64-bit. For disk size, if the VM is only used to run Dify, 20GB is sufficient — and it's recommended to split it into multiple files.
Once created, the virtual machine won't start directly because it's essentially an "empty system." Go to "Edit Virtual Machine Settings", find the CD/DVD option, select "Use ISO image file", and load the Ubuntu 22.04 image (this is equivalent to inserting an installation disc). Then you can power it on.
Key Points During Ubuntu Installation
After booting, you'll enter the graphical installation interface. Select your preferred language. For the installation type, if you only need to deploy Dify via aaPanel, select "Minimal installation"; if you also need to do other Linux development, choose "Normal installation". For disk handling, simply select "Erase disk and install Ubuntu" (note: this only erases the space allocated to the VM, not your host machine). Set your username and password (you can enable auto-login to avoid entering credentials every time), then wait for the installation to finish and reboot.
Deploying aaPanel: The Core Tool for Visual Server Management
aaPanel is the central tool in the entire Dify deployment process — it transforms complex Linux operations into a simple point-and-click experience.
Getting and Running the aaPanel Install Command
On the aaPanel official website, select "Linux Panel" → "Free Edition", then copy the installation command corresponding to your system version (Ubuntu). Back in the virtual machine, open a terminal with Ctrl+Alt+T, right-click to paste the command, and press Enter. The system will ask for your password — note that Linux terminals don't display any characters when you type a password. Just type it correctly and press Enter.

Logging In and Securing aaPanel
After installation, the panel will output login details including the external URL, internal URL, username, and password. For local deployments, use the internal network address (the external IP is dynamic and not suitable for local environments). Copy the address into your browser to access the aaPanel interface.
On first login, you'll need to accept the terms and bind an account. Once inside, you can use the built-in bt command-line tool (short for BaoTa) to improve security:
- Change the default password and username (avoid overly simple passwords)
- Change the panel port (e.g., to something more memorable like 8899)
- Change the security entry path (the default random string is hard to remember; you can customize it)

Complete Steps to Deploy Dify with One Click in aaPanel
This is the most straightforward part of the entire process. aaPanel includes a Docker module that enables one-click deployment of Dify.
Installing the Docker Module
In aaPanel, find the Docker option and click Install, keeping the default installation settings. Docker installation takes some time — just wait patiently. Once done, refresh the panel to see the Docker management interface.
Installing Dify via Docker
In the Docker module, search for "Dify" and click Install, then confirm. You can customize the application name or keep the default.
Troubleshooting Common Dify Installation Issues
In practice, many users encounter installation failures. Here's a complete troubleshooting guide:
Issue 1: BTfile error — Uninstall and search again, then change the version to 1.00 (to specify a stable release).
Issue 2: Network fetch failure — If errors persist after changing the version, it's a network issue. You'll need to modify the VM's DNS configuration:
sudo nano /etc/resolv.conf
Change the nameserver entries to:
nameserver 8.8.8.8
nameserver 8.8.4.4
Save the file (Ctrl+O to confirm, Ctrl+X to exit), then restart the panel and clear the host cache before returning to aaPanel to reinstall.
Issue 3: Docker images cannot be pulled — Go to Docker settings and update the registry mirror URL. aaPanel's help documentation provides several mirror links — try them one by one, then restart Docker and reinstall Dify.
Accessing and Initializing Dify
Once installation succeeds, Dify will enter the "Running" state. Copy its address, change https to http, set the port to 8088, and append /install to open the initialization page. For example: http://192.168.x.x:8088/install.
Set up the admin email, username, and password on this page, and your local deployment is complete. After this, you can close the virtual machine's graphical interface and manage everything through aaPanel.
Summary
With the combination of aaPanel + Docker, deploying Dify locally becomes significantly simpler — even beginners with no Linux experience can follow along and get it running. The entire process can be summarized as: Set up the virtual machine and OS → Deploy aaPanel → Install Docker → One-click deploy Dify. The real challenges usually aren't in the installation itself, but in troubleshooting network and image-pulling issues — once you've mastered the two techniques of DNS modification and registry mirroring, the vast majority of problems can be solved.
Once deployment is complete, you'll have a fully functional AI application development platform where you can build intelligent chatbots, Agents, workflows, and private knowledge bases.
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