Top Open WebUI Alternatives for Running LLMs Locally

Lina Lam· February 7, 2025

Open WebUI is a web interface for running local large language models (LLMs), providing an easy way to interact with AI models. It's a popular choice for privacy-conscious users who want a lightweight self-hosted solution without incurring cloud costs or sending data to external services.

While Open WebUI is a great option for some users, others may need more customization or better integrations. We have curated a list of top Open WebUI alternatives and instructions for quickly setting them up.

Let's get into it!

Overview of Open WebUI Alternatives

Prerequisites

• Have Docker installed and running on your machine.
• Have NodeJS and npm or yarn installed on your machine.
• Have an LLM running locally.

1. HuggingChat

HuggingChat is an open-source chat interface developed by Hugging Face. It provides seamless access to a variety of language models and integrates well with Hugging Face's ecosystem.

If you love tinkering with models on Hugging Face, you might be interested in this one due to its native integrations with Hugging Face's Model Hub and API endpoints.

Strengths & Limitations

Open WebUI vs HuggingChat

HuggingChat is a great option for trying out open models hosted on Hugging Face. It has a slick web UI that's great for quick testing. But for developers, Open WebUI is more customizable. It has a simpler self-hosted setup, better for building with custom LLM workflows.

Getting Started with HuggingChat

HuggingChat can be quickly deployed using Docker.

First, obtain your Hugging Face token.

docker run -p 3000 -e HF_TOKEN=hf_*** -v db:/data ghcr.io/huggingface/chat-ui-db:latest

HuggingChat supports endpoints for OpenAI API-compatible local services as well as third-party providers like Anthropic, Cloudflare, and Google Vertex AI.

For the most up-to-date setup instructions, please visit HuggingChat's official documentation.

2. AnythingLLM

AnythingLLM is an open-source framework designed for users who want to do more than just chat with an LLM locally. It provides local AI agents that can interact with files, databases, and web data—all while running privately on your machine.

Unlike simple chat interfaces, AnythingLLM brings agentic workflows to local models, enabling them to process structured data, search documents, generate reports, and even automate tasks.

Strengths & Limitations

AnythingLLM vs Open WebUI

AnythingLLM is a better choice if you're building document-aware agents or want a plug-and-play RAG setup. That said, it's more opinionated and heavier to configure than Open WebUI. If you're just testing models or building lightweight prototypes, Open WebUI will be easier to set up.

Getting Started with AnythingLLM

We recommend using the Dockerized version for a faster setup.

1. Pull the latest image:

docker pull mintplexlabs/anythingllm

2. Run with Docker:

For Mac/Linux:

export STORAGE_LOCATION=$HOME/anythingllm && \
mkdir -p $STORAGE_LOCATION && \
touch "$STORAGE_LOCATION/.env" && \
docker run -d -p 3001:3001 \
--cap-add SYS_ADMIN \
-v ${STORAGE_LOCATION}:/app/server/storage \
-v ${STORAGE_LOCATION}/.env:/app/server/.env \
-e STORAGE_DIR="/app/server/storage" \
mintplexlabs/anythingllm

For Windows (PowerShell):

$env:STORAGE_LOCATION="$HOME\Documents\anythingllm"; `
If(!(Test-Path $env:STORAGE_LOCATION)) {New-Item $env:STORAGE_LOCATION -ItemType Directory}; `
If(!(Test-Path "$env:STORAGE_LOCATION\.env")) {New-Item "$env:STORAGE_LOCATION\.env" -ItemType File}; `
docker run -d -p 3001:3001 `
--cap-add SYS_ADMIN `
-v "$env:STORAGE_LOCATION`:/app/server/storage" `
-v "$env:STORAGE_LOCATION\.env:/app/server/.env" `
-e STORAGE_DIR="/app/server/storage" `
mintplexlabs/anythingllm;

3. Go to http://localhost:3001 to access the interface.

For the most up-to-date setup instructions, please visit AnythingLLM's official documentation.

3. LibreChat

LibreChat is an open-source AI chatbot platform that integrates multiple LLMs, plugins, and AI tools into a single, free interface. It mimics ChatGPT's design and functionality while adding powerful features such as multimodal support, plugin integration, and conversation customization.

It also offers Code Artifacts, an experimental feature that renders React code, raw HTML, and mermaid diagrams directly in the browser.

Strengths & Limitations

LibreChat vs Open WebUI

LibreChat is built for security-conscious enterprises. It offers robust authentication, persistent storage, and better protection for API keys. However, it comes with a steeper setup process than Open WebUI and may feel heavier for casual users.

Getting Started with LibreChat

1. Download the Project

   • Manual: Visit the GitHub Repo, download the ZIP, and extract it.
   • Using Git: Run:

     git clone https://github.com/danny-avila/LibreChat.git
     

2. Run the App

   • Navigate to the project directory.
   • Create a .env file by copying .env.example and configuring values as needed.
   • Start the app with docker compose up -d. LibreChat should now be running locally. See detailed documentation here.

4. LobeChat

LobeChat is a lightweight and extensible UI framework designed for interacting with various LLMs, both locally and remotely. It prioritizes user experience with a modern design and offers progressive web app (PWA) support.

It integrates multiple AI models and supports features like image generation, text-to-speech, and speech-to-text, as well as plugins to extend its functionality.

Strengths & Limitations

LobeChat vs Open WebUI

LobeChat offers built-in voice support, plugin extensibility, and a polished UI that works well on both desktop and mobile. It also offers a hosted web version, so it's easier to get started than Open WebUI. However, the local setup is more tedious and less lightweight.

Getting Started with LobeChat

Run LobeChat with Docker:

docker run -d -p 3210:3210 -e OLLAMA_PROXY_URL=http://host.docker.internal:11434 lobehub/lobe-chat

Assuming you have already started an Ollama service locally on port 11434, the above command will run LobeChat and connect to your local service.

For the most up-to-date setup instructions, please visit LobeChat's official documentation.

5. Chatbot UI

Chatbot UI is an open-source, self-hosted chatbot interface that allows users to run local or cloud-based AI models with a database-backed storage system.

It provides a sleek and intuitive user experience, making it accessible for both casual users and developers looking to integrate AI into their workflows.

Strengths & Limitations

Chatbot UI vs Open WebUI

Chatbot UI integrates with Supabase for secure storage, API key management, and auth. It's a good alternative to Open WebUI if you prefer a sleek interface with integrated cloud services and don't mind extra setup. That said, if you want a fully self-hosted, privacy-first solution, Open WebUI is a better choice.

Getting Started with Chatbot UI

1. Clone the Repository & Install Dependencies

git clone https://github.com/mckaywrigley/chatbot-ui.git
cd chatbot-ui
npm install

2. Install Supabase & Start Locally

Ensure Docker is installed then run the following to install the Supabase CLI:

• macOS/Linux: brew install supabase/tap/supabase
• Windows:

  scoop bucket add supabase https://github.com/supabase/scoop-bucket.git
  scoop install supabase
  

Start Supabase and set environment variables

In your terminal at the root of your local Chatbot UI repository, run:

supabase start
cp .env.local.example .env.local
supabase status

This creates a .env.local file in your project directory. Populate it with your running Supabase server details.

3. Run Chatbot UI

npm run chat

Access UI at http://localhost:3000
Admin panel: http://localhost:54323/project/default/editor

For more detailed instructions, visit the official GitHub repository or watch this video.

6. Text Generation WebUI

Text Generation WebUI is a versatile web-based interface for running large language models (LLMs). It supports multiple backends, including Transformers, llama.cpp, ExLlamaV2, and TensorRT-LLM.

Inspired by AUTOMATIC1111's Stable Diffusion Web UI, it aims to provide an easy-to-use and feature-rich experience for text generation.

Strengths & Limitations

Text Generation WebUI vs Open WebUI

Text Generation WebUI is more complicated to set up and needs solid local hardware. It's ideal for developers who need broader backend support and finer control. Open WebUI is much easier to get running but trades off some low-level performance tuning and model flexibility.

Getting Started with Text Generation WebUI

1. Clone & Install

git clone https://github.com/oobabooga/text-generation-webui.git
cd text-generation-webui

Run the installation script for your OS:

• Linux/macOS: ./start_linux.sh or ./start_macos.sh
• Windows: start_windows.bat
• WSL: start_wsl.bat

Select your GPU vendor when prompted.

2. Start the Web UI

Once installation is complete, start the UI by running:

./start_linux.sh  # (or the script for your OS)

Access the interface at http://localhost:7860.

3. Download Models

Place models in the text-generation-webui/models folder or download them via the UI. You can also fetch models from Hugging Face using:

python download-model.py organization/model

For more detailed instructions, visit the official GitHub repository.

7. Msty

Msty is an intuitive, offline-first AI assistant designed to simplify local and online AI model interactions. Unlike other AI tools that require complex setups, Docker, or command-line expertise, Msty provides a streamlined, one-click installation with a user-friendly interface.

It supports a wide range of models and has lots of advanced features such as "knowledge stacks," split chats, chat branching, and Deep Research Mode.

Strengths & Limitations

Msty vs Open WebUI

Msty is a beginner-friendly alternative to Open WebUI, with zero setup, a sleek UI, and supports a wider range of models. However, it comes at the cost of flexibility. Open WebUI gives you more control, better local model support, and deeper customization for advanced use cases.

Getting Started with Msty

• Download Msty from the official site.
• Run the installer and follow the setup instructions. No terminal or Docker required.
• Start chatting with local models or enter an API key to chat with online models.

For more detailed instructions, visit the official documentation.

8. Hollama

Hollama is a minimal yet powerful web UI designed for interacting with Ollama servers. It provides a clean, user-friendly interface that supports both local and remote AI models, including OpenAI and reasoning models.

With a focus on simplicity, customization, and performance, Hollama is an excellent choice for those looking to run AI chat models with minimal setup.

Strengths & Limitations

Hollama vs Open WebUI

Hollama focuses on simplicity and a minimal setup process, while Open WebUI is more feature-rich and versatile, offering broader functionality and integrations. Hollama is primarily designed for use with just Ollama.

Getting Started with Hollama

1. Download & Install Locally

• Get a Hollama installer for macOS, Windows, and Linux from the GitHub page.

2. Self-Host with Docker (Recommended for Servers)

To deploy your own Hollama server, run:

docker run --rm -d -p 4173:4173 --name hollama ghcr.io/fmaclen/hollama:latest

Then, open http://localhost:4173 in your browser where you can connect to your Ollama server. You can even download Ollama via the UI.

For more detailed instructions, visit the official GitHub repository.

9. Chatbox

Chatbox is a powerful and user-friendly AI chat client that supports multiple language models online and offline, including ChatGPT, Claude, Gemini Pro, Ollama, and DeepSeek.

It is available as a desktop application for Windows, macOS, and Linux, as well as mobile apps for iOS and Android.

Strengths & Limitations

Chatbox vs Open WebUI

Chatbox is more user-friendly and runs on nearly every platform including mobile with no setup. But it's not fully open-source, and some features are gated behind a subscription—whereas Open WebUI is completely free, self-hosted, and more customizable for developers.

Getting Started with Chatbox

1. Download Chatbox Installer

2. Build from Source (Community Edition)

To run the open-source Community Edition locally, follow these steps:

git clone https://github.com/Bin-Huang/chatbox.git # clone the repository
cd chatbox
npm install # install dependencies
npm start # run the application

For more detailed instructions, visit the official GitHub repository.

10. Ollama UI

Ollama UI is a powerful, open-source frontend that provides a ChatGPT-like experience for local AI models. It works seamlessly with Ollama, allowing users to run LLMs on their own machines with zero cloud dependencies.

It offers a minimal and intuitive design for users who prefer a straightforward, no-frills experience.

Strengths & Limitations

Ollama UI vs Open WebUI

Ollama UI is primarily geared for use with Ollama and is designed for simplicity. But it's limited to that ecosystem and lacks the advanced features, integrations, and flexibility that make Open WebUI better suited for power users and custom workflows.

Getting Started with Ollama UI

Follow these steps to set up Ollama UI on your local machine:

git clone https://github.com/ollama-ui/ollama-ui
cd ollama-ui
make

open http://localhost:8000  # Open in your browser

You can also use the Chrome Extension for a hassle-free experience.

Once installed, you can start chatting with your Ollama models right from your browser.

For more detailed instructions, visit the official GitHub repository.

Bottom Line

While Open WebUI is a solid choice for running local LLMs, there are plenty of alternatives that offer different strengths. Whether you need an easy-to-configure tool like Chatbox, a full-featured UI like Text Generation WebUI, or a Hugging Face-integrated solution like HuggingChat, there's an option that will fit your needs.

We recommend exploring these alternatives to find the best match for your workflow and computing environment.

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