The best graphical interfaces for Ollama (comparison 2026)

#Ollama now has its own interface

The most direct answer to “Ollama GUI” is the Ollama app itself. A new app has been available for macOS and Windows since version 0.10.0 (July 2025): it lets you download models and chat with them without a terminal. The official getting-started guide says to open the app, or run the ollama command in a terminal, then follow the prompts: sign in for cloud models, or choose a local model. On Linux, installation is done with the command-line script, and there is no desktop app.

The application covers the essentials of simple use: conversations, model selection, and adjustable context length via a slider in the settings. It does not replace a third-party interface if you need multi-user accounts, a knowledge base, or preconfigured assistants. It is the right starting point; switch to another interface when you hit a specific limitation.

#Why add a third-party interface

Organized history

Retrieve, search, and sort old conversations.

Knowledge base (RAG)

Drop in documents and query them with a local model; see the introductory guide to local RAG.

Accounts and roles

Share a GPU server with a team, with per-user permissions.

Preconfigured assistants

Save a system prompt, model, and settings under a name.

Multiple providers

Mixing local models and online APIs in the same window.

No interface replaces Ollama: they all need a model server, except those that bundle their own engine, such as Jan with llama.cpp. The guide comparing Ollama, LM Studio, and Jan covers this application choice; this one is limited to interfaces that connect to Ollama.

#Prerequisites: Ollama responds, a model is installed

Before installing an interface, verify that the API responds and that at least one model is available. On Windows and macOS, the server starts with the application; on Linux, it runs as a service.

curl http://localhost:11434/api/tags
ollama pull qwen3.5:4b

A JSON response listing the models confirms that the API is ready. A 3- to 4-billion-parameter model is enough to evaluate an interface: a site benchmark, approximately 2 GB in Q4 for a 3B, 5 GB for a 7-8B, and 9 GB for a 14B.

#Open WebUI: the most comprehensive option, to know before adopting it

Open WebUI is a self-hosted web application that resembles an online assistant: history, RAG, accounts, roles, and web search. It installs cleanly with Docker; the documentation provides the following command, with a fixed secret key so sessions survive container recreation.

docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data -e WEBUI_SECRET_KEY=your-secret-key --name open-webui --restart always ghcr.io/open-webui/open-webui:main

The interface is then at http://localhost:3000, because the machine's port 3000 is mapped to the container's port 8080. The open-webui volume contains conversations, users, and settings: never start the container without it. The --add-host option lets the container reach Ollama on the host through host.docker.internal.

To back up your data, copy the open-webui volume: that's where conversations, accounts, and settings live, and it's the volume that survives image updates. Without Docker, the documentation suggests pip install open-webui followed by open-webui serve, with the interface available at http://localhost:8080 by default. Set DATA_DIR to choose the data location and WEBUI_SECRET_KEY to avoid disconnecting everyone each time a new key is generated.

#Jan, Cherry Studio, LobeChat, and Msty

Jan

Desktop application under the Apache 2.0 license (not AGPL, as older comparisons claim). It runs local models with llama.cpp and can also connect to online providers and custom endpoints. A good entry point if you want open software that does not require Ollama.

Cherry Studio

An AGPL-3.0-licensed, highly active desktop client that brings together several providers (including Ollama) in a single window. Suitable for anyone who switches between local models and online APIs.

LobeChat (LobeHub)

A highly polished web or desktop interface. Its repository indicates the LobeHub Community License; since version 1.0, the license itself specifies that it is based on Apache 2.0 with additional conditions: read it before commercial use.

Msty

Private AI workspace, now presented as the Studio lineup, with Go, Nexus, and Stack for teams. Proprietary product: check the pricing page before choosing, because the offering has evolved.

Among the others, AnythingLLM (MIT license) offers an all-in-one RAG, Page Assist (MIT) is a browser extension for Ollama, and Chatbox is under GPL-3.0. Each has its own guide or page on the site.

#Choose in 30 minutes: the test that replaces comparisons

Interface rankings age quickly because every project releases new versions weekly. A short trial is better: it shows what matters to you, with your hardware and documents. Set aside half an hour and use a single model with 3 to 8 billion parameters for a fair comparison.

1. 01
   Start with the Ollama application

   Talk with a model for five minutes. Note what you’re missing: folders, documents, accounts, assistants. This list defines the actual need.
2. 02
   Install a single third-party interface

   Choose based on the profile table, not both at once. Make sure it can see your Ollama models without downloading them again.
3. 03
   Test three tasks

   A short question, a long-text summary, a multi-step instruction. Compare response readability and time to the first word, not throughput figures.
4. 04
   Test a document

   Drop in a PDF of a few pages and ask three questions whose answers you know. If the cited excerpts are off-topic, the problem lies in chunking and embedding rather than the interface.
5. 05
   Restart

   Close everything, restart, and verify that the history and settings are back. With Docker, that’s the volume’s role.
6. 06
   Test access from another device

   If needed, open the interface from a phone or another computer on the network, while keeping port Ollama closed to the Internet.

After this half hour, keep the interface that bothers you least, not the one with the most features. Switching later costs almost nothing: your models remain in Ollama.

#The features that really matter

#RAG on your documents

Open WebUI and AnythingLLM provide an integrated knowledge base. To understand what happens under the hood (chunking, embeddings, retrieval), read the introduction to local RAG before comparing the interfaces: quality depends more on chunking and the embedding model than on the interface.

#Multiple models and comparison

Comparing two models on the same question helps you choose the right one. Check for this feature in the interface documentation before adopting it, because it varies from product to product and evolves from version to version.

#Assistants and system prompts

An assistant bundles a system prompt, a model, and settings under one name. Almost every interface offers some form of it; the guide to system prompts explains how to write them.

#Decision table by profile, plus licenses

Two notes on these licenses. An AGPL-type license, such as Cherry Studio's, requires you to publish the code for your modifications if you offer the modified software to third parties online: this has no effect on personal use, but should be examined for a commercial product. A permissive license such as MIT or Apache 2.0 offers more freedom, subject to retaining the copyright notices.

#Troubleshooting: the interface cannot see Ollama

Desktop application

Ollama's address must be http://localhost:11434. First test with curl http://localhost:11434/api/tags.

Docker container (Open WebUI)

Inside the container, localhost refers to the container itself: use http://host.docker.internal:11434, with --add-host=host.docker.internal:host-gateway.

Another machine on the network

By default, Ollama listens only on 127.0.0.1. Modify OLLAMA_HOST to expose it, for example with systemctl edit ollama on Linux.

No models in the list

The API responds but no model is installed: run a ollama pull, then refresh the interface.

sudo systemctl edit ollama
# sous [Service] ajouter :
# Environment="OLLAMA_HOST=0.0.0.0:11434"
sudo systemctl daemon-reload
sudo systemctl restart ollama

#Go further

• Open WebUI with Ollama: complete guide
• Jan: the open-source alternative to ChatGPT
• AnythingLLM: RAG tutorial
• Ollama, LM Studio, Jan, or GPT4All
• Local RAG: introduction
• Install Ollama in 5 minutes

• Source: Open WebUI, quick start
• Source: Ollama, startup
• Source: Ollama 0.10.0, new application
• Source: Open WebUI license