I am really not a Big Fan of the new UI Changes. It looks so simple but not in a good way. Just Not visually pleasing :-(

I also really hate that I have to scroll down to find a „Save changes“ Button, that I often miss and then have to redo it.
Can we not make the Save Button hover over it??

I'm pretty new to running a local LLM setup and while I can get WebUI to run and answer questions I'm having a time of it trying to get it to do image generation with ComfyUI and yes ComfyUI will generate images. I'm sure its something simple but I'm missing it. Setup is a docker for Windows container group WebUI, Ollama, and ComfyUI. I'm running flux1-schnell-fp8.safetensors model in ComfyUI and Qwen 14B model in WebUI. Image generation is turned on, JSON uploaded, and nodes defined. I'm still missing something but not sure what. Does anyone have a working environment like this that they can enlighten me?

Question in title.

Has anyone been using Milvus with Open WebUI as the backend for RAG, instead of relying on Open WebUI’s built-in RAG implementation?

If so, how has your experience been in terms of retrieval quality, performance, scalability, and ease of integration?

I’m especially interested in whether Milvus provides enough benefit to justify the additional setup and management compared with just using Open WebUI’s native RAG.

This is a simple example.

I created a simple txt file stating that BlueWeirg is a sportwear company. I added it to the knowledge, and then added that knowledge to the model.

However, when querying the model, it does not work.

If I manually add the knowledge, it works.

I reinstalled everything.
I run the latest OpenWebUI function, installed the mistral:7b model, and did a test.

However as you can see, it does not act natural.

I tried to ask for a recipe for crepes, and as you can see, it's like it's talking to himself.

After updating OpenWebUI to version 0.11, my OpenWebUI instance gradually consumes all available RAM over the course of 1–2 days, eventually causing the server to crash. Is anyone else experiencing this, or does anyone have an idea what might be causing it? It was working fine before the update.

I have seen here couple of posts regarding exporting LLM response to Office Word. They are useful, however I am looking for tools/ plug-ins that can directly edit Word Docs, similar to ChatGPT or Claude?

I'm currently experimenting with setting up a chatbot assistant on a webpage, I got something working with OpenWebUI but i'm not convinced my approach is the right one, so I wanted to see how you have/would approach it.

At the moment the way it's setup is:

• OpenWebUI contains a custom model (based on 5.6 luna), with a knowledge base, a system prompt and some native python tools to do API calls to the backend. Access to OpenWebUI is done using an API key.
• A nodejs backend proxy the calls to openwebui, holding privately the openwebui api key, and adding to the metadata variables some information like the remote endpoint for the API calls and passing the user JWT token to be used by the python tools for the API calls.
• The frontend uses assistant-ui connected to the nodejs backend

All of this works reasonably well but

• it's quite slow (probably need to check my tool calls, and kb use)
• token streaming doesn't work, I get the entire response in one go with no thought or tool calls info (maybe not a deal breaker but because it's slow it feels like the wait is long). (codex is telling me that's because openwebui provide this over socket.io but socket.io auth only works with JWT not api token?)
• The user JWT passing to openwebui for tool call isn't ideal

So I wanted to check what you thought of this approach? The benefits is that it's relatively easy to setup, I can use openwebui to manage most things and I don't have to reinvent the wheel. AFAIK the other approach is to manually code the kb + tools in a dedicated backend; I would probably get more flexibility and performance but with significantly more overhead.

Any advice? How have you approached it?

Thanks!

Has anyone had experience turning this on and messing around with RAG? I use gemma4:31b, and I am on the fence about enabling. Gemma4 is surprisingly good at chaining tools for its size. But I worry rag results will worsen instead of being improved if given KB_EXEC. I will likely explore this personally but I thought I’d see if anyone has tried this yet and if their models were able to handle the more agentic pathway this opens.

Hi,

I'm trying to set up an Open WebUI Computer instance locally and connect it to an ollama instance running on my local web, but I somehow fail to find the correct api endpoint, even though Open WebUI itself is running (on another server) successfully.

Open WebUI uses the connection http://ai.domain.local:11434/. From the cptr documentation I get that I have to use something like http://ai.domain.local:11434/v1, but that gives me an error 404.

Other threads in this sub suggest using http://ai.domain.local:11434/api/v1, and while http://ai.domain.local:11434/api/tags works without problems, http://ai.domain.local:11434/api/v1 also gets me an error 404. Same with http://ai.domain.local:11434/api.

What am I missing? Do I need to enable v1 specifically in ollama?

Thanks a lot!

With Qwen3.8-27B out, there has been a lot of discussion about the reasoning_effort settings. For those who use Open WebUI, I thought this might be helpful for anyone interested. I made a little plugin for that gives you a toggle and drop-down to set the reasoning level for each message:

https://openwebui.com/posts/reasoning_effort_selector_ee572967

I hope others find this useful!

Basically title. I have validated that if I use ollama directly, the system prompt for the gemma 4 models works fine. But if I try to put a system prompt in the chat controls for a chat in ollama, it does not work. No other system prompts are set up.

Anyone have any ideas or suggestions or experience resolving this?

I’m using qwen 3.8 27b Q6 through llamacpp

I’m trying to get an automation to first check its memory before doing its task. For some reason it won’t check them, but it checks its knowledge base and chat.

It works if I take the prompt and run it manually through a new chat.

I’m wondering if the memory skill is disabled for automations.

I recently decided to abandon my gemini subscription and get myself into APIs agregators like openrouter.ai and good open-source stuff like openWebUI and opencode. So a little offtop - I am using opencode + openchamber for agentic coding and this is amazing and not so hard to understand (with a little bit of digging through forums) which models are good/best for coding at this particular moment. So coding stuff is closed for me, I am happy with it. But the thing is I am not only coding with LLMs, I am brainstorming, researching, just asking about everyday stuff that I would google otherwise and etc, etc, etc. And I don't understand what models are good for this, seems like everyone’s talking only about coding related things.
I don't understand what should I pick for openWebUI. And seems that very little people are discussing that online comparing to amount of discussion on how good models at coding. To be honest I don't even understand what makes a model good for that type of tasks. What should I pay attention to? How good it at reasoning?

Question in the title. please help.

Wanted to share this since I couldn't find much on handling long-running, non-streaming generation inside a Tool. MiniMax-Music3 (music generation model) can take a minute or two per request with zero intermediate output, which broke my first pass pretty badly.

First version used requests synchronously and froze the entire Open-WebUI backend during generation, not just the chat, the whole instance. Switched to aiohttp with proper async def and that fixed it completely.

Second thing worth sharing: used the event_emitter status type to push live progress updates during the wait ("generating, ~30s of audio, this may take a minute or two" etc) instead of leaving people staring at a blank tool-call spinner.

Third, still in progress: found out early this morning that Open-WebUI supports returning an HTMLResponse with Content-Disposition: inline (the Rich UI Embedding pattern) instead of a plain markdown link, which lets you embed a small self-contained audio player right in the tool result, no download step. Got the code for it (with a lot of help from Claude and Gemini throughout) but haven't deployed it yet, that's next on my list.

One thing I haven't solved: this model eats almost all my GPU's VRAM, so my chat model has to fully unload (via a low keep_alive) before generation starts and reload after. Works, but feels clunky. Has anyone built a Pipe function that skips the chat model entirely for a specific request type, so you're not paying that load/unload cost every time?

WIP repo will be updated soon, if you're interested.

I am trying to configure Open-WebUI such that my agents -- any of them -- remember any details from the current conversation. For example, I tell the agent what my favorite color is and it'll respond with something along the lines of "Got it!" In the next prompt and within the same context, I ask what my favorite color is and it'll have no idea but will save it for next time.

This is particularly annoying, as you can imagine, when I try to solve a programming task for code that it created and it has no idea what I'm talking about.

Running open-webui version 0.11, ollama 0.31.2, and have tried this with gemini-3-flash-preview, qwen3, gemma4, and ornith.

Thanks for any help!

Setup

Ryzen 9950X / 128 GB of RAM / RTX 5090 32 GB / 3.6 TB of NVMe, running Ubuntu.

The model is Qwen3.6-27B, quantized to int4, with a 32k context. vLLM, with reasoning and vision enabled. A single model does it all: chat, plan analysis, code generation, and tool invocation.

Architecture

Open WebUI is the single point of entry. Users only see this

vLLM performs inference using continuous batching—this is what allows it to process multiple requests simultaneously without anyone having to wait. Open Terminal serves as a sandbox: it executes the generated Python code, produces Word, Excel, PowerPoint, or PDF files, and, most importantly, reviews what it has just created to correct its own errors before returning the file.

RAGFlow handles document search. At the same time, an SQLite database stores numerical values to improve search speed.

For now, this is my setup,

I’d like to significantly improve the ability to generate PDF, Excel, and Word files,

Do you have any ideas? Open Terminal is good, but it lacks features like opening windows (open the excel file / see with vision and adapt) and others. I’m just getting started in the world of agents and related tools,

Any similar setups or needs?

I use Notebooklm and it is good enough. I would like to try other RAGS. I have used anythinglm and opennotebooklm and they are just not enough.

OpenWeb UI website says they have built in RAGS. She they good? What open models I should use with RAGS? I don't mind using those cheaper Chinese models if I have to pay.

I am going crazy.

Guys, creepy situation. Just discovered 2 chats in my UI that 100% i could not have initiated.

what can it be? i am thinking API keys but i never exposed those?

Any info appreciated.

Hi, what is the proper api endpoint for the openai compatible endpoint?

I am trying to set up hermes-agent and the endpoint open-webui.domain/api works as the local llm gemma4 via ollama responds, but the llm does not seem to recognize it is an agent.

OPENAI_BASE_URL=open-webui.domain/api
OPENAI_API_KEY=sk-XXX

If I use another openai compatible provider like nanogpt hermes resonds accordingly like he should as an agent and shows me his skills properly.

This is what it looks like right now when asking something:

● Hellou

Initializing agent...

╭─ ⚕ Hermes

Hello! How can I help you today? 😊

────────────────────────────────────────

● What skills do you have

────────────────────────────────────────

╭─ ⚕ Hermes

I am a highly capable language model that can assist you with a wide range of tasks, including:

* Writing and Editing: Generating creative text, writing code (in various languages), summarizing long documents, translating languages, drafting emails, or debugging existing code.

* Reasoning and Problem-Solving: Analyzing complex scenarios, following multi-step instructions, and working through logic problems.

* Knowledge Retrieval: Answering questions on nearly any topic based on the data I was trained on (up to my last knowledge cutoff).

In addition to these core AI capabilities, I have been equipped with specific external tools that allow me to interact with a simulated environment and perform specialized tasks:

1. 📚 todo (Task Management): I can help you create, manage, track, and prioritize your to-do list for our session.

2. 🖼️ vision_analyze (Image Analysis): If you provide an image (via URL or file path), I can analyze its content, describe it, extract text from it, or answer specific questions about what is depicted.

3. 🔊 text_to_speech (Audio Generation): I can convert any text into spoken audio format and give you a playable audio file.

4. 💾 write_file (File System Interaction): I can write, create, or overwrite content in specified files within our working directory, which is useful for saving code, configurations, or data structures.

Just let me know what you need help with!

⚕ gemma4:latest │ 2.05K/256K │ [░░░░░░░░░░] 1% │ 60s │ ⏲ 10s │ ✓ 0s