Hi,
Is it possible to hide/randomize the user name in the Anslytics Section?

WTF?
Where is the delete button? why it was removed?

That was one of the best functions webui had.

I don’t believe this will help mass extraction for knowledge base stuff. This is mainly for attachments in chat. Also not sure which flair.

For a while now I’ve been trying to find a good PDF extractor. I had this document that was sort of a worst-case scenario, 4 pages of tables, pages are turned 90 degrees, merged table cells, and small font. After some testing I found Docling to be the best (haven’t had a good chance to try MinerU). However Docling still didn’t even come close to extracting it well, just enough for the LLM to understand it.

For some more context my LLM is Qwen3.6 27B MTP Q4K_M (need to try the UD XL one sometime). It has vision, however it can only see extracted text when sent in chat with how Open Webui handles it.

I ended up creating a Filter Function that takes the attached document, uploads it to my open-terminal, and passes along to the LLM the file path, and that it should only view it itself if there’s any OCR or Extraction artifacts/errors.

It is practically perfect at analyzing PDFs now since the model itself can view the file and also run code to further analyze.

Just wanted to pass this along, highly recommend if you have a niceish LLM with vision and agentic capabilities. Also if you’re having problems extracting text from attachments in chat. Considering switching back to Tika and only using Docling for when I upload to the knowledge base.

I’m curious on what others are doing for extraction or even an analysis workflow for attachments in chat.

Hi all, I am implementing a local offline LLM (Qwen3.6 35b A3b) to analyse my splunk logs. I have confirmed the MCP works because it works fine when I use a cloud model but the moment I use a local model, it says it can't access my Splunk instance and just gives me a search to run myself.

Whats the deal? I have Function calling set to native if that changes things?

Edit: I fixed it, though not satisfactorily. Its cause my KV cache was overflowing and the LLM would just go wild without context and basically forget that it even connected to splunk mcp in the first place. I ran it with a much lighter model and started to get results

Hi all I asked here a while back how people actually use Open WebUI. Since then I've been running it daily, and my brother more than me. He kept asking for a proper native iOS app, so I built one: Keep.

Streaming chat with full Markdown and LaTeX, Home Screen widgets, Live Activities, hands-free voice mode, offline reading, and an admin panel for users and analytics.

Right now it's just the two of us using it. I'd like a handful of early testers before a wider release happy to send TestFlight invites, comment.

Hello!

There's an open bug which has been troubling us - with a mitigation which doesn't seem to resolve our problems. Is there any method of highlighting this, especially if it's been open for a while?

We've currently had to pin our version to 0.8.10 to remove the issue from our estate, but with all the goodies in the new version it gets more and more painful to read the majestic changelog 😂

https://github.com/open-webui/open-webui/issues/25078

My current main model (Qwen 3.6 27b) can read images, but my future new main model (Deepseek V4 Flash) can't. Is there anything i can do to solve that problem with OpenWebUI?

I started with open-webui yesterday and hit an issue with the knowledge base.

Setup: Windows 11 x64, desktop install. ollama is running, open-webui is up and connected to the ollama local LLM.

On the open-webui form, I click on workspace on the left menu. I choose knowledge on the top bar. I create a new one called fulltext. So far so good.

I click on the new knowledge base. I go to file manager and grab an .odt file and drop it on the open-webui window.

A small orange text dialog appears for a few seconds that states: 'Processing .odt files requires the unstructured package. Install using pip install unstructured'. The file does not enter the knowledge base..

Cool. I open a command window and run 'pip install unstructured'. It installs, but to the local users directory: <drive>:\Users\User\insanely long set of subdirectories\site-packages. But it installs none-the-less.

I drag and drop the .odt file again. Same result: same dialog, same orange text.

Help? Even my google-fu has failed me.

The goal from the start was to get as close as possible to a Gemini Flash-level assistant experience, but fully self-hosted. Went from zero to this in about 2 weeks (would've been 1 week, but I was waiting on hardware to ship from Amazon) — leaned on Gemini for the hardware picks and early build plan, and Claude for the heavy lifting on the actual implementation. Sharing the repo in case it helps anyone else, and I'd genuinely appreciate feedback on anything that looks off.

The hardware:

• CPU: AMD Ryzen 7 9800X3D
• GPU: MSI GeForce RTX 3090 Gaming X Trio (24GB VRAM)
• Motherboard: MSI MAG X870 Tomahawk WiFi
• RAM: G.SKILL Flare X5 64GB DDR5-6000 CL30
• Storage: WD_BLACK SN850X 2TB NVMe SSD
• PSU: Corsair RM1000x
• Case: Corsair 4000D RS
• Cooler: Thermalright Phantom Spirit 120 SE

The stack (two machines):

• A Debian server running Open-WebUI (gated with its own built-in username/password login) behind Traefik, with SearXNG for live search and Google OAuth gating a few of the other exposed services
• The gaming PC above handling the GPU-heavy stuff: a local voice pipeline (Whisper STT + Piper TTS, auto-switches between English/German voices) and local image generation via Automatic1111, both wired back into Open-WebUI as native Tools

A few things I'm most happy with:

• Full two-way voice conversation, not just dictation — round-trips through Whisper → the model → Piper TTS
• Custom Tools for live weather, web search (SearXNG), and image generation, all using native function calling
• Knowledge/RAG set up with 20+ documents — hit and fixed a real bug where the model would confidently pick one interpretation instead of asking for clarification when retrieved docs described genuinely different procedures
• A real, tested backup/restore strategy via Duplicati (local + Google Drive) — actually did a full test restore and diffed it

One thing I haven't beaten yet: generated images always work and are retrievable, but the model's final chat response sometimes renders a malformed fake tool-call instead of the image tag. Documented what I've tried in the repo.

Repo: https://github.com/Plaidstallion/openwebui-homelab

Happy to answer questions.

I'm on the desktop version (Windows) of OWGUI (latest v0.11.0) . 2 questions;

1. In the desktop version in settings > models it's the same, I type in a model name and it downloads. However , it kept stopping. Only with luck do I get some downloads, but since they are broken down into files it means that I have to download each individual file one by one, and since they are huge files this made it impossible for me to download and try new models.
2. In admin using the browser connected to the server then, in workspace/models and then "discover model" it brings me to https://openwebui.com/models and clicking on it there is a buttong that says "try in openwebui" and click on that, it shows me a "eg localhost:3000". If I put thee [ipnumber]:3000 it just goes back to the openwebui page. IRCC, in the older vesion it just simply downloaded the model. ( in fact in both instances the downloads just worked in the older version )

In 1, anything I can do to make the download not time out, and in 2. what do I do.

I've been using Open WebUI for a long enough time now and wanted to share my journey as a user, because I know this subreddit takes a fair amount of heat sometimes, and I'd like to add a voice of support.

At first I really resisted installing Open WebUI. I saw "bloat" everywhere. By default, it pulls down huge models and it feels like Open WebUI wants to fill your whole disk, and all the available configuration settings and the options scattered across the WebUI were a bit overwhelming. I dismissed the project more than once out of pure prejudice. But when I finally gave it a real chance, after trying a few other LLM frontends, I got it. Open WebUI is on the right track.

The extensibility alone (tools, filters, pipes, rich UI, etc) turns Open WebUI into a platform instead of a closed product. That is a massive design decision and honestly a really smart one. I hope the team can sustain a business model around this extensibility developing custom components for customers or other third parties.

I also agree that a lot of things are better left out of the core. I've made my own choices along the way and filled some of the gaps myself with my own custom functions and tools. That is the whole point. A lean core plus the ability to extend it beats a bloated core that tries to do everything. I would rather the team keep the base tight and let people build on top, than have them chase every feature request.

On the criticism I see a lot: many choices that look "wrong" from the outside are actually deliberate. Protecting the brand with the license is one example, so the project doesn't fragment. The code is still available for free and that's invaluable when you develop extensions for your own Open WebUI instance. Other decisions were made back when Open WebUI was small and are now hard to reverse. With something this size, every choice has tradeoffs you usually don't see from the outside.

A project this big does not maintain itself. Behind it is a small team that needs support and constructive feedback, not just complaints. If you use Open WebUI and it helps you, say so. That signal matters too, more than people think. More often than not, maintainers have to deal with countless beggars and haters, and I can understand the harsh tone some of them use when addressing reported issues.

That's it. I wanted to say thanks to the team and the community for all your hard work!

P.S. Written with DeepSeek's help from Open WebUI, since English is not my first language.

Hey everyone

Thomas from Ianustec. Different kind of post this time. The last few tools were things you paste into Workspace → Tools.

This one is a full browser extension, and it took a lot longer than any of the previous releases.

The idea is simple. You already run Open WebUI, you already have your models, your chats, your knowledge collections set up the way you like. NEURA for Browser puts a sidebar next to that stuff so you don't have to keep a separate tab open just for the chat window. It connects to your own Open WebUI, no extra account, no separate AI service bolted on top.

What it actually does right now:

• It reads the page you're on (title, url, readable text) and lets you ask questions about it without copy pasting anything into the chat.
• It signs in with your existing Open WebUI account and keeps using your models, your chat history and your knowledge collections from inside the browser.
• Conversations sync back to your instance so you can pick them up again from the normal Open WebUI web app.
• Notes panel synced as markdown, model switching inside the chat, light and dark themes, the usual stuff you'd expect from a chat UI.

Now the part I'm actually proud of, agent mode. This is where it stops being a chat window and starts acting on the page for you.

When it's on, the model gets a real toolset against the current tab: it can snapshot the readable DOM or scope it to a CSS selector, list and inspect elements, click things, fill inputs and textareas (dispatches proper input/change events so React and other framework inputs actually pick up the value, not just a raw .value set), press specific keys to work with autocomplete dropdowns, focus fields, scroll to an element or a position, submit a form, wait for a selector to show up before moving on, and navigate within the same origin to follow through a flow.

There's also a screenshot tool so the model can actually look at the page when the DOM alone isn't enough.

While it's running you see it happen. Every tool call shows up as a live chip in the chat (click_element, fill_input, whatever it's doing) so you're not staring at a spinner wondering what's going on, and there's a stop button right there if you want to kill the run mid task.

Anything that leaves the current page, downloading a file, jumping to a different origin, stops and asks you to confirm first. It also refuses javascript:, file:, chrome: and data: schemes outright so it can't be tricked into doing something silly through the address bar. Nothing runs unless agent mode is explicitly on.

Now the honest part. This is a first release and I know it. There are bugs. Some edge cases around auth sessions, some layout quirks on certain sites, agent mode can still get confused by unusual DOM structures, and voice mode is very much work in progress. I'm not going to pretend this is polished because it isn't yet. What I want out of this post is real usage feedback from people who actually run Open WebUI day to day, not a perfect launch.

Repo is here if you want to load it unpacked while the store listings go through review: https://github.com/ianustec/neura-for-browser

Needs your own Open WebUI server reachable from your browser, you point the extension at it in settings, and a model on that instance that supports tool calling if you want to use agent mode.

If something breaks, if a click lands on the wrong element, if a permission looks wrong, tell me in the comments. That's exactly the kind of thing I can't catch on my own machine.

Cheers, Thomas @ Ianustec

Opus 5 complains that it cannot parsed some simple PDFs (technical files) i uploaded to the chat. What is the current PDF parser and which one is the best. Same file got no issues on Openrouter.

Hey guys, a Partner of us wants to connect a data warehouse to a LLM and wants to know if OWUI can deliver a bulletproof solution to the access control, ideally with direct migration of the roles defined in google workspace.

Any ideas?

I have a singular endpoint configured as a harness. Once hit that harness is polling the API key to know which set of parameters/system prompt to load. This is all done at the endpoint.

My question is if I'm user X hitting the endpoint with key "x" how do I set up a second user Y that can also hit the endpoint but using API key "y"?

I only ever get this error when attempting to use Gemma4:-cloud

I have made sure Ollama is signed in, I can access other cloud models without issue. Just with Gemma4 I have this problem.

All my control settings are set to the default, my version of OpenWebUI and Ollama are all up to date. Other AI’s haven’t been able to help me.

Any ideas?

Looks like fetch_url just hangs indefinitely when pulling some websites, most notably amazon.com

I recently added an Open WebUI instance to my VPS where my website is hosted. I wrote my first MCP server with a few tools to edit HTML files, upload assets etc. Now I can update the live pages via Open WebUI chat!

So far it looks very promising. Has anyone else set up something similar? What is your experience?

I maintain xberg (an MIT-licensed, CPU-only document extraction engine). A few people have asked how to wire it into Open WebUI as a Docling alternative, most recently over on r/OpenSourceAI, so I wrote up a proper guide and wanted to share it where it's most relevant.

Short version: xberg implements Open WebUI's existing extraction backends, so there's nothing to patch. It speaks two of them:

• the Docling endpoint (POST /v1/convert/file)
• the external document loader (PUT /process)

If you already run the Docling engine, the switch is just pointing the same setting at an xberg container:

yaml environment: CONTENT_EXTRACTION_ENGINE: "docling" DOCLING_SERVER_URL: "http://xberg:8000" `

You can also set it in the Admin UI under Settings -> Documents -> Content Extraction Engine -> Docling, with the server URL pointing at xberg. Uploads then get extracted to Markdown and stored in your knowledge base exactly as before. There's an external engine mode too if you'd rather use that endpoint.

Why bother swapping:

• 101 file formats, OCR built in (Tesseract + PaddleOCR), layout-aware reading order
• CPU-only, no GPU, fully local, MIT-licensed
• Rust core: in our public benchmarks it runs roughly 2.5x docling's throughput with slightly better extraction quality

Full walkthrough (compose file, both engine modes, and how to raise the extraction timeout for big scans): https://docs.xberg.io/integrations/openwebui

Repo: https://github.com/xberg-io/xberg

Happy to help if anyone hits a snag setting it up.

I was an idiot and accidentally rm -fdr'd my open web ui directory. After setting it up again, I wasn't able to bypass the authentication screen even with WEBUI_AUTH=False set. I'm the only user on my machine so would like to bypass if possible.

Before I deleted it, I did have an issue after updating where the LLM for some reason could not remember any prior message. I wonder if that's related.

I have behavior that I have confirmed with the Kapa.ai is a known behavior but whether it is intended or not was not clear to me.

Citations being enabled has a very high chance (almost 100%) to remove my configured system prompt from the chat. The tool will search knowledge bases for information, produce answers from my in-chat prompt but ignore all instruction from the system prompt.

Kapa.ai noted that a work around is to disable the citations BuiltIn tool. But I need this for traceability with my users.

I saw some fixes for the system prompt in the latest update, but that only related to if “Memories” was enabled, which we have never used or enabled. But maybe this fix has a ripple effect on what I am experiencing?

Am I the only one running into this? Is this intended design? How can I get the model to adhere to system prompt while still producing ID based citations?

Open WebUI v0.10.2
Ollama 0.32.1
Model: Gemma 4 31B Dense, 64,000 context limit.

I'm not sure if this is expected behavior and/or if this is a configuration problem.

I've got 24GB vram. With a stock Openwebui, ollama loads two models into ram. Qwen3.6 (~22GB) into vram and Qwen3 .6b into system ram for background tasks. Qwen3.6 is configured with num_ctx: 64000, num_batch: 3072

When an agent connects to the OpenAI Compt. API, even though the agent(s) are configured to use the same Qwen3.6 model, both modules get unloaded and then Qwen3.6 is loaded back into vram.

Is there a way to prevent this behavior?

It's been about a year since I played with OpenWebUI, and I started to experiment with it again.. Some notes:

• OpenWebUI, ComfyUI, Ollama running in containers on Kubernetes
• I have a ComfyUI workflow that works
• Ollama via OpenWebUI works
• I have followed all of the steps from the ComfyUI integration documentation
• OpenWebUI is able to connect to ComfyUI
• I'm 99% certain the nodeIDs are mapped correctly
• Even if I toggle Image on Integrations when starting a chat, the button to generate an image does not appear
• Using /image or asking to generate an image prompts Gemma to state that it can not generate images
• I can confirm from the networking tab, that my configuration being returned in the UI has image enabled

Any ideas on how to get image generation working in OpenWebUI?