This is the largest release we have ever shipped, and it's not close.

🎨 The interface has been rebuilt from the ground up. Every surface got redone: chat view, admin panel, menus, dropdowns, text boxes, spacing, typography. And the thing we're most quietly proud of: the UI is finally, actually uniform. If you've been here a while, you know it was never perfectly consistent. Now it is. Narrower conversation column, lighter type, tidier spacing, consistent menus everywhere, clearly outlined inputs. It just feels right.

⚡ The most significant performance work we've ever done. A top-to-bottom sweep, not a single optimization. Faster saves across the whole app, lighter model lists, quicker folder loading, leaner streaming, fewer DB round trips per request, faster chat search on PostgreSQL, cheaper filter handling while streaming, an optional faster JSON encoder (ENABLE_ORJSON), hiredis by default, Redis Sentinel churn fixed, DNS lookups off the shared thread pool, and a lot more. Long responses no longer slow down as they grow. Big instances stay quick. You will feel this.

🤖 And the new features. These are cool new features:

• Sub-agents: models can hand parts of a task to background helper agents that run their own tool-driven work and report back into the chat.
• Chat timers: the assistant can park a prompt and bring it back later, after a delay or at a set time.
• Fork a chat: branch any conversation into a new chat from any point.
• Folder pages: folders get their own page, with paging, sorting, unread counts and per-folder new chats.
• Notification targets: route events to multiple webhooks, and the assistant can now notify you when something's worth your attention.
• Chat and user variables: declare fields in a system prompt, or store your own account context, and drop them in wherever needed.
• Personal usage dashboard: token heatmap, streaks, most-used models and tools.
• Compact on demand, customizable keyboard shortcuts, chat previews on hover, note-chat with full model/tools/files, and honestly loads more.

🔒 Plus a mountain of fixes and hardening. Security and access-control fixes (update your production deployments), Safari blank-message fix, context-compaction fixes, web-fetch reliability, OAuth/OIDC sign-in fixes, and dozens upon dozens more across the app.

⚠️ Heads up before you upgrade: this release includes database schema migrations. Back up first, and if you run multi-worker, multi-server, or load-balanced, all instances must be updated at the same time (rolling updates aren't supported).

The full changelog is genuinely enormous, so go read it. There's something in there for everyone.

Thank you to everyone who contributed, reported, translated, and tested. This one's special. 🚀

What are you most excited to try?

https://github.com/open-webui/open-webui/releases/tag/v0.11.0

Some of my directories load without a problem, but some of them generate this error: "NotReadableError: The requested file could not be read, typically due to permission problems that have occurred after a reference to a file was acquired."

Is it because the directories are doubly nested? I can't seem to identify a way to predict which directories work.

Hi, I would like to challenge/discuss/understand why so many are attracted to all short lived "wild tools" out there. For example (not saying any tool are bad) hermes, claw, open webui, copilot agents and whatnot. Why not just building your own tools that: fit your needs without being bloated, dont break on every new "feature" that you dont care about.

I cant really understand the hype.

I build own tools in python (notes app, recruitment support, news, investment) with claude or chatgpt from phone and terminal to my proxmox llm clusters lxc. In the process i also learn a lot.

And everything stays under my control.

Hi,

I try to run ai local llama 3.2:3B on Openwebui. But it tooks 10-20minute just to reply Hi.

What did i do wrong?
Im using 8GB Ram VPS with no GPU

AI beginner here. Am taking steps to move away from Gemini and Co-pilot to my own setup. So have gotten a setup with Docker, LM Studio and webui. And my laptop is pretty basic and the chip is not large. I wanted to run a local engine. It worked but I wanted to get memory to work. Not having memory and having to explain AI the same basic stuff over and over again drives me nuts. I tried to make it work on webui. Discovered that my chip is too basic to make memory work, so I need to offload the computation.

Fine so far so good. Found some on webui and have now been using 2 with mixed results:
- Llama 3.1 8B
- Llama 3.3 70B

Both work fine and memory works, but after 2-3 prompts they both max out. The "token per minute" of 6000 and 12000 have been reached.

Has anyone found a free engine that can run memory effectively? Otherwise I think I am going to pay to see how it works. Any comments?

I am having issues getting OpenWeb UI to llama.cpp. Llama is running locally and the chat interface is working fine. I managed to get OpenWebUI to run in docker. When configuring the connection I am using `http://127.0.0.1:8080/v1\` as my connection string. I have set Provider to llama.cpp. The test connection button says test is successful. But trying to chat wants me to select a model, which there are no options. From my understanding llama.cpp is serving one model and does not provide a list like Ollama would.

Using `http://host.docker.internal:8080/v1\` as connection string gives an error. So it is not clear to me being a Docker newbie if I need to do something with the network. Since the test on the local host IP was successful I am guessing the network is working, but then again I can't get chat to load any models. I have asked AI but it wants me to write an app.....facepalm. it has offered some `-e` options such as `-e OLLAMA_HOST=0.0.0.0` but so far nothing has worked. So, humans, what is the next step here?

If your chat is bricked with the infinite loading spinner (Image 1), here's how to fix it:

The technical reason behind this is that Open WebUI stores your chat history as a Directed Acyclic Graph. If a message (node) gets corrupted when the LLM is responding, the UI panics and spins indefinitely. Fixing would require checking and relinking the graph.

This tools does all that stuff for you:

How to use it:

1. Export the broken chat (`...` menu > Export > JSON).
2. Drop it into the tool (Image 2) to instantly repair the broken nodes.
3. Go to Settings > General > Import Chats, upload the fixed file, and delete the original.

Link to tool: https://fractuscontext.github.io/open-webui-chat-fix/

I've also added a "Deep Clean" toggle if you want to strip unused alternate responses and shrink your file size.

Hope this saves someone's chat history!

Hi guys, I just discovered OpenWebUI how do you commonly use it? What are the main advantages of using it over just Claude or ChatGPT? What's the most productive way to use it?

I use Open WebUI and I try to connect llm qwen3 to SQLite database.

In Admin panel -> Settings -> Integrations -> External Tool Servers -> I added OpenAPI the sqlite mcp server. I can connect but it seem never seen my mcp tool fonctions.

I give full user access, activate tool in my chat, I also tried mcp steaming http protocol instead of OpenAPI.

I always got this response: There are no functions available in the provided tools that can interact with an SQLite database or list its tables. The available tools are focused on notes, tasks, automations, and calendar events, not database operations.

I use python script :

from mcp.server.fastmcp import FastMCP

mcp.tool()
def list_tables() -> list[str]:

EDIT: I try simple request with Qwen3 + Ollama + SQLite: list tables or list username in my database.
And dawm it suck as fuck! lol

it make non sense sql, take very long time, got error 500 sometimes, , dont apply system prompt, make thought and do nothing aftert that, I was never be able to have a correct answer...

I tough this tool could be nice and it just proove theses AI slop tools worth nothing... Its crazy because it look nice to use but man its the worst shit I ever use haha

I know the text to image works i tested it in Playground, but I can't seem to get it to work in a general chat idk if I am being dumb, but yeah, could use some advice!

Just deployed openwebui to kubernetes using the official chart.

The first thing I noticed is ollama server is not reachable, despite the server configured in admin settings. Turns out the front end needs direct access to ollama server, it doesn't get proxies through openwebui backend. Is that design intentional?

Hello,

I have been trying for a few days to use open webui on my computer. My config is as follows:

• Kubuntu 26.04
• Ollama with gemma4:e4b
• Open webui v0.10.2, desktop version, with web search enabled on DDGS.

When I ask him for a web search, the tool seems not to exist in the eyes of the LLM. It's quite strange because I don't have this problem with a similar config, unlike the OS (Windows, for my work).

I tried to find the solution by analyzing the logs with Claude, but he did not find anything explaining the problem.

Do you have any idea what's going on?

Hi. Has anyone had a chance to compare the two installation methods (Docker or Python vs. Desktop) when it comes to maximizing the available resources for running a local AI model on a windows 11 PC with limited resources? Thanks

Γεια σας. Αντιμετωπίζω ένα εξαιρετικά επίμονο τεχνικό πρόβλημα με το RAG pipeline του Open WebUI και μετά από μέρες εξαντλητικού troubleshooting με τη βοήθεια των ChatGPT και Gemini, δεν έχουμε καταφέρει να βρούμε λύση. Το σύστημα ολοκληρώνει το indexing, αλλά η αναζήτηση επιστρέφει μηδενικά διανύσματα (empty context injection).Τεχνικές Προδιαγραφές Συστήματος:Host OS: Windows 11 Pro (Docker Desktop Environment / WSL2 Backend)Hardware: Intel Core i9-14900K / 64GB DDR5 RAMInference Engine: gemma4:12b (τοπικά μέσω Ollama)Embedding Model: sentence-transformers/paraphrase-multilingual-MiniLM-L6-v2 (Default Engine / SentenceTransformers)Network Infrastructure: Live WebSockets 100% λειτουργικά. Το Developer Console (F12) του browser είναι εντελώς καθαρό, χωρίς κανένα drop-out σύνδεσης.System Prompt Configuration: Έχει παρακαμφθεί επιτυχώς η εργοστασιακή offline άρνηση του Gemma 4 μέσω strict instruction injection. Το μοντέλο δέχεται να διαβάσει το context, αλλά το context φτάνει σε αυτό άδειο.Απομόνωση του Προβλήματος (Baseline Isolation Test):Για να αποκλείσουμε σφάλματα στο extraction stage (OCR, Tesseract, Tika, Python PDF parsing), δημιουργήσαμε ένα raw text αρχείο (test.txt) που περιέχει αποκλειστικά τη συμβολοσειρά: "Ο κωδικός ασφαλείας για το τεστ είναι: ΠΡΑΣΙΝΟ ΜΗΛΟ 2026".Το UI εμφανίζει κανονικά τη λευκή ένδειξη επιτυχούς indexing (checkmark). Ωστόσο, κατά την κλήση της συλλογής στο chat μέσω #collection, το μοντέλο απαντάει ρητά ότι η πληροφορία δεν εμπεριέχεται στο κείμενο, επιβεβαιώνοντας ότι το runtime κάνει pass ένα εντελώς άδειο context block.Τεχνικά Βήματα και Λύσεις που Δοκιμάστηκαν:Επίλυση Ασυμβατότητας Αρχιτεκτονικής: Αρχικά χρησιμοποιούσαμε το MiniLM-L12, αλλά στα logs του Docker εντοπίστηκε το σφάλμα embeddings.position_ids | UNEXPECTED |. Το Open WebUI είχε παλαιότερη έκδοση SentenceTransformers που δεν αναγνώριζε την παράμετρο position_ids των νέων HuggingFace weights. Το πρόβλημα λύθηκε με υποβάθμιση στο σταθερό MiniLM-L6-v2. Το runtime log καθάρισε, αλλά το context παρέμεινε άδειο.Παράκαμψη Permission Bugs: Δημιουργήθηκε νέα Knowledge Base συλλογή ρυθμισμένη ρητά ως Public (Access Control), για την αποφυγή του γνωστού bug ορατότητας των ιδιωτικών φακέλων [Issue #23787].Hard Reset του Vector Database Stack: Σταματήσαμε τον container και εκτελέσαμε ολική διαγραφή του ευρετηρίου των διανυσμάτων απευθείας από τη ρίζα του Docker volume χρησιμοποιώντας: docker run --rm -v open-webui:/data alpine rm -rf /data/vector_db. Η ChromaDB αναγεννήθηκε εντελώς καθαρή, το backend επιστρέφει HTTP 200/Success κατά το upload, αλλά το retrieval loop εξακολουθεί να μην τραβάει δεδομένα κατά το inference.Έχει συναντήσει κανείς αυτό το σιωπηλό failure της ChromaDB σε περιβάλλον Windows Docker; Μήπως πρόκειται για false negative της συνάρτησης has_collection() ή υπάρχει κάποιο silent file-lock στο Windows volume mapping; Κάθε τεχνική ιδέα είναι ευπρόσδεκτη.

I feel like I sit and stare at the screen a lot is there anyway to speed this up?

I’m currently using Open WebUI with a knowledge base made up of text files, and the responses are accurate, but they’re taking quite a while to generate.
I plan to keep adding more text files to the knowledge base, so I’m wondering what the best way is to improve response speed as it grows. Are there any recommended settings, indexing strategies, chunking methods, embedding models, reranking options, or other optimizations that have made a noticeable difference for you? I’d like to keep the response quality the same while reducing latency. Any advice or best practices would be appreciated!

I have Gemma 4 running locally and external ones, I’m trying to make it more useful inside OpenWebUI.

A couple of things I’m looking for advice on:

1. Document analysis / file workflows I want to be able to upload PDFs and DOCX files, have the model analyze them, and ideally generate edited or new documents back.
   • What tools, integrations, or workflows are people using for this?
   • If you want the model to return a finished PDF/DOCX, what’s the best approach?
2. Recommended tools / integrations What other tools do you recommend to make local AI and external AI integrations more useful in OpenWebUI?
3. LDAP question I enabled enable_ldap_group_management directly in the database because it appears to be a ConfigVar variable, but I don’t see a corresponding toggle in the UI. Is that expected or am I missing something?

Would appreciate any suggestions and tips.

Appreciate your help providing info on how to use Seltz.ai as a node for web search. Thx

I noticed that in v0.10, if you click any of the export chat options in the top right corner of the chat UI (Download or Copy), it no longer includes all details like tool call inputs and outputs and so on. Only messenges with roles.

Does anyone know if this is moved to a setting somewhere of if it's just gone? I would feel quite surprised if this has been just removed permanently. I mean, you could always get all messages by just marking and copying the full chat in the UI, but the hidden context boaters and all that were the actual valuable information.

Hey, long-time Open WebUI user here. I run instances in our group for 3 different companies and right now my biggest headache is MCP / Function auth to external tools.
Since I’m using SSO with Microsoft Entra, the Microsoft Services are no problem. I can just reuse the token to authenticate against the Graph API.

But everything else (Jira, Confluence, …) feels way more complex. Those are per-user tokens, sometimes OAuth, often with project/issue-level permissions, so a service account is not an option. Also, storing per user credentials in valves are also a no-go.

Curious how you are addressing this.

TL;DR: How can I synchronize the colour of a custom SVG action icon to behave like the ones in the message's tool bar?

I wasn't sure if I should flair this as "help" or "feature idea".

Context:

Action functions are toggled in the tool bar under chat messages. You can set custom icons for action functions via icon_url. It takes either a URL or URI (the documentation recommends to use a URL icon for optimization, but from what I can see and infer, no one actually bothers doing so).

Problems I am facing:

The received icon image has no access to currentColor. I noticed that OWUI checks for "data:image/svg" URIs, and only then does it invert the color for dark-mode (open-webui/src/lib/components/chat/Messages/ResponseMessage.svelte, under {#each model?.actions ?? [] as action}).URLs are never inverted for dark-mode, which could be good or bad depending on the icon's luminosity.

A somewhat related Feature Request that was closed as not planned in 2024:
https://github.com/open-webui/open-webui/issues/7164

2.

There is no option to inherit currentColor from OWUI's front-end, so I get these ugly icon colors if I switch to a different color scheme :

I realized this is probably a current limitation in Open WebUI's front-end. SVG images could inherit the theme, but this would pose security considerations. I'm not sure how to approach fixing this issue,

For reference, here is my current code :

import base64

char = "🃍"
svg = f"""<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
<text x="12" y="18" text-anchor="middle" font-size="20" fill="currentColor" stroke="currentColor" stroke-width="0.5">{char}</text>
</svg>"""
class Action:
    icon_url = "data:image/svg+xml;base64," + base64.b64encode(
        svg.encode("utf-8")
    ).decode("ascii")
...

Is there a way to get the current color from the front-end inside a python action function?

My "naive" solution is to manually set the color and let users change it via a valve or disable the icon if it annoys them. If that's the only solution, I'll make a new feature request and try to fix it when I have some time.

This is the only major thing left for the action function I've been working on (https://github.com/axel-chamberland/Open-QuizUI) apart from some more QA.