https://preview.redd.it/zaf4ngapmgbh1.png?width=1856&format=png&auto=webp&s=dc24bc1f90f797a72892b4c7d802cce43f6bff82

Hey everyone 👋

I'm Thomas, part of the team at Ianustec. A little while ago my colleague Nick posted

here about the open-source suite of Microsoft Office extensions we're building for

Open WebUI — PowerPoint, Word, Excel and Outlook. Thanks for all the encouragement in

that thread!

We said we'd ship it piece by piece, so here's the first one, fully open source:

👉 generate_slides, a tool that creates high-quality native slides, similar to how Claude does it

Repo: https://github.com/ianustec/openwebui-generate-slides

What it does

• Native Office charts (bar, line, area, pie/doughnut, radar, stacked)
• ~25 ready-made layouts: cover, sections, KPI rows, comparisons, timelines, funnels/pyramids, tables, quotes, alerts, image layouts, closing…
• Curated themes + auto theme detection, custom accent color
• Lucide-style icons bundled in the file (no network needed for icons)
• Single self-contained .py — paste it into Workspace → Tools and you're done
• Saves via the Open WebUI Files API and drops a download link right in chat

The screenshots below are straight out of the included example deck 100% generated by the tool.

How to try it

Workspace → Tools → +, paste `generate_slides.py`, enable it for your model, then ask

for "a deck about X". Requires `python-pptx` (auto-installed via the tool's requirements).

This is just the start and we'll keep everything MIT.

Happy to answer questions and take PRs. Cheers 🙌

Thomas, IANUSTEC

Hello, I have question concerning RAG in Open WebUI, I know there are lot of tutorials in youtube, but I cannot understand today's style of speaking fast, editing all pauses away and clicking and making adjustments like adhd person with caffeine in veins instead of blood. I cannot follow those because I am old and stupid.

Question: I have a masterfolder where all my novel related stuff is catalogued, separate word files for e.g. "main charater 30 years old", "main character 50 years old", "alliances", "trade routes", "alliance relations" and so on.

Can I use OpenwebUI so, that if I ask "if main character when 50y old blows syndicate A's base, what are possible results of that action" and then it checkes the files, founds e.g. something in relations-document, and proposes something like "Syndicate B would benefit like this:" and gives few examples.

I understand that outcome depends heavily of the LLM model, so I am not expecting results similar of chatgpt or claude. But my concern is the RAG and indexing it. I have understood that if I modify the documents, I need to index it again?

TL;DR, can OpenWebUI read my documents what I have specified, and make suggestions to my questions based to the information in those documents?

I'm running 0.10.2 and I'm running into this thing that's really messing me up: it seems to be presenting a list of tools to every single session and with every prompt such that models see:

• search_notes / view_note / write_note / replace_note_content — for working with saved notes
• search_knowledge_files / query_knowledge_files / grep_knowledge_files — for searching knowledge bases
• search_chats / view_chat — for searching previous chat conversations
• search_calendar_events / create_calendar_event / update_calendar_event / delete_calendar_event — for calendar stuff
• list_knowledge_bases / search_knowledge_bases / query_knowledge_bases — for listing/searching KBs

The problem is that models seem to feel compelled to actually call at least some of these functions nearly all the time. So lots more tokens with absolutely no benefit.

e.g. I start a conversation about some novel thing I've never engaged a model about before and it immediately starts searching everything I've got (with absolutely zero chance of any relevant data being there)

It used to be the case that you could manually enable memory tools on an as needed basis. Now, it seems, they're enabled by default and you can't disable them? I'm thinking I need to revert. Am I missing something?

well.. About 5 months ago, I had some free time and built an OWUI extension to keep track of my session size and avoid accidentally exceeding the context window limit.

I always also had a concern that session performance might degrade once it exceeds 30-50% capacity (this is something I've noticed during my own testing with different models).

So, I put together a simple extension, published it, and completely forgot about it.

Recently, I discovered that the extension has been downloaded over 3,700 times. Whaaat? I was quite surprised, as I assumed such a niche tool wouldn't have much appeal.

So, I decided to update it.

Here is a brief overview of the plugin: Shows token counts, reasoning/cached breakdowns, context-window utilization, generation time, tokens/sec and message/chat cost below each AI response. Reads provider-reported usage across OpenAI (Chat & Responses API), Anthropic, Gemini, Ollama and llama.cpp, with an optional tiktoken fallback. Cost is shown when your provider/proxy reports it (OpenRouter, LiteLLM) and can optionally be estimated from models.dev prices.

https://openwebui.com/posts/token_usage_display_a94ea72f

Just wanted to share this in case anyone finds it useful. No rocket science or smart AI things, just a simple QoL improvement =)

Professor here. I use Open WebUI in my university classes, and with recent federal regulation changes (and common courtesy), any software we use must meet minimum digital accessibility standards. At my uni that's WCAG 2.1 AA, per ADA Title II / Section 508.

I went through the UI and opened a bunch of PRs to add that compliance. Each one is scoped to a single issue and mapped to a specific WCAG criterion. And they're tested. They cover:

 1. Visible keyboard focus

2. Skip-to-content link + landmarks

3. Dialog roles + labeled inputs

4. Light-theme text contrast

5. Accessible names for icon-only buttons

They're all live and open on the repo now. It'd genuinely help me (and anyone else who needs this for their org) if they get reviewed and merged — and if accessibility matters to you, upvotes/👍 on the PRs signal to the maintainers that people want it.

Happy to answer questions if anyone else is trying to get OWUI through an accessibility review.

I'm running everything locally, on my Linux laptop with no GPU (just integrated graphics) and 16 GB of RAM.

I have ollama 0.31.1 installed and running. I have Open WebUI v0.10.2 installed and running. I'm using model ministral-3:3b because it's smallish and reasonably fast and supports tools. I can chat with the model successfully in OWUI.

But I can't do basic things such as have the model fetch a web page from the internet and tell me things about the page. I have "Web Search" setting enabled, and I tried setting "Function Calling" to "Native" in Advanced Params.

I can't find any simple tools on the internet. Everything seems to want me to install something such as SearxNG and git-clone tools to my disk and run some tool-manager or something via pip or docker. Or wants me to run an OpenAPI server on my laptop or something. Confusing. I've read various OWUI documentation about tools etc.

Is it really this complicated ? There isn't some simple standard web-access tool built into OWUI ? Or some standard web site with curated secure simple tools I can use ?

Thanks for any help.

Would it be possible to implement a summarizing step (e.g., using the task model) on top of the web fetch content before injecting the result into the context of the main model? Often, web pages are 10,000+ tokens long, but a lot of the content is not relevant to the actual request.

​It would be useful if the fetched content were pre-processed by a faster model, alongside a prompt specifying the relevant information. This could be either the initial user prompt or a prompt generated by the main model specifically for this web search.

​This would significantly shorten the context for the main model and speed up processing time. OpenRouter implements a similar strategy with Exa Highlights. I noticed that OpenRouter Chat uses considerably fewer tokens than the same model (e.g., GLM 5.2) in Open WebUI because Exa Highlights returns only 2000-4000 characters per web page.

​Would this be a feature that can be added easily? Maybe somehow utilizing the new context summary pipeline? Or is there an external (self-hosted) tool that implements a similar strategy that can already be used for fetching websites in Open WebUI?

It is possible to create AI Agents in OpenwebUi?

If yes, someone can please help me create one?

Thanks ☺️

We're currently rolling out Open WebUI for our team, but I'm hitting a bit of a wall. A lot of employees are reluctant to drop their personal ChatGPT or Claude accounts.

The biggest roadblock seems to be the fear of losing their "memories", custom instructions, and old chat histories. They don't want to feel like they are starting from scratch. There's also mistrust by the employees thinking the IT department would read all the stupid questions, the employees are asking an LLM why they are preferring paying for their private plan instead of switching to OWUI. Well, I have to admit, that's kind of a bad marketing and bad internal communication, but it still doesn't help at all in a transformation process.

For those of you who have successfully deployed OWUI company-wide: how did you handle this? Any tips, tricks, or best practices to get people on board and make the transition painless regarding their saved context and trust an local on premise solution?

Appreciate the help!

I see all commands that are available in the CLI and telegram bot, but on Open WebUI I don't see how.../model, /help, /commands

Meaning that is not possible to change models, or effort, etc using Open WebUI?

I'm following Raspberry Pi documentation on setting up the Raspi AI Hat + 2, installing it on a Raspberry Pi 5 8G ram. Everything is fine until I inference a model and the chat responds "Server Connection Error." The hailo-ollama console shows the prompt is received but isn't showing a response being generated.

Here's the error I get in the docker logs:

2026-07-03 20:11:38.567 | ERROR    | open_webui.routers.ollama:send_request:149 - Failed to parse error response: 500, message='Attempt to decode JSON with unexpected mimetype: ', url='http://127.0.0.1:8000/api/chat' 
2026-07-03 20:11:38.568 | ERROR    | open_webui.main:process_chat:1512 - Error processing chat payload: Open WebUI: Server Connection Error

With the big OpenWebUI update I thought maybe there was a change they hadn't caught up with in the documentation but tried 0.9.5 and 0.9.6 and couldn't get them to install without errors.

Actually the only difference in my setup from the documentation is I'm running it on a headless Pi so accessing through the local hostname, could that be the problem?

I went to Admin Settings > Models > Qwen whatever > Advanced Parameters and there I set Ollama think to OFF.

That made the model very fast but it now dumps its thinking on to the main chat this way:

https://preview.redd.it/lzthsyqe62bh1.png?width=2268&format=png&auto=webp&s=b13ba8813802b85778f462360f6e4ef8403f6b19

It always ends thinking with a </think> tag and then writes its response.

I tried adding /no_think to the start and end of its prompt but that made no difference.

How do I make it such that it keeps the thinking to itself but just shows me the response? More importantly, how come it is still thinking?

Please help!

I use the latest OpenWebUI with Ollama.

Im hoping its just me but the new "search for skill and tool" selector is absolutely horrendous UI. Previously all the skils were selectible with a checkbox, now you have to click the search skills, select from a drop down, click away from the search bar to clear it and rinse repeat.

Ive tried clearing my cache and trying incognito, i have almost 20 skills and when i load a new model to test, i have to readd them all which takes ages.

How are people managing this? Can I mass add skills and tools to a new model?

Hi everyone,

I'm working to build a highly capable locally hosted with Open WebUI AI assistant inspired by JARVIS. I want it to be fast, deeply conversational, and integrated into my workflow. I want to ask questions like "best manga pirating website?" or something like that which of course normal AIs will refuse to answer.

One of the absolute highest priorities for this build is that it must be completely uncensored and unaligned. I want an assistant that never refuses a request, never gives preachy ethical lectures, and simply answers every question directly and objectively, no matter what.

• Which Base Model? What are the best uncensored/unfiltered models currently available in the Ollama ecosystem that still maintain high intelligence and a good conversational flow?
• System Prompts: Does anyone have a solid system prompt template that effectively forces a strict, compliant, yet sophisticated "JARVIS" persona without triggering built-in refusals? (i cant for the life of me)
• Advanced Parameters: I dont know anything about these any help would be awesome.

If you've built something similar or have a specific Modelfile configuration that works wonders for a raw, unfiltered assistant, I’d love to see it.

https://preview.redd.it/157bp7w4lpah1.png?width=3004&format=png&auto=webp&s=4358d76b684525fc858cd7ae7e7fec24a1142590

I use v0.9.6

I want to use my documents and notes for my STEM studies. But it is unclear to me how I setup a clean RAG that works well with all my study notes.

For example: if I want to use AI to learn for my Bioscience exam, how would this be implemented at best?

Tons of features and fixes in this version and buried way deep in the changelog for this release, but I feel like this is long overdue!

🔀 Native tool calling is now the default. Every chat and model that had not explicitly chosen a tool-calling mode now runs Native, which relies on a model's built-in tool support, while the old behavior has been renamed "Legacy" and made the explicit opt-out; if your models depend on the previous approach you must switch them back to "Legacy" per chat, per model, or globally in your default model parameters to preserve their behavior.

I see in the release notes. Where can one get more information. I am mostly interested if it now saves memories on its own without using your own functions/plugins?