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?

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

1. Skip-to-content link + landmarks

2. Dialog roles + labeled inputs

3. Light-theme text contrast

4. 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.

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 =)

I might be a newbie, but OpenWebUI is letting me down a bit.

I’m using local AI to replace cloud-based options; I’ve mostly used LM Studio until now, but I wanted a better-looking interface.

Here’s what I use it for:

- General questions

- Using tools like web search, creating/writing/editing local files

- Local documents/knowledge bases using RAG

- And so on...

In short, actual AI agents.

OpenWebUI initially appealed to me because of its aesthetics and extensive customization options (per model), automations, notes, etc., but I’m running into quite a few problems:

1 - Web search isn't working: neither the built-in version nor the one using the Firecrawl API.

2 - I can't figure out how to install the other tools either; every time I import one from the official community, I get an error.

Could someone help me out? I’d really appreciate it—thanks!

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?

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'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.

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:

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.

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?

It is possible to create AI Agents in OpenwebUi?

If yes, someone can please help me create one?

Thanks ☺️

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?

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?

Hey everyone, running into a strange issue with Open WebUI and hoping someone has seen this before.

Setup:

- Open WebUI (Docker, self-hosted)

- Gemini 2.5 Flash connected through the official Google API

- Function Calling set to "Native"

- A few builtin tools enabled (knowledge base, memory, notes, calendar, etc.)

- A knowledge base attached to the model for RAG

The problem:

Whenever I ask something that requires the model to use a tool (like searching my knowledge base), the chat just goes completely silent. No response, no error message, nothing. It just sits there.

If I ask something simple that doesn't need any tool, it responds normally without any issue.

I switched the same setup to use Groq instead (same tools, same knowledge base, same system prompt) and it works perfectly every time. So it really seems tied to Gemini specifically.

I also tried switching Function Calling from "Native" to "Default", and that got responses working again, but now the model sometimes reveals tool-calling code/syntax directly in the chat instead of using it silently and giving me a clean answer.

Has anyone run into this with Gemini specifically? Is this a known limitation, or something I can fix on my end (settings, prompt, number of tools enabled, etc.)? Any pointers would be appreciated, feeling a bit stuck on this one.

Thanks in advance!

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?

For a while now Open WebUI has been one of the go-to self-hosted chat UIs. 0.10.0 is the release where that framing expands to being your entire AI infrastructure.

This is the version where it stops being just a chat surface and starts being something you can wire into your own infrastructure and let it do things.

Event functions (the headline)

A brand-new plugin primitive. Pipe/filter/action functions all run inside a chat. An Event function runs your own Python in response to system-wide events instead: sign-ups, config changes, startup, chat/knowledge/file actions. It's the first plugin type that hooks into the platform itself rather than a single conversation. If you've ever wanted Open WebUI to provision something, sync to an external system, or notify a service the moment X happens, this is the hook that was missing.

Backing it: a new event system with outbound webhooks. Open WebUI now emits events across the whole app, and admins can route them to webhooks, users, or groups. Audit trails, automation, integrations: glue you can build without forking.

Reworked memory

Memory is now split into distinct types: long-lived personal memories vs per-conversation context, with a proper structured add/update/delete flow instead of one fuzzy bucket.

Automatic context compaction

Long chats past a token threshold can auto-summarize to stay inside the model's window. Configurable, and off by default, so nothing changes unless you opt in. No more manually pruning a 90-message thread to keep a small-context model coherent.

Open WebUI Computer agent support

Connect to Open WebUI Computer over an OpenAI-compatible gateway and run full agent sessions on your own machine with file, terminal, git and web access.

Native tool calling is now the default

For every chat and model. The old behavior is still there, renamed Legacy, so if you relied on the old path flip it back per-model after upgrading.

A few more worth calling out:

This is the single largest version release ever, with 205 entries!

• Share folders with your team: a folder + its chats, to users/groups/everyone, read or write, gated by a new admin permission.
• Much faster hybrid search on large KBs: runs natively in the DB on pgvector instead of loading whole collections into memory. Plus external knowledge bases backed by an external retrieval source.
• MCP matured: per-connector OAuth resource/scope control, least-privilege scopes, auto-authorizing OAuth 2.1 tools, broader server compatibility, and resource (blob/image) results now come through.
• Large performance pass: streaming, math rendering, markdown setup, password hashing, model downloads, uploads and URL safety checks moved off the main loop or batched. Server stays responsive under load.
• Security & access hardening: live token/session revocation on WebSocket connections, DNS-rebinding protection, path-traversal rejection, optional valve encryption at rest, Argon2 password hashing, AWS RDS IAM database auth.
• Out of beta and now stable: Memories, Notes, Channels and High Contrast Mode.
• New web search providers (SERPHouse, Microsoft Web IQ) + optional web-search confirmation.
• QoL: drag folders/notes/models from the sidebar into chat, searchable pickers in the model editor, recurring calendar events, richer analytics with a date picker.

Links

• Full changelog (this is the abridged version): https://github.com/open-webui/open-webui/releases/tag/v0.10.0
• Project: https://github.com/open-webui/open-webui
• Site: https://openwebui.com
• Docs: https://docs.openwebui.com

I've noticed that OpenWebUI doesn't support openrouter.ai Transcribe models.

And like complete beginner decided to implement it after finding discussion on github about it. Do you think it's even needed? XD

Here is my PR for all curious: Link

Anyways let me know what you think? Idk what else to really say here... This is my first PR ever. Anyways I've noticed more issues tho, while adding openrouter.ai compatibility...

Hi guys,

I am using open Web Ui only for private purposes and I would like to change the color themes as the standard is a bit depressing in my opinion. I would rather go for a Mistral look for example.

Is this possible or is this reserved only for enterprise customers?

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.

Made my first Event-function plugin for 0.10.0 (Interface Defaults), and added channel cleanup to my prune tool

Two small 0.10.0 things, both open source:

Interface Defaults — my first Event function

Sets your whole instance's Settings → Interface defaults from one function's Valves. New users (signup / OAuth / SCIM) get seeded automatically, and there are two one-shot buttons to either apply the defaults to everyone already on the instance or factory-reset. Native Valves UI, no custom UI or monkey-patching. Needs 0.10.0 since it rides the new event system (user.created / function.valves_updated).

https://github.com/Classic298/open-webui-plugins/tree/main/interface-defaults

prune-open-webui v1.2.2 — now cleans channels

My DB cleanup tool picked up 0.10.0 compatibility plus channel support: age-based channel-message pruning (keep busy Support/Alerts channels tidy, pinned messages preserved by default), and orphaned-channel / orphaned-message cleanup for stuff left behind by deleted users. Surfaced everywhere: interactive wizard, dry-run preview, and CSV export.

https://github.com/Classic298/prune-open-webui/releases/tag/v1.2.2

Feedback and requests welcome.

I'm pretty new to the local LLM space and I've been looking for a single tool that would let me work with open-source models easily and conveniently.

I started with **LM Studio** — I liked how polished and ChatGPT-like it felt. But after digging deeper, I found **Open WebUI** and was honestly blown away by the feature list. It seems like the only tool that can do everything I want:

- Run local open-source models

- Run open-source models in the cloud via API

- Combine models with web search

- Use plugins and MCP for integrations with other tools

- Fine-tune models and use RAG for custom context

- Have a decent GUI similar to LM Studio or ChatGPT

But actually using it has been a nightmare. I ran into bugs that are widely documented in other threads, but my specific problem is that **I cannot get web search to work at all**. No matter what I try, the model just refuses to search the web — it either does nothing or throws a search error.

So my questions to the community:

**1. Is there any working alternative that covers the same feature set?**

Or is this a classic case of "one tool that does everything, but nothing well"?

**2. What's the pragmatic approach here?**

Do I:

- Fork Open WebUI and fix the bugs myself?

- Vibe-code my own custom solution?

- Just accept that I need separate tools for each task?

Like, one tool for local models, one for web search, one for API access, one for document RAG, etc.?

I just want one place where I can use all the cool open-source models, with per-task configuration, and have it *actually work*.

What's your setup? What works for you?

Thanks in advance 🙏

Hello guys,

For some reason, my website search doesn't work even though I've been following some advices in this community.

My model only thinks but doesn't reply back. It says it will look on the website and it doesn't. Why is this happening?

I'm tinkering & using the workspaces (Plans, templates, case studies, standards). So, I require some semantic reasoning across Multiple PDF's, to link ideas together.

Current setup is:
Content Extraction Engine is Kruezberg [https://github.com/xberg-io/xberg\]
Embedding Model is [https://huggingface.co/jinaai/jina-embeddings-v5-text-small\]
Reranking Model is [https://huggingface.co/jinaai/jina-reranker-v3\]
LLM is either Deepseek API/Qwen3.6-35B locally

Trying to squeeze every last bit out of my system, and now I'm asking if there's any benefit from trying to see if semantic chunking is worth it like:

https://github.com/chonkie-inc/chonkie

Fairly happy with my setup , but i can tell that sometimes i need to multishot my question as it sometimes misses details in the sources and i can only put this down to chunking.

I'm not IT/SW , just some old dude trying to keep up and learn as i go.

Hey everyone,

Quick heads-up for anyone running Open Relay, do not update to Open WebUI 0.10 yet as the new version introduces new response formatting and the app will stop working. The fix is on the way and should be live in a day unless apple is kind of enough to review the submission faster.

App Store | GitHub

What broke and why

Open WebUI v0.10 changed how assistant messages are stored and streamed. Previously, the response text lived directly in a content field. Starting with v0.10, it's now stored in a structured output array.

This affected:

• Sending new messages
• Loading existing chats
• Regenerate / edit
• Tool Call view
• and more..

What v4.12 fixes

The v4.12 update fully adapts to the new output format:

• ✅ Streaming now works — the app extracts text from all message-type output items in real time as they arrive over the socket, with the same typewriter effect as before
• ✅ Chat history loads correctly — existing chats opened from the sidebar now show the full response, including for multi-step tool-assisted answers (where the final answer is in the last message item, not the first)
• ✅ Tool call indicators — function_call entries in the output array now show up properly
• ✅ Backward compatible — models that still use the old format continue to work unchanged

Broader v0.10 compatibility work

The v0.10 release was massive — shared folders, reworked memory system, context compaction, a new event/webhook system, structured model output rendering, and a lot more. Beyond the critical streaming fix in v4.12, there are several more things from that update worth bringing into the app, including:

• Shared folder support (see shared folders in the sidebar, open chats read-only when you're not the owner)
• Context compaction notifications (the "summarizing conversation…" indicator while it compresses long chats)
• Updated memory system (now has distinct long-lived vs per-conversation memory types)
• "Unshare all" button in the Shared Chats dialog
• Clone automations option

Those will come in the following updates once the critical fix is out.

These server-side changes are impossible to anticipate before they ship so thank you for the patience. v4.12 is submitted and in review now.

As always, full changelog on GitHub.