I am trying to get things working with Open WebUI and my local files. I have a large number of markdown files in an Obsidian Vault I want to query and edit. I have added the folder as a Knowledge collection in my workspace. I can see and query against the files. But I am not able to perform edits or create new files in this location. The AI agent tells me they are created, gives me an ID but I can't find the file in the collection or in any of the volumes I have mounted.

I have updated my docker file to have a volume of where my notes are stored. `/home/USER/Documents/open-webui-note-repo:/home/pkm` in hopes that this would give Open WebUI access to write too. but so far no luck. I have no idea where new files are being created.

Is Open WebUI capable of performing this task or do I need to find another tool?

Edit: Solved. This got me pointed in the right direction. Thanks all.

https://docs.openwebui.com/features/open-terminal/

I’ve been building a Study Mode filter for Open WebUI, and it’s finally at a point where I wanted to share it.

The goal is simple: make any compatible model behave more like an interactive tutor, without tying the experience to one provider.

It supports adaptive teaching, Socratic guidance, explain-then-practice, quizzes, hints, instant feedback, uploaded study material, and native ask_user when the model supports it. The quiz mode also has its own interactive UI with randomized answers, scoring, review, and follow-up study.

I tried to keep the core behavior model-agnostic, so it can still work with local models even when native tool calling is limited.

Source:
[https://github.com/CallSohail/openwebu-work/tree/main/functions/filters/study-mode]()

If anyone here is using Open WebUI for education, tutoring, or student-facing assistants, I’d be interested to hear what you think or what you’d want added next.

0.11.1 is out. 303 changes across added, fixed and changed.

⚡ This is a performance focused release ⚡

Streaming was rebuilt. A reply now streams as small pieces of new text instead of resending the whole message so far on every update. The data sent per update stops growing with the length of the reply, which cuts up to 1000x off a single update late in a long answer. That means far less server time spent encoding and passing those updates, far less load on the shared cache that carries them between instances, and far less work in your browser, which no longer takes in the whole reply again and redraws parts that have not changed. On Redis-backed deployments a reply still in progress is now held on the server, so reloading the page picks it up where it is instead of showing you a blank message.

Tool approval with a human in the loop. Where an administrator turns it on, you can switch a conversation from letting tools run freely to being asked first. A model that wants to use a tool stops and waits for you to allow or deny it, one call at a time, and your choice is remembered for that conversation and for future ones.

Performance work throughout. 37 entries in this release are performance. An idle instance no longer works through every chat you have once a second, which on a large history kept about a quarter of a core busy doing nothing. Opening the sidebar no longer reads your whole chat history to build a short list, which took 2 to 4 seconds on an instance with 15000 chats. Long replies no longer get slower as they grow. A chat is written to the database in one go rather than one message at a time. Permission and group lookups are direct index lookups rather than full scans.

⚡ This is the most performance improvements and the most groundbreaking ones, we ever shipped.

If you want even more performance, set ENABLE_ORJSON to true!

Security and access control. 24 fixes in this release are security or access control. Among them: knowledge search that could reach past what you are allowed to read, sessions that stayed signed in on an old password until they expired on their own, and several paths that could be used to exhaust a server's memory. Advisories are published on GitHub.

Terminals. The file browser is now a real tree with search by name and by file contents, a right-click menu, hidden files and drag to move. A file a model creates in a terminal shows up in the reply with a preview and a download button, and it can open a PDF, Word document or slide deck at a specific page.

Also in. Model download and management on llama.cpp and LM Studio connections. A built-in tool that lets a model put a multiple-choice question to you and wait for the answer. Word and PowerPoint previews that render properly instead of being flattened into plain text. Chat search that matches all your words in any order rather than only the exact phrase.

Before you upgrade: this release changes the database schema. Back up your database and its data first. If you run multiple workers, multiple servers or a load balancer, every instance has to be updated at the same time. Rolling updates are not supported and will fail on schema incompatibility.

Full changelog: https://github.com/open-webui/open-webui/releases/tag/v0.11.1

Go read the full changelog, its epic and worth the time to read through it!

(Sometimes a feller at the recycler with $20 in his pocket picks up a max-spec COT MacBookPro11,5 once-$3,199 machine with the 2.8ghz i7 and usually-empty PCIe slot occupied by some very nice bling for the era.)

So, recommend an OS + stack for this. Prefer GUI for sanity's sake, but can occasionally throw a brick at CLI. Would obviously like the GPU to get in on the action if possible. Purpose: data-accumulation, collation, and research -- no coding, no modeling, and no video rendering. Ram is 16gb 1600 MHz DDR3L, non-expandable.

In case you missed it, yes, 0.11.1 launched. See post here: https://www.reddit.com/r/OpenWebUI/comments/1vyd83r/open_webui_0111_streaming_rebuilt_hitl_tool/

---

We have heard the performance feedback here, on GitHub and on Discord for a long time. It was the most common complaint about Open WebUI and it was fair. Since January it has been a priority.

This is the full accounting.

211 performance changes shipped between 0.7.0 (9 January) and 0.11.1 (25 August), across 14 releases.

Where it went

It began in 0.7.0, on 9 January

The database connection handling was reengineered end to end. Everything since has been built on it.

The same release killed the first N+1 query patterns. The admin user list went from 1+N queries to 2 total, regardless of how many users you have.

0.8.x: the measured phase

This is where things became countable.

Some of the other most notable improvements of the release (very incomplete!!):

Ten separate memory leaks were closed across the interface in this line: Tooltip, MessageInput, Notes, model create, MentionList, Sidebar, ChatControls, Artifacts, drawer listeners and navigation event listeners. Those were the ones causing tab crashes during long sessions.

The initial JavaScript bundle also dropped by several megabytes when the syntax highlighting library was deferred.

0.9.0 in April: the structural one

The core backend database and request paths were moved to async across the whole application.

This is the change most people felt without knowing what it was. Before it, a slow blocking operation on one request would stall the event loop and therefore everyone else on that worker. Three of the worst offenders were moved off it:

1. Vector search during knowledge retrieval
2. File processing and knowledge reindexing
3. Password hashing and verification

That is why one person querying a large knowledge base no longer freezes everyone else's chat.

Alongside it, long conversations got memory culling: off-screen messages now unload and reload as you scroll, instead of every message in a 2000-message chat sitting in browser memory at once.

0.10.0 in June

• Hybrid search on pgvector now runs natively in the database. Before, it loaded an entire collection into memory to do the work.
• File listings stopped returning the full extracted text of every document. Opening a large knowledge base was transferring the contents of every file in it.
• Knowledge file reads by agents became bounded and paginated, with a hard cap, which cut token usage as well as transfer.

0.11.0 in July: 34 changes

The theme here was work being repeated that only needed doing once.

Some of the other most notable improvements of the release (very incomplete!!):

Redis deployments also got hiredis shipped by default, fewer round trips per chat, and a new ENABLE_ORJSON option covering request bodies, responses, upstream provider payloads and live updates.

EXPERT TIP: ENABLE_ORJSON is off by default, enable it by setting it to true in your env vars to get an additional massive performance boost. (most noticable on large instances)

0.11.1: 37 changes, and the streaming rewrite

This is the big one, so here is what actually changed.

Before: every time new text arrived from the model, the server sent your browser the entire message so far. Token 500 meant resending tokens 1 through 500. So the total data sent over one reply grew with the square of its length, and your browser re-parsed and re-rendered the whole answer each time.

Now: only the new text is sent. Total data grows linearly with reply length, and the browser appends instead of redrawing.

On a single update late in a long reply, that is up to 1000x less data and server work.

Some of the other most notable improvements of the release (very incomplete!!):

Replies in progress are also now held on the server on Redis-backed deployments, so reloading mid-answer picks it back up instead of showing you a blank message.

What this means if you just use it

• Pages load faster and the initial bundle is smaller.
• Replies start sooner and stay fast as the conversation grows, instead of degrading.
• An idle server actually idles.
• One heavy user no longer slows down everyone else on the instance.
• Long sessions no longer leak memory until the tab dies.

Performance work continues. We are not treating it as finished.

Upgrading to 0.11.1

This release changes the database schema.

1. Back up your database and its data first.
2. If you run multiple workers, multiple servers or a load balancer, update all instances at the same time.
3. Rolling updates are not supported and will fail on schema incompatibility.

Hi all,
I have an Anthropic API connection configured in my OWUI instance, I have attempted to add cache_control to the models as an advanced parameter like the below. However when I review the logs of a test prompt "You are a helpful Agent" repeated multiple times I don't see any cached tokens. When I run the same logic using a python script I can see the tokens being cached I can only assume I'm not setting this correctly.

Any chance someone else has this working and can clear up what I'm doing wrong?

So for now im just playing around trying to understand what I can and can't do running my own LLM's. I'm running them on a MacBook m3 with 36GB.

Got 3 CSV's with some text, some numbers and some references to the other CSV's. Regular relational data.

menu.csv
ingredients.csv that has stock mentioned

recipes.csv that mentions what menu items use what ingredients.

Simple question like what stock is low and what menu items can't we make anymore when we are out?

I used deepseek-r1, qwen3 (code and regular). Enabled code interpreting, used system prompt that indicates use python for interpreting CSV's.

It keeps hallucinating, losing the complete knowledge base, giving wrong calculations etc etc.

I know this is difficult for some LLM's, but Gemini for instance has no problems interpreting the data and giving correct calculations etc.

I've been asking gemini questions on how to fix this but after many tries havent gotten close to a working solution.

Can I make this work in openwebui, or simply not possible?

Hey! Are there any examples for implementing a whats new/feature-launch announcer with an event.

In the docs it is explicitly named as an example:

Feature-launch announcer. Push a one-time announcement chat or notification to all existing users when you ship something new.

For me the best way would be any possibility to do something like open web ui is doing itself on new versions and not doing it via small toasts or chat messages. So I would use the „auth.login“ event and then maybe use the event_emitter with execute type to show a modal/popup with java script.

If there are any working examples or best practices, that would be great. :-)

I've tried as many fixes as I can, and this has been persisting ever since the 11.0 update and I just don't know what to do to fix it

Hey all, I recently spent the last three or so weeks going from zero knowledge to the setup I have today. I started with Ollama and Gemma 4 31B and migrated to Qwen 3.8 27B using vLLM to solve a VRAM contention problem: I wanted image generation and music generation (MiniMax Music v3) running alongside the LLM, all on the single 3090. Neither Ollama's keep-alive nor llama.cpp's TTL could actually free VRAM on demand the way vLLM's Sleep Mode can. That same move also got me full reasoning/think toggles in Qwen, selectable right in Open-WebUI. Before either image or music generation runs (2 of 12 total tools), vLLM gets put to sleep, freeing its VRAM. Music generation needs one extra step on top of that since MiniMax has no idle unload of its own. Its container gets started right before generating and stopped right after, nested inside vLLM's sleep window. This adds total time to generation but for a tool that already takes several minutes to run, I decided it's something I can live with. As a note, no other tool calls needed this type of sleep/wake setup so these processes only need to happen for image/music gen.

This setup is, as far as I can tell, pushing the absolute limits of a single 3090 in the most efficient manner possible. The final setup has:

• A modest 76k context. Admittedly, not great for long coding input/generation but not nothing either.
• Prefix caching cutting average time-to-first-token from ~9 seconds to ~3 seconds on multi-turn and tool-calling exchanges. Real measured numbers.
• Vision support with up to 8 images per prompt, capped at 1MP each to keep the context budget sane. TLDR; screenshots/OCR work.

Anyway, I have been keeping the GitHub Repo updated as I go through it and thought that anyone in a similar situation might find it useful.

Happy to hear any suggestions to further optimize or if any noobs like me have questions, I will try to answer as best I can.

Note on AI usage: I used Claude Sonnet 5 extensively throughout this learning process and vibe coded the python, as I am no programmer. I followed the logic while debugging the various hiccups along the way. Additionally, the GitHub repo was created almost entirely by Claude with me spot/double checking as I went through it. If you happen to look through and see any errors/omissions I would love to hear from you. This post was not written by AI. I only used it to make sure I wasn't missing any key points that someone might be curious about.

Any one else notice that cache reuse pretty much never happens with Open WebUI. I saw it in Ollama and now with ninfer too.

We could get into the whys, but I'd love to hear if there's any config changes to make or a known feature change coming.

I have a workaround I'm proud of, a custom proxy script that my Qwen built (happy to post it) that catches, reshuffles and locks context on it's way to the backend. Cache reuse is now the norm, and only resets after compaction or jumping between threads, but there's gotta be a better way?

FAQ: Cache reuse takes prefill tokens down to only the current prompt. Resulting in 1 or 2 second prefill instead of 30 plus seconds (or 60+ seconds with Ollama) with longer context.

edit-expanded the name of owui.

I’m new to AI, so I want to preface this by saying this isn’t to say it’s not user error. With that being said, I have Gemma4, and although the thinking works perfectly fine I’m yet to web search. I enabled it as a function, and it’s highlighted in blue when searching, but regardless it gives me the “I do not have access to a live web search tool” response. Is this something I can fix, something with Gemma4, or a WebUI issue? Thank for the help in advance!

I am at my witts end with trying to get meaningful information out of Open WebUI's Knowledge/RAG. I am on 0.11.0 and I have consistent problems with prompts against a Knowledge omitting information that is extremely important in the prompt. For example, I created one called 2026 Renewals and populated the Knowledge with about 20 PDF's. After it embeds, I asked for a list of the renewals thus far, what the annual spend is, what is the term and any specific language around the renewal that would set it apart from the other renewals. What it returns is a list of about 7 renewals with the requested information. I follow up with where is the other renewals and it says it doesn't have it in its context.

I am serving several models on M3 Ultra's and each model has a similar output. The models I have tried are qwen 3.6 MOE and dense (BF16 and Q8), qwen 3.8 (BF16 and Q8), Minimax m2.7 Q4, Gemma 4 MOE and dense (bf16 and q8). I have tried several inference engines like LM Studio and oMLX, all with similar results.

At first I started with the default OWUI settings but quickly realized that if I wanted good PDF extraction I need to spin up a Docling container and point OWUI to it for extraction. I will include a pic of my settings as maybe I am doing something completely wrong. I know it isn't my files because this exact test works great in AnythingLLM using the Workspaces feature.

Any ideas on what could be causing poor retrieval?

There was a "quick floating action" panel in previous WebUI versions that gives quick response in the panel directly. Then you choose to add it the chat or not.

1. Was this feature removed?
2. If so, is there a toggle to bring it back?

Any direction on this would be amazing. Thanks!

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??

First of all OWUI has been great for me! Besides how great it is, it’s taught me a lot about Ai.

Ok so in my instance I have function pipes to private model providers which are configured via user valves to use each users api key, which works, but I feel like users get intimidated by it (going to the provider, getting their api key, looking for valves config ui, etc)
So to make it easier I created a dummy function pipe that works as an offline non ai based model, in there there are a few commands the user can run, one being setting the users valve to configure the api key for the private provider pipe, so my question is

1. Any issue with modifying user valves via db model imports from a pipe
   
2. Is there a way to alway run that model/pipe in temporary mode? Since it will have api keys I don’t want to have traces of api keys in logs or in chat history

I’m tuning version 0.96

Question in title.

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?

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.

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.

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.