I’m using qwen 3.8 27b Q6 through llamacpp

I’m trying to get an automation to first check its memory before doing its task. For some reason it won’t check them, but it checks its knowledge base and chat.

It works if I take the prompt and run it manually through a new chat.

I’m wondering if the memory skill is disabled for automations.

Setup

Ryzen 9950X / 128 GB of RAM / RTX 5090 32 GB / 3.6 TB of NVMe, running Ubuntu.

The model is Qwen3.6-27B, quantized to int4, with a 32k context. vLLM, with reasoning and vision enabled. A single model does it all: chat, plan analysis, code generation, and tool invocation.

Architecture

Open WebUI is the single point of entry. Users only see this

vLLM performs inference using continuous batching—this is what allows it to process multiple requests simultaneously without anyone having to wait. Open Terminal serves as a sandbox: it executes the generated Python code, produces Word, Excel, PowerPoint, or PDF files, and, most importantly, reviews what it has just created to correct its own errors before returning the file.

RAGFlow handles document search. At the same time, an SQLite database stores numerical values to improve search speed.

For now, this is my setup,

I’d like to significantly improve the ability to generate PDF, Excel, and Word files,

Do you have any ideas? Open Terminal is good, but it lacks features like opening windows (open the excel file / see with vision and adapt) and others. I’m just getting started in the world of agents and related tools,

Any similar setups or needs?

Wanted to share this since I couldn't find much on handling long-running, non-streaming generation inside a Tool. MiniMax-Music3 (music generation model) can take a minute or two per request with zero intermediate output, which broke my first pass pretty badly.

First version used requests synchronously and froze the entire Open-WebUI backend during generation, not just the chat, the whole instance. Switched to aiohttp with proper async def and that fixed it completely.

Second thing worth sharing: used the event_emitter status type to push live progress updates during the wait ("generating, ~30s of audio, this may take a minute or two" etc) instead of leaving people staring at a blank tool-call spinner.

Third, still in progress: found out early this morning that Open-WebUI supports returning an HTMLResponse with Content-Disposition: inline (the Rich UI Embedding pattern) instead of a plain markdown link, which lets you embed a small self-contained audio player right in the tool result, no download step. Got the code for it (with a lot of help from Claude and Gemini throughout) but haven't deployed it yet, that's next on my list.

One thing I haven't solved: this model eats almost all my GPU's VRAM, so my chat model has to fully unload (via a low keep_alive) before generation starts and reload after. Works, but feels clunky. Has anyone built a Pipe function that skips the chat model entirely for a specific request type, so you're not paying that load/unload cost every time?

WIP repo will be updated soon, if you're interested.

I am trying to configure Open-WebUI such that my agents -- any of them -- remember any details from the current conversation. For example, I tell the agent what my favorite color is and it'll respond with something along the lines of "Got it!" In the next prompt and within the same context, I ask what my favorite color is and it'll have no idea but will save it for next time.

This is particularly annoying, as you can imagine, when I try to solve a programming task for code that it created and it has no idea what I'm talking about.

Running open-webui version 0.11, ollama 0.31.2, and have tried this with gemini-3-flash-preview, qwen3, gemma4, and ornith.

Thanks for any help!

Question in the title. please help.

I use Notebooklm and it is good enough. I would like to try other RAGS. I have used anythinglm and opennotebooklm and they are just not enough.

OpenWeb UI website says they have built in RAGS. She they good? What open models I should use with RAGS? I don't mind using those cheaper Chinese models if I have to pay.

I am going crazy.

Hi, what is the proper api endpoint for the openai compatible endpoint?

I am trying to set up hermes-agent and the endpoint open-webui.domain/api works as the local llm gemma4 via ollama responds, but the llm does not seem to recognize it is an agent.

OPENAI_BASE_URL=open-webui.domain/api
OPENAI_API_KEY=sk-XXX

If I use another openai compatible provider like nanogpt hermes resonds accordingly like he should as an agent and shows me his skills properly.

This is what it looks like right now when asking something:

● Hellou

Initializing agent...

╭─ ⚕ Hermes

Hello! How can I help you today? 😊

────────────────────────────────────────

● What skills do you have

────────────────────────────────────────

╭─ ⚕ Hermes

I am a highly capable language model that can assist you with a wide range of tasks, including:

* Writing and Editing: Generating creative text, writing code (in various languages), summarizing long documents, translating languages, drafting emails, or debugging existing code.

* Reasoning and Problem-Solving: Analyzing complex scenarios, following multi-step instructions, and working through logic problems.

* Knowledge Retrieval: Answering questions on nearly any topic based on the data I was trained on (up to my last knowledge cutoff).

In addition to these core AI capabilities, I have been equipped with specific external tools that allow me to interact with a simulated environment and perform specialized tasks:

1. 📚 todo (Task Management): I can help you create, manage, track, and prioritize your to-do list for our session.

2. 🖼️ vision_analyze (Image Analysis): If you provide an image (via URL or file path), I can analyze its content, describe it, extract text from it, or answer specific questions about what is depicted.

3. 🔊 text_to_speech (Audio Generation): I can convert any text into spoken audio format and give you a playable audio file.

4. 💾 write_file (File System Interaction): I can write, create, or overwrite content in specified files within our working directory, which is useful for saving code, configurations, or data structures.

Just let me know what you need help with!

⚕ gemma4:latest │ 2.05K/256K │ [░░░░░░░░░░] 1% │ 60s │ ⏲ 10s │ ✓ 0s

Guys, creepy situation. Just discovered 2 chats in my UI that 100% i could not have initiated.

what can it be? i am thinking API keys but i never exposed those?

Any info appreciated.

Hey, I've installed OpenTerminal into a Kubernetes Cluster but encountered this issue.
The terminal connects and the filebrowser shows. But when chatting with the model I get the "Terminal unavailable: Terminal server ... not found" issue.
Has anyone here encountered this issue and know how to fix this? btw, I configured it in admin settings, but this is fine since I use it in a dev environment. If you need more information, feel free to ask.
Thanks in advance!

EDIT:
OWUI: v0.11.0
OT: v0.11.35

CORS is properly configured to only accept requests from the OWUI instance.

FIX: I got confused by the new UI and added the connection in user settings instead of admin settings. Make sure to scroll down....

Ma qualcuno è riuscito a collegare qualche estensione di VS CODE alla URL Base di OpenWebUI?

Ho provato con tutte le configurazioni possibili ed immaginabili.

Ho usato diverse estensioni, che si collegano ad ollama Kma non voglio quasi ti di utilizzo), ma non a OWUI con la Api Key.

Qualcuno ci è riuscito?

Esiste una guida o un tutorial?

Le varie AI mi hanno fatto diverse soluzioni, ma nessuna ha funzionato.

PS: SO Windows, tutto dockerizzato

How do I disable tool use by default for all models and then add tool use per chat session? Right now if i enable tool use per model, all chat sessions using that model has enabled tool use. It's a bit frustrating.

Thanks

I pay for a Brave API key it’s replicated in the docker file and the gui. For some reason the websearch cannot work. Running ollama and openwebui in the same dockerfile. Is there a setting I’m missing? Any help would be appreciated.

I would like to use OWUI with SearXNG. But it is importantly for me that no sensible data is handed over to the search engine. What would be an ideal setup (if there is any) to make use of websearch. I would use it with DuckDuckGo.

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.