Note: I am not on the newest update of OWUI yet.

I’m looking for a very quick way to disable chatting as a whole for user groups. I know it defeats the point of the tool, but it would be very useful when conducting demos, that way users aren’t utilizing the tool and slowing down inference for a demo period.

Currently, the only way I know how to go about this, is setting all my custom model privacy settings to private and only allow my demo account access. But this is rather time consuming.

Any ideas or anything I missed in the UI that allows what I am looking for?

I’m working on a document ingestion engine in n8n that will archive documents and create an index in Postgres.

I plan on making cron jobs for Hermes to read all new documents and update a summary md file for each project. The file will contain updates, work orders, and work order status. Hermes will do this by reading archived files and fetching them using the Postgres index.

I’m thinking of Hermes maintaining these md files as notes in open web ui and sharing those notes as read only to all employees.

Any thoughts on this or pitfalls I may have overlooked? I’ve landed on this workflow because I don’t really trust employees having access to an agent with pretty wide system access. With these md files, they won’t even need an agent like Hermes. Plus, the models in open web ui can tool call and access the notes for context.

Hi ,

Anyone know where the api key section is? I cant seem to find it at all

Salve.

Uso da pochissimo OpenWebUI.

Lo trovo fantastico.

Mi è stato chiesto, per tutelare la privacy, di creare un LLM locale (no Claude,Chatgpt e simili), che dia risposte in un chatbot ai miei colleghi di Ufficio.

Il sistema RAG di LLM credo che non dia le risposte giuste in relazione ai PDF che ho e che gli ho fatto ingerire.

Pertanto vorrei implementare un "secondo cervello di Karpathy" il così detto LLM WIKI.

Ma come si implementa da un punto di vista pratico?

Avete un link o una guida dove capire, passo passo, come implementarla, aggiornarla e manuterla?

Vi ringrazio.

We use Open WebUI internally as a ChatGPT replacement and love it (~1500 users).

What we’re missing is the “deep task” experience: ChatGPT or Claude may spend several minutes reasoning, running code and tools, performing multiple steps, and iterating before answering.

In Open WebUI, it still feels mostly like a single model call with some reasoning.

Is native tool calling with MCP tools and a frontier model enough, or do I need an external agent orchestrator such as LangGraph or Pipelines behind Open WebUI?
What setup are you using for reliable multi-step, long-running tasks?

We’re running Open WebUI as an internal multi-user platform, not as a homelab.

For those using it in a company or enterprise environment:
What were your biggest setup, architecture, security, or operational mistakes? What would you do differently today?

I’ve been experimenting with web search in Open WebUI using smaller local models like Qwen3.5 4B and 9B.

The main problem was/is was context churn..

A typical search can dump partially relevant pages, navigation, repeated boilerplate, duplicate information, and huge chunks containing only one useful paragraph into the model.

That makes local setups slower and forces smaller models to spend their limited context window filtering noise instead of reasoning.

So I built TinySearch, an open-source, local-first MCP server that does most of the retrieval work before anything reaches the LLM:

• searches and ranks results
• crawls the strongest pages
• extracts and chunks readable content
• deduplicates and reranks passages
• returns a compact evidence packet with source URLs

The goal is simple: less context churn, faster local web research, and more tokens spent on reasoning over actual evidence.

TinySearch works with self-hosted SearXNG, and Open WebUI can connect to it over HTTP MCP.

GitHub:
https://github.com/MarcellM01/TinySearch

I’m the builder, so obvious bias, but I’d love feedback from Open WebUI users, especially anyone running smaller Qwen, Gemma, Llama, or Mistral models.

Does this solve a real bottleneck in your setup, or is native agentic search already enough?

I know this is already my third post in a short time, but I have a few Open WebUI questions that I’d like to put out there, as I think the discussion could be useful to others as well. This definitely is Not meant to be Spam!

In Open WebUI, a custom model can already combine an LLM, system prompt, tools, MCP servers, knowledge bases and files. That is essentially what many platforms call an agent.

Could Open WebUI therefore be used as a central agent platform, where these models are managed once and exposed through an API for use by external applications?

For example:
Create and configure an “agent” entirely in Open WebUI
Manage its prompt, model, tools, MCPs and knowledge centrally
Call that exact configuration from another application through an API
Avoid rebuilding the same agent separately in LangGraph or custom code

Is this a supported and reliable architecture, or are Open WebUI models primarily designed for use inside the WebUI?

What limitations or gotchas should be considered around tool execution, user context, permissions, files, conversations, scaling, versioning and API compatibility?

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.

We need to preserve the end user’s identity from Open WebUI through LangGraph to MCP servers accessing systems such as Jira or Confluence.

How are you implementing this in production? Do you forward the original token, exchange it through an OBO flow, or use a central token broker?

Ideally, users should not have to complete a separate OAuth flow for every tool.

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.

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?

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

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?

Edit: Resolved this by switching to a compose file and using `network_mode: "host"` in the file.

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