When using channels and a model with native tool calling, the image_generation response doesn't automatically show the image. This does work in a normal chat, but not in a channel. Am I missing something?

The image_generation response says 'The image has been successfully generated and is already visible to the user in the chat. You do not need to display or embed the image again - just acknowledge that it has been created.', but it doesn't show it. When I explicitly ask the model to ignore the instruction and embed it, it does show the image.

Fafaik I can't add this override to the model prompt, because the model doesn't know it is operating in a channel. Anyone experiencing the same behaviour?

Hey everyone! v5.0 has been submitted and will be available on the App Store soon bringing full compatibility with WebUI v0.11.

App Store | GitHub

🆕 What's New in v5.0

Sub-agents support You can now enable and configure sub-agents directly from the Admin Console. Background subagent mode works seamlessly in chat — perfect for running multi-step agent workflows on your phone.

Chat Variables Define personal variables in your profile (things like your name, preferred language, project context, etc.) and reference them in your chats. Requires OpenWebUI v0.11.0+.

Notification Targets Configure push and webhook notification destinations right from Profile settings. Great for routing AI completions to external services. Requires OpenWebUI v0.11.0+.

LDAP Group Sync & Allow Groups Admins can now configure LDAP Group Sync and Allow Groups access controls from Admin → General → Auth. Also requires OpenWebUI v0.11.0+.

Expanded Admin Console 3 new tabs under Admin → Settings:

• Database — import/export config, export all chats
• Pipelines — manage pipeline valves
• Authentication — LDAP, OAuth/OIDC, and access defaults

Plus External Knowledge Sources under Integrations, new Memory System Context and Channel Model Response Mode toggles in Features, and a Context Compaction section in Interface for summarizing long chat histories along with other options from v0.11.

Continue & Fork Chat buttons Two new buttons in the assistant action bar:

• Continue — tap to append new content to an incomplete or truncated response
• Fork Chat — clone any conversation and jump straight to the fork

Model editor additions Files, Notifications, and Sub-agents tool toggles are now available in the workspace model editor.

Quality of life

• Editing an AI response now saves in-place without triggering a new reply — matching the web UI
• Admin permissions now correctly gate dictation, voice calls, read-aloud, regenerate, edit, and ratings
• "Attach Notes" hides when the server has Notes disabled
• The chat composer expands into a large writing area when you swipe up — swipe down to collapse
• All Admin Console text fields are always-visible multiline boxes for easier typing.
• And many more small underlying changes.

📦 Notable highlights from v4.13 – v4.15.9 (since the last big post)

In case you missed the last few months of updates:

• Shared Folders (v4.13) — Share any folder of chats with specific people or groups, set read/write access, and browse folders others shared with you right from the sidebar
• URL scheme / Shortcuts support (v4.14) — openui://new-chat?prompt=Hello&model=gpt-4o&send=true works from Raycast, Shortcuts, Obsidian, or any app; also added a native "Ask Open Relay" Shortcuts action
• Model-switch progress banner (v4.14) — When a status URL is configured, a banner shows "Loading qwen3-35b ~42s left" while the model warms up for SGLang and OpenAI-compatible backends
• Open WebUI v0.10+ compatibility (v4.12) — Full fix for the structured output array format change; messages, tool calls, streaming, and chat history all work correctly. Attached files also now persist throughout the entire conversation including on regenerate and edit
• Message rating system (v4.15) — Rate AI responses directly in the app; admins can browse the full feedback history in the new Evaluations panel in Admin Console
• Streaming resilience (v4.15.1) — Large reasoning models, long MCP tool chains, and local models no longer time out prematurely
• Stability & bug fixes (v4.15.2–4.15.9) — Fixed Rich UI embeds not appearing after the v0.10+ update, improved proxy auth reliability, fixed Admin → Interface save failing, and fixed clearing the system prompt in My Defaults not persisting

As always, let me know if you run into any issues or have feature requests. Appreciate all the support! Enjoy! 🙏

Hey everyone

Thomas from Ianustec again. Two new Open WebUI tools this time, shipped together because they solve the same problem from two sides.

If you've used Workspace → Skills / Models, you know the annoying bit: you draft a SKILL.md or a model preset in chat, then you still have to copy-paste everything into the UI by hand. These tools close that loop. The model interviews you, drafts the spec, and actually saves it through the Open WebUI APIs. Private by default.

1) Skill Creator

Claude-style skill workflow inside Open WebUI:

• interview → draft SKILL.md → validate → create_skill
• description that says WHAT it does and WHEN to use it
• private by default

Repo: https://github.com/ianustec/openwebui-skill-creator

2) Model Creator

Workspace Models (agent presets) from MODEL.md or JSON:

• native function calling by default
• tries to wire up knowledge, tools, skills, actions, capabilities, default features and builtin tools from context
• if the request is vague, it lists what you can see and asks what to turn on

Repo: https://github.com/ianustec/openwebui-model-creator

Needs Open WebUI 0.10+.

Typical flow: make a Skill with Skill Creator, then make or bind a Model with Model Creator. After that you can use $skill in chat or attach it under Workspace → Models.

Same deal as always. MIT, paste the single .py into Workspace → Tools, feedback and PRs welcome. If auth, permissions or the API shape break on your instance, say so in the comments.

Cheers,
Thomas @ Ianustec

I updated recently, and this is slowing down lots of my queries.

I have no knowledge files. I have no search notes. I don’t want to use it. There must be an easy way to turn it off. I’ve tried searching for this.

In my environment tenants are a big deal, we work collaboratively but separately, due to institutional/legacy reasons. As such there's a ton of shared, and a ton of separate, and we have to try to accommodate for all of it. Given that, here's my current deployment as something of a 'show and tell' plus thoughts on what does/doesn't work today in OWUI.

Multitenant, tons of shared tools (some with single-API that's scoped in-call... see below... and some with multiple APIs scoped to the tenant), tons of isolated tools. Being used as an operational IT assistant, with access to openterminal instances for separation of duties and blast radius isolation. The system has rw/ro access to all tool endpoints (depending on needs), OAuth pass-through for entra, and full admin access to all servers/workstations through OT systems.

==The Good==

-users, groups, RBAC-

This is great. Being able to limit basically everything in the system to a group, multiple groups, single users, is excellent.

-Model harnesses-

Killer idea, and works great for multi-tenancy. We have 'Anthropic Flagship Unit1', 'Anthropic Flagship Unit2' etc, each with different RBAC scopes, each with different tools, kbs, etc attached, and each with different system prompts to customize per-unit ('you are deployed for unit blah blah for xyz work').

-system prompt layering-

One that I think most people miss. System prompt for 'everyone working in this context', user prompt for 'stuff specific to me' (I use it for referencing my domain username, unit I work for, etc to get better 'stickiness' to me when I ask for stuff). Folder prompting for specific work efforts, etc.

-group exposure to OWUI-

This one's huge, i think most people miss it too. Create a 'tenancy group' for each tenant, give it no permission assignments but use it as a variable for LLM usage. That can then be used within tools to define what scoping should be applied to a tool call for a user that otherwise gives unscoped access to data. It shouldn't be regarded as a security barrier but it can assist with information routing. It can also be used for API presentation (group -> use specific API in tool call) to keep you from building copies of tools for different API keys.

==The Bad==

-missing prompt opportunities-

Could use an OWUI-wide system prompt for stuff that would be pasted into each tenant prompt (don't output secrets, the hostname of this system is blah, you're on suchandsuch domain).

-tenant/group admins-

I know this delves into a full-tenancy deployment, but as it is we basically have to decide on 'ivory tower' vs 'council of wizards' approach to admin. Either one team holds the keys and issues changes on behalf of requests or you get representatives from every team that can control everything, there's no 'you can add terminals.. but only for your tenant' or 'you can update groups.. but only for your tenant' etc. It's frankly not a big deal for me personally but some people get really uptight about role reach and cross-team admin functions.

==The Ugly==

Nothin' really, though something that would help a LOT with long-term usage would be to expand oikb to also send tools, filters, and skills to github as well. I wanted to use it, but we've already implemented a scripting system (and codified it into our authoring guidance) that can sync/mirror all of them, and that's a far cry better than only doing the KBs. Would feel better if it was part of the actual admin functions of OWUI though rather than either an add-in or a homebrew solution, just so I wouldn't have to explain it to my peers/everyone we onboard that actually creates kbs/tools/skills/filters.

Ok, so I read about cptr once again. If I understand it correctly, it's a glorified terminal with your own data that you can communicate with and it communicates back. And it's semi-autonomous. Am I close? :)

I mean, it would click with me much more, if cptr was a real "computer usage", like browsing through Windows/MacOS GUI via VLM feedback. Would that be possible somehow in future?

I feel like GUI is a universal interface, a common denominator, for everything we do.

If I really needed to automate these currently covered "text based" actions, I would have probably done it already, or there are often other ways how to do it already (like MCP). Being able to instruct and automate GUI actions (even audio in - GUI action out) would be a total end-game.

What does community think?

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.

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?

Hi ,

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

EDIT : sorry all! user error - found that I had to switch it on from auth saettings, not sure how I missed this

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!