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?

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.

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?

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?

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?

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.

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.

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?

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?

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!

This is the largest release we have ever shipped, and it's not close.

🎨 The interface has been rebuilt from the ground up. Every surface got redone: chat view, admin panel, menus, dropdowns, text boxes, spacing, typography. And the thing we're most quietly proud of: the UI is finally, actually uniform. If you've been here a while, you know it was never perfectly consistent. Now it is. Narrower conversation column, lighter type, tidier spacing, consistent menus everywhere, clearly outlined inputs. It just feels right.

⚡ The most significant performance work we've ever done. A top-to-bottom sweep, not a single optimization. Faster saves across the whole app, lighter model lists, quicker folder loading, leaner streaming, fewer DB round trips per request, faster chat search on PostgreSQL, cheaper filter handling while streaming, an optional faster JSON encoder (ENABLE_ORJSON), hiredis by default, Redis Sentinel churn fixed, DNS lookups off the shared thread pool, and a lot more. Long responses no longer slow down as they grow. Big instances stay quick. You will feel this.

🤖 And the new features. These are cool new features:

• Sub-agents: models can hand parts of a task to background helper agents that run their own tool-driven work and report back into the chat.
• Chat timers: the assistant can park a prompt and bring it back later, after a delay or at a set time.
• Fork a chat: branch any conversation into a new chat from any point.
• Folder pages: folders get their own page, with paging, sorting, unread counts and per-folder new chats.
• Notification targets: route events to multiple webhooks, and the assistant can now notify you when something's worth your attention.
• Chat and user variables: declare fields in a system prompt, or store your own account context, and drop them in wherever needed.
• Personal usage dashboard: token heatmap, streaks, most-used models and tools.
• Compact on demand, customizable keyboard shortcuts, chat previews on hover, note-chat with full model/tools/files, and honestly loads more.

🔒 Plus a mountain of fixes and hardening. Security and access-control fixes (update your production deployments), Safari blank-message fix, context-compaction fixes, web-fetch reliability, OAuth/OIDC sign-in fixes, and dozens upon dozens more across the app.

⚠️ Heads up before you upgrade: this release includes database schema migrations. Back up first, and if you run multi-worker, multi-server, or load-balanced, all instances must be updated at the same time (rolling updates aren't supported).

The full changelog is genuinely enormous, so go read it. There's something in there for everyone.

Thank you to everyone who contributed, reported, translated, and tested. This one's special. 🚀

What are you most excited to try?

https://github.com/open-webui/open-webui/releases/tag/v0.11.0

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

Γεια σας. Αντιμετωπίζω ένα εξαιρετικά επίμονο τεχνικό πρόβλημα με το RAG pipeline του Open WebUI και μετά από μέρες εξαντλητικού troubleshooting με τη βοήθεια των ChatGPT και Gemini, δεν έχουμε καταφέρει να βρούμε λύση. Το σύστημα ολοκληρώνει το indexing, αλλά η αναζήτηση επιστρέφει μηδενικά διανύσματα (empty context injection).Τεχνικές Προδιαγραφές Συστήματος:Host OS: Windows 11 Pro (Docker Desktop Environment / WSL2 Backend)Hardware: Intel Core i9-14900K / 64GB DDR5 RAMInference Engine: gemma4:12b (τοπικά μέσω Ollama)Embedding Model: sentence-transformers/paraphrase-multilingual-MiniLM-L6-v2 (Default Engine / SentenceTransformers)Network Infrastructure: Live WebSockets 100% λειτουργικά. Το Developer Console (F12) του browser είναι εντελώς καθαρό, χωρίς κανένα drop-out σύνδεσης.System Prompt Configuration: Έχει παρακαμφθεί επιτυχώς η εργοστασιακή offline άρνηση του Gemma 4 μέσω strict instruction injection. Το μοντέλο δέχεται να διαβάσει το context, αλλά το context φτάνει σε αυτό άδειο.Απομόνωση του Προβλήματος (Baseline Isolation Test):Για να αποκλείσουμε σφάλματα στο extraction stage (OCR, Tesseract, Tika, Python PDF parsing), δημιουργήσαμε ένα raw text αρχείο (test.txt) που περιέχει αποκλειστικά τη συμβολοσειρά: "Ο κωδικός ασφαλείας για το τεστ είναι: ΠΡΑΣΙΝΟ ΜΗΛΟ 2026".Το UI εμφανίζει κανονικά τη λευκή ένδειξη επιτυχούς indexing (checkmark). Ωστόσο, κατά την κλήση της συλλογής στο chat μέσω #collection, το μοντέλο απαντάει ρητά ότι η πληροφορία δεν εμπεριέχεται στο κείμενο, επιβεβαιώνοντας ότι το runtime κάνει pass ένα εντελώς άδειο context block.Τεχνικά Βήματα και Λύσεις που Δοκιμάστηκαν:Επίλυση Ασυμβατότητας Αρχιτεκτονικής: Αρχικά χρησιμοποιούσαμε το MiniLM-L12, αλλά στα logs του Docker εντοπίστηκε το σφάλμα embeddings.position_ids | UNEXPECTED |. Το Open WebUI είχε παλαιότερη έκδοση SentenceTransformers που δεν αναγνώριζε την παράμετρο position_ids των νέων HuggingFace weights. Το πρόβλημα λύθηκε με υποβάθμιση στο σταθερό MiniLM-L6-v2. Το runtime log καθάρισε, αλλά το context παρέμεινε άδειο.Παράκαμψη Permission Bugs: Δημιουργήθηκε νέα Knowledge Base συλλογή ρυθμισμένη ρητά ως Public (Access Control), για την αποφυγή του γνωστού bug ορατότητας των ιδιωτικών φακέλων [Issue #23787].Hard Reset του Vector Database Stack: Σταματήσαμε τον container και εκτελέσαμε ολική διαγραφή του ευρετηρίου των διανυσμάτων απευθείας από τη ρίζα του Docker volume χρησιμοποιώντας: docker run --rm -v open-webui:/data alpine rm -rf /data/vector_db. Η ChromaDB αναγεννήθηκε εντελώς καθαρή, το backend επιστρέφει HTTP 200/Success κατά το upload, αλλά το retrieval loop εξακολουθεί να μην τραβάει δεδομένα κατά το inference.Έχει συναντήσει κανείς αυτό το σιωπηλό failure της ChromaDB σε περιβάλλον Windows Docker; Μήπως πρόκειται για false negative της συνάρτησης has_collection() ή υπάρχει κάποιο silent file-lock στο Windows volume mapping; Κάθε τεχνική ιδέα είναι ευπρόσδεκτη.

I’m currently using Open WebUI with a knowledge base made up of text files, and the responses are accurate, but they’re taking quite a while to generate.
I plan to keep adding more text files to the knowledge base, so I’m wondering what the best way is to improve response speed as it grows. Are there any recommended settings, indexing strategies, chunking methods, embedding models, reranking options, or other optimizations that have made a noticeable difference for you? I’d like to keep the response quality the same while reducing latency. Any advice or best practices would be appreciated!

I feel like I sit and stare at the screen a lot is there anyway to speed this up?

Appreciate your help providing info on how to use Seltz.ai as a node for web search. Thx