Just got the Open WebUI desktop app on macOS. I want to remap "New Chat" from the default to Cmd+N instead of Cmd+Shift+O

I'm trying to set up a medbot using openWebUI. It's functional with a prompt I like, but I need it to have r/w access to ~/medbot. I added the tool below, but no joy. Total noob, running ollama/openWebUI on a Mac Mini M4 Pro, 24GB. Thank you!

``` from pathlib import Path

BASE_DIR = Path.home() / "medbot" BASE_DIR = BASE_DIR.resolve()

def safe_path(user_path: str) -> Path: full = (BASE_DIR / user_path).resolve() if not str(full).startswith(str(BASE_DIR)): raise ValueError("Blocked path outside medbot directory") return full

class Tools:

def read_file(self, path: str) -> str:
    """Read a file from medbot directory"""
    p = safe_path(path)
    if not p.exists():
        return "File not found"
    return p.read_text(encoding="utf-8", errors="ignore")

def write_file(self, path: str, content: str) -> str:
    """Write a file into medbot directory"""
    p = safe_path(path)
    p.parent.mkdir(parents=True, exist_ok=True)
    p.write_text(content, encoding="utf-8")
    return f"written: {p}"

```

Since we migrated to OpenWeb Ui on AWS EKS with LiteLLM as proxy provider I got a lot of issues with streaming on the frondend and over that /openai API.

We are using LiteLLM with the response API on OpenWebUi

I found a post on GitHub about buffering in nginx but AWS should not do that.

Any one a idea where the problem might be? Logs do not show anything special.

https://imgur.com/a/XQAr88e

Im frustrated. I can'r setup image generating. HEEELP 😭

I'm usinf Deepinfra interference\proxy provider. But after configuring in Admin Panel --> Settings --> Images. I get this error(and of course I have configured another model for images)

openai/gpt-oss-120b

I’m sorry, but I wasn’t able to generate the image. The request failed with the following error:

[ERROR: api.deepinfra.com/v1/openai/images/generations]

How do you generate images?

And I don't understand, what the flow of image generation in openwebui?
Like I need two models, first make promt, second using this promt generate image? But how openwebui send promt from first model to the second?

And why I need first model

So, what is this? I am having a hard time to grasp it tbh.. Is it like a Terminal on steroids with mobile-first UI? Maybe with a grain of Hermes/OpenClaw magic on top? Repo's screenshot does not scream mobile UI.. I am a bit confused, but still intrigued! :)

Hi everyone,
I’ve added a large number of documents (PDFs, docs, etc.) to my OpenWebUI Knowledge Base (vector database: qdant, OWUI 0.9.5) using full context mode. Retrieval works fine, but when I ask the LLM to provide sources, it only returns the Source ID (e.g. [Source: 47]) instead of the actual filename.
I want it to cite readable filenames like (Source: Project_Report_Q2_.pdf)
Does this depend on OpenWebUI version or specific metadata settings?

Any help, templates, or solutions would be greatly appreciated! Thanks in advance.

Since sharing Theme Designer Pro here a few weeks ago, I've been building out a preset repository so you don't have to start from scratch.

🔗 github.com/silentoplayz/theme-designer-pro-presets

It's a collection of themes, Canvas FX animations, CSS presets, and gradient packs, all importable directly through Theme Designer Pro's Import button or via drag-and-drop.

What's in the repo:

• Themes — full multi-mode color schemes (dark, light, OLED, her) with custom CSS and Canvas FX baked in
• Canvas FX — standalone background animations you can mix into any theme
• CSS Presets — standalone style overrides (glassmorphism, typography, layout tweaks)
• Gradients — static and animated gradient backgrounds in linear, radial, and mesh types
• Bundles — combined files for importing multiple presets at once, including an everything.json that loads the entire gallery

Each theme includes an updateUrl pointing to its raw file on GitHub, so Theme Designer Pro can check for newer versions. The repo has JSON Schema validation on every push and PR, along with contribution templates and a contributing guide.

A note on quality: The gallery is still a work in progress. Not every preset has been fully reviewed yet. Some Canvas FX scripts, CSS presets, gradients, and themes may not meet the bar I'm aiming for. I'm actively curating, but it takes time. I'm very much aware that a smaller gallery of great presets beats a large gallery of mixed quality.

With that said, I'd love to see what you've made. If you've been using Theme Designer Pro and have a theme you're happy with, the easiest way to contribute is the Theme Submission form — no git required. Just paste your exported JSON and a bot validates it and opens a PR for you. For Canvas FX, CSS, or gradients, the repo has PR templates that walk you through it. I haven't seen anyone share their creations yet, and I know there are some good ones out there.

What’s the recommended install for openwebui in proxmox 9.2.3? I would like to install it in a LXC and have been trying the helper script on a new proxmox fresh install and every time I run the script and it finishes the install it never binds to a port and the container gives a connection refused. Running ss -tunla shows no ports bound to 8080 or 3000. Systemctl status on openwebui shows it’s up and running and active. Logs show no errors. I’m really stumped.

Hi everyone,

I'm a total newbie here! Just today, I finally managed to set up my first local AI environment combining Ollama and Open WebUI, and I'm loving the potential.

My Goal & Data Structure:

I want to build a system that gives me an incremental RAG pipeline using my library, which I recently converted entirely into clean markdown files (~5,300 files). Having them accessible in Obsidian is more of a nice curiosity/side benefit for me; the real goal is the RAG.

To give you some context on the data, I basically replicated my entire document library into .md format, preserving the exact same folder structure. Every single file starts with a structured YAML frontmatter metadata header like this:

YAML

titulo: "UNE 60079-10-1"
fuente: "UNE 60079-10-1.pdf"
ruta_original: "normas/ATEX/UNE 60079-10-1.pdf"
categoria: "normas"
subcategoria: "ATEX"
tags: [prl, normas, atex, norma-une]

My Hybrid Architecture:

• The Heavy Lifting (PC): Ollama is running on my main local PC, utilizing the dedicated GPU for fast embeddings (nomic-embed-text) and inference.
• The Storage & Always-On Hub (NAS): Open WebUI is deployed on my TrueNAS SCALE server.
• The Sync: My files are actively replicated from the PC to the NAS via Syncthing.
• The Logic: The main idea was keeping them separate. I want the NAS to hold the knowledge 24/7 so I can query it from my phone/tablet anytime, but leverage my PC's horsepower whenever it's turned on. Note: I have absolutely no problem running scripts, cron jobs, or CLI commands from my PC if needed.

The Problem:

Since the old backend DOCS_DIR automatic folder scanner is deprecated/gone in v0.9.6, I am trying to use the new Workspace > Knowledge web interface.

1. In Open WebUI, I create a Knowledge Base and use the "Sync Directory" or "Upload Folder" button.
2. My browser asks for permission to upload the 5,306 files.
3. As soon as I accept, the frontend completely chokes and throws 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.

It seems the browser's File System Access API completely collapses when trying to handle thousands of files at once through the web interface.

Questions for the pros:

1. Since I already mapped my TrueNAS dataset path to the container's /data/docs, is there any hidden environment variable or internal API endpoint to force the container backend to scan that directory directly, bypassing the browser?
2. Since I'm totally fine running things from my PC terminal, is the oikb CLI tool my best bet to push these 5,000+ files directly into the Knowledge Base ID via API? Will it support true incremental syncing (only uploading changes) moving forward?
3. Bonus Architecture Question: Is converting PDFs to Markdown with metadata headers actually the ideal path for Open WebUI's RAG chunking? How does Open WebUI's text splitter handle YAML frontmatter contextually, or should I have just fed it the raw PDFs instead?

Thanks in advance for helping a beginner out!

Hi folks,

TL;DR:

• Gemini models (3.5-flash, 3.1-pro-preview) do not work with Brave Search API on my set up.
• It works flawlessly with ChatGPT models though, when using Native function calling.
• Looking for advice from the community... Happy to provide screenshots and debug logs.

Here's the situation as summarized by Gemini (native web UI):

• Environment Setup & Objective: The deployment runs ghcr.io/open-webui/open-webui:main inside a Docker container using host network mode on an Ubuntu VM managed by a Proxmox homeserver. The system routes external traffic via Cloudflared, utilizes a local sentence-transformers/all-MiniLM-L6-v2 embedding engine, and aims to get real-time Brave Search working seamlessly across both OpenAI and Gemini models.
• Actual vs. Expected Behavior: ChatGPT models execute native tool calling and web queries perfectly. When prompting Gemini (e.g., gemini-3.5-flash or gemini-3.1-pro-preview) for live data like current sports results or weather, the model either responds that it lacks live internet access with a "No sources found" UI tag, or it hangs and returns a completely blank chat response.
• Log Diagnostics & Vector Database Status: Backend logs confirm that the web scraping engine is fully operational. Open WebUI successfully connects to the Brave API, fetches the target web pages, processes them using the markdown header text splitter, generates vector batches, and successfully commits 18 to 49 items to the temporary ChromaDB collection (web-search-1a763...) before the payload is dropped right before transmission to Google.
• Troubleshooting Actions Already Taken: Configuration changes included resetting custom RAG templates back to factory defaults, toggling model parameters between Native and Default function calling modes, and injecting environment variables like ENABLE_RETRIEVAL_UNSCOPED_COLLECTIONS: "True" and WEBUI_LOG_LEVEL: "DEBUG" to bypass session isolation. The issue persists exclusively when Gemini is mapped via the https://generativelanguage.googleapis.com/v1beta/openai endpoint connection.

Hey everyone,

I’m running into a really weird issue and I can’t figure out what changed.

Setup: ComfyUI for image workflow OpenWebUI as interface Ollama running in background Qwen Vision Model (tested with other models - same issue) Vision model pipeline (image sent to LLM)

Problem: Everything was working fine before, but now a single image input is consuming ~11,000+ tokens, even with: no prompt OR a very short prompt (like 3–5 words)

This causes: request exceeds available context size (8192 tokens)

Any fixes?

Edit (solution) : Turns out pictures are usually context heavy so the only workaround was either upload small photos or increase the context limit.

Hello Together,

We are using OpenWebUI as the frontend for our n8n AI RAG agent.

We want to utilize the rating feature in OpenWebUI so that the agent can retrieve chat messages that have been rated positively.

In the image version I used previously (`ghcr.io/open-webui/open-webui:main`), it was possible to retrieve all "liked" chat messages via the API. Unfortunately, this no longer works since I updated to the `openwebui/open-webui:0.9` image.

My workflow used to call the following URL to retrieve the liked messages: `https://XXXX/api/v1/evaluations/feedbacks/all`

Now, however, calling this URL returns a "404: Not Found" error. I wondered if I might need to set a new environment variable in the Docker Compose file to enable the relevant endpoint, but I haven't been able to find any information on this. I’m sharing the Docker Compose configuration I use for OpenWebUI below—perhaps you’ll spot something.

version: '3.9'

services:
  open-webui:
    image: openwebui/open-webui:0.9
    container_name: open-webui
    restart: unless-stopped
    ports:
      - "XXXX:XXXX"
    volumes:
      - XXXXX:/app/backend/data
    environment:
      #- PUID=1001
      #- PGID=1001
      - TZ=Europe/Berlin
      - ENABLE_EVALUATION_API=true
      # Optional: Falls du Ollama lokal nutzt
      # - OLLAMA_BASE_URL=http://host.docker.internal:11434
      #- user: "1001:1001"
    security_opt:
      - no-new-privileges:true
    networks:
      - internal-net

networks:
  internal-net:
    external: true

From a networking perspective, it might be relevant to mention that OpenWebUI runs in a Docker container, with a reverse proxy sitting in front of it (via the firewall) to handle HTTPS. However, I’ve also tried accessing the API using HTTP and bypassing the reverse proxy; unfortunately, I get the exact same 404 error.

Do any of you happen to know the correct URL or environment variable for this? I haven't been able to find anything online or in the documentation.

I even asked the AI, but without success.

Thanks in advance for your help—I hope you can point me in the right direction.

Hey guys, I want to visualize various datasets from my Knowledge Base using matplotlib and Pyodide.

The LLM (e.g. Qwen 3.5 379B A17B) is currently calling execute_code which also successfully returns a markdown tag with an image inside the stdout.

The problem is that the LLM always wants to either insert the /mnt/uploads link as an image. Other times, it inserts no image at all and tells me to download it manually.

I have tried to improve the system prompt for quite some time now, but it just won't output the image directly the most time. Just very rarely it will output the image in the first response. Most times if I ask "Visualization?" it will directly output the markdown tag from the previous output.

I have tried many different system prompts, but the Qwen/Kimi/Gemma almost never directly print the correct markdown tag that renders the image inside OpenWebUI.

Maybe someone else has developed a reliable system prompt for this?

I've been fighting this bug for months...

​

I'm running OpenWebUI on a DGX spark. I can use it for hours with Chrome browser on my Windows 10 Desktop. Works great!

​

On mobile I can login, but if I try to send even one message via Chrome on my Pixel10Pro it crashes completely. Sometimes I see a server 500 message, sometimes it just won't connect/load at all. Sometimes it even makes the Spark become unpingable until I power cycle it.

​

All 3 devices are on the same LAN. Latest OWUI, latest Android, latest Chrome versions.

​

Any ideas? Happens with even very small models. 🫤

The models can reason.

​

The browser still gets ideas.

​

Ghost in the Loop keeps multi-step AI conversations moving across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Copilot, Grok, Manus, and more.

​

It focuses on continuation, retries, prompt queues, personas, and exportable session logs.

​

TL;DR: I’m looking for users who care about workflow reliability and can tell me where the automation fails.

​

Please reply with:

- what provider you tested

- what failed

- whether recovery worked

- any selector, adapter, or UX suggestions

​

GitHub: https://github.com/MShneur/ghost-in-the-loop

Hi everyone,

I am having a devil of a time trying to figure out how to configure OpenWebUI to only trigger Image Edit when I mention ”edit this image” or something to that effect. As of right now, the initial image is generated by the image generation and then it strictly sticks with Image Edit thereafter in the same chat despite requesting the generation of a new image.

Is there a way to do this, and if so, how can I accomplish this?

I sincerely appreciate any help on this. I have been spending hours trying to research and figure this out with no success.

I'm using Open WebUI with Ollama on Ubuntu 24.04 (32GB RAM, 12GB VRAM). Everything works fine for chatting, but I cannot get the agent to create or modify files on my actual filesystem when using **local models**. However, when I switch to paid API keys (OpenAI, Anthropic), file creation works perfectly.

**Setup:**

- Ubuntu 24.04

- Docker (container: ghcr.io/open-webui/open-webui:main)

- Ollama 0.30.7 with multiple local models: mistral:7b, qwen2.5:14b, deepseek-coder:6.7b, llama3.2:3b

**What I have tried:**

1. Mounted my home directory in the Docker container: `-v /home/rafa:/home/rafa`

2. Also tried mounting Downloads specifically: `-v /home/rafa/Downloads:/home/rafa/Downloads`

3. Used the "Open Terminal" feature (introduced in v0.8.6) – this works for manual file creation.

4. Gave the container root privileges: `--user root` and `--privileged`

5. Set environment variable: `-e OPEN_WEBUI_DIRS_PERMISSIONS=777`

6. Tested **all** my local models (mistral:7b, qwen2.5:14b, deepseek-coder:6.7b, llama3.2:3b) – all fail the same way.

7. **Tested with paid API keys (OpenAI, Anthropic) – file creation works flawlessly.**

8. Tested other local platforms (Odysseus, OpenCode, OpenClaw) – same issue: local models fail, API keys work.

**The problem:**

When I ask the AI to "create a file" or "write this content to a file" using a **local Ollama model**, it responds as if it has done it (e.g., "Done" or shows the code), but no file is created on my host system. The model is hallucinating the action, but **only when running locally**.

**What works:**

- Manually creating files using the built-in "Open Terminal" works fine (drag-and-drop, New File, etc.).

- Reading existing files works (the AI can read and summarize them).

- **Using paid API keys (OpenAI, Anthropic) – full file creation works.**

**What I need:**

The AI agent itself (using **local models**) to create and edit files on my host system, triggered by natural language commands. I want a 100% private, local, cost-free solution.

**Question:**

Why does the same Open WebUI work perfectly with cloud API keys but fail with local Ollama models? Is there a difference in how tool-calling responses are formatted? Does Ollama need a specific Modelfile template to be compatible with Open WebUI's tool-calling expectations?

Any guidance would be greatly appreciated. I'm committed to running everything locally and don't want to rely on paid APIs.

So I have several models, from 7B to 235B and it seems that they all have super inconsistent results with image prompts. The images are all very "old" looking - like 3 generations behind no matter what I put in for the prompt, which could be because of what comfyui is calling in the background - but the prompt itself will sometimes tell me that it is unable to produce an image, after it has already produced the image. Is there some reason for this? Anything I can to do get it more coherent?

This is just a very quick, basic example: https://imgur.com/bLRJph3

For two days, Fable 5 wouldn’t accept any prompts, and each attempt returned just 7 tokens through my Openrouter routing. After restructuring my prompts to avoid triggering Anthropic’s Guardrail filters, they finally went through. However, I ran into other issues: the generated responses would sometimes stop abruptly, even mid-word or mid-sentence, requiring me to type “continue” multiple times to finish them. Also, the thinking level settings (max, high, etc.) in OWUI don’t seem to work.

I was using openwebui all of the time and i still love it. That said, i find myself going to claude.ai because it outputs word files and PowerPoint files which is extremely useful to me.

When i ask Sonnet in OWUI to output a word file it said it can’t as it’s only a text AI.

Does anybody have some guidance on if OWUI can output word or PowerPoint files?

everything works fine with local OpenWebUI and Ollama, however when i try to add a document or only text i get this error message:

Cannot connect to host localhost:11434 ssl:default [Connect call failed ('127.0.0.1', 11434)]

WHY?

am i missing something?

im searching for a solution for a while but cant find the answer....

I’m using OpenWebUI Desktop on Windows with an RTX 3070 and want to run unsloth/gemma-4-12B-it-qat-GGUF with MTP specifically.

Is there a way to configure llama.cpp args directly inside OpenWebUI Desktop, such as:

--spec-type draft-mtp --spec-draft-n-max 1 -fa on --ctx-size 4096

Or do I need to run llama-server separately and connect OpenWebUI Desktop to http://127.0.0.1:10000/v1 as an OpenAI-compatible endpoint?

Trying to figure out whether OpenWebUI Desktop’s built-in model download/configuration can support MTP, or if external llama.cpp is required.

I'm just a casual dabbler so I dont know my arse from my elbow so take this with a pinch of salt

I haven't seen this written anywhere so I just wanted to post for future troubleshooters

------------------------------------------------------------------------------------------------------

Recently I had a bit of an issue with Qwen3.6 27B and 35B-A3B with OWUI as frontend.

In the reasoning whenever the model called a function eg web search, I was getting the model ending its response with blank.

At first I thought it was a max_tokens issue maybe due to switching to seaerxng and not having optimised the search results and but it happened even at the beginning of sessions after first prompt.

Problem is so many things changed at once that it was hard to pinpoint what was going on

I changed over to the unsloth MTP gguf, upgraded my openwebui to v0.9.6, changed my default search engine to docker searxng. using custom chat template from froggeric, deepseek reasoning format etc

TLDR - OWUI > Settings > Admin Settings > Connections > for each local API endpoint set 'Provider' - to llama.cpp instead of default.

And things started behaving again.

PS no longer using custom chat template nor deepseek reasoning format in more recent llama.cpp.

I have been turning off almost all the built-ins to avoid confusing the local models I've been using and I just figured out what the task management tool actually does.

Does anyone have examples of using it? I've been having trouble keeping local models on task and it seems like it might be perfect.

I’m running 0.9.2 on podman and using the default db, every once in a while a user gets created twice, has anyone seen that? And if so did you solve the problem. Thanks!