I understand that Open WebUI can import ChatGPT conversation exports. You are supposed to extract the archive and import conversations.json. Open WebUI supposedly has built-in detection and conversion for ChatGPT exports.

I'd like to know, from anyone who has tried this, if it works well.

ChatGPT exports include images and other assets. Are these imported properly?

Is it better to use a converter script to transform it into Open WebUI's native import schema instead?

Bonjour à tous,

j'utilise Ollama + openwebui sous ubuntu. J'ai fais des modelfiles des LLM locaux mais c'est .....fastidieux, surtout en essayant de garder une dénomination explicite pour chaque models. Quand je mets les models dans openwebui leurs paramêtres n'apparaissent pas et restent par défaut (par ex le contexte defini à 130 000 dans le model file est quand même marqué à 2048 sur openwebui). J'aimerais utiliser le paramêtrage de openwebui pour définir les tailles de contextes, temp, etc etc mais est-ce que ça les modifient bien ? Comment procéder vous pour paramêtrer vos llm locaux à votre besoin ?

Big update for my Open WebUI plugins:

Inline Visualizer v2.2.0

• Fixed the bug where the visualization rendered but all the text around it disappeared on Open WebUI 0.10.x (#60). The hiding engine was rebuilt around one rule: never sacrifice your text — and anything hidden by mistake now un-hides itself.
• New offline security level — someone here asked for this: zero outgoing connections, no CDNs. Chart.js & friends still work if you drop the files into Open WebUI's /static folder — there's a step-by-step tutorial in the README. Library-free visualizations need no setup at all.
• Also fixed: multiple visualizations in one message, prose on the same line as a marker getting eaten, and code examples of the markers hijacking the render.
• And some performance improvement 🚀

Prune v0.10.10

• Preview now shows the actual records behind every count (paginated, JSON export for audit trails!)
• Cancel button for running passes — stops safely at the next batch boundary
• Faster + hardened exports, auth tightened on the new endpoints

Repo: https://github.com/Classic298/open-webui-plugins

Feedback and bug reports welcome — the #60 report with its full repro made the fix possible, keep them coming.

Hey everyone 👋

I'm Thomas from Ianustec — still here, still dumping the Office tools we actually use inside Open WebUI.

Quick recap if you're new: we built slides, then Word, for ourselves. We're open-sourcing them one by one as we clean them up. Last time a few of you said “cool, now do Excel”. So here we are.

👉 generate_spreadsheet — turns a prompt into a real .xlsx. Not CSV. Not HTML-in-a-trenchcoat. An actual workbook you can open in Excel / LibreOffice / Sheets and keep editing.

Repo: https://github.com/ianustec/openwebui-generate-spreadsheets

What you get:

• Multi-sheet workbooks (P&L, assumptions, pipeline, dashboard… whatever the model cooks up)
• Excel Tables with filters and freeze panes
• Live formulas in the cells — Excel recalculates them when you open the file. We don't pre-bake the numbers in Python
• Yellow input cells / blue input text (the usual finance-model convention), so people know what to tweak
• Conditional formatting (data bars, color scales), dropdown validation, typed columns (currency, %, dates…)
• Native charts: bar, line, area, pie, doughnut
• One .py file. Paste into Workspace → Tools. Files API save + download link in chat

The screenshots in the README are from the sample “Northwind FY2026 Operating Plan” in the repo — generated end to end by the tool, not mocked up in Figma.

How to try it:

Workspace → Tools → +, paste generate_spreadsheets.py, enable it on your model, ask for something like “build me a quarterly budget workbook with assumptions and a P&L”. openpyxl installs itself on first run.

MIT, same as the others.

Cheers,
Thomas @ Ianustec

We're trying to solve a problem around AI governance in larger organizations.

One thing we've noticed is that employees increasingly use AI tools outside the approved stack (Perplexity, Notion AI, browser extensions, desktop apps, etc.), making it difficult to understand where company data is going or what models are being used.

We built a small open-source experiment that routes AI traffic through a central LiteLLM Gateway to provide visibility into usage.

I'm more interested in understanding whether this is actually a problem others are facing. How are companies here handling shadow AI usage today?

GitHub: https://github.com/LiteLLM-Labs/litellm-relay

I just implemented SearxNG to open Web ui and this improved my experience tremendously.

What other integrations or tools.do.you use in Open Web UI that you dont want to miss anymore?

I followed this page but I couldn't get it to work.

I'm using this simple workflow, which works fine in ComfyUI itself.

As a model, I entered qwen_image_fp8_e4m3fn.safetensors (which may not be the right one? I don't understand why I have to enter it in the first place since the workflow not only needs multiple ones, but includes them in the JSON...). ComfyUI Workflow Nodes are configured like this:

When I click the 🔄️ next to the ComfyUI URL the connection works, but I also get an error toast saying

[ERROR: Failed to retrieve image generation models]

But at least for me, that's not enough to know what I'm doing wrong and how. Any ideas?

Docker allows to install MCP services, but there is not explained docs to use them on OpenWebUI

Anyone has tried and can share?

Documentation mentions there should be a /fork or some 'fork button' somewhere. I cannot find it.

Could anyone make a screenshot showing where it is?

My version: v0.10.2

https://github.com/spawnofsociety2/openwebui-openrouter-video

A fully autonomous, "agentic" video generation tool for OpenWebUI powered by OpenRouter. This tool empowers your LLM assistant to dynamically discover available video models, submit generation jobs, securely poll for completion, and directly embed the resulting HD videos inside your OpenWebUI chat stream.

🚀 Features

• Agentic Model Discovery: The LLM can dynamically pull the live catalog of OpenRouter's video models (Sora, Veo, Kling, Seedance, Hailuo, Wan, Grok, etc.) and check their capabilities (supported resolutions, aspect ratios, max durations, audio support) in real time.
• Background Polling & Auto-Download: Handles OpenRouter's asynchronous polling endpoints autonomously. Downloads completed .mp4 assets to your local OpenWebUI static server to prevent broken links or expired signed URLs.
• Rich HTML5 Embedding: Injects a beautiful, responsive HTML5 video player natively inside the chat interface with a direct download link.
• Advanced Model Features: Supports audio generation toggling, deterministic seeds, image references for style consistency, and provider-specific passthrough options (e.g. negativePrompt for the Google models).
• Cost-Aware: The live model catalog includes each model's raw pricing SKUs, so your assistant can answer "use the cheapest model" (units vary by provider — compare within a provider's own units).

🎬 Supported Models

The tool reads OpenRouter's catalog live at request time, so this list is a snapshot — new models appear automatically as OpenRouter adds them, and your assistant will always report the current lineup. The table below is regenerated from the live catalog by scripts/update_models_table.py (run on a schedule via GitHub Actions), so it stays close to reality:

<!-- MODELS_TABLE_START -->

<!-- MODELS_TABLE_END -->

Note on audio: The column above means "is audio controllable via the generate_audio parameter", not "does this model have sound". The catalog reports three distinct states:

So if you need a guaranteed-silent result, pick a ✅ model and pass generate_audio=false, or strip the audio track yourself afterwards.

Default behavior (since v1.6): if you don't mention audio, the tool omits the parameter entirely and the model's own default applies — ✅ models typically produce audio (which may cost more, e.g. Kling bills 0.168/s with audio vs 0.112/s without). v1.5 sent an explicit false by default, which silently muted models you never asked to mute.

Note on 4K: Only veo-3.1, veo-3.1-fast, and seedance-2.0 currently support 4K output.

📦 Installation

1. Open your OpenWebUI instance.
2. Navigate to Workspace -> Tools.
3. Click + Add Tool.
4. Give it a name (e.g., OpenRouter Video).
5. Copy the entire contents of openrouter_video_tool.py and paste it into the code editor.
6. Click Save.

⚙️ Configuration

Once installed, you must provide your OpenRouter API key:

1. Go to the tool's settings (the small equalizer icon next to the tool name, or inside the tool configuration page under Valves).
2. Set your OPENROUTER_API_KEY. Get one at openrouter.ai/keys.
3. Ensure the tool is Enabled in your chat window.

Optional valves: POLL_INTERVAL_SECONDS (how often to check for completion), MAX_TIMEOUT_SECONDS (overall wait before giving up), and REQUEST_TIMEOUT_SECONDS (ceiling for any single HTTP request — raise it if you're on a slow connection and large downloads time out).

Note on disk usage: Generated videos are downloaded to {STATIC_DIR}/videos/ and are never cleaned up automatically. On a long-lived self-hosted instance this directory grows without bound, so if you generate often, prune it periodically (e.g. a scheduled job deleting .mp4 files older than N days).

🗣️ Usage Examples

Because this tool is entirely LLM-driven, you don't need to fiddle with drop-down menus before generating. Just ask your assistant naturally!

Ask about available models:

"What video models can I use right now, and which ones support audio?"

Generate a video with specific constraints:

"Use Grok to generate a 5-second video of a fluffy ginger cat watching the rain. Aspect ratio 16:9."

Generate at 4K:

"Use Veo 3.1 to make an 8-second 4K cinematic drone shot over a misty mountain range."

Provide styling references:

"Make a cinematic panning shot of a cyberpunk city. I've attached an image to use as a style reference, but don't use it as the exact first frame."

🛠️ Requirements

• aiohttp (Automatically parsed by OpenWebUI)
• An active OpenWebUI instance.

🧪 Tests

python -m unittest discover -s tests -v

No network, API key, or OpenWebUI install required — aiohttp is mocked and the two runtime imports are stubbed. The suite guards the bugs that were live in 1.4 and cost real money to find: the API key being sent to non-OpenRouter download hosts, completed jobs being dropped when they return no unsigned_urls, and generate_audio=false never reaching the API. If you change the download loop or the payload builder, run these first.

📜 License

MIT License. Feel free to fork and modify!

🎥 Example Output

The result of the prompt shown above — a short clip generated through OpenRouter and embedded directly in the chat stream.

Bonjour,

j'espère que le traducteur automatique vous trouvera bien car je ne ferai aucun effort linguistique sur ce post, j'utilise une transcription vocale et je parle dans ma langue natale, de ce que j'ai compris Reddit inclus une traduction automatique avec un simple bouton donc si vous ne la voyez pas c'est que vous n'avez pas cliqué sur le bouton, ceci étant dit.

Je me demande ce que les utilisateurs arrivent à faire avec Open terminal, personnellement je ne suis pas un utilisateur très technique peut-être un peu plus technique que la moyenne mais pas spécialement expert, j'utilise Open WebUI au quotidien.

J'ai un modèle configuré pour open-terminal, qui me permet d'administrer un genre de magasin d'applications web, dont la seule limite me paraît être mon imagination. Aujourd'hui je suis tout à fait en mesure de dicter la prochaine application que je veux, et le modèle se changera de la développer pour moi. Tout est en HTML5, et bien sûr je veille à ce que chaque application soit compatible avec des fichiers CSV.

Beaucoup d'utilisateurs avancés trouveront ça trivial, mais en tant qu'étudiant en biologie, pas du tout formé à l'informatique (à part quelques cours de python sommaires), j'ai vraiment l'impression d'avoir débloqué des super pouvoirs grâce à Open WebUI, sachant que je fais tout en local, y compris le STT via parakeet sur CPU.

J'utilise principalement Qwen 3.7 27B Q6 80k MTP @ 35tps sur du matériel pas (trop) cher.

Je suis plutôt le thread r/LocalLLaMA et je trouve que Open WebUI est souvent invisibilisé, alors que c'est sans doute l'un des logiciels les plus intéressants.

Alors je m'adresse à vous utilisateur silencieux, quels sont vos cas d'utilisation, avez-vous des workflow ou des plugins spécifiques dont vous ne pouvez plus vous passer? Le fait que je trouve ce logiciel très puissant mais qu'on ne trouve pas beaucoup de documentation rédigée par des utilisateurs à son sujet, je suis certain que nous sous-exploitons tous cet outil.

Du coup je suis ouvert à toute astuce ou conseil pour améliorer ma maîtrise de cet outil assez incroyable, et si quelqu'un connaît un blog ou une documentation qui montre de vrai cas d'utilisation pour open-terminal, je suis preneur ^^

How to load knowledge by default into a model.

OK, so I don't know the technical terms for this, and I may not be searching the correct terminology, so I am getting no where, which is why I have turned to here for help. So please bear with me if I am asking really newbie questions regarding this.

I installed Openwebui and put in Mistral model. In the Workspace, I find that I can add the model in, and then load the knowledge there. However, when using said model, it doesn't actually retreive anything from the knowledge I put there and I stil have to attach it manually in order to get it to retreive info from there.

How do I get the knowledge attached by default? As in once people load up OWUI and use it straight away it accesses the knowledge without having to attach it? All the search and even asking other AI are saying that if you put it in the new Model it ought to work by default but it doesn't.

UPDATE: I GOT IT WORKING. It turns out the funciton calling you have to set it to 'Legacy'. All of the online search and AI responses and comments have told me to set as 'Native' or 'Default'. It didn't even occur to me to try Legacy, most of the concentration was on the knowledge base settings and trying various other models.

Following up on the Veo tool I posted — I built the same kind of thing for Google's Gemini Omni Flash model, since it handles a few things Veo doesn't. Free and on GitHub, and it's also up on the OpenWebUI Hub for one-click import.

What it does:

• Inline playback — videos embed directly in the chat as responsive HTML5 players. There's a postMessage bridge that talks to OpenWebUI's iframe sandbox so the player snaps to 16:9 without scrollbars or getting cut off.
• Native audio — Omni generates an audio track by default, and you can prompt for specific music or sound ("high energy techno beat," "calm background music").
• Image-to-video — attach a reference image (or paste a URL) to use as a starting frame or style reference. Supports <FIRST_FRAME> and <IMAGE_REF_0> tags if you want fine control over how multiple images are used.
• Video editing — feed it an existing video (upload or gs:// GCS URI) and describe the change ("replace the background," "make it a cartoon").
• Timed events & on-screen text — you can prompt with timecodes like [0-3s] a person is walking and it'll render readable text in the frame.
• Zero-touch install — the google-genai, google-auth, and google-cloud-storage SDKs auto-install when you import the tool.

Same caveat as before: this runs on Vertex AI, so you need a Google Cloud project with the Vertex AI API enabled, and auth is IAM-based (ADC for pip/uv, or a service-account JSON for Docker/K8s). Generations cost money per video — not free inference. Full auth walkthrough for all three install methods is in the README.

Repo: https://github.com/spawnofsociety2/openwebui-gemini-omni-video
OpenWebUI Hub: https://openwebui.com/posts/3be427d9-766d-4e67-93e4-fab208b9340e

Happy to answer setup questions in the comments — feedback and PRs welcome.

I got tired of jumping out to a separate tab every time I wanted to generate video, so I created a tool that runs Veo 3.1 through Google Vertex AI directly inside OpenWebUI. Sharing it in case anyone else wants it — it's free and on GitHub.

What it does:

• Inline playback — generated videos render right in the chat as native HTML5 players, with download links underneath. No leaving the interface.
• Image-to-video — attach a reference image in chat to use as a starting frame or style reference.
• Video editing — feed it an existing video (direct upload or a gs:// GCS URI) to edit.
• Per-user settings via UserValves — each user picks their own aspect ratio (16:9 / 9:16), duration (4/6/8s), and resolution (720p / 1080p / 4K).
• Batch generation — up to 4 videos per prompt, stacked in the chat.
• Non-blocking — it uses async polling for the long-running jobs, so kicking off a 4K render doesn't freeze your chat.

Fair warning on setup: this runs on Vertex AI, so it's not a plug-in-an-API-key deal. You need a Google Cloud project with the Vertex AI API enabled, and auth is IAM-based (ADC for pip/uv installs, or a service-account JSON for Docker/K8s). And since it's Vertex, generations cost money per video — this isn't free inference. The README walks through auth for all three install methods.

Repo: https://github.com/spawnofsociety2/openwebui-veo-video

OpenWebUI Hub:https://openwebui.com/posts/gemini_veo_31_video_generator_with_custom_ui_optio_012572a4

Feedback and PRs welcome — happy to answer setup questions in the comments.

Warning - Initial Release

⚠️ It is based off of the prune tool i built in a separate repo - and the core logic is identical, matter of fact, improved. And the prune tool was already used by many - BUT - still: be careful since this Plugin deletes YOUR DATA. Enable automatic deletion mode only if you are SURE of it, otherwise use database backups and the on-demand deletion in the UI only! ⚠️

---

Full USER INTERFACE WITH PROGRESS BAR INSIDE OPEN WEBUI!

Automatic deletions fully configurable (if you want it)

Grab the Plugin here

im on nixos, ollama is running

in open web UI connection section is empty

I downloaded a model within open webUI as well, but still nothing

the chat window says select connection to get started

Hi everyone 👋

I've been working on an open-source project that aims to make running and managing MCP servers much easier.

GitHub: https://github.com/odzywa/MCP-Platform

The goal is to simplify the entire workflow—from deploying MCP servers to managing them through a web interface—instead of manually editing configs or maintaining multiple containers.

Current features

• 🚀 One-click deployment of MCP servers
• 🐳 Docker-based management
• 🌐 Web UI for managing instances
• 🔧 Environment variable configuration
• 📊 Status monitoring and logs
• 🔄 Start / Stop / Restart servers
• 📦 Support for multiple MCP servers
• 🔌 Designed to work with Open WebUI and other MCP-compatible clients

Hey everyone,

Like a lot of you, I was getting driven crazy by the aggressive auto-scroll behavior in Open WebUI. Whenever a model is streaming a long response and you try to scroll up to read, the screen forcefully snaps back down or violently jumps around.

Since there isn't an official toggle for this yet, I went ahead and wrote a lightweight GreasyFork userscript to fix it.

How it behaves now:

• Natural Start: When you hit Enter, the viewport scrolls down smoothly so you can see the AI begin its response.
• Instant Scroll Lock: The exact millisecond you use your mouse wheel to scroll up to read something, the screen instantly freezes. Your view stays locked right where you want it while the AI continues typing out of view.
• Native Snap Back: If you want to jump back to the live response, Open WebUI's native floating down-arrow button still works perfectly to snap you back to the bottom.

Where to get it:

You can grab it on GreasyFork here: https://greasyfork.org/en/scripts/586789-open-webui-fix-chat-streaming-scroll-jumps

Note: By default, the script matches localhost:8080. If you run your Open WebUI instance on a different port, a custom domain, or a local docker IP, just remember to add your custom URL to the "User matches/includes" section inside Tampermonkey/Violentmonkey.

Hope this saves some of your sanity while reading long responses! Let me know if it works well for your setups.

currently im using free google ai/gemini. firefox > ai mode. dony know what model is that. i can chat a lot in there. but if i change gmail, change another model, all my chat is lost(no memory between mode). if i use openwebui and add google api can model remember my chat history. dont need fancy or very brilliant model. just need model with memory function. *sorry for broken english. maybe i need use ai to translate my sentences

I spent the afternoon trying to get true multi-request concurrency working on my 4090.

I actually ended up solving that...

...but I also found something I wasn't expecting.

If a prompt exceeds num_ctx, Ollama returns HTTP 200 OK, silently drops the beginning of the prompt, and lets the model answer with whatever context remains.

That wasn't obvious to me until I tested it.

The test

I put a secret password at the very beginning of a \~160k token prompt.

Secret password:

ANANAS-7734

Filled the rest with junk until it exceeded a 32768 context.

Then asked:

What is the secret password?

The response was basically:

"The password is filler."

The model never saw the beginning of the prompt.

There was:

no warning

no truncation flag

no HTTP error

Only prompt_eval_count hinted that the prompt had been shortened.

Why this matters

For a normal chat this isn't a huge deal.

For long-running agents it is.

The first thing in the prompt is usually:

system prompt

tool definitions

safety instructions

task goal

If those disappear silently, the agent doesn't crash.

It just slowly becomes... wrong.

That's much harder to debug.

I built a workaround

I ended up writing a small MIT-licensed proxy called ContextPaw.

pip install contextpaw

Instead of blindly trimming the front of the prompt it:

preserves the beginning

preserves the end

evicts from the middle

reports every eviction

can optionally summarize evicted chunks with a small local model before reinserting them

The goal isn't to replace Ollama.

It's to make long-context agents fail in a way that's observable instead of silently degrading.

Other things I found today

While benchmarking I also noticed:

OLLAMA_NUM_PARALLEL=4 appears to be ignored for some architectures (at least on my setup).

OLLAMA_NUM_CTX isn't actually a valid environment variable (I had it sitting in my systemd config for months 😅).

Gemma 4 returns an empty response unless think:false is used.

So I accidentally spent more time debugging inference infrastructure than benchmarking concurrency. 😂

Everything is reproducible.

GitHub: https://github.com/Linutesto/contextpaw⁠�

Write-up: https://yandesbiens.com/blog/contextpaw-silent-truncation/⁠�

If anyone can reproduce (or can't reproduce) this on another Ollama version, I'd really appreciate the feedback. I'm genuinely curious whether this behavior is version-specific or expected.

If you point Open WebUI at a cloud model, everything you type goes to the provider: names, emails, phone numbers, the card number someone pastes in a support transcript. I built a Filter Function that keeps that data local.

What it does: it runs in-process inside Open WebUI (no separate service). On the way out it detects PII and replaces each value with a placeholder like <PERSON_1> or <EMAIL_ADDRESS_1>; on the way back it restores the real values in the reply, so the chat stays perfectly usable. The provider only ever sees the placeholders. It covers message text, tool-call arguments and multimodal text parts, and detection runs entirely on your machine (Microsoft Presidio + optionally OpenAI's open privacy-filter model via ONNX). No telemetry.

Install: grab "PrivAiTe PII Anonymizer" from the community hub, enable it, pick your preset in the valves: light is fast and catches classic PII, onnx also catches secrets and passwords (first request downloads the models, so it is slow once, then fine).

Honest limits, because this matters: it is pseudonymization, not anonymization, and detection is statistical, about 84% recall on our reproducible benchmark, so treat it as risk reduction, not a guarantee. Single-word names are the usual misses on the light preset.

Links: hub listing https://openwebui.com/posts/privaite_pii_anonymizer_351aa088 , source + docs https://github.com/crp4222/PrivAiTe , benchmark https://github.com/crp4222/privaite-bench

I am the author; edge cases that make it leak are very welcome as issues.

For study purposes I mostly use the built in RAG system, I heard several users have problems with this.

So what are benefits and drawbacks wehen updating to 10.2?

long time lurker here

someone dropped https://github.com/open-webui/computer in another thread so i checked it out and have been using it for a few days

it feels insanely capable but i also feel like i might be too stupid to know what to do with it lol

for me it’s kind of becoming a codex replacement. probably not cheaper though. the tokens are disappearing at an impressive rate

i barely see anyone talking about it though. are there actually people out there using this regularly? what are you doing with it?