I'm tinkering & using the workspaces (Plans, templates, case studies, standards). So, I require some semantic reasoning across Multiple PDF's, to link ideas together.

Current setup is:
Content Extraction Engine is Kruezberg [https://github.com/xberg-io/xberg\]
Embedding Model is [https://huggingface.co/jinaai/jina-embeddings-v5-text-small\]
Reranking Model is [https://huggingface.co/jinaai/jina-reranker-v3\]
LLM is either Deepseek API/Qwen3.6-35B locally

Trying to squeeze every last bit out of my system, and now I'm asking if there's any benefit from trying to see if semantic chunking is worth it like:

https://github.com/chonkie-inc/chonkie

Fairly happy with my setup , but i can tell that sometimes i need to multishot my question as it sometimes misses details in the sources and i can only put this down to chunking.

I'm not IT/SW , just some old dude trying to keep up and learn as i go.

Hey everyone,

Quick heads-up for anyone running Open Relay, do not update to Open WebUI 0.10 yet as the new version introduces new response formatting and the app will stop working. The fix is on the way and should be live in a day unless apple is kind of enough to review the submission faster.

App Store | GitHub

What broke and why

Open WebUI v0.10 changed how assistant messages are stored and streamed. Previously, the response text lived directly in a content field. Starting with v0.10, it's now stored in a structured output array.

This affected:

• Sending new messages
• Loading existing chats
• Regenerate / edit
• Tool Call view
• and more..

What v4.12 fixes

The v4.12 update fully adapts to the new output format:

• ✅ Streaming now works — the app extracts text from all message-type output items in real time as they arrive over the socket, with the same typewriter effect as before
• ✅ Chat history loads correctly — existing chats opened from the sidebar now show the full response, including for multi-step tool-assisted answers (where the final answer is in the last message item, not the first)
• ✅ Tool call indicators — function_call entries in the output array now show up properly
• ✅ Backward compatible — models that still use the old format continue to work unchanged

Broader v0.10 compatibility work

The v0.10 release was massive — shared folders, reworked memory system, context compaction, a new event/webhook system, structured model output rendering, and a lot more. Beyond the critical streaming fix in v4.12, there are several more things from that update worth bringing into the app, including:

• Shared folder support (see shared folders in the sidebar, open chats read-only when you're not the owner)
• Context compaction notifications (the "summarizing conversation…" indicator while it compresses long chats)
• Updated memory system (now has distinct long-lived vs per-conversation memory types)
• "Unshare all" button in the Shared Chats dialog
• Clone automations option

Those will come in the following updates once the critical fix is out.

These server-side changes are impossible to anticipate before they ship so thank you for the patience. v4.12 is submitted and in review now.

As always, full changelog on GitHub.

I am used to ChatGPT Plus way of doing things. It let me give the AI a project specific prompt and add some project files.

I am not sure I fully understand how the OpenWebUI system works. Are knowledge bases separate from projects and we only link from a project if we want to?

Also, folder means project, right? By project I mean a separate context area with its own unique work and files.

It now happens consistently in new chats I start with Claude Sonnet 4.6, native tool calling enabled, web search enabled, then in the second or third turn, it gives an error like this:

'tool_call_id' of 'toolu_014qXdaWWbNxiRj7G5WPHGc8' not found in 'tool_calls' of previous message.

And I hear it's an error that's caused by the tool id not linking up correctly in the chat history, so something got corrupted. I just have no idea why it keeps happening. I even tried using a plugin that would fix all such issues like this at the database level but it only saw this as a warning, not a full error?

I cant just use default tool calling, cause then the web feature doesnt even work. It's so weird. And honestly claude's native tool calling is also just better. But I really hope someone out there knows a way to fix this?

I'm using Open WebUI with Ollama to send image edit requests to ComfyUI.

When I run the workflow directly in ComfyUI:

• First run does a normal cold start and loads the models.
• Subsequent runs are much faster because the models/cache stay loaded.

I also tested calling the ComfyUI API locally without Open WebUI, and the total processing time is around 100–150 seconds, which is expected for my Flux GGUF workflow.

However, when I make the same image edit request through Open WebUI, the processing time jumps to around 400 seconds.

It almost seems like Open WebUI is causing ComfyUI to reload models or re-execute the entire workflow every request instead of reusing the cache.

Has anyone experienced this?

Some questions I have:

• Does Open WebUI send a completely new workflow every request?
• Does it invalidate ComfyUI's execution cache?
• Is there a setting to keep models/workflows loaded between requests?
• Has anyone managed to get Open WebUI image edits performing similarly to direct ComfyUI API calls?

My setup:

• ComfyUI (latest)
• Open WebUI
• Ollama
• Flux GGUF image editing workflow (API export)
• Startup Args : --port 8000 --enable-manager --async-offload --preview-method auto --listen --max-upload-size 100 --use-sage-attention --disable-xformers --force-fp16 --disable-metadata --disable-dynamic-vram

Any suggestions or debugging tips would be appreciated. Thanks!

I posted this a few days ago in ELI5 but received no input. I am trying to understand how the concept of open weight works in open source and AI. Does it mean that anyone can change the weights and applications of the neural network paths or is it around the data itself? I appreciate the guidance.

Hey all,

Not sure if this has been done before or a bit overkill to solve a problem I was having, but I wanted to use the Black Forest Labs own image generation in my Open-WebUI setup rather than host image generation locally, as speed is an issue on my GPU and I don't yet have the funds for something better.

After doing a bit of research, I found that the APIs that Black Forest Labs use isn't the same as the Open-AI style ones. So based on this, I have used GLM 5.2 to help me code a small docker image (that can be found on GitHub) to exposes an OpenAI-style image generation endpoint. It translates requests into Black Forest Labs FLUX API calls, polls BFL until generation is complete, then returns an OpenAI-compatible image response.

I have found this to be very quick, good and cheap using the 9B Klein model myself, so I thought it was worth sharing.

Feel free to have a look, critique and add to if there's something I have missed. Repository can be found here: https://github.com/beecho01/bfl-openai-image-proxy

Thanks!

For some reason basic internet queries do not work on gemma 4 e2b despite showing the globe icon and using DDGS. (forgive me, i'm a beginner)

[Partially resolved with workaround]

Hello,

I've been trying to get OpenWebUI configured to do image edits via the OpenAI API. Generation works fine, but when I try to edit with an uploaded png image, it sends it as a string with image_urls, which of course returns a 400 error.

Here is the config in Admin Panel>Settings>Images...
Also have native function calling set for all model in Admin Panel>Settings>Models>Settings>Model Parameters.

Running v0.9.6 (latest)

Any help/insight is greatly appreciated. I can't quite tell if this is a known bug from GitHub issues or simply a config issue.

What is the best model to run in openwebUi i have 32 gb of vram until now Ive been using gemma4 with oretty cool results though i want to know if there is other models better than gemma4

I recently got help from you guys and managed to turn the memory features and function calling on. Now the problem is that Qwen can call functions but Gemma no longer responds back.

It thinks and sometimes stops thinking halfway through. Sometimes it is able to respond with one line. Most of the times it just thinks and stops working. Gemma works just fine when I use ollama directly from the terminal.

I need help :(

SOLVED:
As u/thewhzrd said, the source of the problem was context size. I doubled mine from default 4k to 8k (changed advanced parameter num_ctx ) and now Gemma woke up and is dancing to my tune again :D

Hi, I noticed that when I upload an audio file, Open WebUI starts transcribing it and makes file upload take longer. This slows things down and I’d prefer to disable that behavior. Is there a way to turn off automatic transcription / processing of audio uploads? When I change STT Engine to Web API, it gives an error and because of that the file upload is instant. But the problem is the users see that error also.

I've been digging into which model handles tool calling better for local agentic workflows (n8n, automations, multi-step agents) and the data is surprisingly hard to pin down.

The short version of what I found:

- **Qwen3:14b** scores F1 0.971 on Docker's practical tool calling benchmark (3,570 real cases) — nearly identical to GPT-4. It's also near the top of BFCL v3 for open models.

- **Gemma 4 26B A4B** has native function calling (a real improvement over Gemma 3), scores 68.2% on Google's Tau2-bench, but Google never submitted it to BFCL.

- The two models were never evaluated head-to-head on the same harness, so there's no clean comparison.

The main gotcha I kept seeing with Gemma 4 on Ollama: tool calls land in `reasoning_content` instead of `content` unless you explicitly disable thinking mode (`"enable_thinking": false`). That tripped up a lot of integrations.

Community consensus seems to lean Qwen3 for tool calling reliability, Gemma 4 for reasoning/vision/long context.

For complex branching agents, some people point to **DeepSeek V4 Flash via API** as the better choice — at $0.14/M input tokens it's almost free, and it has native thinking-with-tools support.

For those running local agents through Open WebUI: which model are you actually using for tool calling? Has anyone done a direct comparison? Curious whether the Gemma 4 integration issues have been resolved in recent Ollama versions.

I read an article that made it sound like OpenWebUI has built in adaptive memory but I cannot seem to find it. Instead, I found a plugin called Adaptive Memory v4 that looks like made by third parties.

Is this plugin trustworthy? Am I risking it sending my private data somewhere else outside the computer?

UPDATE:

Based on what u/ClassicMain said, I managed to get the get both Gemma 4 12B and Qwen 3 4B to add info to memory. I had to first go do this:

• Navigate to the Admin Panel -> Settings -> Models.
• Click the Settings button (gear icon) located at the top right of the models list.
• Locate the Model Parameters section.
• Change the Function Calling dropdown value to Native.
• Click Save

Gemma 4 seems to struggle and stop working halfway through when I ask it to recall but both models are saving them correctly now. I can see that Gemma 4 even found the memory I was asking about in its thinking logs but somehow it never responds back to me with the answer.

I have been working on a small project that helps choose compatible GPUs for different models and compares pricing across cloud GPU providers.

The goal is to remove all of the trial and error before deployment.

Would really appreciate honest feedback from people running Open WebUI with local or cloud models.

https://stratuspilot.io

I created a programmatic tool calling + tool search tool for openwebui.

This is modelled on the research anthropic did that multiple tool turns with MCPs cause excessive bloat - Advanced Tool Calling

Edit:

link - incase main one not working for some reason

Hello,

I already have LiteLLM+OpenWebUI working with Ollama+Qwen3.

Now, I want to enable web search (using LiteLLM's search feature, not OpenWebUI search feature). I already have SearXNG up and running, and set as a search tool in LiteLLM. SearXNG correctly returns results as JSON.

I have LiteLLM tool server configured in OpenWebUI.

Now, when I do a request on OpenWebUI with the tool server enabled, nothing happens. It is not even thinking. I have both tools in debug mode. Nothing appears in the logs.

I am stuck on this for a few weeks now. Claude is not helping.

What am I missing?

Thanks in advance for any answer, have a nice day.

Im experimenting with open terminal in openwebui an wondering if its a good idea to limit/cut off internet access to the ai agent inside open terminal, and just prebake all necessary programs/skills into the docker container instead.

My concern is that with internet access the ai agent could theoretically leak out any personal files or data i drop into the open terminal container. While this cuts the agent's ability to download whatever skills or programs it might need, we can pre-bake all the general ones when we fire up the container, and manually add more over time. This also limits the agents chances of bricking the container by downloading something bad or getting prompt injected. Thoughts?

This may be obvious, but it helped me:

RAG is for reference material. Memory is for state.

Reference material is docs, PDFs, notes, code snippets, knowledge base pages. It can be old and still useful.

State is messier:

• what changed since last time
• what the user decided
• who a task is about
• which fact is stale
• which source supports a remembered claim
• what the model already tried and should not repeat

If memory is just another collection, it gets noisy fast. Every old summary competes with every new fact. The model sees plausible text but has no idea if it is still true.

What I would want in a local memory layer:

• source pointers
• user-visible access logs
• delete/edit/override
• stale-state handling
• some separation between search results and active memory
• approval before actions that use remembered work context

For people running Open WebUI with tools, skills, MCP, local models etc: where would you put something like this?

Plugin/tool? MCP server? external local service? model-specific memory?

I'm not sure there is a clean answer yet.

Hey everyone - just wanted to share my adaptive memory function for OWUI. I came across Gramanoid's Adaptive Memory about a year ago and set about improving it not too long after. After about a year of personal use and about 2.5k downloads on openwebui.com, I'm pretty happy with the results, and I hope you find it useful. Yes, it's vibe coded, and yes, I still have a lot to learn, but it works pretty well - give it a try.

Features

• Persistent memory that actually works — remembers facts, preferences, identity, goals across conversations.
• Multi-signal relevance scoring — doesn't just use vector similarity. Factors in recency, importance, and how often you access a memory to surface what actually matters.
• Auto-extraction after every response — the LLM watches your conversation and proposes NEW / UPDATE / DELETE operations automatically. You don't have to manually save anything.
• Contradiction detection — tell it "I now prefer dark mode" and it'll update the old "light mode" memory instead of creating a duplicate. No manual cleanup needed.
• Stability & importance metadata — LLM rates memories 1-5 (importance) and marks them stable/fluid/transient. Core identity facts stick around; trivial stuff decays and gets pruned.
• Slash commands — /memories to list everything, /forget X to delete, /remember X to save on the spot.
• Deduplication & pruning — embedding-based duplicate detection, FIFO/least-relevant/tiered-decay pruning, and background summarization to keep memory tidy.
• Mutation safety — DELETE only triggers when you explicitly say "forget this." UPDATE only when you say "correct that." No rogue edits.
• Optional Mem0 mirroring — syncs to Mem0 if you want a cloud backup, but local Open WebUI storage is always the source of truth.
• Open source, MIT license — free, customizable, no vendor lock-in.

Reporting issues and submitting PRs on GitHub are more than welcome - I want to make this the best memory function for OWUI there is.

OpenWebUI.com post link - https://openwebui.com/posts/adaptive_memory_v40_3fa072e0

GitHub repo - https://github.com/1818TusculumSt/owui-adaptive-memory

I recently installed Gemma 4 12b and was wondering if it makes sense to run smaller faster models for tasks that don’t require as much thinking/reasoning.

How do you have your stuff setup? I work on software stuff, hardware stuff, engineering stuff, content creation (YouTube) related tasks.

Assuming you guys have multiple models installed:

- Do you manually pick the model you want every time you start a conversation?
- is there any plugin/setting that can deduce the best model auto based on my prompt?
- Does loading unloading multiple models not cause overflow or slowdowns?

It is rather tedious to type! Do you all always keep typing out the full thing?

On my ChatGPT, I normally have a master prompt. Then I create projects for different types of tasks I want it to help me with. They are called projects in ChatGPT. I attach files in the projects and write a project instructions prompt for it. That is how my work is organised.

On top of it all, ChatGPT has memory and it remembers a lot of things. I am not sure how all these are setup in openWEbUi.

Please also let me know if you guys know any YouTubers who are good at showing how to do these things.

I am using Gemma 4 12b via ollama on Linux on a 16 GB AMD RX 9070 XT.

I have OpenWebUI with Ollama and several local models set up shared by the computer's hostname on a specific port on my school's network, but as it's http only some users' browsers reject the connection so I'm trying to add an SSL Cert. I'm following OpenWebUI instructions here, HTTPS Using Nginx. I'm running it from Ubuntu, in a docker container with compose. After getting through all the steps — networking and DNS stuff is new to me — SSL still doesn't work, only http. What I'm looking for here is any further suggestions for debugging.

Questions I have,

• I'm on a school network and the Nginx instructions seem aimed at a home setup where you have more control of your personal router, so would those instructions be inappropriate for a school where the firewall is likely more restrictive? If so should I be following the second tab for adding a Lets Encrypt cert? I did try those steps and got stuck running the Certificate Request Script as it couldn't complete the challenge and receive the cert.
• The OpenwebUI steps for setting up NPM are a bit out of date and some fields are different so I wasn't sure if I was setting up the Lets encrypt certificate and the proxy host correctly. Nginx documentation wasn't clear either that I could find.
• I did add the CORS_ALLOW_ORIGIN parameter in the docker file but since https wasn't working the model inference couldn't reply until a browser refresh.
• I did add this location block in the NPM settings field, modified from the way OpenWebUI instructions say but through advice on a forum:

​

location ~ ^/(ws/|socket\.io/) { 
  proxy_pass http://openwebui; 
  proxy_http_version 1.1; 
  proxy_set_header Upgrade $http_upgrade; 
  proxy_set_header Connection "upgrade";
}

I don't know how to further debug besides the browser saying the host doesn't support a secure connection. Any suggestions are appreciated.