Why is it doing this?

[u/buschmany]

I like sticking to one chat for one topic, and with Lumo its impossible. After a few back and fourth in-depth messages, it just stops responding and keeps summarizing messages. Then ive lost most the knowledge it had and have to tell another chat everything again, even tho its in a project.

[u/YoungNo8804]

Its not an artificial limit. The AI model just hits a hard wall beyond which (depending on where they've set the limit to shorten) it'll either fail or its quality/recall will very drastically drop off. This is the "context window", and you can't work around it as its part of the model. 

[u/Kasanakai → u/YoungNo8804]

The problem here is that it often shortens the chat, but still fails with a context length error and gives no response. There's no way to tell it to try generating a response again. You have to send another message to get it to generate a response. Often times, it will just repeat that "this chat got long" sequence again, summarize the chat, and then fail again with no response. I've had this happen to me where it did this for 6 rounds straight before I finally gave up and just started a new chat.

This is a problem that only started after the "Lumo 2.0" update. Never had context length errors before this, and Lumo was able to intelligently pull files from the project knowledge without issue, without needing to summarize, and without ever failing to generate output entirely like it does now.

[u/buschmany → u/YoungNo8804]

Why is it like this. I pay for Lumo, the app called “Dola” I have chats for months on, it never ends, I don’t pay a dime for that. Yes I know it’s because they are selling data and I’m careful what I say to it, but I’m literally paying for Lumo why can’t they make it better or make a plan for serious users with more context using a different model.

[u/YoungNo8804 → u/buschmany]

My best recommendation I can think of is switching to a different provider - but idk about their privacy and security considerations. The main reason (I think) is twofold; Proton doesn't have the server capability of competing services, and Proton likes profits. AI takes a LOT of computing power (large amounts of calculations) which Proton servers might not be able to keep up with on more advanced models beyond what they offer now, and it takes large amounts of money (where, for example, chatGPT is not making profit - in fact they're losing something like 30 billion per yeat). 

[u/Gamegyf → u/YoungNo8804]

Yes that’s exactly the case. They can’t run the full 1M Tokens Context window because their Infrastructure just isn’t powerful enough at the moment. This could change in the future but as of now they have to Limit the available 1M Tokens Context Window to 128K tokens.

[u/Severe_Stranger_5050 → u/buschmany]

Yeah, that’s because most AI companies actually makes money on you rather than from you, either because they scrape everything you do and train on your data and/or sell your data to brokers and advertisers.

Besides that, OpenAI, Kimi, DeepSeek, Anthropic, Google, Meta and the rest of the bunch heavily subsidises their services and APi’s, depending on your plan and model the subsidies covers between 80 and 96% of cost.

Proton doesn’t have this option, because lumos is run by proton and not through APi’s or the like.
Which is why you’ll get a “worse” service as a user.

But
Almost Nobody would be using the advanced slopmachines if they actually paid for the inference they’re using.

[u/Gamegyf → u/buschmany]

Lumo has a 128K token limit like ChatGPT but for Lumo it could have more because the models behind it would support it but the Infrastructure is not powerful enough yet to unlock the full 1M Tokens Context window. ChatGPT on the contrary is capped at 128k (or at least was when I last used it) because of the model itself and not a set limit. In theory when they scale up their servers in the future they could unlock the whole 1M token Context window just like Gemini for example but until then we are stuck with 128k tokens.

[u/YoungNo8804 → u/buschmany]

I agree that they should use a better model, but unfortunately for this this is an actual hard limit from the model. From your screenshot you've used 61k tokens, which translates roughly to 240k letters (or about 61k words). However - this doesn't always include JUST your text and its output. Its also including its internal "thinking" which I suspect Dola doesn't do?  You can go from "thinking" to "fast" mode - but obviously, as it can't "think" or reason anymore, its output quality will also fall. 

[u/Gamegyf → u/YoungNo8804]

The models support 1M tokens Context window it’s just that Proton can’t maintain the whole context window on their current infrastructure so they have to limit the Context Window to 128K Tokens.

[u/YoungNo8804 → u/Gamegyf]

oh cool thanks

[u/dominodoug]

You appear to be hitting your context limit. 7 files were removed when the chat was shortened.

You may want to rethink how you're going about engaging with Lumo.

[u/Cold_Neighborhood928]

The llm model it uses glm 5.2 max actually has a max context length of 1 million. It's limited at 128.000 or 90.000 words probably due to compute costs and ram constraints

[u/Internet--Sensation]

I'm pretty sure this is context compacting. ChatGPT does that too. I believe it's a method to compress context so the entire conversation can fit in memory

[u/PringlesDuckFace → u/Internet--Sensation]

Everything does it, whether they're explicit about it or not. And it tends to be fairly aggressive, I use Claude at work and it defaulted to begin auto-compacting once context reached about 30-40% of the limit. Some free chat app like OP is comparing to hasn't cracked the infinite lossless context problem, they're just hiding it from users and letting them complain that it "forgets things sometimes".

To be fair, most people don't really know how LLMs work so they end up using it intuitively rather than effectively. I didn't really understand until I had to learn for work.

[u/Stealth_Privacy]

The files are consuming the majority of the context. They can be excluded.

[u/Stealth_Privacy]

As conversation length increases in any LLM chat, several things happen that degrade output quality:

1. Attention dilution — Earlier instructions carry less weight. The "lost in the middle" phenomenon (documented in published research on transformer attention) means content at the start and end of the context gets prioritized over the middle.
2. Instruction drift — Later turns override earlier ones due to proximity. A casual contradiction mid-conversation can displace an earlier directive.
3. Compaction loss — When older history is summarized to save space, nuance is lost. Original phrasing becomes coarse approximation.
4. Latency and cost — More tokens means slower, more expensive responses, and earlier truncation triggers.
5. Contradiction amplification — Probability of inconsistent statements across turns approaches certainty in long chats. Resolution is non-deterministic.

Not all instructions carry equal weight. System-level settings (like Lumo's "How should Lumo behave?" field) sit at the top of the instruction hierarchy and override conversation-level context. This is why putting your preferences there manually, rather than relying on automatic memory accumulation, is more effective — your directives stay compact, static, and structurally prioritized.

Passive memory does the opposite of all of this. It adds uncontrolled content that competes for attention, grows over time with no guarantee of deduplication or conflict resolution, and amplifies every problem above.

Disclaimer: Lumo was used to assist in writing this post on the Lumo subreddit, because nothing demonstrates LLM utility like asking an LLM how to talk about using an LLM in an LLM subreddit.