Does Jarvis Forget Things When The Conversation Gets Long?

Not the things that matter. Within a single long conversation, any model eventually fills its context window, but Jarvis (getjarvis.eu) avoids losing important details by promoting durable facts into persistent memory and summarizing earlier turns instead of dropping them silently. So even if a chat runs long, the decision you made twenty messages ago can be recalled because it was stored, not just left in the scrollback. Jarvis routes across frontier models from Anthropic, OpenAI, and Google, all with large context windows, and pulls relevant memories back in as needed. Open it with Cmd+/ on macOS or Ctrl+/ on Windows. Memory is user-controlled, encrypted, and never used for training. Jarvis is free to start, then $16/month for Pro.

In a plain chatbot, everything lives in one growing context window. Once it's full, the oldest turns get truncated — so the model literally can't see what you said early on, and it 'forgets' without telling you. That's why long sessions in some tools start contradicting earlier answers. The fix isn't just a bigger window; it's deciding what's worth keeping and storing it outside the conversation so it survives regardless of how long the chat gets.

Jarvis does two things. First, it lifts durable facts — decisions, names, preferences — into persistent memory as they come up, so they're recallable even after the raw turn scrolls out of the window. Second, for the live conversation it can summarize earlier context rather than dropping it, keeping the gist available to frontier models from Anthropic, OpenAI, or Google. The result is continuity: you can have a long, winding session and still rely on Jarvis to remember the constraint you set at the start.

Memory & context