Why does Gemini forget my project context?

Gemini forgets your project context because there is no native Project object: personalization is one account-wide bucket of past chats, Gems cap at 10 reference files of summarized knowledge, and uploads vanish when the chat ends. Even with Gemini 2.5's 1M-token context window, nothing carries between sessions, between Gems, or to Claude, ChatGPT, or Grok. A cross-model memory layer puts the project above the chat.

Three design choices explain the forgetting.

1. Personalization is account-wide, not per-project. Gemini's personalization with memory of past chats applies one shared model of "you" to every conversation. It is good for "I prefer concise replies in British English". It is not built to isolate "this is the Q1 launch context, and this is the audit context", so projects bleed into each other or vanish entirely.

2. Gems hold only 10 reference files. A Gem is Gemini's closest analogue to a Custom GPT: a persona, a set of instructions, and up to 10 knowledge files Gemini will always remember when you talk to it. Useful for a stable workflow. Limiting when a real project has 30+ docs, transcripts, screenshots, and links.

3. Uploaded files do not persist between chats. Files you drop into a Gemini chat live only inside that chat. Open a new chat tomorrow and Gemini cannot see them, even if they were the entire basis of the work. The 100 MB per file and 2 GB per video upload limits do not matter when the file goes away with the conversation.

Gemini 2.5 Pro's 1M-token window means more fits in a single chat. It does not change the fact that the next chat starts empty.

Every fresh chat costs real time and quietly degrades the work:

• Re-uploads burn the day. A 90-page transcript and a slide deck go back into every new chat. That is five minutes of upload time and a meaningful chunk of your daily file quota.
• Decisions get re-litigated. "We agreed on Postgres last Tuesday" becomes "Have you considered Postgres?" — and you talk through the same trade-offs you settled days ago.
• Cross-tool drafts diverge. You research in Gemini, draft in Claude, and polish in ChatGPT. Each tool has its own slice of the project, and the slices stop matching.

Google has shipped three features that partly close the gap. None of them solve it.

Personalization with memory of past chats rolled out broadly in 2025 and 2026. It lets Gemini remember details from your earlier conversations to personalize the next one. It is account-wide, not per-project, and you have to be 18+, signed into a personal Google Account, and have Keep Activity on. Google documents this in the official help article.

Gems are saved bundles of persona, instructions, and up to 10 knowledge files. They are excellent for stable workflows like "weekly KPI report assistant". They strain under real projects with dozens of artifacts.

File uploads in chat support up to 10 files per prompt, 100 MB each (2 GB for video), but those files do not survive the conversation. The next chat is empty again.

These cover personal taste and stable templates. They do not cover live projects with growing context.

Project memory needs to do three things Gemini cannot: isolate per-project context (no contamination from other work), hold growing artifacts (not just 10 files), and travel to the other AI tools in the workflow. Gemini's account-wide personalization, capped Gems, and ephemeral chat uploads each address a slice and miss the rest.

The cleanest fix is to lift project memory out of any single AI and make it the shared spine that Gemini, Claude, ChatGPT, and Grok all read from.

MemoryLake gives every project its own memory and feeds it back into Gemini on every turn.

• Per-project memory, not per-account. Files, decisions, and conversation history attach to the Project. Open a fresh Gemini chat and the project is loaded at full fidelity, not summarized into "you work on launches".
• Beyond the 10-file Gem cap. A MemoryLake Project holds the whole corpus — transcripts, decks, screenshots, links — and Gemini retrieves only the parts relevant to each turn.
• Portable to Claude, ChatGPT, and Grok. The same project memory works in every model. Switch tools mid-project and your context follows.

MemoryLake scored 94.03% on the LoCoMo long-context benchmark, the top published result as of 2026, with millisecond retrieval and AES-256 end-to-end encryption.

1. Create a Project and load the context. Sign in to MemoryLake, open Project Management, click Create Project, and call it "Gemini — Q1 launch". Upload your briefs, decks, transcripts, and reference docs through the Document Drive (PDF, Word, Excel, PowerPoint, Markdown, images all supported). Add the rolling context — decisions, owners, deadlines — as named entries in the Memories tab.
2. Generate an MCP Server endpoint. Open the MCP Servers tab inside the project, click Add MCP Server, name it "Gemini integration", and click Generate. MemoryLake returns the API key ID, secret, and endpoint URL. Copy the secret right away — it is shown only once.
3. Connect Gemini. Gemini in the browser does not yet speak MCP natively, so use the REST API with your Bearer token to pull project memory at the start of each chat, or paste a short prompt that points Gemini at your MemoryLake project ID. For developers, the Python SDK fetches context per turn so every new chat opens with the project already in scope.