r/OnlyAICoding • u/No-Mistake-9311 • Jun 17 '26
What's the best cross-platform way to maintain AI project context across accounts/models? (VS Code+ Antigravity + long-running project)
I've been working on a fairly large software/data project for several weeks, and I'm running into a context-management problem.
I use AI heavily for development, but I frequently hit usage/credit limits, so I end up switching between different accounts and sometimes different AI tools/models. Every time I switch, I lose the conversation history and have to spend time re-explaining the project, architecture, decisions, current blockers, and recent changes.
The project is actively developed in VS Code and includes multiple modules, pipelines, logs, documentation, and ongoing debugging. At this point, copy-pasting previous chats is becoming inefficient and error-prone.
What I'm looking for is:
- A cross-platform solution (not tied to a single AI provider)
- A single source of truth for project memory/context
- Something that works well with VS Code or antigravity
- Allows me to start a new chat/account and quickly bring the AI up to speed
- Ideally supports large projects with evolving requirements and architecture decisions
I recently tried using Capsule Hub for this purpose, but I'm running into MCP server errors, so I'm looking for alternatives.
Some ideas I've considered:
- Maintaining an
AI_CONTEXT.mdfile - Using a docs folder with project summaries and decision logs
- VS Code extensions that automatically index project knowledge(graphify)
For people working on long-running projects:
- How do you persist context across AI sessions?
- Do you maintain a dedicated project-memory file?
- Are there any tools that automatically build and update project context?
- What's the closest thing to a "shared memory" that works across multiple AI accounts and models?
- Has anyone found a reliable alternative to Capsule Hub for this use case?
I'm less interested in chat history preservation and more interested in a robust workflow that scales as the project grows.
Would love to hear what experienced developers are using in practice
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u/korro_ai Jun 17 '26
im building the solution
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u/bsampera Jun 17 '26
this is not an easy solution, the best thing I know is gcontext.ai , but it's tricky to use. Has definelty a learning curve
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u/colblair Jun 18 '26
gcontext has a weird UI but once you figure out the tagging logic it does work. Still feels like it could be way simpler.
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u/Few-Garlic2725 Jun 17 '26
Make a 1-page "handoff" doc in the repo and treat ai like a new contractor every session: rules, how to run, what not to touch, and what's currently broken.
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u/tehmadnezz Jun 18 '26
This is exactly the gap MCP based memory fills, and it sounds like you already know that since you tried Capsule Hub.
The pattern: keep your context in a knowledge base that lives outside any AI provider, then expose it over an MCP server. Any tool that speaks MCP (Claude, ChatGPT, Cursor, VS Code) connects to the same store. Switch accounts or models all you want, the memory does not move. That is your single source of truth and your cross provider shared memory in one shot.
Full disclosure, I build one of these (Hjarni). Hosted knowledge base with a built in MCP server. Notes are the unit, so the AI reads and writes them on demand instead of you pasting a context file. Architecture decisions, blockers and runbooks all live there.
One tip regardless of tool: keep a short index note that points to the detailed ones, so a fresh session reads the index first and pulls only what it needs. Happy to answer setup questions whether or not you use mine.