AI Context Flow
One context & memory that works across all your AIs.
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AI Context Flow reaches 4200+ users <3

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AI Context Flow reached 4200+ users in 10 months with new paid users coming in every week - proving that context management is a growing pain for AI users. 

If you are also frequently repeating context to different AIs, try AI Context Flow and make your life a little easier.

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congrats on the growth. genuinely curious how the "works across all your AIs" part handles the mismatch in context window size and format between models - if I build up a long context talking to one model and then switch to a smaller-context one, does it compress/summarize down to fit, or is it on me to know which context is safe to carry over

the context is stored and organized in buckets, when you move to a new one and are having a conversation, it will bring in the related context to your prompt, so you are not stuffing everything word for word from one chat to another, but rather letting your AIs pull in the right information at the right time. The mechanism to do this is via Model Context Protocol (MCP)
just to clarify, your entire content is intact and if you ask the AI to bring in everything word for word, it will do it. But otherwise it will bring in the information as much as needed

 got it, that's a better tradeoff than I assumed - retrieval on demand instead of a full dump every time. how does it decide what counts as "related" when pulling from a bucket, is that similarity search over embeddings, or something more structured like tags/recency? mainly curious whether there's a failure mode where something relevant just wasn't similar enough on paper to get pulled in, even though a human would obviously connect it to the current conversation

 semantic search basically. We have developed our internal search mechanism based on a combination of vector embeddings + file system (markdown) based search.

Conflicts are surfaced but we do not filter based on time since our core uer population is knowledge workers where recency is not that important as compared to having the information available at the right time.

Recency and temporal reasoning matters more in companionship use cases or tracking changing preferences of a user, not in work related use cases.