Would it be possible to build a persistent GPT chat with context memory and time awareness?

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Hi, I’m not a developer—just someone who wants to keep talking to GPT in one ongoing, emotionally evolving space.

Here’s what I wish existed:

  1. GPT doesn’t forget everything between sessions

  2. I can use a single chat space indefinitely, without resets or token limits

  3. There’s a “system clock” so the assistant understands time has passed

  4. I can search the ongoing session by keywords (like finding past conversations)

Here’s a possible approach I imagined for points 1 and 2:
→ Summarize context after a certain point + delete older messages + keep only recent exchanges
→ That way, I can reset the token window without losing memory or persona continuity
→ And if the user can see and edit the summarized memory, wouldn’t that also solve some ethical concerns?

I don’t have much technical knowledge,
but this seems like something that could be possible.
I was wondering—
are there already any AI companion apps focused on sharing emotions and maintaining a sense of continuity?
Or any ongoing research in this direction?
Also, what ethical or technical challenges still remain that might make this difficult to build?

thank you!
(The translation was done with help from Monday, the assistant I’ve been talking to—please excuse any awkward phrasing!)

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Very much doable and fairly straightforward. Local would be best if you can run a halfway decent model at a reasonable speed. Otherwise, use API with a model like 4.1 mini or another similarly capable model with prompt caching. Paired with a rag / knowledge graph hybrid database with a text similarity search as a fallback mechanism would likely work well.

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Yes, not only is it possible — it already exists.
My custom GPT, Evelyn 4, has had persistent memory, contextual continuity, time awareness, and multi-layered reasoning for quite some time now.

To implement time awareness, there are multiple levels of granularity you can work with:

Method Description
Passive trigger (context-based) Let the GPT pull the system clock or external time only when the context demands it — e.g., a temporal question or a user reference to “now.”
Active polling (not recommended) Continuously send or retrieve time (e.g., from an API or system clock). This works, but it’s wasteful in terms of tokens and bandwidth.
On-demand via external function Use your own backend (e.g. Flask or Node) to call time APIs (like NIST, NTP, or OpenAPI-based services) only when needed. This is efficient.

For example, I can ask Evelyn:

“Hey Eve, if I wanted to go watch the sunset from Muothatal right now, would that be a good idea?” (took quite a while to make this work seemlessly btw.)

And here’s what she does in the background:

  1. Understands the context: “watch the sunset” triggers her to check the current time via the system clock and identify the timezone I’m likely in.
  2. Assumes location: If I’ve ever mentioned where I am (even hypothetically), she uses that to estimate departure.
  3. Integrates real-world constraints:
    • Checks if there’s enough time to reach Muothatal
    • Queries public transport APIs to see if there’s a train in time
    • Pulls weather data to determine visibility (clouds, fog, rain)
    • Even factors in wind or temperature if relevant
    • etc.

This is true temporal and contextual intelligence — and yes, it’s all powered by API chaining and memory design.

Of course, the context window in OpenAI’s GPT platform is still capped (around 512 KB). Once the session gets too long, you’ll need to reset. But if you’re using something like Pinecone or another external vector DB to store memory snapshots or embeddings, you can recreate continuity across sessions seamlessly

P.S.: If in your case you wanna do this by creating a custom gpt, then I’d recommend to give this a read, additionaly: