Customer Support AI Agent with Memory

Hacker News

Hi, I am Bobur, a Software Engineer. We built an open-source customer support AI agent that can remember past conversations. Most chatbots treat every message like it’s the first one. This one keeps context over time, remembering what the user said earlier, what issues they faced, and what pages they visited. It can be added to any website with a small widget. The goal is to make support more personal and reduce repeated questions. We’re still improving the memory layer and feedback loop, and would love feedback on how it behaves with real customer data. Thanks!

Visit product View on Hacker NewsOct 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, context · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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