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Chatwoot 4.0 – Open-source intercom alternative with AI agents

Hacker News

Chatwoot 4.0 – Open-source intercom alternative with AI agents

A couple of years ago, we launched Chatwoot here on Hacker News as an open-source alternative to Intercom. Back then, it was a scrappy live chat widget with a shared inbox and just enough backend logic to make things work. What made it stand out was that you could self-host it, own your data, and actually read the code. Since then, things have grown. We got into Y Combinator, has grown into a platform used by thousands of companies around the world. Some teams use it for a few hundred conversations a week. Others run it at massive scale, handling tens of millions of messages across channels. Through all of that, we’ve stayed focused on keeping it open-core, self-hostable, and developer-first. Today, we’re back on HN to share Chatwoot 4.0 — the biggest update we’ve shipped so far. The most visible change in Chatwoot 4.0 is Captain, our new AI agent. It’s built into the platform and can automatically handle repetitive support questions. Under the hood, it uses OpenAI, and it’s fully opt-in. Nothing gets sent anywhere unless you configure it to. Right now, it generates responses based on your Help Center articles, and we’re actively working on adding actions (tool calling capabilities). We’ve already tested it with Shopify and Linear APIs. It works great and opens up some interesting automation paths. Additionally, we’ve reworked core parts of the system. The UI has been fully revamped using Vue 3. It’s faster, cleaner, and easier to extend. On the backend, we focused heavily on performance. Some deployments now handle over 200 million messages, so we’ve optimized querying to make things faster and more predictable. We’ve also added smarter caching, reduced duplicate fetches, and cleaned up both frontend and backend logic to better handle high-throughput workloads. We also rebuilt the Help Center module. It’s now a proper self-service portal. Markdown-based, cleaner UI, better search, and support for custom domains. If you’re already using Chatwoot, the upgrade is available now. If you remember our original launch or you’re looking for a support tool you can actually own and extend, we’d love for you to check it out again. Code: https://github.com/chatwoot/chatwoot Docs: https://www.chatwoot.com/docs Demo: https://app.chatwoot.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
97%97% 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
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active, shopify · Missing: arr, mrr, revenue
22%22% 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, smart · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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