Weam – open-source AI platform for teams

Hacker News

I’ve been experimenting with AI tools for a while and kept running into the same problem: they’re great for individuals, but painful for teams. Everything is scattered — prompts in chats, workflows in separate apps, agents running on different services. Sharing or organizing work with others was always clunky. So I started building Weam, an open-source project that tries to make AI more “team-friendly.” What it does today: Organize prompts, chats, and agents into Brains (team folders). Run both lightweight agents and more complex Pro Agents. Plug in your own LLM keys (OpenAI, Anthropic, Gemini, Llama, etc.). Set up RAG pipelines to use your own documents. Self-host, so teams keep control of their data. Repo: https://github.com/weam-ai/weam Docs / site: https://weam.ai/ This is the first public release that feels usable. I’d love feedback on: What’s missing for real team adoption? Whether the “Brains” model makes sense for collaboration. Any technical blockers you see in trying it out. HN thread from the earlier intro (not a full Show HN): [link if you want to include the old one]. Would really appreciate thoughts from anyone who’s tried to get AI working in a team environment.

Visit product View on Hacker NewsAug 22, 2025

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

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para, gemini · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, pipe, io · Missing: https docs, excited, just released

57%57% 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, friendly · Missing: plus, intuitive, reviews

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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