Bushchat – open-source graph LLM interface

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Hey, When working on complex projects involving multi-document or multiple tasks process, I've stumbled upon a problem that LLMs can't guide themselves efficiently through context. I've seen some tools for LLM graph interface ( https://branchcanvas.com/ , http://grafychat.com/ ) but: - They are closed source and cost $ - They are ugly (subjective) or clunky - They do not support merging the branches back So I built Bushchat: an opensource, browser-based tool that turns LLM conversations into a tree structure, allowing you to expand and merge back when needed. This is basically LLM 'thinking' but with manual thread control, which increases the reliability of steering the azimuth of the thought process, and efficiently work on context. In the future I envision that making this a collaborative tool where users could share and work together on live data trees or part of their context with each other could increase the productivity in heavy-AI reliant teams. It's free, hosted on gh-pages and you can access it https://bushchat.xyz/ or run it yourself https://github.com/srakai/bushchat Everything is DOM based, there is no server, but you need your API key or local LLAMA with OpenAI compatible API. Also disclaimer: There is posthog analytics, which is used to collect solely usage metrics (gh-pages does not facilitate that). Messages, API keys, responses, chat names are out of scope ( https://bushchat.xyz/privacy ). Happy to hear your thoughts!

Visit product View on Hacker NewsDec 30, 2025

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

85%85% 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: efficiently, compatible · Missing: supports, reddit linkedin, podcasting

75%75% 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, ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface, efficient · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

11%11% 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.

Incorrect prediction on native model

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