Argumentree.AI provides Collective AI Reasoning through structured argumentation. Several large language models take turns building one hierarchical pro/con argument tree for a yes/no question: models with web search go first, the first model builds the tree, each next model reviews the arguments already on it and adds its own, and then every model rates every argument. This goes far beyond flat pro/con lists: the hierarchical structure and the ratings reveal which arguments have consensus (most models agree), which are contested, and where evidence is thin. Every argument carries a stance (pro or con), a category label, its evidence, the name of the model that wrote it, and every model's rating. The 4-step process is: Ask (pose a yes/no question), Argue (the models build and review the tree in turn), Rate (every model rates every argument), Consensus (see what they agree on). When most models agree, you have higher confidence. When they disagree, you've found a genuinely contested question. Argumentree.AI is available at argumentree.ai with a free tier for evaluation.
Go beyond flat pro/con lists. Several AI models build and review one hierarchical argument tree with evidence, then rate every argument to reveal the complete landscape of any debate.
Try Structured ArgumentationArguments are organized as parent-child trees. Top-level arguments address the main question; child arguments support or counter their parent. You can drill into any branch to see the full chain of reasoning.
Every argument carries an explicit stance — for or against the proposition. This makes it instantly clear which side of the debate each claim supports, even deep in the argument tree.
Each argument carries its evidence, and the web-search models attach the sources they found on the live web, so you can check a claim yourself.
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AI-powered structured argumentation uses several large language models to build a formal argument tree with pro and con positions, each backed by evidence. Unlike free-form AI chat, arguments are organized hierarchically — every claim has a parent, a stance (for or against), its evidence, and the model that wrote it. This structure makes it possible to see the complete landscape of a debate at a glance.
When you pose a yes/no research question, the models take turns: models with web search go first, the first model builds the tree, and each next model reviews the arguments already on it and adds its own for and against the proposition. Top-level arguments address the main question directly, and child arguments support or counter their parent. Each argument includes a stance (pro or con), a category label, its evidence, and the model that wrote it — and then every model rates every argument.
Yes. The structured argument trees with evidence are well-suited to exploring hypotheses and mapping the arguments around a research question. Every model rates every argument, which shows you where the models agree, where they split and where evidence is thin. Share the link to a published run with colleagues; check the sources before you cite anything.
Three key differences: (1) Several models from different vendors work on the same tree, each reviewing the arguments already there and adding its own, so one model's blind spots are less likely to go unnoticed. (2) Arguments are hierarchically structured, not flat lists — child arguments challenge or support parent claims. (3) Every model rates every argument, so you see agreement and disagreement across models rather than a single model's self-assessment.
Arguments are categorized by the AI models based on the topic domain — for example, economic, legal, ethical, environmental, technical, or social. Categories are labels that help you navigate large argument trees.
Build structured pro/con trees with evidence from several AI models — free to start.
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