Dialogue with an LLM as a Tree, Not a Linear Chat

In the process of working with large dialogues and complex interaction scenarios with LLMs, it quickly becomes apparent that a linear representation of conversation stops being convenient. While a dialogue is short, the familiar “list of messages” model works reasonably well. But as complexity grows — when alternative formulations, hypotheses, clarifications, and returns appear — a linear chat begins to hinder rather than help.

When a dialogue branches, returning to important decisions, clarifications, or key conclusions requires either constant scrolling back or keeping a large amount of information in the user’s memory. At this point, the dialogue stops being a working tool and starts to resemble a poorly structured log.

One critically useful element in such systems is the ability to explicitly mark individual messages — using color, tags, or other visual markers. This makes it possible to highlight significant nodes in the dialogue and quickly return to them later without reviewing the entire conversation history. This is especially important in long sessions, where the dialogue is used not as a chat, but as a workspace for reasoning, analysis, and decision-making.

Equally important is the ability to save the content of individual messages as notes, separated from the main conversational flow. Such notes can capture conclusions, ideas, or intermediate results and be used independently of how the conversation continues to evolve. The dialogue remains dynamic, while the knowledge extracted from it becomes structured and accessible.

I implemented a prototype and tested this approach in practice. It turned out that the ability to return to a specific point in the dialogue, continue the conversation from there, and pass to the LLM only the conversation history up to that selected point fundamentally changes the quality of the model’s responses. The LLM begins to maintain context in the way the user understands it, rather than in the way it happened to form within a linear sequence of messages.

The screenshot below shows an example of a dialogue with its history represented as a tree of messages.

It is important to emphasize that this is not just another UI feature. This approach changes the very context of interaction between the user and the LLM. The user gains the ability to consider the subject of discussion from multiple perspectives, without having to keep all of them in memory at once and without scrolling back to find the necessary message.

In practice, two key shifts occur. First, the user gains explicit control over which exact dialogue history is passed to the model at a given moment. Second, cognitive load is significantly reduced: attention is focused on a specific branch of reasoning rather than on trying to remember what was discussed earlier. It is precisely this combination — accurate context selection and reduced cognitive load — that produces a qualitatively new effect when working with LLMs.

Moreover, a tree-based representation can be applied not only to messages, but also to AI agent decisions. This opens the possibility — even post hoc — to return to individual decision steps and change the agent’s subsequent behavior without restarting the entire process from scratch. For agentic systems, where transparency, reproducibility, and control are critical, this approach appears particularly promising.

Of course, in simple scenarios users will likely prefer to remain with the familiar interaction model of “list of dialogs — dialog,” without an additional tree layer. However, when working on more complex tasks — for example, when discussing multiple hypotheses or alternative solutions — such an interface element as a message tree is likely to be perceived not as unnecessary complexity, but as a natural and highly useful tool.

Based on my experience, this type of functionality quickly stops feeling experimental and starts to feel like a missing component of current LLM clients. As agentic AI continues to evolve and requirements for controllability, reproducibility, and responsibility increase, representing dialogue as a structure rather than a linear chat is highly likely to become a de facto standard for advanced LLM interaction scenarios.