Flowith

Flowith moves AI conversation onto an infinite canvas. A normal chat tool is one linear thread from top to bottom; Flowith turns each question and answer into a node on the canvas, so you can branch a new question off any node and lay out different lines of thought side by side without overwriting each other.

That format suits divergent work. Brainstorming topics, drafting proposals or digging into a complicated question usually means trying several directions at once; on a canvas they are all visible, and you can have different models answer the same prompt and compare them directly. It has since added an agent mode that plans, searches and produces a complete result on its own across multiple steps.

The cost is a learning curve. If you are used to a chat box, a canvas can feel disorienting at first, and it gets messy as nodes pile up. It suits heavy users willing to adjust how they work, not people who just want to ask one thing and get one answer.

Key features

• Canvas conversations: Every exchange is a node; branch from any node at any time and keep several threads alive.
• Model comparison: Send the same question to multiple models at once and compare the output.
• Agent mode: Give it a goal and it breaks down the steps, gathers material and compiles a result — suited to longer tasks.
• Knowledge base: Upload your own material and have the AI draw on it when answering.
• Varied output: Beyond text, it can generate images, web pages and more.

How to use

1. Go to flowith.io and sign up with email or a Google account.
2. Type your first question on the canvas and pick a model.
3. To try another angle, pull a new branch from the relevant node instead of overwriting the original thread.
4. For a multi-step task you want done independently, switch to agent mode and spell out the goal and the deliverable format.
5. Put frequently used material into the knowledge base and call on it in later questions.

Best for

• Brainstorming and topic selection: several directions laid out side by side for easy comparison.
• Comparing models: same prompt, multiple models, the clearest way to see which suits your task.
• Long research tasks: let the agent gather sources and assemble a report.
• Knowledge management: turn scattered notes into a personal knowledge base you can reuse.

Strengths and limitations

Strengths

• Non-linear canvas interaction that suits divergent thinking and comparing options.
• Many providers’ models in one tool, without hopping between sites.
• Agent mode can handle longer, multi-step tasks.

Limitations

• The canvas takes some getting used to, and busy canvases get cluttered.
• Credits go quickly, especially on agent tasks and premium models; the free allowance is limited.
• Long agent runs are inconsistent and sometimes drift off-topic or come back rough, needing a human to step in.