Vitral

Use AI with notes, code, terminals, and files in one practical workflow.
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Freemium, Paid
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Freemium, Paid
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Start a project in Vitral by setting up a workspace for the task you’re doing, then keep everything you need in view at the same time: a model chat for decisions, a notebook for requirements, and an editor or terminal for execution. When drafting content, you can ask an LLM for outlines or rewrites, paste the result into a notebook, and iterate with versions and context saved alongside source notes and reference files. For software work, you can plan in the chat, generate or refactor code in the editor, run commands in the built-in terminal, and keep outputs, errors, and fixes linked to the same workspace so you don’t lose the thread between steps.

Teams use Vitral to review documents and datasets by indexing project material, searching across past work, and pulling relevant snippets back into the current task. If you need to work with images, you can drop in screenshots or visuals to extract details, check UI issues, or produce quick assets through image generation, then store the results next to the related specs and prompts. Longer jobs can be delegated to dedicated agents that handle repeated actions, coordinate multi-step routines, or watch for changes while you focus on higher-level decisions. When workloads require compute, you can spin up managed instances to run web services, experiments, or automation without rebuilding your environment each time. Credits make it straightforward to switch between model providers as needs change, whether you’re comparing outputs, optimizing cost, or selecting the best model for a given stage of work.

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Review Summary

Features

  • Multi-pane workspaces for chat, notes, code, files, and terminals
  • Markdown and structured model output
  • Rich-text notebooks and integrated editor
  • Web-based interactive terminals
  • Image understanding and image generation
  • Search and indexing across projects
  • AI agents for automation and coordination
  • Managed compute instances for running services and tasks
  • Support for multiple LLM providers
  • Credit-based usage

How It’s Used

  • Drafting and revising documents with tracked context
  • Planning, coding, running, and debugging in one workspace
  • Creating and maintaining reusable prompt and knowledge libraries
  • Reviewing and extracting info from screenshots and visuals
  • Generating quick graphics for product or marketing
  • Automating recurring workflows with custom agents
  • Comparing model outputs across providers
  • Running prototypes, scripts, or web services on managed compute

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