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LabFrame is an agentic AI platform for research teams. Each agent is an AI researcher that owns a dedicated computer — an isolated cloud VM with its own filesystem, installed packages, shell, and tools. You describe what you need, and the agent works autonomously: searching the web, writing code, generating documents, building websites, and delivering finished artifacts. It is not a chatbot. It is an AI researcher with its own machine.

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Who is it for?

LabFrame is built for R&D teams, university labs, and national labs — anyone whose work involves literature reviews, data analysis, technical writing, course material, or building prototypes. Instead of doing the tedious parts yourself, you delegate them to an AI agent that works on a real computer and hands you the finished result.

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Core ideas

Dedicated computer per agent

Each agent gets its own isolated cloud VM with a full Linux environment. Files, packages, and state persist across sessions — nothing resets when the conversation ends.

Automated research

Set a goal with acceptance criteria and the agent works autonomously — searching, reading, analyzing, and writing until the job is done. Check back when it’s finished.

Real deliverables

PDFs, datasets, spreadsheets, slide decks, deployed websites. Artifacts you can download, share, and use — not just text in a chat window.

Team collaboration

Invite collaborators into the same agent and project. Everyone shares the same computer, conversation, and results in real time.

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How it works

  1. You create an agent. LabFrame provisions a dedicated cloud VM for it.
  2. You describe what you need: “Build a literature review on battery degradation models with a comparison table.”
  3. The agent works on its own computer — searching the web, reading papers, writing LaTeX, compiling to PDF — and presents you with the finished file.
  4. You click the file link in chat to preview it, or open the agent’s workspace to browse everything it created.
  5. You send a follow-up: “Add a section on solid-state electrolytes and regenerate.” The agent iterates on the same files in the same workspace.
For larger tasks, use Goal Mode to define acceptance criteria and let the agent run autonomously across multiple steps. It plans, executes, checks its work, and iterates until the goal is met — all on its own machine, without you watching.

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Next steps

Quickstart

Create your first agent and get a deliverable in under two minutes.

Agents

How agents work — dedicated VMs, system prompts, and models.

Goal Mode

Delegate complex research tasks and let the agent work autonomously.

Workspace

Browse, preview, and manage the files on your agent’s computer.