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Open Computer Use and open-terminal solve the same core problem — give an LLM a place to run code — with fundamentally different architectures. This is not a ranking; it’s a map of trade-offs.
We respect the open-terminal team. If you spot an inaccuracy, open an issue — we want this fair and factual.
Claude.ai and OpenAI Operator are cloud-only, not self-hosted — the overview table has them for reference.

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Overview

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Architecture and isolation

Open Computer Use creates a fresh Docker container per chat session. If the AI breaks something — installs wrong packages, corrupts files, fills disk — only that chat is affected. Next chat starts clean. Containers are GC’d after idle timeout; per-container limits (2 GB RAM, 1 CPU default) are enforced. open-terminal runs a single container (or bare-metal process) shared across sessions. Multi-user mode creates OS-level accounts with isolated home directories, file-ownership enforcement, and path validation. For container-per-user, the separate Terminals project orchestrates dedicated containers. Why it matters. Non-technical users + agent executing arbitrary code is the worst case for a shared env. Container-per-chat makes every session disposable. Trade-off. 2 GB per container is a ceiling, not an allocation — idle or light tasks use very little. Chromium and LibreOffice can push the ceiling. open-terminal is lighter but shares kernel and network between users.

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MCP tools — different design philosophies

Open Computer Use — 5 high-level tools: open-terminal — 15+ fine-grained tools across files, processes, and notebooks. Trade-off. Few powerful primitives (the AI uses bash_tool for search, process mgmt, everything) vs. fine-grained operations that don’t require shell knowledge.

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Security

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What Open Computer Use offers that open-terminal doesn’t

  • Document creation skills — 13 built-in with scripts and templates for pptx/docx/xlsx/pdf
  • Skill auto-injection — structured instructions in the system prompt + per-user skills via Settings Wrapper
  • Live shared browser — Playwright + CDP, AI automates via CDP, user watches/interacts in the same Chromium
  • Claude Code sub-agent — model selection, session resume, cost tracking, auto-configured MCP servers
  • Server-side file preview — renders from any MCP client, not tied to Open WebUI
  • Container-per-chat isolation
  • Persistent terminal via ttyd + tmux; full escape hatch
  • Pre-installed stack (~180 packages: LibreOffice, Playwright, Tesseract, OpenCV, ImageMagick, GitLab CLI, fonts, ML libs)
  • Vision AI skill
  • Multi-client MCP tested with Open WebUI, n8n, OpenAI Agents SDK, LiteLLM
  • Container resurrection — saved metadata recreates GC’d containers with same volumes/env
  • Smart tool output — bash_tool streams with heartbeats, caps output, semantic exit codes

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What open-terminal offers that we don’t

  • Jupyter notebooks — per-session kernels
  • Bare-metal mode — pip install, no Docker
  • Port proxy — HTTP reverse-proxy to localhost services
  • Lightweight image variants (slim ~200 MB, alpine ~100 MB)
  • Document text extraction as API endpoint (11 formats)
  • Process stdin — send input to running processes
  • Session CWD tracking
  • Runtime package install via env vars
  • Docker-in-Docker — Docker CLI + Compose + Buildx pre-installed
  • TOML config files
  • Per-process JSONL logs with retention
  • Simpler setup — single docker run

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When to choose what

Open Computer Use — workflows that need browser automation, document creation, or Claude Code sub-agent. Container-per-chat isolation. Multi-client MCP. open-terminal — terminal-first workflows, especially in Open WebUI. Native integration, minimal setup, Jupyter, port proxy, image variants from ~100 MB. Both together. Open WebUI supports connecting to both — pick per task.