Ross ROSS = Recommend OSS · open-source software intelligence for agents

bytebot-ai/bytebot

Bytebot is a self-hosted AI desktop agent that automates computer tasks through natural language commands, operating within a containerized Linux desktop environment. observed · 2026-08-28

github.com/bytebot-ai/bytebot · homepage · TypeScript · Apache-2.0 (permissive) · archived observed · 2026-08-28

Health v2 · maintenance only

10/100

  • Activity 41
  • Release rhythm 35
  • Longevity 41

Flags: no_releases archived

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 576
  • days_rel: n/a
  • days_push: 355
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

11089 stars · 1504 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Bytebot is a self-hosted, open-source AI desktop agent that operates a full containerized Linux desktop, using screen vision, mouse, and keyboard to complete tasks described in natural language. It supports multiple LLM providers (OpenAI, Anthropic, Gemini) and exposes an MCP interface, running entirely on your own infrastructure via Docker.

Use cases

  • automate downloading invoices from vendor portals
  • fill out web forms and data entry automatically
  • process PDFs and spreadsheets with AI
  • run multi-app workflows without scripting
  • self-hosted alternative to RPA tools like UiPath
  • give an AI agent its own computer to complete tasks

When to choose

  • you need task automation across arbitrary desktop apps and websites without writing scripts
  • privacy matters and tasks must stay on your own infrastructure
  • UI changes frequently and brittle RPA scripts break
  • you want to use your own LLM API keys with no usage limits

When to avoid

  • you need simple API-to-API integration where a script is cheaper and faster
  • you cannot run Docker or spare container resources
  • tasks require guaranteed deterministic outcomes that LLM-driven agents can't assure
  • you need a fully managed cloud service rather than self-hosting

Facets

application · maturity active

agent-framework computer-vision workflow-automation mcp llm-inference self-hosted artificial-intelligence large-language-models developer-tools self-hosted computer-use desktop-automation agentic-ai rpa containerized-desktop natural-language-automation ai-agents automation docker linux web-server typescript

3 sources

Member repositories

RepositoryRoleHealth v2
bytebot-ai/bytebotmain10

For agents

markdown · JSON · MCP: product_card(name="bytebot-ai/bytebot")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem