Bytebot
by Bytebot · Open-source AI agent with its own containerised Linux desktop
BenchRank score
Screenshots of Bytebot
Homepage
Overview
Bytebot is an open-source desktop agent that runs in a containerised Linux desktop with a browser, terminal, file system, code editor and password manager. You describe a task in plain English and it reads the screen, then clicks and types through the UI across applications. It runs locally with Docker compose, on Railway, or on AWS, GCP or Azure.
- Best for
- Teams automating multi-app desktop tasks on their own infrastructure without writing scripts.
- Pricing
- Bytebot itself is free under the Apache 2.0 licence, with no listed licence or subscription fees; the stated costs are your AI provider's API fees (described as a few cents per task) and the infrastructure you run it on.
- Runs on
- Self-hosted
Strengths and trade-offs
Strengths
- Open source under Apache 2.0, self-hosted via Docker compose
- Full Linux desktop: browser, terminal, VS Code, file system
- Works with Anthropic, OpenAI, Google Gemini or LiteLLM proxy
- Screenshots before and after every action for inspection
Trade-offs
- No hosted option; you run and pay for the container infrastructure
- Requires your own AI provider API key, charged per task
- Applications limited to what runs on Ubuntu Linux
- Support is community GitHub and Discord, not a paid SLA
How Bytebot markets itself
A structured read of the promise, proof and page design on Bytebot’s captured homepage.
Homepage capture
“Desktop agents that use computers like a human — at cloud scale.”
- Angle: AI-native
- Hero: Product screenshot
Pricing
Published plans and prices from Bytebot’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Bytebot pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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Is this your product?
Claim Bytebot to manage its profile. Claiming lets you suggest edits to the descriptive fields — it never changes scores or rankings.