Bytebot

Bytebot is an open-source AI desktop agent, with the core idea of "give AI its own computer." Unlike traditional automation tools or browser-only agents, Bytebot runs in a containerized Linux desktop environment and can view the interface through the screen like a person, using the touchpad, keyboard, and screen to perform clicks, scrolling, and keystrokes, thereby completing complex multi-step workflows across multiple applications. Users only need to describe the task in natural language, and Bytebot will launch a brand-new, sandboxed computer to execute it.

[Why choose a desktop agent] Bytebot believes that a desktop agent is the ideal automation solution because it works like a person, so it is universally compatible with any software, without relying on pre-mapped elements or specific APIs.

[Main capabilities]

• Natural language driven: No need to write code or flowcharts; directly enter instructions such as "go to amazon.com and filter by this week's best-selling products."
• Cross-application operation: Collaborate across multiple applications in one workflow, not limited to web interfaces, and can also use desktop applications.
• Built-in tools: Comes with a browser, file system, password manager, terminal, code editor, etc., and can customize and install any Ubuntu Linux application, with Firefox, Thunderbird, VS Code, and common office tools preinstalled.
• Secure login and 2FA: Can open a browser, use password managers such as Bitwarden or 1Password to securely fill in usernames and passwords, and handle two-factor authentication steps.
• Human-agent collaboration: During task execution, users can take over the desktop for control at any time, and then resume letting the agent continue execution.
• Fully auditable: Every operation includes screenshots before and after execution for easy inspection.
• Scale expansion: Can scale from a single agent to hundreds running in parallel.

[Typical use cases]

• Financial operations: Access bank portals (including 2FA), download transaction files, reconcile accounts.
• Customer onboarding: Navigate between CRM, banking, and verification systems.
• HR operations: Collect employee data from various systems and ensure consistency.
• Document processing: Read PDFs, extract data from spreadsheets, process emails.
• Quality assurance: Test applications, reproduce defects, perform visual regression testing.
• Data entry: Fill out forms, transfer information between systems, update databases.
• Web automation: Monitor websites, extract data, handle multi-step workflows.
• Automated development workflows: Open a terminal to scaffold a new Web application, install dependencies and run the development server, launch VS Code to modify code, then use the browser to verify the changes on the local live server.
• Technical research and summarization: Autonomously research technical data online, download and read complex documents such as PDFs, extract key information, and generate structured summary reports.

[Usage and deployment] Bytebot is fully self-hosted on the user's own infrastructure, data does not leave their own servers, and each desktop runs in an independent Docker container, completely isolated from the host system. Users have full control over data, AI provider keys, and security policies, and there are no cloud components unless actively added. Getting started process: Clone the code repository, add the AI provider API key, run the Docker Compose command, then access http://localhost:9992 to start automating. It can run locally with Docker Compose, on Railway, or be deployed on AWS/GCP/Azure. The runtime environment is persistent, and authentication information can be set once and remembered for subsequent tasks.

[Supported AI Providers] Out of the box, multiple AI providers are supported: Anthropic Claude (officially recommended, suitable for complex reasoning and visual understanding), OpenAI GPT models (fast and reliable, suitable for general automation), Google Gemini (an alternative option for diverse use cases), and LiteLLM Proxy (for custom model deployments). Users need to provide the API key for the selected provider.

[Pricing and Licensing] Bytebot itself is completely free and open source, licensed under Apache 2.0, with no Bytebot license fees, subscription fees, or usage limits. Users only need to cover the API costs of the selected AI provider (usually a few cents per task) and the infrastructure costs of running the Docker container (an ordinary server or even local operation can be used).

[Differences from Traditional RPA and Scrapers] Traditional RPA tools require designing flowcharts, writing scripts, and mapping every possible scenario; Bytebot uses AI to understand intent from plain English descriptions, automatically adapts to UI changes, and handles unexpected pop-ups, making it suitable for any application, not just pre-mapped elements. Traditional scrapers tend to break after a website update, while Bytebot's AI vision semantically understands the interface, looking for buttons such as "Add to Cart" rather than specific HTML selectors, so it can continue working after a website changes its layout, styles, or structure.

[Integration with Other Tools] Bytebot can integrate seamlessly with existing setups, for example complementing automation tools such as Puppeteer and Playwright, and automation scripts or extensions can be installed and triggered within the Bytebot environment.

[Operational Limitations] Tasks continue executing until they are completed or need help, and users can intervene at any time. Regarding details such as specific interface language support and mobile apps, official materials are not yet available.