Buda AI

Buda AI is a coding tool. Buda is a cloud workspace for running a coordinated team of AI agents across coding, sales, marketing and operations, with persistent memory, browser automation, shared drives and no local hardware to manage.

Buda is a cloud-native workspace built around the idea of running agents as a company. Instead of a single chatbot, it lets a team set up specialist agents for coding, sales, marketing, operations, finance and research, each with its own role, files and memory. The pitch is that nothing needs to be installed or kept running locally: no dedicated machines, no daemons, no laptop left awake overnight. In practice, work is organized in a workspace with an Organizer that dispatches tasks and isolated, persistent agent computers that carry them out. Each agent has a private Agent Drive for working files, while a shared Space Drive holds team knowledge and final outputs. Agents can build and deploy websites, generate images and video, run commands and install packages in a terminal sandbox, and drive a browser to open pages, extract text, click and capture screenshots. Code changes can be reviewed as diffs and rolled back. Skills and automations turn repeated workflows into reusable procedures, and chat channels can be connected so requests arrive from messaging apps and finished work comes back there. A marketplace offers ready-made agents, skills and whole teams that can be installed and adapted. The product is aimed at small teams, developers, operations staff and creators who want several agents working in parallel without managing infrastructure. Company controls are listed as well: shared workspaces, roles, audit logs, SSO and private deployment options. The model picker on the homepage shows choices such as Claude Sonnet 5 and Gemini 3.1 Flash. Among alternatives, Buda positions itself against locally hosted agent tools such as OpenClaw, trading hardware control for convenience, persistence and reviewability. Compared with general chat assistants, it emphasizes file storage, approval of outputs and coordination across agents rather than single conversations. Teams that need everything on their own hardware may find the cloud-first approach limiting, while those who want a quick start with one workflow and room to expand will find the model straightforward.