Bike4Mind

Bike4Mind is an open-core AI workbench for builders, teams, enterprises, developers, researchers, and regulated industries. It is built around a neutral runtime that routes work through a model router and a single workbench and API, so tasks can move among frontier APIs and open-weight models without re-architecting workflows. The product’s stated aim is continuity when a provider raises prices, revokes access, or removes a model.

Its core features include chat, agents, voice, CLI, RAG, collaboration, images, notebooks, artifacts, and data lakes. The page describes model swapping mid-session and says the workbench treats models as interchangeable peers. It also says the system can run any model, from OpenAI and Anthropic to open-weight models on a user’s own hardware, and that open-weight lanes run via vLLM. Named open-weight models include Qwen, Llama, and DeepSeek. Data lakes are described as supporting DOCX, PDF, images, code files, and more, with vectorized, searchable storage and retrieval into any session.

Bike4Mind also includes artifacts that are versioned like code and rendered in a locked-down sandbox on the user’s machine. The listed artifact types on the page include live React components, in-browser Python, Mermaid diagrams, charts, and chess boards. For longer-running work, it offers agents and quests with tools such as code REPL, search, and MCP, under hard budget guards. Collaboration is supported through shared sessions and team workspaces, and the enterprise features named on the page are SSO and role-based permissions.

It can be started hosted in one click or run inside a user’s own AWS environment, and the page states that data never leaves the user’s AWS in that deployment. Bike4Mind is source-available under BSL 1.1, with Apache-2.0 arriving automatically in two years. The page also says it is an open-core platform and includes a pricing page.