LLMStack

LLMStack is an AI project builder tool. A no-code environment for chaining models from OpenAI, Cohere, Stability AI and Hugging Face with imported data sources, then publishing chatbots and agents with granular sharing permissions.

LLMStack is a no-code platform for assembling generative AI applications, chatbots and agents on top of your own data. It is aimed at teams that want a working AI app without writing a backend: product managers, operations staff, marketers and small engineering groups who would rather wire components together than maintain glue code. The pitch is speed, and the homepage frames the whole product around minutes rather than sprints. The building blocks are model chaining and data import. LLMStack connects to major model providers including OpenAI, Cohere, Stability AI and Hugging Face, and you can chain calls between them so the output of one model feeds the next. On the data side, the platform ingests web URLs, sitemaps, PDFs, audio files, PowerPoint decks, Google Drive and Notion imports, which usually means a retrieval workflow: load source material, connect it to a model, then expose the result through a chatbot or app interface. In practice a typical build starts with a data source, moves to model selection and chaining, and ends with a shareable app. Collaboration is treated as a first-class feature. Apps can be published publicly or restricted to named individuals through a granular permission model, and viewer and collaborator roles let several people edit the same app together. That makes LLMStack closer to a shared workspace than a personal prompt sandbox. Where it fits: lighter than a hand-coded stack on LangChain or similar frameworks, and more oriented toward multi-model chaining and private data than single-purpose chatbot widgets. Buyers comparing it should weigh the no-code abstraction against the control a code framework gives, and check current provider and data connector support directly, since the homepage does not detail limits, pricing or hosting.