StartupOS

What is StartupOS?

StartupOS is a single Next.js application that turns a one-line startup idea into a set of deliverables produced by an orchestrated pipeline of LLM calls. It generates strategy documents, branding, a working prototype, and quality scores, with an option to run inference on local GPUs or via cloud API mode.

The repository is structured as a demo-oriented “pipeline in one app,” where the same codebase can be used across inference environments by changing configuration (for example, local GPU versus cloud inference variables).

Key Features

• One-line idea input that drives a multi-step LLM pipeline: The app accepts a short idea and runs it through successive LLM calls to produce strategy, branding, prototype output, and evaluation scores.
• Orchestrated workflow with a single DAG-style chain: The pipeline is implemented as a workflow orchestrator that chains agents/codegen/evaluation and then triggers regeneration steps.
• Structured outputs using Zod schemas: Each “dimension” in the evaluation is described as a combination of an LLM call and a Zod schema, and the code generation flow includes file parsing and build looping.
• Quality scoring with a multi-dimension rubric: Brain evaluation uses LLM scoring prompts with multiple “dimensions,” plus a “cortical map”/rubric approach described in the repo overview.
• Optional infrastructure depending on runtime environment: The core demo can run without optional components; GPU telemetry parsing, queue-based scaling (BullMQ/Redis), and additional servers (TRIBE v2 Python) are described as optional paths.
• Streaming and persistence in the demo app: The app includes SSE streaming and persistence to a .data/ directory as part of its standard demo behavior.

How to Use StartupOS

• Clone the repository and follow the setup instructions in the included README (the repo contains configuration files such as package.json, next.config.ts, and an .env.example).
• Provide the required environment configuration for the inference mode you want to run (local GPU option or cloud API mode). The repo overview notes that the cloud “demo mode” uses the same source tree with different inference environment variables.
• Start the Next.js app and enter a one-line startup idea.
• Run the pipeline end-to-end to generate strategy/branding/prototype outputs and quality scores; the workflow supports streaming (SSE) and persists intermediate/final results as part of the demo flow.

Use Cases

• Hackathon-style feasibility demos: Teams can use the repo’s “single Next.js app + orchestrated pipeline” pattern to rapidly demonstrate idea-to-prototype flow under tight time constraints.
• Local GPU evaluation: If you have NVIDIA DGX Spark hardware available, you can run the pipeline on local GPUs for a self-contained demo environment.
• Cloud-based judge-friendly demos: For reviewers without access to local GPU hardware, you can run the same codebase in cloud mode by setting inference-related environment variables.
• Prototype iteration with automated review loops: The workflow includes codegen and evaluation with regeneration steps, which is suited to exploring multiple iterations of a generated prototype based on scoring.
• App-embedded scoring rubric experiments: Because the evaluation is implemented as multiple dimensions (each tied to an LLM call and a Zod schema), you can study or adjust the rubric boundaries within the pipeline.

FAQ

• Does StartupOS require a message queue (BullMQ/Redis) or a specific hardware setup? No. The repo overview says the core happy path runs inline in the workflow/API, and queues are not a prerequisite for the demo. GPU telemetry and queue-based scaling are described as optional.

• Is the cloud demo a separate, pre-built product? The repo states that the cloud “demo mode” is the same source tree with cloud inference APIs configured via environment variables, not a secretly pre-built second product.

• What kinds of outputs does the pipeline produce? The overview lists strategy documents, branding, a working prototype, and quality scores.

• How are outputs validated or structured? The overview describes “dimensions” as LLM calls paired with Zod schemas, and also mentions file parsing and validation-related behavior in the codegen and evaluation steps.

Alternatives

• Single-agent or chat-based idea-to-prototype workflows: Tools that rely on a conversational LLM without a DAG-style orchestrated pipeline may be simpler but typically provide less structured, repeatable multi-step generation and scoring.
• Low-code workflow automation with LLM steps: Automation platforms can chain LLM calls for drafting/evaluation, but may not produce a tight “single Next.js app” prototype flow with integrated streaming and persistence as described here.
• Open-source multi-agent orchestration frameworks: Frameworks that support multiple agents and tool calls can replicate the multi-step behavior, but differ in whether they ship as a single Next.js demo app with the same end-to-end pipeline structure.
• Local LLM inference UIs focused on chat only: Local UIs can run models on your hardware, but they generally won’t implement the same idea-to-strategy-to-prototype pipeline and rubric-based scoring out of the box.