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Launch

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Basic Information

Launch is an open-source full-stack platform designed to assemble, configure, and deploy autonomous AI agents from a browser-based interface. The repository provides a developer-oriented toolkit to name and configure custom agents that pursue user-defined goals by generating tasks, executing actions, and learning from outcomes. It bundles an automatic setup CLI that provisions environment variables, a database, a FastAPI backend, and a Next.js frontend so developers can bootstrap a working agent environment locally. The project integrates LLM tooling and common web and data technologies to accelerate building goal-driven autonomous agents. The repo is presented as a beta effort with public documentation, a roadmap, and community contribution scaffolding to guide development and deployment.

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App URL
https://github.com/AIFlow-agent/Launch
Github Repository
https://github.com/AIFlow-agent/Launch/blob/main/README.md

App Details

Features
Launch includes an automatic setup CLI that configures environment variables and API keys and provisions a MySQL-compatible database. The codebase contains a FastAPI backend and a Next.js 13 TypeScript frontend. Authentication is provided via Next-Auth.js. The stack uses Prisma and SQLModel as ORM layers and Planetscale for hosting compatibility. UI is styled with TailwindCSS and HeadlessUI. Schema validation uses Zod on the frontend and Pydantic on the backend. LLM tooling is integrated via LangChain. The README documents prerequisite tools including Node.js, Git, Docker and mentions optional integrations for OpenAI, Serper, and Replicate API tokens. The repo also includes platform and db folders, setup scripts for macOS/Linux and Windows, public docs, a roadmap, and contributor/sponsor acknowledgments.
Use Cases
Launch reduces the friction of standing up autonomous agent projects by providing an opinionated, end-to-end developer template that includes frontend, backend, database, authentication, ORM, and LLM tooling. The bundled CLI and setup scripts automate environment and service configuration so teams can focus on agent design and prompts instead of infrastructure. Integration with LangChain and support for common model APIs lets developers prototype goal-driven agents that generate and execute tasks. The full-stack template and included docs and roadmap make it easier to iterate, contribute, and deploy locally or to cloud database providers, which speeds experimentation and reduces time to a working autonomous agent demo or prototype.

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