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Basic Information

PostBot 3000 is an open-source example project that demonstrates how to build a streamed-response AI agent and generate artifacts for social posts. The repository includes two main parts: an agent service implemented in Python using LangGraph for AI workflows and FastAPI to expose APIs, and an agent UI built with Next.js and the Vercel AI SDK. It shows end-to-end wiring of a model layer (the README references gpt-4o-mini), state and caching with Upstash Redis, authentication with Clerk, and a TailwindCSS frontend. The project provides a running demo, architecture images, and step-by-step local setup instructions using poetry for the backend and npm/yarn/pnpm for the frontend. It is intended as a hands-on template for developers to study and reuse.

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Features
Open-source full-stack agent example with separate agent-service and agent-ui codebases. Streaming of generated post content as artifacts is demonstrated in UI screenshots and a workflow diagram. Backend uses LangGraph to model AI workflows and FastAPI to serve API endpoints. Frontend is implemented with Next.js and the Vercel AI SDK and styled with TailwindCSS for real-time interactions. The stack references gpt-4o-mini as the model, Clerk for authentication, and Upstash Redis for storage or caching. The README includes runnable local setup steps, .env guidance, and commands for poetry and node package managers. The repository also links to a deployed demo and inspiration sources to aid learning.
Use Cases
This repository helps developers learn how to build and connect an AI workflow backend with a responsive frontend by providing working code and clear setup steps. It illustrates modeling agent behavior with LangGraph, streaming outputs as artifacts to a UI, and exposing functionality via FastAPI. Inclusion of authentication, Redis integration, and a modern frontend stack shows common patterns useful for production-style agents and deployment on Vercel. The documented local run instructions reduce onboarding friction and accelerate prototyping. Developers can reuse the code, inspect the workflow diagram and UI examples, and adapt the agent and integrations to their own models and deployment needs.

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