PostBot 3000 provides an open-source framework for building AI agents that stream responses and generate artifacts, targeting developers seeking to implement similar AI-powered solutions. The project offers a complete stack for creating and deploying these agents, simplifying the process of integrating advanced AI capabilities into applications.

How It Works

The agent utilizes LangGraph for orchestrating complex AI workflows and FastAPI for building a robust backend API. Responses are streamed to the user interface, mimicking the experience of platforms like Claude, and generated content is presented as artifacts. The architecture separates the agent service from the user interface, promoting modularity and scalability.

Quick Start & Requirements

• Install: Clone the repository, create .env files in agent-service and agent-ui directories with necessary API keys.
• Backend: Navigate to agent-service, run poetry install, then poetry run uvicorn app.main:app --reload.
• Frontend: Navigate to agent-ui, run npm install (or yarn/pnpm), then npm run dev (or yarn/pnpm).
• Prerequisites: OpenAI API key, Python 3.x, Poetry, Node.js, npm/yarn/pnpm.
• Access: Open http://localhost:3000 in your browser.
• Docs: GitHub Repository

Highlighted Details

• Leverages LangGraph for sophisticated AI agent workflows.
• Employs FastAPI for a performant Python backend API.
• Integrates Next.js, TailwindCSS, and Clerk Auth for a modern frontend.
• Streams AI-generated content and artifacts to the user.

Maintenance & Community

The project is maintained by ahmad2b. Community engagement is encouraged via email, LinkedIn, and Twitter. A GitHub star is requested to help with project visibility.

Licensing & Compatibility

The project's code is open-source. Specific licensing details are not explicitly stated in the README, but availability on GitHub suggests a permissive license suitable for most development and commercial use cases.

Limitations & Caveats

The project is presented as a demonstration and may require further configuration or adaptation for production environments. Specific details on scalability, error handling, and deployment best practices are not elaborated upon in the README.