Build a Full-Stack LLM App in Python

.. with just a prompt

In today’s newsletter:

  • Build a Full-Stack LLM app in Python

  • RAGLite: Python toolkit for building RAG apps with PostgreSQL or SQLite

  • AI Terminal Agent with MCP Support

Reading time: 2 minutes.

I built an LLM app in Python (both frontend and backend) with just a prompt!

(didn’t write a single line of code)

Reflex is a Python library for building full-stack web apps in pure Python.

With the new Reflex Build, you can now create internal tools and dashboards using AI with just a single prompt in Python.

Here's what I built:

  1. Described the app I wanted, shared a screenshot of Google AI Studio, and asked it to recreate it.

  2. Reflex generated the entire frontend and backend in Python, with a live preview.

  3. Added another prompt to integrate an OpenAI model, connect the backend, and complete the full workflow.

Here’s why this is a game changer:

Most prompt-to-app tools are designed to generate apps using JavaScript or TypeScript stacks, not Python.

Reflex Build handles both frontend and backend in Python, making it accessible for Data Scientists, ML Engineers, and AI Engineers to build dashboards and AI apps in just Python.

Python toolkit for building RAG apps with PostgreSQL or SQLite!

Introducing RAGLite, a toolkit for building RAG Apps using PostgreSQL or SQLite in just pure Python.

Key Features:

  • Choose any LLM provider, including local llama-cpp-python models

  • Choose either PostgreSQL or SQLite as a keyword & vector search database

  • Choose any reranker with rerankers, including multilingual FlashRank as the default

It’s 100% Open Source

Warp (AI Terminal Agent) just added MCP support, allowing your terminal to access real-time external context.

You can connect a GitHub MCP server to access, analyze, and interact with code inside the terminal, letting your AI terminal reference live external context while assisting you with coding.

It is free and easy to set up.

That’s a Wrap

That’s all for today. Thank you for reading today’s edition. See you in the next issue with more AI Engineering insights.

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