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Build Fullstack AI Agent Apps with Gemini and CopilotKit

Job to be done: Build fullstack AI agent applications for content generation and code analysis

🇳🇬 Ways to use this in Nigeria

Ideas to get you started, adapt to your situation.

  • Student

    Build a custom 'Post Generator' AI app as a final year project to help small businesses on Instagram draft engaging product captions, showcasing your fullstack AI development skills.

  • Entrepreneur

    Create a unique 'Post Generator' AI tool for your content creation business, allowing you to rapidly draft tailored social media updates for your niche audience (e.g., financial literacy tips), enhancing your brand's output.

  • 9-5 employee

    As a software engineer, build an internal 'Stack Analyzer' AI tool to help your team quickly understand the tech stack of new or legacy codebases, streamlining onboarding and project assessment.

What this is, in plain English

This is a developer tutorial that builds two AI agents inside one app: a Post Generator that drafts social media content, and a Stack Analyzer that looks at a GitHub project and tells you what technologies it is built with. It is “fullstack”, meaning it covers both the part users see (the frontend) and the behind-the-scenes part that does the work (the backend).

It uses four main pieces: Next.js (builds the frontend), FastAPI (the Python backend), LangGraph (structures the agent’s steps), and CopilotKit (drops a ready-made AI chat interface into the app and connects it to the backend agent). Be honest about the level: this is an advanced, multi-file build, and the full code lives in the source guide and its repository. This page explains how it fits together and the realistic order of building it, so you know what you are taking on and what you can make with it.

What you can use it for

  • A content generator product. A caption or post drafter tuned to a niche (financial tips, product launches) you could sell or use for your brand.
  • A codebase analyzer. A tool that reads a repo and explains its tech stack, handy for onboarding or sizing up legacy projects.
  • Any app with a built-in AI assistant. CopilotKit makes adding an in-app AI chat that can actually do things straightforward.
  • A portfolio piece. A real fullstack AI app is strong evidence of skill for a job or client.
  • Learning fullstack AI development. Building both halves teaches how a frontend, a backend, and an agent connect.

Tools you need

  • Google Gemini (freemium): the AI model that does the reasoning and writing.
  • CopilotKit (freemium): an SDK (a code toolkit) that adds an AI chat interface to your app and wires it to your agent.
  • LangGraph (free): structures an agent as a series of steps (“nodes”), like gather context, then generate.
  • Next.js (free): builds the frontend (the pages and interface) and its API routes.
  • FastAPI (free): the Python backend that hosts the LangGraph agents.

How it actually works

The shape is: a Next.js frontend talks to a Python (FastAPI) backend that runs the LangGraph agents, with CopilotKit connecting the two. The realistic order:

  1. Set up the frontend. Start a Next.js project and add the CopilotKit packages (@copilotkit/react-core, @copilotkit/runtime, @copilotkit/react-ui). Wrap the app in the CopilotKit provider so the AI chat is available everywhere.
  2. Build the Post Generator agent (backend). In your Python agent/ folder, define a LangGraph workflow with steps to gather context (for example a web search) and then generate the post with Gemini.
  3. Expose it through FastAPI. Register that agent in your FastAPI app as an API endpoint (a web address your frontend can call).
  4. Build the Post Generator UI. Add a page in Next.js with CopilotKit’s chat components so users can type a topic and see the drafted post.
  5. Build the Stack Analyzer the same way. A LangGraph workflow that pulls a repo’s metadata, README, and code files, then asks Gemini to infer the tech stack; expose it via FastAPI and give it its own UI page that takes a GitHub URL.
  6. Connect the frontend to the backend. In your Next.js API route, forward requests from the UI to the right FastAPI endpoint.
  7. Run and test. Start both servers (Next.js and FastAPI), give the Post Generator a topic, and give the Stack Analyzer a GitHub URL to confirm each works.

Words you’ll see, explained

  • Fullstack: covering both the frontend (what users see) and the backend (the behind-the-scenes logic).
  • Frontend / backend: the visible app versus the server that does the heavy work.
  • SDK: a code toolkit that gives you ready-made building blocks (CopilotKit is one).
  • LangGraph: a library for laying out an agent as connected steps, called nodes.
  • Node: one step in that agent workflow (gather context, generate, and so on).
  • API endpoint: a web address on the backend that the frontend calls to get work done.
  • Next.js / FastAPI: the frameworks for the frontend and the Python backend here.

Original source

Based on a DEV Community guide by Anmol Baranwal showing how to build fullstack AI agent apps by combining Gemini, CopilotKit, LangGraph, Next.js, and FastAPI.

Notes & variations

  • Do you even need all this? For a simple one-off generator, Google AI Studio’s no-code Build is far quicker. This stack pays off when you want a real, custom app with an embedded assistant that takes actions.
  • Free-tier tip: Gemini and CopilotKit have free tiers, and Next.js and FastAPI run free locally, so you can build the whole thing at no cost.
  • Common pitfall: mis-wiring the API routes between Next.js and FastAPI. Double-check the endpoint URLs and the request and response shapes if you get connection errors.
  • Tip for better results: for sharper stack analysis, have the agent parse specific config files directly (like package.json or requirements.txt) when gathering context.

Keep going

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