Here's How To Build Fullstack Agent Apps (Gemini, CopilotKit & LangGraph)
Blog post from CopilotKit
On September 23, 2025, Anmol Baranwal and Arindam Majumber discussed the complexities developers face when creating AI agents and introduced a practical solution by building two agents using a full-stack setup. The Post Generator drafts LinkedIn/X content with live web search, while the Stack Analyzer inspects GitHub repositories to generate structured reports. Utilizing a tech stack that includes Next.js for the frontend, FastAPI for the backend, CopilotKit, LangGraph workflows, and Google Gemini, the guide illustrates the architecture and concept of these agents, highlighting the integration of these technologies to streamline the development process. The backend agents are built with FastAPI and LangGraph, incorporating structured-output tools and prompts for generating and analyzing content, and the frontend is developed with Next.js to render the agent's output. The article provides a comprehensive walkthrough of setting up the system, from installing dependencies to defining agent workflows, aiming to simplify the process and enable developers to focus on building scalable AI applications.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Coding Assistant | 5 | 1,248 | 236 | 92 | +16% |
| Multi-agent systems | 4 | 470 | 101 | 50 | +55% |
| Real-time | 4 | 4,881 | 1,155 | 268 | -10% |
| AI Agents | 3 | 3,101 | 601 | 194 | +4% |
| LLM | 2 | 4,410 | 670 | 222 | -3% |
| MCP | 1 | 3,632 | 330 | 137 | -26% |
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