How to Build a Stateful AI Agent with FastAPI, LangGraph, and PostgreSQL.
The article discusses how to build a production-ready AI agent using FastAPI, LangGraph, and PostgreSQL. It highlights the challenges of stateless APIs in AI systems and proposes a stateful architecture to maintain context and improve performance. The author provides insights into implementing this architecture to ensure reliability and scalability in AI applications.
- ▪Stateless APIs can lead to performance issues in AI systems due to their inability to maintain context over time.
- ▪LangGraph offers a stateful architecture that allows for persistent state management and improved AI orchestration.
- ▪Using FastAPI with asynchronous capabilities can enhance server performance during high traffic.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3939917) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Shahzaib S Posted on May 19 How to Build a Stateful AI Agent with FastAPI, LangGraph, and PostgreSQL. #ai #python #langgraph #agents Your AI demo worked perfectly in development. You opened a local notebook, wrote a clean prompt wrapper, and watched the model respond beautifully to your test queries. It felt like magic. Then production traffic hit. User sessions started losing memory. API latency exploded under concurrent requests.
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