Practical frameworks for building production AI applications.
Applied AI frameworks bridge the gap between raw model APIs and production-ready applications. Instead of building everything from scratch β routing logic, UI, memory management, deployment β these frameworks provide opinionated structures that handle the common patterns so you can focus on your specific use case.
The landscape spans from visual builders (Dify, Flowise) where non-developers can create AI workflows by dragging nodes, to developer frameworks (Vercel AI SDK, FastAPI patterns) that give programmatic control with production-grade tooling. Choosing the right framework depends on your team's skills, deployment target, and how much customization you need.
Dify
Open-source visual AI workflow builder. Drag-and-drop interface for building chatbots, RAG apps, and agent workflows. Supports multiple LLM providers, built-in knowledge base, and API deployment.
n8n
Workflow automation platform with powerful AI nodes. Connect LLMs to 400+ integrations (Slack, email, databases, APIs). Self-hostable, event-driven, and ideal for business process automation with AI.
Flowise
Visual LangChain builder β create LLM chains and agents by connecting nodes in a browser UI. Lower barrier to entry than coding LangChain directly. Good for prototyping RAG and agent workflows.
Vercel AI SDK
TypeScript-first framework for building AI-powered web applications. Handles streaming, tool calling, multi-step interactions, and generative UI in React/Next.js. The standard for AI web apps.
FastAPI + LLM Patterns
Python backend patterns: async streaming endpoints for LLM responses, WebSocket connections for real-time chat, dependency injection for provider switching, and structured output validation with Pydantic.
Streamlit & Gradio
Rapid prototyping frameworks for AI demos. Streamlit creates data apps with Python scripts. Gradio builds ML model interfaces with automatic API generation. Both deploy to the cloud in minutes.
LangServe & LangGraph
LangChain's deployment and stateful workflow tools. LangServe deploys chains as REST APIs. LangGraph builds complex multi-step agent workflows with state management, cycles, and human-in-the-loop.
CrewAI & AutoGen
Multi-agent orchestration frameworks. CrewAI assigns roles and tasks to collaborating agents. AutoGen (Microsoft) enables conversational agent teams. Both simplify building agent systems.
Low-Code AI Platforms
Platforms like Zapier AI, Make.com, and Bubble with AI plugins. Enable non-developers to build AI-powered workflows. Trade flexibility for speed and accessibility.
Choosing a Framework
Non-technical team: Dify/Flowise. Automation: n8n. Web app: Vercel AI SDK. Python API: FastAPI. Prototype: Streamlit/Gradio. Complex agents: LangGraph. Always prototype before committing to a framework.
Visual Workflow BuilderTool that lets you create AI applications by connecting nodes in a drag-and-drop interface rather than writing code.
StreamingDelivering LLM responses token by token in real-time rather than waiting for the complete response.
LangGraphLangChain's framework for building stateful, multi-step agent workflows with cycles and branching logic.
Low-Code AIPlatforms enabling AI application development with minimal programming through visual interfaces and pre-built components.