# Apply Technique: Comparison: AI Code Editors (Cursor, Windsurf) vs. Agentic Frameworks (CrewAI, LangGraph)
## What This Is
This document compares two types of AI tools for developers. The first type, like Cursor and Windsurf, are code editors that act as smart assistants to help write code faster. The second type, like CrewAI and LangGraph, are frameworks for building automated teams of AI 'workers' to complete complex tasks, like research or data analysis.
Source: https://uibakery.io/blog/cursor-vs-windsurf
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://uibakery.io/blog/cursor-vs-windsurf) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Analyze the current project's `README.md` and source files to determine if the user's primary goal is to enhance their interactive code editing experience or to build a new, standalone multi-agent autonomous workflow.
- If the goal is a multi-agent workflow, compare the project's requirements against the characteristics of CrewAI (fast prototyping, high-level configuration) and LangGraph (fine-grained control, stateful execution) to recommend the most suitable framework.
- Based on the user's selected framework, scaffold a new project directory by either installing `crewai` and creating a `main.py` with a basic `Crew` and `Task` structure, or by installing `langgraph` and creating a `graph.py` with a sample `StatefulGraph` and a few nodes.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it