# Apply Technique: Agentic AI Frameworks: Comparing IDE-based vs. Library-based Approaches
## What This Is
This guide compares two types of AI coding tools: integrated editors like Cursor for daily developer assistance, versus powerful frameworks like LangGraph and CrewAI for building custom, automated agent teams. It helps decide whether to enhance an existing developer workflow or build a specialized, automated system for complex tasks.
Source: https://www.digitalapplied.com/blog/mcp-vs-langchain-vs-crewai-agent-framework-comparison
## 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://www.digitalapplied.com/blog/mcp-vs-langchain-vs-crewai-agent-framework-comparison) 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
- Scan the user's project to understand its size, complexity, and primary goals (e.g., rapid prototyping, daily coding, building a complex pipeline).
- Based on the project analysis, recommend a path: Suggest IDE-based agents (Cursor, Windsurf) for enhancing individual developer productivity, or a framework (LangGraph, CrewAI) for building a custom multi-agent system.
- If the user selects a framework, create a starter 'main.py' file for them. For CrewAI, instantiate a basic 'Crew' with two agents and one task. For LangGraph, set up a simple graph with two nodes and define the entry and exit points.
## 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