# Apply Technique: AI Agent Frameworks: When to Use CrewAI vs. LangGraph
## What This Is
To build autonomous AI agents for your business, two main frameworks dominate: CrewAI and LangGraph. Use CrewAI for rapidly prototyping team-based workflows like content creation, and choose the more powerful LangGraph for building complex, resilient, and production-ready applications like data analysis pipelines.
Source: https://www.turing.com/resources/ai-agent-frameworks
## 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.turing.com/resources/ai-agent-frameworks) 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 requirements and prompt them to choose between a 'Rapid Prototype' (recommending CrewAI) or a 'Complex/Production' workflow (recommending LangGraph).
- Based on the user's choice, initialize a new project: for CrewAI, install the 'crewai' and 'crewai-tools' packages and generate a main.py with a basic two-agent (e.g., Researcher, Writer) crew structure. For LangGraph, install 'langgraph' and generate a boilerplate file with a state graph, agent nodes, and conditional edges.
- Connect the chosen framework to the user's preferred LLM by locating their API key (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY) in the workspace's .env file. Instantiate the LLM client (e.g., ChatOpenAI, ChatAnthropic) and pass it to the agent configuration.
## 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