techniqueintermediateGeneral AI

Agentic AI Frameworks: CrewAI vs. LangGraph for Orchestration

This guide compares two leading approaches for building automated AI agent teams. CrewAI is best for quickly creating simple, role-based agent systems, while LangGraph offers more powerful, fine-grained control for complex, production-ready workflows. The choice depends on whether the project prioritizes speed and simplicity or robustness and scalability.

AI SETUP PROMPT

Paste into Claude Code or Codex CLI — it will scan your project and set everything up

# Apply Technique: Agentic AI Frameworks: CrewAI vs. LangGraph for Orchestration

## What This Is
This guide compares two leading approaches for building automated AI agent teams. CrewAI is best for quickly creating simple, role-based agent systems, while LangGraph offers more powerful, fine-grained control for complex, production-ready workflows. The choice depends on whether the project prioritizes speed and simplicity or robustness and scalability.

Source: https://agathon.ai/insights/best-ai-consultancies-for-2026-navigating-the-agentic-era

## 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://agathon.ai/insights/best-ai-consultancies-for-2026-navigating-the-agentic-era) 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 user's current project requirements to determine if a rapid prototyping framework (CrewAI) or a production-grade state machine (LangGraph) is more suitable for their agentic workflow needs.
- If CrewAI is chosen for rapid development, scaffold a new Python project by installing the `crewai` and `crewai[tools]` packages, then generate a `main.py` file with boilerplate for defining a simple two-agent crew (e.g., a researcher and a writer).
- If LangGraph is selected for complex, durable workflows, install `langgraph`, `langchain`, and a compatible model SDK. Then, generate a starter script that defines a graph with at least two nodes and a conditional edge to demonstrate state management.

## 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

3,241 charactersCompatible with Claude Code & Codex CLI

MANUAL SETUP STEPS

01Analyze the user's current project requirements to determine if a rapid prototyping framework (CrewAI) or a production-grade state machine (LangGraph) is more suitable for their agentic workflow needs.
02If CrewAI is chosen for rapid development, scaffold a new Python project by installing the `crewai` and `crewai[tools]` packages, then generate a `main.py` file with boilerplate for defining a simple two-agent crew (e.g., a researcher and a writer).
03If LangGraph is selected for complex, durable workflows, install `langgraph`, `langchain`, and a compatible model SDK. Then, generate a starter script that defines a graph with at least two nodes and a conditional edge to demonstrate state management.

FIELD OPERATIONS

Automated Market Research Crew

Build a CrewAI system with a `ResearcherAgent` that scrapes financial news sites for a given stock ticker and a `FinancialAnalystAgent` that summarizes the findings, performs sentiment analysis, and generates a daily investment brief.

Dynamic Customer Support Routing Graph

Create a LangGraph application that takes a customer support ticket, uses a classifier node to determine the topic (e.g., 'billing', 'technical', 'sales'), and routes the ticket through different processing paths with human-in-the-loop approval steps before generating a final response.

STRATEGIC APPLICATIONS