techniqueadvancedGeneral AI

Multi-Agent AI Frameworks: A Landscape Comparison of IDEs and Backend Systems

Instead of using one single AI assistant, new frameworks allow you to create teams of specialized AI agents that work together to solve complex problems. This guide compares popular options, from agentic coding assistants built directly into your editor (like Cursor) to powerful backend systems (like CrewAI and LangGraph) for building sophisticated, automated workflows.

AI SETUP PROMPT

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

# Apply Technique: Multi-Agent AI Frameworks: A Landscape Comparison of IDEs and Backend Systems

## What This Is
Instead of using one single AI assistant, new frameworks allow you to create teams of specialized AI agents that work together to solve complex problems. This guide compares popular options, from agentic coding assistants built directly into your editor (like Cursor) to powerful backend systems (like CrewAI and LangGraph) for building sophisticated, automated workflows.

Source: https://getstream.io/blog/multiagent-ai-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://getstream.io/blog/multiagent-ai-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 to identify the primary programming language, project size (e.g., monorepo vs. microservice), and any existing AI tool configurations (e.g., LangChain usage in requirements.txt or package.json).
- Based on the project analysis, recommend a suitable agentic framework. For complex, multi-step backend processes, suggest LangGraph or CrewAI. For enhancing individual developer productivity in a VS Code environment, suggest evaluating Cursor or Windsurf.
- If the user chooses a backend framework like CrewAI, create a new directory named `ai_crew`, generate a `main.py` with boilerplate for a two-agent crew (e.g., 'researcher' and 'writer'), and create a `requirements.txt` file with `crewai` and `crewai-tools`.

## 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

3,233 charactersCompatible with Claude Code & Codex CLI

MANUAL SETUP STEPS

01Scan the user's project to identify the primary programming language, project size (e.g., monorepo vs. microservice), and any existing AI tool configurations (e.g., LangChain usage in requirements.txt or package.json).
02Based on the project analysis, recommend a suitable agentic framework. For complex, multi-step backend processes, suggest LangGraph or CrewAI. For enhancing individual developer productivity in a VS Code environment, suggest evaluating Cursor or Windsurf.
03If the user chooses a backend framework like CrewAI, create a new directory named `ai_crew`, generate a `main.py` with boilerplate for a two-agent crew (e.g., 'researcher' and 'writer'), and create a `requirements.txt` file with `crewai` and `crewai-tools`.

FIELD OPERATIONS

Automated Code Refactoring Crew

Build a multi-agent system using CrewAI. One agent ('CodeScanner') identifies code smells and anti-patterns in a codebase. A second agent ('RefactorExpert') receives the findings and generates refactored code snippets. A third agent ('TestGenerator') writes unit tests for the refactored code to ensure functionality is preserved.

Dynamic API Documentation Generator

Use LangGraph to create a stateful agent that introspects a web framework's routes (e.g., FastAPI or Express.js). The agent graph would have nodes for: 1) parsing route definitions, 2) analyzing request/response models, 3) generating OpenAPI documentation, and 4) creating human-readable Markdown docs with code examples.

STRATEGIC APPLICATIONS