Choosing Your Stack

Choosing a framework is one of the most consequential early decisions. Use this matrix to narrow your options:

Criteria	Weight	Questions to Ask
Language	High	Is your team Python-first or TypeScript-first?
Model Lock-in	High	Do you need to swap models, or are you committed to one provider?
Multi-Agent	Medium	Do you need multiple agents collaborating?
MCP Support	Medium	Do you want standardized tool integration via MCP?
Production Readiness	High	Are you prototyping or shipping to production?
Community Size	Medium	How important is community support and examples?

Most agent frameworks are Python-first, but TypeScript options are growing fast.

Choose Python if:

• You need the widest selection of frameworks (LangGraph, CrewAI, OpenAI Agents SDK, etc.)
• Your team has data science or ML experience
• You are building backend-only agent services
• You need mature libraries for NLP, embeddings, and vector stores

Choose TypeScript if:

• You are building full-stack web applications with agent features
• You want type safety and better IDE support
• You are already using Next.js, Vercel, or similar web stacks
• You prefer frameworks like Vercel AI SDK, Mastra, or CopilotKit

// TypeScript agent with Vercel AI SDK
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const { text } = await generateText({
  model: openai("gpt-4o"),
  system: "You are a helpful assistant.",
  prompt: "Explain AI agents in one paragraph.",
});

Not every problem needs multiple agents. Here is how to decide:

Single-Agent (Start Here)

• Task is well-defined with a clear scope
• One set of tools is sufficient
• Simpler to debug, test, and deploy
• Frameworks: OpenAI Agents SDK, PydanticAI, Smolagents

Multi-Agent (When Needed)

• Task requires distinct expertise areas (researcher + writer + reviewer)
• You need parallel execution of independent subtasks
• Different agents need different model configurations or tools
• Frameworks: LangGraph, CrewAI, Microsoft AutoGen, Google ADK

A common anti-pattern is starting with multi-agent when a single agent with good tools would suffice. Start simple, measure, then add complexity only when needed.

Based on common scenarios, here are our recommended starting points:

Use Case	Recommended Stack	Why
Quick prototype	OpenAI Agents SDK	Minimal code, fast iteration
Production API service	LangGraph + LangSmith	State management, observability, persistence
Content creation pipeline	CrewAI	Role-based agents, natural collaboration model
Full-stack web app	Vercel AI SDK + Next.js	Streaming, React integration, model-agnostic
Document Q&A / RAG	LlamaIndex	Best-in-class data ingestion and retrieval
Enterprise / Azure	Microsoft AutoGen	Azure integration, multi-language, enterprise SLAs
MCP-native tooling	Claude Agent SDK	Deep MCP integration, safety-first design
Learning / education	Smolagents	Minimal API, code-first, easy to understand

Before committing to a framework, check these signals:

• GitHub stars and recent commit activity — A framework with 10k+ stars but no commits in 3 months is a red flag.
• Documentation quality — Can you find a quick-start guide that works on the first try?
• Production case studies — Are real companies using it in production?
• Breaking changes — Check the changelog. Frequent breaking changes indicate an unstable API.
• Community support — Is there an active Discord, Slack, or GitHub Discussions?

Visit the Frameworks page for a side-by-side comparison of all major options, including GitHub stars, license, and MCP support status.