The AI Agent Framework Decision Tree: 9 Frameworks, One Clear Choice

Most developers pick the wrong AI agent framework. Not because they're bad developers, but because they ask the wrong question: "Which framework is best?" The right question is: "Which framework fits my specific use case?"

Let's cut through the noise. Here are 9 frameworks, each with a clear purpose, and a decision tree to help you choose.

The 9 Frameworks, Explained

LangChain

Python/JS framework for LLM-powered agents

The OG

Tool Calling

Agent Loops

Reasoning

The original gangster of AI frameworks. LangChain set the standard for building LLM-powered agents with its composable chains, tool integrations, and reasoning loops. If you're new to agents, start here.

Best for: General-purpose AI agents, RAG applications, tool-based agents

LangGraph

State-machine / graph framework built on LangChain

Control Freak

State Machines

Branching

Loops & Retries

When you need absolute control over multi-step workflows. LangGraph uses a finite state machine approach that lets you define exactly how your agent moves between states, handles errors, and branches on conditions.

Best for: Complex, multi-step agents with conditional logic

AutoGen

Microsoft's multi-agent framework

Collaborative

Agent Chats

Human-in-Loop

Research-Backed

Microsoft's research-backed approach to multi-agent systems. Agents talk to each other, collaborate on complex tasks, and can bring humans into the loop when needed. Production-ready for enterprise scenarios.

Best for: Collaborative and autonomous agents, Microsoft ecosystem

CrewAI

Role-based multi-agent framework

Team Player

Clear Roles

Defined Goals

Orchestration

Think of it as your AI team. Each agent has a role (researcher, writer, reviewer), clear goals, and responsibilities. The mental model is intuitive: build a team, delegate tasks, get results.

Best for: Task delegation and team-style agents

LlamaIndex

Data + agent framework for LLM applications

Data Expert

Strong RAG

Structured Data

Agent Ready

The data person's choice. If your agent's superpower is accessing and synthesizing external knowledge, LlamaIndex offers the most mature toolkit for data ingestion, indexing, and retrieval.

Best for: Knowledge-driven agents, RAG applications

Semantic Kernel

Microsoft's SDK for AI orchestration

Enterprise

Plugin System

Prompts + Tools

Memory

Microsoft's enterprise SDK with a clean plugin architecture. Combines prompts, tools, and memory seamlessly. If you're in the Microsoft ecosystem and need production-grade agents, this is your choice.

Best for: Production-grade agents, Microsoft ecosystem

Haystack

Open-source framework for LLM pipelines

Search Expert

Modular

Strong Search

Python-First

The search specialist. Haystack's modular architecture makes it perfect for building search-based applications. Strong RAG support, Python-first design, and enterprise-ready features.

Best for: Search-based and enterprise agents

Google ADK

Open-source Agent Development Kit

Open Source

Workflow Agents

Multi-Language

Built-in Streaming

Google's open-source framework for building production AI agents. Supports Python, TypeScript, Go, and Java. Model-agnostic with LiteLLM integration for 40+ providers. Workflow agents (Sequential, Parallel, Loop) for predictable pipelines or LLM-driven dynamic routing.

Best for: Multi-language teams, production agents, workflow orchestration

Mastra

TypeScript AI framework from the Gatsby team

TypeScript Native

40+ Models

Built-in Evals

Local Playground

From the team behind Gatsby, Mastra is built on Vercel's AI SDK for TypeScript developers. All-in-one framework with agents, workflows (graph-based), RAG, memory, and MCP support. Integrates with React, Next.js, and Node.js.

Best for: TypeScript/JavaScript developers, modern web stack, Vercel ecosystem

Quick Reference Table

Framework Comparison at a Glance

Choose based on your primary use case

Framework	Type	Best For	Superpower
LangChain	General	RAG, tool-based agents	Composability
LangGraph	Control	Complex multi-step workflows	State machines
AutoGen	Multi-Agent	Collaborative agents	Human-in-the-loop
CrewAI	Team	Task delegation	Role-based design
LlamaIndex	RAG	Knowledge-driven agents	Data access
Semantic Kernel	Enterprise	Production agents	Plugin system
Haystack	Search	Search-based apps	Modular architecture
Google ADK	Open Source	Multi-language, workflow agents	Sequential/Parallel/Loop
Mastra	TypeScript	Modern web stack	Built on Vercel AI SDK

The "right" framework isn't a singular choice. It's the one that matches your use case, tech stack, and control requirements.

Katonic: Deploy Any Framework, Zero Lock-in

Here's the thing: choosing a framework is just the beginning. Getting it to production is where most teams struggle. That's why we built Katonic to support all of these frameworks natively.

Full-Stack Agent Platform

Brain + Body + Guardrails on your infrastructure

Katonic's platform doesn't force you into a single framework. Bring your LangGraph agents, your CrewAI teams, your AutoGen conversations. We handle the deployment, UI, and governance so you can focus on building.

LangGraph CrewAI AutoGen LlamaIndex Semantic Kernel Haystack Google ADK Mastra 10+ More

Framework Agnostic

Deploy agents built with any framework. No vendor lock-in.

Generative UI

Auto-generated frontends for your agents. No frontend code required.

Sovereign Deployment

On-premises, private cloud, or air-gapped. Your data never leaves.

The Bottom Line

Stop asking "which framework is best." Start asking "which framework fits my use case." Here's the cheat sheet:

• Starting out? → LangChain
• Need control? → LangGraph
• Multi-agent collaboration? → AutoGen or CrewAI
• Knowledge-driven? → LlamaIndex
• Microsoft ecosystem? → Semantic Kernel
• Search specialist? → Haystack
• Multi-language / workflow agents? → Google ADK
• TypeScript / modern web? → Mastra

And when you're ready to go to production? Katonic deploys them all.