§ Developer guide · 16 min read
Most developers pick the wrong AI agent framework. LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, Semantic Kernel, Haystack, Google ADK, Mastra. Here's how to choose the right one.
Arjun Sanchala
Engineering
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.
§ 01
Python/JS framework for LLM-powered agents
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.
State-machine / graph framework built on LangChain
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.
Microsoft's multi-agent framework
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.
Role-based multi-agent framework
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.
Data + agent framework for LLM applications
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.
Microsoft's SDK for AI orchestration
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.
Open-source framework for LLM pipelines
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.
Open-source Agent Development Kit
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.
TypeScript AI framework from the Gatsby team
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.
§ 02
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.
§ 03
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.
Deploy agents built with any framework. No vendor lock-in.
Auto-generated frontends for your agents. No frontend code required.
On-premises, private cloud, or air-gapped. Your data never leaves.
§ 04
Stop asking "which framework is best." Start asking "which framework fits my use case." Here's the cheat sheet:
And when you're ready to go to production? Katonic deploys them all.
Arjun Sanchala
Engineering · Katonic AI
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Bring your LangGraph, CrewAI, or AutoGen agents. Katonic handles deployment, UI, and governance.
