Save 3 months of detours: A Practical Guide to Selecting AI Agent Frameworks
God Translation Bureau is a compilation team under 36Kr, focusing on fields such as technology, business, the workplace, and life, and highlighting new technologies, new ideas, and new trends from abroad.
Editor's note: This article is a hard - won summary of testing 12 AI agent frameworks: from no - code tools to hardcore development stacks, it helps you avoid three months of trial - and - error pitfalls and find the right "agent partner". This article is a compilation.
There is indeed a lot of hype around AI agents.
But building one yourself? That's a whole different ballgame.
Earlier this year, I set out to develop a personal AI agent to automate my daily workflows - email replies, report generation, schedule management, content drafting, and even debugging code snippets. I thought I could finish it in a week.
I was wrong.
It took me three months. Why?
Because choosing the right AI agent framework is harder than developing the agent itself.
Now that I've tried out more than a dozen frameworks, from fancy visual development tools to deeply customizable code - first technology stacks, I'm going to share this guide. It would have been great if someone had written it when I was just starting out.
First, why are AI agent frameworks so important?
Let's get one thing straight: an AI agent is not just a fancy chatbot.
It's a system that can do the following:
Perceive (through inputs like text, voice, and tools)
Plan (decide what to do)
Act (trigger APIs, run tools, delegate tasks)
Learn (utilize memory, context, and history)
The framework provides a structure for these capabilities. Without a framework, you're just piecing together APIs and hoping GPT doesn't mess up your production database during a hallucination.
It's thanks to the framework that agents become reliable, modular, and scalable.
Now, let's talk about what frameworks are available.
1. No/Low - code Stars - Best for Developers and Teams in a Hurry
n8n
Use case: You want to connect an AI agent to over 700 real - world applications without coding.
Think of n8n as a steroid - pumped Zapier - but with full developer freedom. With its growing AI modules, you can develop agents that can do the following:
Respond to Slack messages
Analyze received emails
Query databases
Invoke GPT - 4 or Claude for reasoning
It's visual, powerful, and can be deployed on your own server. I used it to automate a sales agent process in less than an hour.
Flowise
Use case: You like LangChain but hate YAML.
This is a drag - and - drop visual development tool designed specifically for chaining large language models. Imagine clicking to combine:
Prompt templates
Memory modules
Retrieval engines
Action tools (such as browsing or code interpreters)
It's like a visual Lego set for GPT.
Langflow
Use case: You're prototyping agents with LangChain but don't want to write all the code by hand.
Langflow is between no - code and low - code. It offers the same visual comfort as Flowise but allows for more in - depth customization when needed.
Rivet
Use case: You care about visual debugging, transparency, and AI flow graphs.
Rivet is the Figma of the AI agent world. It's beautifully designed, supports collaboration, and lets you intuitively check what your agent is thinking at each node. I found it very useful for explaining processes to non - technical customers.
2. Code - First Frameworks - Built for Developers, Highly Scalable
Now it's time to get serious.
These frameworks assume you're familiar with Python (or.NET in some cases) and want to create truly autonomous, production - ready agents.
LangGraph
Development team: The LangChain team
Key advantage: Graph - based reasoning + memory
LangGraph lets you define how an agent makes decisions among multiple paths. It's like playing a "Choose Your Own Adventure" game - but with GPT at the wheel and built - in error handling.
Use it if you want your agent to have the following capabilities:
Reflect on past actions
Retry or branch based on results
Handle state during long - term conversations
This framework is great for multi - agent negotiation, research processes, or customer service processes.
CrewAI
Specifically designed for agent team collaboration
Core concept: Role - based collaboration
CrewAI lets you define "roles", such as developers, analysts, and editors - each driven by an agent persona. Then you assign them tasks, and they communicate with each other to solve problems.
I used CrewAI to create a system with 3 agents that can brainstorm blog articles, draft them, and then automatically publish them. The results were surprisingly good.
Bonus: Their ecosystem now includes a no - code platform called Studio.
AutoGen (by Microsoft)
Use case: You need enterprise - level reliability
AutoGen is modular, highly testable, and designed for enterprise integration. You can define agents, tools, memory, and strategies within one framework.
It's great for conversational AI, document agents, or any task that requires multiple calls to GPT.
SuperAGI
Use case: You want an end - to - end autonomous agent technology stack
SuperAGI offers:
Vector database integration
A user interface for monitoring and controlling tasks
Agent telemetry
An agent marketplace
It's not just a framework. It's an infrastructure.
3. Specialized Frameworks for Specific Workflows
Sometimes, you don't need a one - size - fits - all solution. You need the right tool for the job.
UFO
Focus area: UI automation (Windows applications)
It's great for interacting with legacy systems like Excel, CRM, and desktop tools.
LiveKit
Focus area: Real - time voice agents
It enables agents that can speak, respond, and interact in real - time scenarios.
I combined it with Whisper and GPT - 4 Turbo to develop a real - time voice AI receptionist.
Agent Zero
Focus area: Customizable modular agents
It's great for research projects and building internal tools. It's lightweight, open - ended, and logic - first.
SmoLagents Framework (by Hugging Face)
Focus area: Prototyping with Hugging Face tools
It's great for rapid experimentation. The syntax is simple, and the iteration speed is fast.
4. The Framework Battle: The Ecosystem Matters
Now, it's not just about the framework itself - what's more important is the ecosystem around it.
LangChain + LangGraph + LangSmith Ecosystem
A full - stack solution for LLM agents
Debugging, monitoring, vector storage, and tool support
A large community
CrewAI + CrewAI Studio Ecosystem
No - code and code interfaces
A developer network and open templates
An excellent onboarding experience + enterprise support
Microsoft Technology Stack
AutoGen + Semantic Kernel + Azure AI Ecosystem
Seamless integration with.NET and enterprise systems
New Players to Watch in 2025
This field is evolving rapidly. The following frameworks are relatively new but show real potential:
Julep — Orchestration of complex tasks with hierarchical plans
MGX — An agent system with reflective logic
QuantaLogic — Reasoning based on Tree - of - thought + ReAct
Guardrails AI — Providing guardrails and security measures for LLM outputs
✅ Choosing the Right Framework: A Simple Guide
Don't Just Choose One
The most important lesson I've learned? You don't have to stick to one framework.
In fact, my current setup is as follows:
n8n for triggering workflows
CrewAI for brainstorming + content writing
LangGraph for managing logical branches
LangSmith for monitoring everything
UFO for automating local UI applications
It's not about taking sides. The key is to build a technology stack that works together.
AI agents are no longer science fiction - they're tools. If 2024 was the year of prompts,
then 2025 will be the year of agent frameworks.
So, what are you developing? Which framework are you betting on?
Share your favorite frameworks in the comments - especially if they're not on this list. I'll try them out myself and update this article.
Translator: boxi.