The best AI agent framework depends on your goal. To build AI products, use LangGraph or CrewAI. To automate business work without much code, use n8n. To automate coding, use OpenHands.
This guide sorts 12 tools by what you want to do. You get real prices, licenses, limits and a clear pick for each goal. If you want the general top 10, read our best AI agent frameworks guide first.
Quick answer
- AI development (building AI apps): LangGraph for control, CrewAI for speed.
- Business development and operations: n8n for most teams, Dify if you want a visual AI app builder.
- Software development: OpenHands for a full coding agent, Claude Agent SDK to build your own.
- Cheapest way to start: every open-source tool here is free to run yourself. You pay for model calls and hosting.
Which type of tool do you need?
People search for “AI agent frameworks” and mean three different things. Pick your goal first, and the list gets short.
AI development
You write code to build an AI product or feature. You need a code library with tools, memory and control.
Business development
You want AI to handle leads, emails, reports or support. A visual builder with many app connections saves months.
Software development
You want AI to write, fix and test code in your projects. You need agents that work with files, tests and Git.
Last updated: September 21, 2026. Prices and star counts change, so check the official site before you pay.
How each tool is judged
- Fit for the goal: does it solve this job, not just any job?
- Ease of start: how fast a beginner gets a working agent.
- Real cost: what you pay besides model calls.
- License: can a company use it freely?
- Health: recent updates and community size.
Comparison table
| Tool | Goal | Code needed | License | GitHub stars |
|---|---|---|---|---|
| LangGraph | AI development | Yes (Python, JavaScript) | MIT | 42.1K |
| CrewAI | AI development | Yes (Python) | MIT | 58.8K |
| OpenAI Agents SDK | AI development | Yes (Python) | MIT | 29.6K |
| PydanticAI | AI development | Yes (Python) | MIT | 20.1K |
| n8n | Business | Little or none | Sustainable Use License | 205.6K |
| Dify | Business and AI apps | Little or none | Modified Apache 2.0 | 156.8K |
| Langflow | Business and AI apps | Little, Python for extras | MIT | 155.1K |
| Lindy | Business (hosted) | None | Closed, paid | Not open source |
| OpenHands | Software | Optional | MIT | 88.7K |
| Claude Agent SDK | Software | Yes (Python, TypeScript) | Anthropic terms | 8.1K (Python repo) |
| MetaGPT | Software | Yes (Python) | MIT | 70.5K |
| SWE-agent | Software | Command line | MIT | 20.4K |
Best AI agent frameworks for AI development
These are code libraries. You use them to build your own AI product, like a support bot, a research tool or an internal assistant. The first two are the safest bets for most teams. See the full ranking in our top AI agent frameworks list, and our Python guide if you work in Python.
LangGraph
Best for control- Best forProduction agents that must not fail halfway
- LanguagePython, JavaScript
- Cost to start$0 (MIT license)
You draw your agent as a graph. Each step is a node, and lines say what happens next. LangGraph saves state as it goes, so an agent can pause for a human to approve something, then continue. It can also restart after a crash.
That makes it a strong pick when you need a clear record of what the agent did. The price is more code and a new way of thinking. Install it with pip install langgraph.
The library is free. Its tracking tool, LangSmith, has a $0 Developer plan with 5,000 base traces a month. The Plus plan is $39 per seat per month.
Pros
- Saved state and human approval
- Works with many model providers
- Clear, auditable steps
Cons
- Steeper learning curve
- More code for simple jobs
CrewAI
Fastest to learn- Best forA first multi-agent project
- LanguagePython
- Cost to start$0 (MIT license)
CrewAI treats agents like a small team. Each agent gets a role, a goal and a short backstory. You add tasks, and the crew runs them. A researcher finds facts, then a writer turns them into a report.
Install it with pip install crewai. Its cloud platform has a free Basic plan with 50 workflow runs a month. Agents can loop and burn tokens, so set a spending limit on your model account from day one.
Pros
- Easy to read and learn
- Large community (58.8K stars)
Cons
- Less control than a graph tool
- Loops can waste tokens
OpenAI Agents SDK
Least code- Best forSimple agents on OpenAI models
- LanguagePython
- Cost to start$0 code, you pay for model calls
This SDK is small on purpose. It has three core ideas: agents, handoffs (one agent passes work to another) and guardrails (checks on inputs and outputs). It also includes sessions for memory and tracing. Install it with pip install openai-agents.
It works best with OpenAI models. Other providers work through adapters, but some built-in tools may not.
Pros
- Very little to learn
- Guardrails and tracing included
Cons
- Closely tied to OpenAI
- You design memory and state yourself
PydanticAI
Best for clean Python- Best forDevelopers who like typed code
- LanguagePython
- Cost to start$0 (MIT license)
PydanticAI comes from the team behind Pydantic, the data checker many Python developers use. Inputs, tools and outputs are all typed and validated, which catches mistakes before users see them. You switch models by changing a text string. Install it with pip install pydantic-ai.
Pros
- Typed, checked outputs
- Easy model switching
Cons
- Python only
- Best if you know type hints
Best AI agent frameworks for business development
Business teams rarely need a code library. They need agents that connect to email, a CRM, spreadsheets and chat, and that a non-engineer can fix. These tools give you a visual builder and hundreds of app connections. The best AI agent frameworks for business development are the ones your team can maintain without a developer on call.
Tip
A common setup uses two layers. A visual tool like n8n handles triggers, emails and app connections. It calls a small LangGraph or CrewAI service only for the step that needs deep reasoning.
n8n
Best for business teams- Best forLead handling, reports, support and internal workflows
- PriceFree self-hosted, cloud from 20 EUR/month billed yearly
- Free planCommunity Edition, self-hosted
n8n is a visual workflow builder with an AI Agent node. You drag blocks, connect apps and let an agent choose tools. It has hundreds of integrations, so a lead form can flow into a CRM, an AI step and a Slack message with no code.
Cloud pricing is by workflow run. Starter is 20 EUR per month billed yearly for 2,500 executions. Pro is 50 EUR per month for 10,000. Business is 667 EUR per month for 40,000 and adds single sign-on. Enterprise is custom.
Read the license. n8n uses the Sustainable Use License, not a standard open-source one. You can use it for your own business, but you cannot resell n8n itself as a service.
Pros
- Non-engineers can maintain it
- Huge app library
- Free to self-host
Cons
- Not a standard open-source license
- Complex reasoning needs code beside it
Dify
Best AI app builder- Best forChatbots and knowledge assistants on your own documents
- PriceFree plan, paid from $590/year per workspace
- Free planYes: 200 message credits, 5 apps
Dify lets you build AI apps in a browser. You upload documents, design a workflow and publish a chatbot or an agent. It fits support desks and internal help tools.
The Sandbox plan is free with 200 message credits, 5 apps and 1 team member. Professional is $590 per year with 5,000 credits a month and 3 members. Team is $1,590 per year with 10,000 credits a month and 50 members. You can also self-host the Community version for free.
The license is Apache 2.0 with extra conditions. Selling Dify itself as a multi-user service may need a commercial license, so read it first.
Pros
- Visual builder with built-in document search
- Free self-hosting
Cons
- Cloud free plan is small
- License has extra conditions
Langflow
Best visual builder for developers- Best forTeams that mix visual building with Python
- LanguagePython
- Cost to start$0 (MIT license)
Langflow gives you a drag-and-drop canvas for agents and document search. You can also write Python components when the blocks are not enough. That mix suits a small team where one developer supports several non-developers.
Install it with pip install langflow. It needs Python 3.10 to 3.14.
Pros
- MIT license
- Visual plus code in one tool
Cons
- Fewer business app connections than n8n
- You host it yourself
Lindy
Best hosted, no setup- Best forNon-technical teams who want agents without hosting
- PriceFrom $29.99 per user per month
- Free planNo, only a 7-day trial for some sign-ups
Lindy is a paid, hosted product. You describe a task, and it runs agents that work in Slack and connected apps. There is nothing to install or host.
Plans use credits. Plus is $29.99 per user per month with 3,000 credits. Pro is $99.99 with 15,000. Max is $199.99 with 35,000. Everyday tasks cost 2 to 250 credits each, and credits do not roll over.
Pros
- No setup or hosting
- Approval steps before agents act
Cons
- Not open source
- Credits are hard to predict
Best AI agent frameworks for software development
Here you have two paths. You can use a ready coding agent that works on your repository, or you can build your own coding agent from a toolkit. The best AI agent frameworks for software development cover both.
OpenHands
Best coding agent platform- Best forTeams that want an open-source coding agent
- LanguagePython SDK, command line
- Cost to start$0 (MIT license), plus model costs
OpenHands is an open-source platform for AI coding agents. Agents edit files, run commands and test their work. It has a command-line tool, and a Python Software Agent SDK if you want to build your own agents on top. It reaches 88.7K GitHub stars and is updated daily.
The older local GUI is deprecated. Its replacement is Agent Canvas, an open-source browser client. There is also a managed OpenHands Cloud, but its prices are not public. The current release needs Python 3.12 or 3.13.
Pros
- MIT license
- CLI, SDK and cloud options
- Very active project
Cons
- Setup takes more effort than an editor plugin
- You pay for model use
Claude Agent SDK
Best to build your own- Best forCustom agents that read files and run commands
- LanguagePython, TypeScript
- Cost to startYou pay for Claude API use
This SDK gives you the same agent loop and tools that power Claude Code, as a library. Built-in tools read, write and edit files, run commands and search. It adds hooks, subagents, permissions and MCP support. Install it with pip install claude-agent-sdk.
You sign in with an API key. It is built around Claude models, so it is not the tool for mixing many providers.
Pros
- Strong file and command tools built in
- Permissions and subagents
Cons
- Made for Claude models
- Cost follows your API bill
MetaGPT
Best for an AI software team demo- Best forTurning one idea into specs, design and code
- LanguagePython 3.9 to 3.11
- Cost to start$0 (MIT license)
MetaGPT copies a software company. Agents play the product manager, architect and engineer. From one sentence, it produces user stories, designs, code and documents.
It is popular, with 70.5K stars. But the last code update was in January 2026, and it needs an older Python (below 3.12). Use it to learn multi-agent design, and be careful before you build a product on it. Install it with pip install --upgrade metagpt.
Pros
- Clear team-of-roles idea
- Creates full project documents
Cons
- Slow recent updates
- Old Python version needed
SWE-agent
Best for bug fixing- Best forFixing GitHub issues automatically
- LanguageCommand line, Python
- Cost to start$0 (MIT license)
SWE-agent comes from a research team at Princeton and Stanford. It gives a model a clean way to browse a code repository, edit files and run tests, so it can fix real issues. It is small and simple, and it is updated often.
Pros
- Focused on one job
- Simple and readable
Cons
- Less polished than a full platform
- Mostly command-line
Editor tools like Cline and Aider are coding assistants, not frameworks. Both are Apache 2.0 licensed and free, and you pay only for the model. Pick them if you want help inside your editor or terminal, and pick a framework if you want to build an agent product.
What these tools really cost
The code is free for most tools here. The bills come from model calls, hosting and paid platform tiers.
| Tool | Free option | Paid option |
|---|---|---|
| LangSmith (for LangGraph) | $0, 1 seat, 5,000 base traces | Plus: $39 per seat per month |
| CrewAI cloud | 50 workflow runs a month | Enterprise: custom |
| n8n | Self-hosted Community Edition | From 20 EUR/month (billed yearly), 2,500 executions |
| Dify | Sandbox (200 credits) or self-host | $590/year, or $1,590/year for teams |
| Lindy | None | $29.99 to $199.99 per user per month |
| OpenHands, SWE-agent, MetaGPT, Langflow | Free, MIT license | Model calls and hosting only |
| OpenAI, Claude, Gemini and others | Depends on the model | You pay per token used |
Watch out for loops
An agent that keeps retrying can spend real money. Cap the steps in your code and set a spending limit on your model account. This matters most for coding agents, which run many model calls per task.
Which AI agent framework should you pick?
| If you… | Pick |
|---|---|
| Build an AI product and need saved state and approvals | LangGraph |
| Want a working team of agents fast | CrewAI |
| Use OpenAI models and want the least code | OpenAI Agents SDK |
| Want typed, checked Python output | PydanticAI |
| Run business workflows with many apps | n8n |
| Build chatbots on your own documents in a browser | Dify |
| Have one developer supporting a non-technical team | Langflow |
| Want no hosting and no setup, and can pay per seat | Lindy |
| Want an open-source agent that works on your repo | OpenHands |
| Want to build a custom coding agent | Claude Agent SDK |
| Want to study multi-agent software teams | MetaGPT |
| Want to fix GitHub issues automatically | SWE-agent |
How to start in one week
Day 1: name one job
Write one sentence, like “sort new leads and draft a reply.” A small clear job beats a big vague one.
Day 2: pick by goal
Use the table above. Choose one tool only. Do not compare five.
Days 3 and 4: build with two tools at most
Give the agent a small set of tools. More tools cause more wrong choices.
Day 5: add limits and logs
Cap the steps, set a spending limit and turn on tracing so you can see each step.
Days 6 and 7: test on real cases
Run 20 real examples. Add a human check before the agent sends email or changes data.
Common mistakes to avoid
- Using a code framework for a no-code job. If your team cannot maintain Python, pick a visual tool.
- Skipping the license. n8n and Dify have extra terms. Read them before you sell anything built on them.
- Trusting output blindly. Always add a check before an agent acts on customers or code.
- Building on a slow project. Check the last update date. An idle repo can leave you stuck on old Python.
- Ignoring costs. Coding agents and long workflows use many model calls. Watch the bill on day one.
Our pick
For AI development, start with CrewAI to learn fast, and move to LangGraph when you need control. For business work, use n8n first, and add Dify when you need document chatbots. For software development, use OpenHands if you want a ready agent, and the Claude Agent SDK if you want to build your own. Whatever you choose, set step and cost limits before the first run.
Frequently asked questions
What is the best AI agent framework for business development?
n8n is the best fit for most business teams, because a non-engineer can build and fix workflows and it connects to many apps. Add a code framework only for hard reasoning steps.
What is the best AI agent framework for software development?
OpenHands is the strongest open-source choice for a ready coding agent. To build your own, the Claude Agent SDK gives you file and command tools out of the box.
Do I need to code to use an AI agent framework?
Not always. n8n, Dify and Lindy work with little or no code. LangGraph, CrewAI and PydanticAI need Python.
Are AI agent frameworks free?
Most are free to run yourself. You still pay for model calls, and you may pay for hosting, tracking or a cloud plan.
Should I use n8n or LangGraph?
Use n8n when the job is a business workflow across apps. Use LangGraph when you are building an AI product in code and need saved state and strict control. Many teams use both.
Is MetaGPT still worth using?
It is good for learning how a team of agents can plan software. Its last code update was January 2026 and it needs Python below 3.12, so avoid it for a new product.