Quick answer: There is no single best AI agent framework. LangGraph wins for complex, stateful control flow; CrewAI for role-based teams of agents; AutoGen for conversational multi-agent research; the OpenAI Agents SDK for a lean official path; and Mastra for TypeScript teams. If you want the least abstraction, a plain loop over a model plus tools still works. Every one of them needs a way to read the live web, which is the part most tutorials skip.
I have shipped agents on most of these. The framework matters less than people think, and the tools you give an agent matter more. But the framework does shape how fast you move and how much you fight the abstraction later, so the choice is worth getting right.
The short version: which one when
| Framework | Language | Model | Best for | Learning curve |
|---|---|---|---|---|
| LangGraph | Python, JS | Graph / state machine | Complex branching, loops, human-in-the-loop | Medium to high |
| CrewAI | Python | Role-based crews | Teams of specialized agents with clear roles | Low to medium |
| AutoGen | Python | Conversational agents | Multi-agent chat, research, code generation | Medium |
| OpenAI Agents SDK | Python, JS | Lightweight loop + handoffs | Official, minimal, provider-aligned builds | Low |
| Mastra | TypeScript | Workflows + agents | JS/TS teams shipping to the web | Low to medium |
| DIY (loop + tools) | Any | Your own loop | Full control, minimal dependencies | Low to write, high to maintain |
Framework details and APIs move quickly, so treat the specifics below as a snapshot and check each project's docs before you commit.
LangGraph
LangGraph, from the LangChain team, models an agent as a graph. You define nodes (units of work, usually a model call or a tool call), edges (what runs next), and a shared state object that flows through the graph. Because you control the edges, you can express loops, branches, retries, and points where a human approves a step before it continues.
That control is the reason to reach for it. If your agent needs to try something, check the result, and decide whether to loop back or move on, a graph expresses that cleanly where a flat "agent, go" call gets messy. The cost is that you write more scaffolding up front, and the mental model takes a little while to click.
Pick LangGraph when the control flow is the hard part: multi-step workflows, cycles, and anywhere a person needs to sit in the loop.
CrewAI
CrewAI organizes work around roles. You define agents (a "researcher", a "writer", a "critic"), give each a goal and a backstory, and assign tasks. The crew then executes those tasks, sequentially or in a more managed process, passing work between members.
The abstraction is intuitive, which is CrewAI's biggest strength. Non-experts can read a crew definition and understand what it does. It is a strong fit when your problem genuinely decomposes into distinct jobs. We wrote a full walkthrough in the CrewAI multi-agent guide if you want to see the roles pattern in practice.
The trade-off: the role metaphor can add ceremony to problems that are really just one agent with a few tools. If you do not have distinct roles, you may be paying for structure you do not need.
AutoGen
AutoGen (from Microsoft Research) frames multi-agent systems as conversations. Agents talk to each other and to tools, and the framework routes messages between them until a task is done. A common pattern is an assistant agent that proposes code and a user-proxy agent that executes it and feeds back results.
AutoGen shines for research-style and code-generation tasks where back-and-forth is the point. It is flexible and has strong support for the "agents debating until they converge" pattern. The flip side is that conversational systems can be harder to make deterministic, and you will spend time tuning termination conditions so agents do not talk forever.
OpenAI Agents SDK
The OpenAI Agents SDK is a deliberately small library for building agents as a loop over a model with tools, plus handoffs between agents and guardrails. It leans on function calling and keeps abstraction to a minimum, which makes it easy to read top to bottom.
Reach for it when you want an official, low-ceremony path and you are comfortable being aligned with one provider's conventions. It is a good default for teams that want to start simple and add structure only when they hit a wall. As always, check the current docs, because the SDK is young and evolving.
Mastra
Mastra is a TypeScript framework for agents and workflows, aimed at people already building web apps in JS/TS rather than dropping into Python. It gives you agents, tools, workflows, memory, and evals in a way that fits the Node ecosystem.
If your product is a TypeScript app and you do not want a Python service just to run an agent, Mastra keeps everything in one language and one deploy. We go deeper in the Mastra AI guide. The trade-off is a smaller ecosystem than the Python-first tools, though it is growing.
DIY: a loop and some tools
You do not need a framework to build an agent. An agent is a model, a set of tools, and a loop that calls the model, runs whatever tool it asks for, feeds the result back, and repeats until done. If that sounds simple, it is, and for many production systems it is the right amount of machinery.
DIY wins when you want full control, minimal dependencies, and no abstraction to reverse-engineer when something breaks. It loses when you need features a framework gives you for free: state persistence, handoffs, tracing, evals. Our post on what an AI agent actually is breaks down the loop in plain terms.
The thing every framework needs: web access
Here is the part the framework comparisons gloss over. None of these tools ship with a reliable way to read the live web. They give you the orchestration; they do not give you fresh, clean data. An agent that cannot read a URL or run a search is stuck with whatever the model memorized during training, which goes stale and was never complete.
So whichever framework you choose, you will add a web tool. The messy way is to wire up your own headless browser, proxies, and HTML parsing, then maintain it. The clean way is one API call that returns readable text.
curl https://link.sc/v1/fetch \
-H "Authorization: Bearer lsc_..." \
-d url="https://example.com/pricing" \
-d format="markdown"
For open-ended questions, search returns full page content, not just snippets, so the agent can reason over the actual text:
curl https://link.sc/v1/search \
-H "Authorization: Bearer lsc_..." \
-d query="site reliability engineering hiring trends 2026"
You expose that as a single tool in your framework of choice. In LangGraph it is a node, in CrewAI a tool on an agent, in the OpenAI Agents SDK a function tool, and in a DIY loop just a function. The link.sc docs have per-framework snippets, and there is an MCP server at mcp.link.sc if your stack speaks the Model Context Protocol.
How to actually choose
Do not agonize. Use this order:
- If your team writes TypeScript and ships web apps, start with Mastra.
- If your problem splits into clear roles, try CrewAI.
- If the control flow is complex (loops, approvals, branching), use LangGraph.
- If you want the leanest official path, use the OpenAI Agents SDK.
- If you want multi-agent conversation and code execution, look at AutoGen.
- If you value control over convenience, build the loop yourself.
Then, no matter what you picked, give the agent a real web tool before you give it a personality. A well-fed agent on a boring framework beats a clever agent that cannot see today's data.
Building agents on any of these frameworks? Give yours reliable web access with link.sc and skip the browser plumbing.