LangGraph, CrewAI, and AutoGen compared honestly, plus the total-cost truth: the framework is free, the production system is not. When open source agents actually win.
Codestreaks Team
You have three tabs open: LangGraph, CrewAI, AutoGen. Each README shows a multi-agent demo in under fifty lines. The licenses are MIT or Apache, the examples run on the first try, and the pitch to your boss writes itself: the software is free, so the project should be cheap.
We build agents for a living, 30+ shipped to production since 2024, and most of them sit on open source foundations. So this is not a takedown. Open source AI agents are the right call more often than not. But the license price and the system price are different numbers, and the gap between them is where agent projects quietly die. This guide covers the major frameworks honestly, the costs the READMEs skip, and the situations where open source genuinely wins.
There are dozens of open source AI agent frameworks. Three define the categories that matter, and most of the rest are a variation on one of them.
LangGraph models an agent as a graph. Nodes do work, edges decide what runs next, and the whole thing carries explicit state that you define. That design buys you the two features production teams actually end up needing. Checkpointing lets you pause an agent mid-run, wait for a human approval, and resume without losing anything. Explicit state means you can inspect and replay what the agent did last Tuesday.
The cost is that you write more code and think harder up front. LangGraph is the least magical of the three, which is exactly why we reach for it most. Magic is what you end up debugging in week six. Pick it when the workflow is multi-step, needs human-in-the-loop gates, or has to survive an audit.
CrewAI organizes agents into role-based crews (a researcher, a writer, a reviewer) and handles the delegation between them. It produces a working demo faster than anything else here, and for linear, role-shaped tasks that speed is real value, not a trap.
The ceiling shows up when you need fine-grained control over failure: what happens when the third agent in the chain returns garbage, how retries work, where state lives when a run dies halfway. The abstractions that made the demo fast start fighting you. Teams either accept the ceiling or rebuild on something lower level, and the rebuild is cheaper if you see it coming.
AutoGen, from Microsoft, treats a multi-agent system as a structured conversation between agents. It is strong for experimentation and research-flavored work, and the conversation model maps naturally to problems where agents genuinely need to negotiate.
It has also been reorganized repeatedly: a community fork (AG2), a ground-up rewrite, and a planned consolidation into Microsoft's unified agent tooling alongside Semantic Kernel. None of that makes the code bad. It does mean that building on it today includes a line item for future migrations. That churn is not unique to AutoGen, but it is most visible there, and it is a fair preview of what betting on any young framework involves.
Smolagents from Hugging Face is worth a look if you want minimal, code-first agents. OpenAI's agent tooling is clean but pulls you toward one vendor's way of working. And here is the option no framework page will tell you about: a single-purpose agent is often a few hundred lines of your own code, a loop that calls a model, executes tools, and checks results. A framework earns its place when you need checkpointing, parallel branches, and durable state, not before. Some of our smaller production agents use no framework at all.
This is the total-cost truth, and it applies no matter which name you picked above. Across the agents we have shipped, the orchestration layer (the part the framework provides) is the minority of the effort. The majority is everything the framework cannot know about your business. You will build all of the following yourself:
From the field. The most common way an engagement starts for us: a team arrives with a framework demo that impressed everyone in a meeting and then fell apart on real data. The framework was never the problem. The pilot ran on hand-picked inputs, nothing was logged, and there was no eval suite, so when answers went wrong nobody could say which ones or why. Most of our work is reliability engineering, not the first demo. Moving from 90% to 99% reliability is where the engineering lives, and it lives outside the framework.
Open source is the right foundation when at least one of these is true:
It loses in situations teams talk themselves out of seeing:
Our pricing, since vague ranges help nobody. A single-purpose agent (one workflow, one integration surface) runs $8k-$20k and ships in 3-4 weeks. A multi-step workflow agent runs $20k-$45k over 5-7 weeks. An enterprise platform with approvals, audit logs, and staged autonomy runs $45k-$60k+ across 8-12 weeks, delivered in phases. Most of these are built on the same open source frameworks covered above; you are paying for the engineering around them, because that is where the work is.
Either way, you get 100% code ownership, repo included, plus 30 days of post-launch support. That matters double with open source: the entire point of building on open foundations is that you own the result. If an agency builds on a free framework and still will not hand you the repo, walk away.
The license is free. The production system is not. Budget for the evaluation suite, logging, integrations, hosting, and ongoing inference ($50-$2,000 a month is typical). The framework is usually the cheapest line on the invoice.
It depends on the shape of the work. LangGraph for multi-step workflows that need control, checkpointing, and human approval gates. CrewAI for fast, role-shaped, linear tasks. AutoGen for research-flavored multi-agent experiments. For a simple single-purpose agent, a few hundred lines of your own code may beat all three.
Yes. Every major framework speaks to local or self-hosted models. Expect a quality gap against frontier hosted models on hard reasoning tasks, which is exactly why you need an eval suite before you commit either way: it turns the hosted-versus-local question into a measurement instead of a debate.
If you have engineers with real capacity and the agent is core to your product, build. If the agent supports the business rather than being the business, or you need it live this quarter, a fixed-price build you fully own usually costs less than the internal detour. The honest tiebreaker is the maintenance question: who owns this in month six?
We take on two engagements per quarter, so we scope carefully and say no when the fit is not there. If you are weighing a framework build against hiring it out, book a free 30-minute scoping call. You will get a straight read on whether your workflow needs LangGraph-grade orchestration or two hundred lines and an eval suite, and what either path costs. We reply within two business days.
Book a scoping call or see how we run these builds at AI agent development.
