S02E01: I Built the Same Agent in 6 Orchestrators
LangGraph vs CrewAI vs AutoGen vs OpenAI Swarm vs Pydantic AI vs LlamaIndex. Same 3-step research agent built in each — DX, cost, reliability scorecard.
The 6-Framework Agent Decision Guide
The same 3-step agent, built in 6 orchestrators — which to standardize on, and why.
Show Notes
The experiment
The question every engineering leader is asking in 2026: which agent framework do we standardize on? So I built the same three-step research agent in six of them — LangGraph, CrewAI, AutoGen, OpenAI Swarm, Pydantic AI, and LlamaIndex — and compared developer experience, cost, reliability, and debuggability, apples to apples. The Season 2 opener of CTAIO Labs.
The two camps
The six split faster than expected into two camps, and that single design choice drives everything downstream — debuggability, reliability, how each fails under load:
- Opinionated state — the framework owns state for you (LangGraph, Pydantic AI).
- Defer state to you — you manage it (OpenAI Swarm, CrewAI).
The six, in one line each
- LangGraph — agent as a directed graph, explicit typed state; most mature, best docs; shipped three breaking changes in six months.
- CrewAI — Crew/Agents/Tasks abstraction; the easiest on-ramp, ~30 lines of Python; defers state to you.
- AutoGen (Microsoft) — everything framed as conversations between agents; strong enterprise backing.
- OpenAI Swarm — intentionally minimal (Agents + handoffs); an educational reference, least production-ready.
- Pydantic AI — type safety at the agent layer; lowest impedance to standard Python engineering.
- LlamaIndex Agents — agent primitives on a RAG heritage; strongest when the agent is mostly retrieval.
The verdict
Two frameworks separated from the pack, and it's close: LangGraph and CrewAI — two fundamentally opposed architectures that both win. CrewAI gets you to a working agent fastest; LangGraph gives you explicit control and typed state for when things go wrong. The tiebreaker isn't the tool, it's your team's priority. The other four are situational: Pydantic AI for typed-Python shops, LlamaIndex when it's mostly retrieval, AutoGen for Microsoft-enterprise stacks, and Swarm for learning, not production.
What I got wrong
I expected LangGraph's boilerplate to be a deal-breaker for small teams. It wasn't — the typed state graph earns its keep the moment an agent run goes sideways.
Timestamps
- 00:00 — Intro
- 01:32 — The experiment: the same agent in six frameworks
- 05:34 — Two camps: opinionated state vs. defer-to-you
- 07:00 — The six, framework by framework
- 25:30 — What I got wrong predicting this
- 31:49 — The verdict: LangGraph & CrewAI, neck-and-neck
- 40:56 — Outro & what's next in Season 2
Links
Transcript coming soon. Until then, the companion article covers everything we talked about in this episode.
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