The 6-Framework Agent Decision Guide
The same 3-step agent, built in 6 orchestrators — which to standardize on, and why.
Updated: 2026-06-19 · Distilled from the full lab report — no signup required to read the article.
What's inside the one-page scorecard
- All 6 frameworks mapped to the two camps that decide everything downstream
- The neck-and-neck top two (LangGraph & CrewAI) and the tiebreaker question
- When each of the other four is actually the right call
- The dependency trap: one framework pins 40+ conflicting LangChain versions
The bottom line: LangGraph and CrewAI separated from the pack and it's close — two 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.
Frequently asked questions
Which AI agent framework should I use in 2026?
LangGraph and CrewAI are the neck-and-neck top two. Pick CrewAI to reach a working agent fastest (~30 lines); pick LangGraph for explicit control and typed state when runs go sideways. The other four are situational: Pydantic AI for typed-Python shops, LlamaIndex for retrieval-heavy agents, AutoGen for Microsoft stacks, Swarm for learning.
Is LangGraph or CrewAI better?
They win on opposite axes. CrewAI is the easiest on-ramp — a working agent in ~30 lines that defers state management to you. LangGraph owns state in an explicit typed graph, which costs more boilerplate but pays off the moment an agent run needs debugging. Choose by your team's priority, not the tool.
What is the hardest part of building an AI agent?
Not the framework. Across all six, the same hard problem remained: making the agent stop, retry, and hand off reliably over many runs. Debuggability matters more than feature completeness — and transitive dependency bloat is a real risk (one framework pins 40+ conflicting LangChain versions).