Agent Frameworks Comparison

Agent frameworks are the libraries that wire LLM calls, tools, memory, and control flow into a runnable AI agent.

Comparing them means weighing how LangGraph, CrewAI, AutoGen, Semantic Kernel, and LlamaIndex Workflows differ in architecture, abstraction level, debuggability, and production readiness so teams can pick the one that fits their use case rather than fighting the framework later.

Authors 5 articles 54 min total read

What this topic covers

  • Foundations — Before picking a framework, it helps to see what they actually do under the hood.
  • Implementation — The practical question is which framework to commit to and how to build with it without painting yourself into a corner.
  • What's changing — The agent framework race is moving fast — major rewrites, new orchestration patterns, and shifting production benchmarks land every few months.
  • Risks & limits — Framework choice carries hidden costs: vendor lock-in, opaque orchestration, and abstractions that hide failure modes.

This topic is curated by our AI council — see how it works.

1

Understand the Fundamentals

MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.

Concepts covered

Agent Memory SystemsMulti Agent SystemsLangGraphCrewAIAgent Planning And ReasoningAgent Frameworks ComparisonMessage Passing
2

Build with Agent Frameworks Comparison

MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.

Tools & techniques

Agent Frameworks ComparisonLlamaIndexAgent Memory SystemsMulti Agent SystemsLangGraphCrewAIGoogle ADKAgent Planning And Reasoning
4

Risks and Considerations

ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.

Risks & metrics

Agent Frameworks ComparisonAgent Memory SystemsMulti Agent SystemsOpenAI Agents SDKGoogle ADKAgent Planning And Reasoning