Framework comparisons usually devolve into feature tables that help nobody. The more useful question is: what does each of these think the hard part is? Because they disagree, and that disagreement is what should drive your choice.
The four bets
LangGraph — the hard part is state and control flow
LangGraph models an agent as a graph: nodes are steps, edges are transitions, and there is an explicit state object threaded through. It is deliberately low-level — LangChain describes it as an orchestration framework and runtime for long-running, stateful agents, with higher-level agent abstractions built on top of it rather than baked in.
The bet is that real agents need durable state, conditional branching, retry logic, human approval gates, and the ability to resume after a failure three steps in. If your workflow genuinely needs "if the confidence score is low, route to a human, wait for their input, then continue" — that is what LangGraph is for.
Cost: more upfront design. You are drawing a graph, not describing a crew. For a simple linear task it is more machinery than the problem warrants.
CrewAI — the hard part is expressing multi-agent collaboration
CrewAI uses a role-based mental model: each agent has a persona, a set of tools, and a task, and they operate as a crew. The abstraction is intuitive enough that teams consistently get a working multi-agent prototype running faster than with most alternatives.
The bet is that the difficulty is conceptual — reasoning about how multiple agents divide work — and that a role metaphor makes that tractable.
Cost: the abstraction that makes prototyping fast can get in the way when you need precise control over execution order or failure handling. Excellent for getting to a demo; teams sometimes migrate when requirements sharpen.
LlamaIndex Workflows — the hard part is the data
LlamaIndex came from retrieval and still shows it. Its Workflows layer is event-driven orchestration, but the surrounding strength is document ingestion, parsing, indexing and retrieval strategy.
The bet is that for most enterprise systems, the agent loop is not the hard part — getting a 200-page scanned contract into a form the model can reason over is. If your problem is document-heavy, that framing is correct more often than not.
Cost: lighter on complex multi-agent orchestration than LangGraph.
Google ADK — the hard part is getting to production on GCP
Google's Agent Development Kit is opinionated and batteries-included: session management, a browser-based debugging UI, code execution support, and a CLI that exposes agents as services without writing server boilerplate.
The bet is that most of the pain is operational plumbing, and that removing it matters more than framework flexibility — provided you are on Google Cloud.
Cost: the opinions and the gravity toward GCP. Genuinely fast if that is your platform; less appealing if it is not.
Choosing without agonising
- Complex stateful workflow, conditional routing, human approval gates, must resume after failure → LangGraph
- Multiple specialised agents dividing a task, prototype speed matters most → CrewAI
- Document-heavy, retrieval quality is the actual problem → LlamaIndex Workflows
- Already on Google Cloud, want deployment plumbing solved → ADK
- Single model call with two or three tools → no framework. Write the loop. Seriously.
That last line matters more than the rest. A large share of "agents" in production are one LLM call, a tool, and a retry — perhaps eighty lines of plain Python. Adding a framework to that buys you dependency surface and an abstraction to debug through, in exchange for structure you did not need.
What genuinely transfers between frameworks
Framework choice is more reversible than vendors imply, because the parts that take longest to get right are not framework-specific:
- Your tool definitions — and increasingly these are portable by design. MCP exists precisely so a tool integration is not welded to one framework.
- Your prompts and rubrics.
- Your evaluation dataset — which is usually the single most valuable artefact your team owns, and is entirely framework-agnostic.
- Your observability instrumentation, if you built it on OpenTelemetry GenAI conventions rather than a framework-native tracer.
- Your guardrails, when implemented as a layer rather than framework hooks.
Teams that invest in those five and stay relatively thin on framework-specific abstractions can migrate in days. Teams that push business logic deep into framework primitives cannot.
The honest recommendation
Pick the framework that matches the problem you have today. Keep evaluation, observability, guardrails and tool definitions outside it. Revisit in six months without embarrassment — this space moves, and a framework choice is not a marriage.
What is not optional is the surrounding discipline. A CrewAI system with a solid eval suite and full tracing will beat a LangGraph system with neither, every time.
Our AI Orchestration programme covers multi-agent design, tool integration and production agent patterns hands-on — HRD Corp SBL-KHAS claimable for eligible Malaysian employers.
