The Multi-Agent Framework I Actually Use (And Why I Stopped Using the Others)

I went through the same evaluation you're going through right now. LangGraph vs CrewAI vs OpenAI Agents SDK vs Google ADK. I read the comparison articles. I evaluated each one against my OpenClaw setup. And I ended up with a take that most of the "experts" won't tell you: the framework doesn't matter as much as the orchestration patterns underneath it.

Let me explain what I mean — and give you the practical breakdown I wish I'd had.

What I Was Actually Choosing Between

Every multi-agent framework is solving the same problem: how do multiple AI agents share state, handle failures, and decide who acts next? They just take different approaches to the primitives.

LangGraph — Graph-based with persistent state checkpoints. Every transition is logged. You can pause the graph mid-execution, wait for human input, then resume. This is the one I'd recommend for anything where auditability matters or where agents need to recover from failures gracefully.

CrewAI — Role-based. You define agents with specific roles ("researcher", "writer") and tasks, then the framework handles handoffs between them. Intuitive for business process automation. Less flexible for complex state management.

OpenAI Agents SDK — Handoff-native. Agents explicitly transfer control to each other with full context. Clean mental model, but it's Python-first and locked to OpenAI models. If you're on the OpenAI stack, this is the lowest-friction choice.

Google ADK — Most recent entrant. Built for more complex, multi-agent-native workflows. Still maturing but the Google ecosystem integration is real if you're building in that environment.

The Decision That Actually Matters

Here's what the comparison articles skip: you're not choosing a framework — you're choosing an orchestration pattern. And the pattern you choose has downstream consequences that the framework comparisons don't tell you.

Pattern 1: Handoffs (OpenAI Agents SDK model)

• Agent A does its work, hands off to Agent B with the full conversation context
• Simple to reason about, simple to debug
• Scales poorly beyond 8-10 agent types — the handoff graph becomes unmanageable
• Best for: Simple workflows with clear sequential steps, teams already on OpenAI

Pattern 2: Shared State Graph (LangGraph model)

• All agents read/write to a shared state object
• Transitions are checkpointed — you can replay any step
• Graph structure enables conditional routing that's invisible in handoff models
• Best for: Complex workflows, regulated industries, anything where auditability is required

Pattern 3: Role-Based (CrewAI model)

• Agents are assigned roles, tasks are assigned to roles, framework handles routing
• Fastest to prototype for business process automation
• Harder to debug when things go wrong — the routing is implicit
• Best for:原型 (prototypes), straightforward business workflows, non-technical team members who need to read the agent definitions

What I Actually Run in OpenClaw

Here's where it gets practical. I run OpenClaw 24/7 with multiple agents. My pattern:

1. One "manager" agent per domain — this is the agent that receives requests and decides what needs to happen
2. Specialized sub-agents for execution — research, writing, code review, whatever your domain needs
3. State flows through OpenClaw's session system — not through the framework

I don't actually use an external multi-agent framework for most of this. OpenClaw's session system, with spawned subagents and session targeting, handles the coordination layer directly. The multi-agent frameworks become relevant when I need:

• Complex workflow orchestration with branching and conditional logic I can't cleanly express in prompts
• Regulatory audit requirements that demand checkpointed state transitions
• Team members who need to read and modify agent definitions without understanding OpenClaw internals

In those cases, I've gravitated toward LangGraph for the checkpointing and auditability. The graph structure maps cleanly onto OpenClaw's session model — you can think of each session as a graph node, and the state object as the session context.

The One-Line Decision Framework

If you're choosing today and you don't want to go deep:

• Start with OpenClaw's built-in session/sessionTarget as your coordination layer — it's already there, already production-tested
• Add LangGraph if you need checkpointed failure recovery or regulated-industry audit trails
• Add CrewAI if you have non-technical stakeholders who need to read agent role definitions
• Use OpenAI Agents SDK only if you're locked to the OpenAI ecosystem and have simple handoff requirements

The framework is not the product. The orchestration pattern is the product. Choose the framework that forces you to think clearly about your pattern — not the one with the best marketing.

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My setup: OpenClaw 2026.5.7, running 3 manager agents across separate session targets with shared memory. LangGraph used only for the workflow I run as a separate service thatOpenClaw talks to via API calls.

Links: LangGraph documentation | CrewAI documentation | OpenAI Agents SDK | Google ADK documentation