Educational deep dive

AI agent frameworks compared for delivery teams.

Use this reference to choose between OpenClaw, LangGraph, CrewAI, AutoGen, and MetaGPT based on orchestration style, governance needs, and deployment speed.

5 frameworksFeature tableUse-case matrix

Feature comparison table

Focus on operational fit, not hype cycles. The strongest choice is the one your team can run safely and iterate quickly.

Framework	Orchestration	State model	Strengths	Tradeoff	Best fit
OpenClaw	Event-driven DAG + agent contracts	Checkpoint snapshots + vector memory adapters	Strong control plane, custom routing, auditability	Needs architecture discipline from day one	Teams building bespoke production workflows
LangGraph	Stateful graph execution and loops	Typed state object with deterministic transitions	Excellent for tool chains and resumable flow control	Complex graphs can become verbose quickly	Engineering-led teams shipping reliable assistants
CrewAI	Role-based task handoff among agents	Context passing between crew roles	Fast setup, intuitive multi-agent role design	Less granular control for advanced runtime constraints	Teams optimizing for rapid delivery and experimentation
AutoGen	Conversation loops among programmable agents	Message-first context windows and tool calls	Great for collaborative reasoning and simulation	Requires careful guardrails for long-running loops	Research-heavy and experimentation-first organizations
MetaGPT	Company-style SOP pipeline across specialist roles	Task docs and role artifacts through each phase	Clear role decomposition and planning artifacts	Heavier runtime and process assumptions	Teams that prefer structured SDLC-style automation

Use-case matrix

Match framework choice to delivery context. Ratings reflect implementation speed, governance fit, and maintenance burden.

Use case	OpenClaw	LangGraph	CrewAI	AutoGen	MetaGPT
MVP prototype in under one week	Good	Good	Best fit	Good	Fair
Regulated workflow with strict review gates	Best fit	Best fit	Fair	Fair	Good
Complex tool orchestration with retries	Best fit	Best fit	Good	Good	Fair
Autonomous content and campaign ops	Good	Good	Best fit	Good	Best fit
Research and multi-agent debate loops	Good	Good	Fair	Best fit	Good

FAQ

Next: evaluate your stack using the tooling ecosystem guide and map implementation sequence with the agentic lifecycle model.

Which framework should a small product team start with?

CrewAI and LangGraph are usually the fastest to production for small teams. Choose CrewAI for speed and role clarity, or LangGraph for deterministic control.

When does OpenClaw make more sense?

OpenClaw is ideal when you need strong governance, detailed observability, and a custom control plane that can enforce internal policies.

Can teams combine frameworks instead of picking one?

Yes. Many teams pilot with CrewAI or AutoGen, then move critical paths into LangGraph or OpenClaw as reliability and governance requirements grow.

How do these framework choices connect to business outcomes?

Use a lifecycle model with clear owner handoffs, then tie framework selection to delivery speed, governance risk, and conversion impact in production.

Decision support

Need help selecting a framework?

We can map your constraints, risk profile, and product goals to the right framework stack in a focused architecture session.

Book architecture review See production proof