Guide · Updated June 13, 2026
LangGraph vs CrewAI vs AutoGen Workflow Patterns
Agent workflow users need help choosing between graph orchestration, multi-agent collaboration, and simpler local CLI workflows. The right answer depends on state, control, observability, and risk tolerance.
When graph workflows fit
Graph workflows are useful when the task has state, branches, retries, or review checkpoints. LangGraph-style systems are a strong fit for long-running or stateful agents where observability and control matter.
- Use for research pipelines, triage loops, code change review gates, and multi-step decision flows.
- Require explicit state, node boundaries, retry behavior, and failure handling.
- Avoid for simple one-shot prompts where a local command is enough.
When multi-agent flows fit
CrewAI Flow-style systems are useful when teams want structured coordination around multiple agents or crews. They need stricter review because more moving pieces can hide permission and data-flow mistakes.
- Identify which agent owns each task.
- Log intermediate decisions and tool calls.
- Keep external writes behind approval gates.
Recommendation for this marketplace
For public discovery, list graph and flow repositories when there is visible source evidence, a runnable example, and a reviewable state model. For paid local workflow apps, start with narrower code change workflows before selling general orchestration.