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Framework Comparison

This is a narrow, technical comparison intended to clarify scope, not market against other projects.

LangGraph

  • Strong fit when you want graph-oriented control flow and state transitions.
  • nano-agent-stack is more opinionated about organizational structure: departments, managers, workers, and approval boundaries.
  • If your primary abstraction is a graph, LangGraph may be the more direct tool. If your primary abstraction is a team or department hierarchy, nano-agent-stack is easier to reason about.

CrewAI

  • CrewAI emphasizes collaborative agent roles and task delegation flows.
  • nano-agent-stack is intentionally smaller and lower-level. It focuses more on infrastructure boundaries such as policies, trace hooks, memory interfaces, and provider abstraction.
  • CrewAI may be faster for higher-level agent app assembly. nano-agent-stack is more useful when you want to expose execution structure as reusable infrastructure.

AutoGen

  • AutoGen provides flexible multi-agent interaction patterns and model-centric workflows.
  • nano-agent-stack puts more weight on explicit operational topology and auditable runtime boundaries.
  • If you need conversational agent interplay first, AutoGen may be the stronger fit. If you need developer-facing infrastructure primitives for department-style orchestration, nano-agent-stack is narrower but more focused.

Summary

nano-agent-stack is not trying to be the broadest agent framework. It is trying to be a clean open-source base for modeling organizations as auditable, human-supervised multi-agent systems.