framework comparison

Klawty vs AutoGen

AutoGen from Microsoft Research pioneered conversation-based multi-agent patterns and remains a powerful research tool. Klawty is built for production: task execution, proposal workflows, security sandboxing, and native channel integrations.

compare --autogen --klawty
FeatureAutoGenKlawty
BackingMicrosoft Researchdcode technologies
ParadigmConversation-based multi-agentOS with task execution + proposals
LanguagePython + .NETJavaScript / TypeScript
ChannelsNone20+ native
SecurityBasic Docker sandboxDocker + policy engine + PII + integrity
MemoryConversation history only6-tier + Qdrant + context threads
Managed hostingNoneYes (from 79€/mo)
DashboardNoneCLI + TUI + web portal
Production proofResearch-grade8 agents, 1,000+ tasks/mo in production
Community55.9K stars302K+ (OpenClaw base)
When to choose

AutoGen

AutoGen is a strong choice for research and experimentation with multi-agent conversation patterns. Its Microsoft backing and active research community make it ideal for exploring new agent paradigms.

  • +You are researching multi-agent conversation patterns
  • +You need .NET support alongside Python
  • +Your project is experimental and flexibility matters more than production features
  • +You want Microsoft ecosystem integration
When to choose

Klawty

Klawty is the right choice when you are deploying agents to production with real business operations, security requirements, and cost control needs.

  • +You are deploying agents to production, not running experiments
  • +You need native channel integrations (Discord, Slack, Telegram)
  • +Security and compliance are requirements, not nice-to-haves
  • +You want managed hosting with a web portal and cost tracking
  • +You need proven production reliability (1,000+ tasks/mo track record)

Ready to try Klawty?

Free and open source. Clone, install, deploy your first agent in under 5 minutes.

Get Klawty on GitHubOr go managed from 79€/mo