Which is more popular, AIOS or LobeHub?

LobeHub has more GitHub stars (82,957 vs 6,440).

Which is more actively developed, AIOS or LobeHub?

LobeHub had more commits in the last 90 days (2,430 vs 19).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

LobeHub is growing faster: +435 GitHub stars in the last 30 days vs +165 for AIOS.
Pick AIOS for: aIOS: AI Agent Operating System. Pick LobeHub for: lobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling.

From GitHub data refreshed daily.

AIOS: AI Agent Operating System

LobeHubopen-source

🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.

+Comprehensive resource management with dedicated modules for LLM, memory, storage, and tool management
+Dual interface support with both Web UI and Terminal UI for flexible development workflows
+Modular architecture separating kernel and SDK concerns, allowing focused development on either system-level or application-level features

-High complexity as an operating system-level solution may present steep learning curve for developers
-Requires understanding of both kernel and SDK components for full utilization
-Appears to be primarily research-focused, potentially limiting production readiness

•Development and deployment of complex LLM-based AI agents requiring comprehensive resource management
•Building computer-use agents that need VM control and computer contextualization capabilities
•Research projects exploring AI agent operating system architectures and agent ecosystem development

Which is more popular, AIOS or LobeHub?: LobeHub has more GitHub stars (82,957 vs 6,440).
Which is more actively developed, AIOS or LobeHub?: LobeHub had more commits in the last 90 days (2,430 vs 19).
Should I use AIOS or LobeHub?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.