Which is more popular, AIOS or Eidolon?

AIOS has more GitHub stars (6,442 vs 492).

Which is more actively developed, AIOS or Eidolon?

AIOS had more commits in the last 90 days (19 vs 0).

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

Eidolon has had no commit in 21 months; AIOS is actively maintained (19 commits in the last 90 days).
AIOS is growing faster: +165 GitHub stars in the last 30 days vs +1 for Eidolon.
Pick AIOS for: aIOS: AI Agent Operating System. Pick Eidolon for: the first AI Agent Server, Eidolon is a pluggable Agent SDK and enterprise ready, deployment server.

From GitHub data refreshed daily.

AIOS: AI Agent Operating System

Eidolonopen-source

The first AI Agent Server, Eidolon is a pluggable Agent SDK and enterprise ready, deployment server for Agentic applications

+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

+Service-oriented architecture with built-in HTTP servers eliminates deployment complexity and makes agents production-ready by default
+Excellent agent-to-agent communication through well-defined interfaces and dynamic tool generation from OpenAPI schemas
+Highly modular design allows easy swapping of components (LLMs, RAG, tools) without vendor lock-in, enabling rapid adaptation to AI advances

-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

-Relatively small community with 485 GitHub stars may mean limited ecosystem and third-party integrations
-Service-oriented approach may introduce overhead for simple single-agent use cases that don't require distributed architecture
-Documentation and examples appear limited based on basic quickstart guide mention, potentially steeper learning curve

•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

•Enterprise multi-agent systems requiring scalable deployment and agent-to-agent communication in production environments
•Organizations needing to frequently swap AI components (different LLMs, RAG systems) without rebuilding entire agent infrastructure
•Development teams building agent services that need to integrate with existing microservice architectures via standard HTTP APIs

Which is more popular, AIOS or Eidolon?: AIOS has more GitHub stars (6,442 vs 492).
Which is more actively developed, AIOS or Eidolon?: AIOS had more commits in the last 90 days (19 vs 0).
Should I use AIOS or Eidolon?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.