auto-dev vs langgraph
Side-by-side comparison of two AI agent tools
auto-devopen-source
🧙AutoDev: the AI-native Multi-Agent development platform built on Kotlin Multiplatform, covering all 7 phases of SDLC.
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| auto-dev | langgraph | |
|---|---|---|
| Stars | 4.4k | 28.0k |
| Star velocity /mo | 45 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.6145646079458494 | 0.8081963872278098 |
Pros
- +基于Kotlin Multiplatform的统一架构，实现真正的写一次到处运行
- +覆盖SDLC全部7个阶段的专业化AI代理，提供端到端开发支持
- +支持8个以上平台的原生体验，包括主流IDE、桌面、移动和Web端
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
Cons
- -3.0版本仍处于Alpha阶段，可能存在稳定性问题
- -iOS平台功能仍在生产就绪阶段，可能功能不够完整
- -作为多平台解决方案，可能在某些特定平台上的体验不如专门为该平台优化的工具
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
Use Cases
- •大型软件项目需要统一的跨平台开发体验和完整生命周期管理
- •分布式团队成员使用不同操作系统和开发环境时的协作开发
- •希望在移动端进行代码审查或轻量级开发任务的移动办公场景
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions