langgraph vs open-llms
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
open-llmsopen-source
📋 A list of open LLMs available for commercial use.
Metrics
| langgraph | open-llms | |
|---|---|---|
| Stars | 28.0k | 12.7k |
| Star velocity /mo | 2.5k | 52.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8081963872278098 | 0.4171987579270238 |
Pros
- +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
- +专注于商业友好许可证的模型，为企业应用提供明确的法律保障
- +提供全面的模型元数据，包括参数规模、上下文长度、检查点链接等关键信息
- +持续维护更新，拥有活跃的社区贡献者和较高的 GitHub 关注度
Cons
- -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
- -仅是静态文档列表，不是可直接使用的工具或 API 服务
- -在快速变化的 LLM 生态中，信息可能存在滞后性
- -缺乏性能基准测试和模型间的详细比较数据
Use Cases
- •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
- •企业寻找可商业部署的开源 LLM 替代方案，避免专有模型的许可费用
- •研究者快速筛选适合特定研究项目的开源模型和相关论文资源
- •开发者评估不同开源模型的规模和能力，为项目选择最合适的模型架构