langgraph vs llm-guard
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
llm-guardopen-source
The Security Toolkit for LLM Interactions
Metrics
| langgraph | llm-guard | |
|---|---|---|
| Stars | 28.0k | 2.8k |
| Star velocity /mo | 2.5k | 142.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8081963872278098 | 0.4561154149793364 |
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
- +全面的安全覆盖：提供从输入净化到输出检测的完整安全链，包括数据泄露防护、有害内容检测和提示注入攻击防护
- +生产就绪且易于集成：开箱即用的设计，支持Python库和API两种部署方式，可无缝集成到现有LLM工作流中
- +模块化扫描器架构：提供多种专用扫描器（匿名化、代码检测、主题过滤等），可根据具体需求灵活配置和组合
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
- -持续开发状态：文档中提到仓库在不断改进和更新中，可能存在API变更或功能稳定性问题
- -高级功能依赖性：使用更高级功能时需要自动安装额外的依赖库，可能增加部署复杂性
- -Python版本要求：仅支持Python 3.9及以上版本，对旧版本Python环境不兼容
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
- •企业级LLM应用安全防护：为生产环境中的聊天机器人、内容生成系统等添加安全防护层，防止敏感数据泄露
- •提示注入攻击防护：保护LLM应用免受恶意用户通过精心构造的提示来绕过系统限制或获取未授权信息的攻击
- •内容审核和合规性检查：对LLM生成的内容进行自动检测和过滤，确保输出符合企业政策和法规要求