OpenHuman vs rigging

Side-by-side comparison of two AI agent tools

Short answer

  • OpenHuman is growing faster: +2,510 GitHub stars in the last 30 days vs +2 for rigging.
  • Pick OpenHuman for: openHuman is the fastest, cheapest, most efficient open-source agent harness. Pick rigging for: lightweight LLM Interaction Framework.

From GitHub data refreshed daily.

O
OpenHumanopen-source

OpenHuman is the fastest, cheapest, most efficient open-source agent harness. Written in Rust

riggingopen-source

Lightweight LLM Interaction Framework

Metrics

OpenHumanrigging
Stars40.5k418
Star velocity /mo2.5k1.736842105263158
Commits (90d)22.8k39
Releases (6m)100
Downloads (30d, npm + PyPI)—1.8K
Overall score0.93082273956958560.4010959722216466

Pros

    • +结构化输出支持：通过 Pydantic 模型提供类型安全的 LLM 响应处理，减少数据解析错误
    • +广泛的模型兼容性：集成 LiteLLM、vLLM 和 transformers，支持几乎所有主流语言模型
    • +生产就绪的架构：内置异步批处理、跟踪支持、错误处理等企业级功能

    Cons

      • -相对较新的项目：GitHub 星数较少（407），社区生态和文档可能不如成熟框架完善
      • -依赖性较重：依赖 LiteLLM、Pydantic 等多个外部库，可能增加环境配置复杂度

      Use Cases

        • •企业级 AI 应用开发：需要集成多个 LLM 提供商并确保类型安全的生产环境
        • •大规模内容生成：利用异步批处理能力进行大量文本、数据的自动化生成
        • •多模型实验和比较：通过连接字符串轻松切换不同模型进行性能评估
        View OpenHuman DetailsView rigging Details

        FAQ

        Which is more popular, OpenHuman or rigging?
        OpenHuman has more GitHub stars (40,486 vs 418).
        Which is more actively developed, OpenHuman or rigging?
        OpenHuman had more commits in the last 90 days (22,774 vs 39).
        Should I use OpenHuman or rigging?
        Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.