llm.ts vs rigging

Side-by-side comparison of two AI agent tools

Short answer

  • llm.ts has had no commit in 41 months; rigging is actively maintained (39 commits in the last 90 days).
  • rigging is growing faster: +2 GitHub stars in the last 30 days vs +-0 for llm.ts.
  • Pick llm.ts for: call any LLM with a single API. Pick rigging for: lightweight LLM Interaction Framework.

From GitHub data refreshed daily.

llm.tsopen-source

Call any LLM with a single API. Zero dependencies.

riggingopen-source

Lightweight LLM Interaction Framework

Metrics

llm.tsrigging
Stars213418
Star velocity /mo-0.158730158730158721.746031746031746
Commits (90d)039
Releases (6m)00
Overall score0.13478448564358370.4197411780115613

Pros

  • +Unified API that abstracts complexity across 30+ models from multiple providers (OpenAI, Cohere, HuggingFace)
  • +Extremely lightweight with zero dependencies and under 10kB minified size, suitable for any environment
  • +Batch processing capability to send multiple prompts to multiple models in a single request with standardized response format
  • +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

  • -Requires managing API keys for each provider separately, increasing configuration complexity
  • -Limited to older generation models with no apparent support for newer models like GPT-4 or Claude 3
  • -No streaming support mentioned, which may limit real-time applications
  • -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

  • •A/B testing and benchmarking different LLMs with identical prompts to compare output quality and characteristics
  • •Building LLM comparison tools or research platforms that need to evaluate multiple models simultaneously
  • •Prototyping applications that require provider flexibility without committing to a single LLM vendor
  • •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 llm.ts DetailsView rigging Details

FAQ

Which is more popular, llm.ts or rigging?
rigging has more GitHub stars (418 vs 213).
Which is more actively developed, llm.ts or rigging?
rigging had more commits in the last 90 days (39 vs 0).
Should I use llm.ts 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.
llm.ts vs rigging (2026): GitHub Stats, Features & Which to Choose