llama-cpp-agent vs rigging
Side-by-side comparison of two AI agent tools
Short answer
- llama-cpp-agent has had no commit in 6 months; rigging is actively maintained (39 commits in the last 90 days).
- llama-cpp-agent is growing faster: +6 GitHub stars in the last 30 days vs +2 for rigging.
- Pick llama-cpp-agent for: python framework for LLM chat, structured output, function calling, RAG, and agent chains. Pick rigging for: lightweight LLM Interaction Framework.
From GitHub data refreshed daily.
llama-cpp-agentfree
Python framework for LLM chat, structured output, function calling, RAG, and agent chains
riggingopen-source
Lightweight LLM Interaction Framework
Metrics
| llama-cpp-agent | rigging | |
|---|---|---|
| Stars | 659 | 418 |
| Star velocity /mo | 5.684210526315789 | 1.736842105263158 |
| Commits (90d) | 0 | 39 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | 603 | 1.8K |
| Overall score | 0.18581044753131928 | 0.4010959722216466 |
Pros
- +引导采样技术让未微调模型也能进行函数调用和结构化输出
- +支持多种后端提供商(llama-cpp-python、TGI、vllm等)提供良好兼容性
- +功能全面涵盖聊天、函数调用、RAG和代理链等核心能力
- +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
- -项目已不再维护，官方建议迁移到其他框架
- -对于简单用例可能存在过度设计的复杂性
- -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
- •构建具有函数调用能力的对话代理系统
- •实现带文档检索的RAG应用程序
- •从LLM中提取结构化数据和执行复杂的代理链工作流
- •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
FAQ
- Which is more popular, llama-cpp-agent or rigging?
- llama-cpp-agent has more GitHub stars (659 vs 418).
- Which is more actively developed, llama-cpp-agent or rigging?
- rigging had more commits in the last 90 days (39 vs 0).
- Should I use llama-cpp-agent or rigging?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.