Gorilla vs llama-cpp-agent
Side-by-side comparison of two AI agent tools
Short answer
- Gorilla is growing faster: +41 GitHub stars in the last 30 days vs +6 for llama-cpp-agent.
- Pick Gorilla for: gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). Pick llama-cpp-agent for: python framework for LLM chat, structured output, function calling, RAG, and agent chains.
From GitHub data refreshed daily.
Gorillaopen-source
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)
llama-cpp-agentfree
Python framework for LLM chat, structured output, function calling, RAG, and agent chains
Metrics
| Gorilla | llama-cpp-agent | |
|---|---|---|
| Stars | 13.0k | 659 |
| Star velocity /mo | 40.578947368421055 | 5.684210526315789 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | — | 603 |
| Overall score | 0.22885397334190424 | 0.18581044753131928 |
Pros
- +提供业界领先的Berkeley Function Calling Leaderboard，为LLM工具调用能力评估设立标准
- +支持复杂的多轮对话和多步骤函数调用评估，包含状态管理和错误恢复机制
- +活跃的学术研究社区，持续更新评估方法和数据集，与LMSYS等知名平台合作
- +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
Cons
- -主要面向研究用途，对于生产环境的实际应用指导有限
- -文档信息不够完整，缺乏详细的实施和部署指南
- -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
Use Cases
- •AI研究人员评估和比较不同LLM的函数调用能力表现
- •开发团队基准测试自己的AI智能体在复杂工具集成场景中的性能
- •学术机构研究多模态AI系统在真实世界任务中的工具使用效果
- •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
FAQ
- Which is more popular, Gorilla or llama-cpp-agent?
- Gorilla has more GitHub stars (13,041 vs 659).
- Which is more actively developed, Gorilla or llama-cpp-agent?
- Gorilla had more commits in the last 90 days (0 vs 0).
- Should I use Gorilla or llama-cpp-agent?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.