Gorilla vs Langroid
Side-by-side comparison of two AI agent tools
Short answer
- Gorilla has had no commit in 6 months; Langroid is actively maintained (102 commits in the last 90 days).
- Gorilla is growing faster: +41 GitHub stars in the last 30 days vs +27 for Langroid.
- Pick Gorilla for: gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). Pick Langroid for: harness LLMs with Multi-Agent Programming.
From GitHub data refreshed daily.
Gorillaopen-source
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)
Langroidopen-source
Harness LLMs with Multi-Agent Programming
Metrics
| Gorilla | Langroid | |
|---|---|---|
| Stars | 13.0k | 4.1k |
| Star velocity /mo | 40.578947368421055 | 26.526315789473685 |
| Commits (90d) | 0 | 102 |
| Releases (6m) | 0 | 10 |
| Overall score | 0.22885397334190424 | 0.6024826648773315 |
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
- +独立架构设计，不依赖Langchain等框架，避免了复杂的依赖关系和潜在的兼容性问题
- +基于Actor模型的多智能体范式，提供清晰的抽象和直观的消息传递机制
- +支持几乎所有LLM模型，具有出色的模型兼容性和灵活性
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
- -相对较新的框架，生态系统和第三方集成相比成熟框架仍有差距
- -学习曲线需要理解多智能体概念，对初学者可能有一定门槛
- -社区规模相对较小（3943 stars），可能在遇到复杂问题时获得帮助的资源有限
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
- •构建需要多个AI智能体协作的复杂业务流程自动化系统
- •开发智能客服系统，不同智能体负责不同专业领域的问题处理
- •创建AI驱动的内容生成管道，多个智能体分工完成研究、写作、审核等任务
FAQ
- Which is more popular, Gorilla or Langroid?
- Gorilla has more GitHub stars (13,041 vs 4,111).
- Which is more actively developed, Gorilla or Langroid?
- Langroid had more commits in the last 90 days (102 vs 0).
- Should I use Gorilla or Langroid?
- 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.