guidance vs Instructor
Side-by-side comparison of two AI agent tools
Short answer
- Instructor is growing faster: +215 GitHub stars in the last 30 days vs +67 for guidance.
- Pick guidance for: a guidance language for controlling large language models. Pick Instructor for: structured outputs for llms.
From GitHub data refreshed daily.
guidanceopen-source
A guidance language for controlling large language models.
Instructoropen-source
structured outputs for llms
Metrics
| guidance | Instructor | |
|---|---|---|
| Stars | 21.8k | 14.0k |
| Star velocity /mo | 66.63157894736841 | 214.57894736842107 |
| Commits (90d) | 0 | 93 |
| Releases (6m) | 0 | 4 |
| Downloads (30d, npm + PyPI) | 11.8K | 8.4M |
| Overall score | 0.253336309574155 | 0.5756266090102762 |
Pros
- +Pythonic interface that integrates naturally with existing Python workflows and familiar programming patterns
- +Constrained generation capabilities that guarantee output syntax and structure using regex and context-free grammars
- +Multi-backend support allowing seamless switching between different model providers and local/cloud deployments
- +极简API设计：只需定义Pydantic模型即可获得结构化输出，相比传统方法大幅减少代码复杂度
- +内置Pydantic集成：提供强类型验证、IDE智能提示和自动错误处理，确保数据质量和开发体验
- +自动化处理机制：内置JSON解析、验证错误处理和失败重试，无需手动管理复杂的错误场景
Cons
- -Requires Python programming knowledge, limiting accessibility for non-technical users
- -Learning curve for advanced constraint features like context-free grammars and complex regex patterns
- -Dependent on backend availability and may require additional setup for specific model types
- -Python生态限制：基于Pydantic构建，仅支持Python环境，无法在其他编程语言中使用
- -依赖LLM质量：提取准确性完全依赖于底层语言模型的理解能力，模型局限性会直接影响结果
- -功能范围有限：专注于结构化数据提取，不支持复杂的多轮对话、推理链或智能体工作流
Use Cases
- •Structured data extraction from documents or conversations where output must conform to specific JSON schemas or formats
- •Building conversational AI applications that require controlled dialogue flows and predictable response structures
- •Cost-effective alternative to fine-tuning when you need specific output formatting without retraining models
- •从非结构化文本中提取实体信息，如从客户反馈中提取用户资料、产品特征和情感倾向
- •将自然语言输入转换为API就绪的结构化数据，如将用户查询转换为数据库查询参数
- •处理文档和消息转换为数据库模式，如将邮件内容解析为CRM系统的标准化记录格式
FAQ
- Which is more popular, guidance or Instructor?
- guidance has more GitHub stars (21,786 vs 13,971).
- Which is more actively developed, guidance or Instructor?
- Instructor had more commits in the last 90 days (93 vs 0).
- Should I use guidance or Instructor?
- 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.