guidance vs llama-cpp-agent
Side-by-side comparison of two AI agent tools
Short answer
- llama-cpp-agent has had no commit in 6 months; guidance is actively maintained.
- guidance is growing faster: +67 GitHub stars in the last 30 days vs +6 for llama-cpp-agent.
- Pick guidance for: a guidance language for controlling large language models. Pick llama-cpp-agent for: python framework for LLM chat, structured output, function calling, RAG, and agent chains.
From GitHub data refreshed daily.
guidanceopen-source
A guidance language for controlling large language models.
llama-cpp-agentfree
Python framework for LLM chat, structured output, function calling, RAG, and agent chains
Metrics
| guidance | llama-cpp-agent | |
|---|---|---|
| Stars | 21.8k | 659 |
| Star velocity /mo | 66.63157894736841 | 5.684210526315789 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | 11.8K | 603 |
| Overall score | 0.253336309574155 | 0.18581044753131928 |
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
- +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
- -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
- -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
- •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
- •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, guidance or llama-cpp-agent?
- guidance has more GitHub stars (21,786 vs 659).
- Which is more actively developed, guidance or llama-cpp-agent?
- guidance had more commits in the last 90 days (0 vs 0).
- Should I use guidance 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.