guidance vs LMQL
Side-by-side comparison of two AI agent tools
Short answer
- LMQL has had no commit in 16 months; guidance is actively maintained.
- guidance is growing faster: +67 GitHub stars in the last 30 days vs +9 for LMQL.
- Pick guidance for: a guidance language for controlling large language models. Pick LMQL for: a language for constraint-guided and efficient LLM programming.
From GitHub data refreshed daily.
guidanceopen-source
A guidance language for controlling large language models.
LMQLopen-source
A language for constraint-guided and efficient LLM programming.
Metrics
| guidance | LMQL | |
|---|---|---|
| Stars | 21.8k | 4.2k |
| Star velocity /mo | 66.82539682539682 | 9.047619047619047 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Overall score | 0.2638438459635918 | 0.20321882837913116 |
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
- +Native Python integration makes it accessible to existing Python developers while adding powerful LLM capabilities
- +Constraint-based programming with the `where` keyword provides precise control over LLM outputs and behavior
- +Seamless combination of traditional programming logic with LLM reasoning in a single, unified language
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
- -As a specialized language, it requires learning new syntax and concepts beyond standard Python programming
- -Limited to LLM-focused use cases, making it less suitable for general-purpose programming tasks
- -Relatively new with 4,161 GitHub stars, indicating a smaller community compared to mainstream programming languages
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
- •Building conversational AI applications that require complex logic and constraint-based response generation
- •Creating automated content analysis and generation systems with precise output formatting requirements
- •Developing interactive AI tutoring systems that combine algorithmic assessment with natural language reasoning
FAQ
- Which is more popular, guidance or LMQL?
- guidance has more GitHub stars (21,785 vs 4,218).
- Which is more actively developed, guidance or LMQL?
- guidance had more commits in the last 90 days (0 vs 0).
- Should I use guidance or LMQL?
- 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.