Guidance
Programming paradigm and library for controlling LLM generation with interleaved prompting, control flow, and constrained output in one program.
- Well documented
- Permissive license
Overview
Guidance lets a developer write a single program that mixes plain text prompts, control flow (loops, conditionals), and constraints on exactly what the model can generate next - regex patterns, choice lists, or structured formats - rather than making multiple separate calls to an LLM API and stitching results together in application code. This can also make generation faster by skipping tokens that are already determined by the constraints.
Guidance is a fit for teams that want fine-grained, programmatic control over the generation process itself, a compared alternative to Outlines (also in this catalog) - both constrain LLM output, with Guidance emphasizing an interleaved program-and-generation authoring style.
Why it's in this catalog
Catalog rating: ★★☆☆☆ (2/5)
Structured-output generation library, meaningful overlap with Outlines/Instructor in the same category.
- Emerging
- Vendor-backed
- Categories
- AI & Machine Learning
- Languages
- Python
- License
- MIT
Related Projects
Outlines
Apache-2.0Library for constraining LLM output to a specific structure - JSON schema, regex, grammar - guaranteeing valid, parseable results.
Actively maintained
- AI & Machine Learning
Instructor
MITLibrary for getting reliable, structured data output from LLMs using function-calling APIs and Pydantic models for validation.
Actively maintained
- AI & Machine Learning
Milvus
Apache-2.0Open source vector database built for scalable similarity search and AI applications.
Actively maintained
- AI & Machine Learning
- Database
MLflow
Apache-2.0Open-source platform for managing the machine learning lifecycle: experiment tracking, model packaging, versioning, and deployment.
Actively maintained
- AI & Machine Learning
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