GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Guidance is a specialized tool written in Jupyter Notebooks that provides a unique language to control large language models (LLMs) across multiple backends such as Transformers, llama.cpp, and OpenAI. It's open-source,轻
Good fit when
- When you need a specific language to finely control various LLM backends including Transformers, llama.cpp, and OpenAI
- If your project requires integration with the MIT-licensed codebase for flexibility and ease of modification
Avoid when
- When your project is strictly confined to using only one type of backend which you can manage without a specialized control language
- If your development environment does not support or prefer Jupyter Notebooks, Guidance may not be the best choice
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (108d since push)
- As of Sep 7, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 7, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/guidance-ai/guidanceSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Guidance is a tool that uses a specialized language to control and interact with various large language model backends including Transformers, llama.cpp, OpenAI, etc. It can be installed easily via PyPI and supports multiple backend systems.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Sep 7, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Install Guidance is available through PyPI and supports a variety of backends (Transformers, llama.cpp, OpenAI, etc.). If you already have the backend required for your model, you can simply run
For agents
This page has a .md twin and JSON over the API.