--- title: "guidance vs llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/guidance-ai-guidance-vs-simonw-llm" tools: ["guidance-ai-guidance", "simonw-llm"] --- # guidance vs llm *GraphCanon updated Aug 8, 2026* ## Verdict Pick guidance if 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; pick llm if decision-critical facts for 'llm'. [guidance](https://github.com/guidance-ai/guidance) reports 22k GitHub stars, 1.2k forks, and 316 open issues, last pushed May 21, 2026. [llm](https://llm.datasette.io) has 12k stars, 939 forks, and 664 open issues, last pushed Aug 5, 2026. Figures are from public GitHub metadata via [guidance's repository](https://github.com/guidance-ai/guidance) and [llm's repository](https://github.com/simonw/llm). | | [guidance](/tools/guidance-ai-guidance.md) | [llm](/tools/simonw-llm.md) | | --- | --- | --- | | Tagline | A guidance language for controlling large language models. | Access large language models from the command-line | | Stars | 21,706 | 12,324 | | Forks | 1,198 | 939 | | Open issues | 316 | 664 | | Language | Jupyter Notebook | Python | | Adopt for | 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 | Decision-critical facts for 'llm' | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [guidance](/tools/guidance-ai-guidance.md) | [llm](/tools/simonw-llm.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 78d | 2d | | Open issues (now) | 316 | 664 | | Owner type | Organization | User | | Full report | [trust report](/tools/guidance-ai-guidance/trust.md) | [trust report](/tools/simonw-llm/trust.md) | ## Shared compatibility - **Python**: [guidance](/tools/guidance-ai-guidance.md) - Python runtime; [llm](/tools/simonw-llm.md) - Python runtime ## Decision facts: guidance - **Adopt for:** 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 ## Decision facts: llm - **Requirements:** - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities. - **Adopt for:** Decision-critical facts for 'llm' - **License detail:** Apache-2.0 ## Choose when ### Choose guidance if… - guidance is primarily Jupyter Notebook; llm is Python. - License: guidance is MIT, llm is Apache-2.0. - Tags unique to guidance: backend support, control language, language-models, pip-installable. - When you need a specific language to finely control various LLM backends including Transformers, llama.cpp, and OpenAI ### Choose llm if… - llm is primarily Python; guidance is Jupyter Notebook. - License: llm is Apache-2.0, guidance is MIT. - Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities.. - Tags unique to llm: ai, llms, openai. - - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ## When NOT to use guidance - 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 ## When NOT to use llm - - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models. ## Common questions ### What is the difference between guidance and llm? guidance: A guidance language for controlling large language models.. llm: Access large language models from the command-line. See the comparison table for live GitHub stats and shared categories. ### When should I choose guidance over llm? Choose guidance over llm when guidance is primarily Jupyter Notebook; llm is Python; License: guidance is MIT, llm is Apache-2.0; Tags unique to guidance: backend support, control language, language-models, pip-installable; When you need a specific language to finely control various LLM backends including Transformers, llama.cpp, and OpenAI. ### When should I choose llm over guidance? Choose llm over guidance when llm is primarily Python; guidance is Jupyter Notebook; License: llm is Apache-2.0, guidance is MIT; Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities.; Tags unique to llm: ai, llms, openai; - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ### When should I avoid guidance? 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 ### When should I avoid llm? - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models. ### Is guidance or llm more popular on GitHub? guidance has more GitHub stars (21,706 vs 12,324). Stars measure visibility, not whether either tool fits your constraints. ### Are guidance and llm open source? Yes - both are open-source projects on GitHub (guidance: MIT, llm: Apache-2.0). ### Where can I find alternatives to guidance or llm? GraphCanon lists graph-backed alternatives at [guidance alternatives](/tools/guidance-ai-guidance/alternatives) and [llm alternatives](/tools/simonw-llm/alternatives) ([guidance markdown twin](/tools/guidance-ai-guidance/alternatives.md), [llm markdown twin](/tools/simonw-llm/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/guidance-ai-guidance-vs-simonw-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, guidance or llm? guidance: Steady. llm: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for guidance and llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [guidance trust report](/tools/guidance-ai-guidance/trust); [llm trust report](/tools/simonw-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=guidance-ai-guidance`](/api/graphcanon/graph?tool=guidance-ai-guidance) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_