--- title: "llm vs llmflows" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/simonw-llm-vs-stoyan-stoyanov-llmflows" tools: ["simonw-llm", "stoyan-stoyanov-llmflows"] --- # llm vs llmflows *GraphCanon updated Sep 20, 2026* ## Verdict Pick llm if decision-critical facts for 'llm'; pick llmflows if lLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. [llm](https://llm.datasette.io) reports 12k GitHub stars, 978 forks, and 689 open issues, last pushed Sep 2, 2026. [llmflows](https://llmflows.readthedocs.io) has 708 stars, 35 forks, and 19 open issues, last pushed Feb 20, 2025. Figures are from public GitHub metadata via [llm's repository](https://github.com/simonw/llm) and [llmflows's repository](https://github.com/stoyan-stoyanov/llmflows). | | [llm](/tools/simonw-llm.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Tagline | Access large language models from the command-line | Simple, Explicit and Transparent LLM Apps | | Stars | 12,473 | 708 | | Forks | 978 | 35 | | Open issues | 689 | 19 | | Language | Python | Python | | Adopt for | Decision-critical facts for 'llm' | LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution. | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm](/tools/simonw-llm.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 4d | 574d | | Open issues (now) | 689 | 19 | | Stars delta | +149 (30d) | +1 (30d) | | Open issues delta | +25 (30d) | 0 (30d) | | Full report | [trust report](/tools/simonw-llm/trust.md) | [trust report](/tools/stoyan-stoyanov-llmflows/trust.md) | ## Shared compatibility - **Python**: [llm](/tools/simonw-llm.md) - Python runtime; [llmflows](/tools/stoyan-stoyanov-llmflows.md) - Python runtime ## 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 ## Decision facts: llmflows - **Pricing:** freemium - The library is free to use, but additional services or support might incur costs. - **Requirements:** Min 2 GB RAM; Python environment is required. - **Adopt for:** LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency. - **License detail:** LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution. ## Choose when ### Choose llm if… - License: llm is Apache-2.0, llmflows 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: openai. - - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ### Choose llmflows if… - License: llmflows is MIT, llm is Apache-2.0. - Pricing: The library is free to use, but additional services or support might incur costs.. - Requirements: Min 2 GB RAM; Python environment is required.. - Tags unique to llmflows: chatgpt, gpt-4, llm, llm-inference. - When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process. ## 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. ## When NOT to use llmflows - Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy. - Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features ## Common questions ### What is the difference between llm and llmflows? llm: Access large language models from the command-line. llmflows: Simple, Explicit and Transparent LLM Apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm over llmflows? Choose llm over llmflows when License: llm is Apache-2.0, llmflows 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: 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 choose llmflows over llm? Choose llmflows over llm when License: llmflows is MIT, llm is Apache-2.0; Pricing: The library is free to use, but additional services or support might incur costs.; Requirements: Min 2 GB RAM; Python environment is required.; Tags unique to llmflows: chatgpt, gpt-4, llm, llm-inference; When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process. ### 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. ### When should I avoid llmflows? Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy. Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features ### Is llm or llmflows more popular on GitHub? llm has more GitHub stars (12,473 vs 708). Stars measure visibility, not whether either tool fits your constraints. ### Are llm and llmflows open source? Yes - both are open-source projects on GitHub (llm: Apache-2.0, llmflows: MIT). ### Where can I find alternatives to llm or llmflows? GraphCanon lists graph-backed alternatives at [llm alternatives](/tools/simonw-llm/alternatives) and [llmflows alternatives](/tools/stoyan-stoyanov-llmflows/alternatives) ([llm markdown twin](/tools/simonw-llm/alternatives.md), [llmflows markdown twin](/tools/stoyan-stoyanov-llmflows/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/simonw-llm-vs-stoyan-stoyanov-llmflows.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm or llmflows? llm: Very active. llmflows: Dormant. 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 llm and llmflows? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm trust report](/tools/simonw-llm/trust); [llmflows trust report](/tools/stoyan-stoyanov-llmflows/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=simonw-llm`](/api/graphcanon/graph?tool=simonw-llm) - 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/_