--- title: "LMFlow vs llmflows" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/optimalscale-lmflow-vs-stoyan-stoyanov-llmflows" tools: ["optimalscale-lmflow", "stoyan-stoyanov-llmflows"] --- # LMFlow vs llmflows *GraphCanon updated Aug 16, 2026* ## Verdict Pick LMFlow if lMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment; pick llmflows if lLMFlows is designed for developers seeking a streamlined way to build and deploy language model applications with an emphasis on transparency and simplicity. [LMFlow](https://optimalscale.github.io/LMFlow/) reports 8.5k GitHub stars, 825 forks, and 88 open issues, last pushed May 22, 2026. [llmflows](https://llmflows.readthedocs.io) has 707 stars, 35 forks, and 19 open issues, last pushed Feb 20, 2025. Figures are from public GitHub metadata via [LMFlow's repository](https://github.com/OptimalScale/LMFlow) and [llmflows's repository](https://github.com/stoyan-stoyanov/llmflows). | | [LMFlow](/tools/optimalscale-lmflow.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Tagline | An Extensible Toolkit for Finetuning and Inference of Large Foundation Models | Simple Explicit Transparent LLM Apps | | Stars | 8,486 | 707 | | Forks | 825 | 35 | | Open issues | 88 | 19 | | Language | Python | Python | | Adopt for | LMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment. | LLMFlows is designed for developers seeking a streamlined way to build and deploy language model applications with an emphasis on transparency and simplicity. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LMFlow](/tools/optimalscale-lmflow.md) | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 72d | 541d | | Open issues (now) | 88 | 19 | | Stars delta | Unknown | +2 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/optimalscale-lmflow/trust.md) | [trust report](/tools/stoyan-stoyanov-llmflows/trust.md) | ## Shared compatibility - **Python**: [LMFlow](/tools/optimalscale-lmflow.md) - Python runtime; [llmflows](/tools/stoyan-stoyanov-llmflows.md) - Python runtime ## Decision facts: LMFlow - **Adopt for:** LMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment. - **License detail:** Apache-2.0 ## Decision facts: llmflows - **Adopt for:** LLMFlows is designed for developers seeking a streamlined way to build and deploy language model applications with an emphasis on transparency and simplicity. ## Choose when ### Choose LMFlow if… - License: LMFlow is Apache-2.0, llmflows is MIT. - Tags unique to LMFlow: deep-learning, instruction-following, language-model, pretrained-models. - You require an extendable framework to fine-tune or conduct inference operations on large foundational models where a user-friendly chatbot UI can be integrated using Gradio. ### Choose llmflows if… - License: llmflows is MIT, LMFlow is Apache-2.0. - Tags unique to llmflows: ai, gpt-4, llm, llm-inference. - If you need a Python framework that prioritizes the clarity and ease of use in developing language model apps. ## When NOT to use LMFlow - You do not need a Python-based solution for your large foundation model tasks, or if your projects specifically require languages other than Python. - Your project requires commercial use with simplified authorization processes, since LMFlow demands signing a specific document to obtain authorization for commercial use. ## When NOT to use llmflows - Do not choose LLMFlows if advanced customization or integration with more complex frameworks is required for your project. - Avoid using this tool in scenarios where you need real-time adaptive features that are highly dynamic, as LLMFlows emphasizes explicitness which can limit flexibility. ## Common questions ### What is the difference between LMFlow and llmflows? LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. llmflows: Simple Explicit Transparent LLM Apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose LMFlow over llmflows? Choose LMFlow over llmflows when License: LMFlow is Apache-2.0, llmflows is MIT; Tags unique to LMFlow: deep-learning, instruction-following, language-model, pretrained-models; You require an extendable framework to fine-tune or conduct inference operations on large foundational models where a user-friendly chatbot UI can be integrated using Gradio. ### When should I choose llmflows over LMFlow? Choose llmflows over LMFlow when License: llmflows is MIT, LMFlow is Apache-2.0; Tags unique to llmflows: ai, gpt-4, llm, llm-inference; If you need a Python framework that prioritizes the clarity and ease of use in developing language model apps. ### When should I avoid LMFlow? You do not need a Python-based solution for your large foundation model tasks, or if your projects specifically require languages other than Python. Your project requires commercial use with simplified authorization processes, since LMFlow demands signing a specific document to obtain authorization for commercial use. ### When should I avoid llmflows? Do not choose LLMFlows if advanced customization or integration with more complex frameworks is required for your project. Avoid using this tool in scenarios where you need real-time adaptive features that are highly dynamic, as LLMFlows emphasizes explicitness which can limit flexibility. ### Is LMFlow or llmflows more popular on GitHub? LMFlow has more GitHub stars (8,486 vs 707). Stars measure visibility, not whether either tool fits your constraints. ### Are LMFlow and llmflows open source? Yes - both are open-source projects on GitHub (LMFlow: Apache-2.0, llmflows: MIT). ### Where can I find alternatives to LMFlow or llmflows? GraphCanon lists graph-backed alternatives at [LMFlow alternatives](/tools/optimalscale-lmflow/alternatives) and [llmflows alternatives](/tools/stoyan-stoyanov-llmflows/alternatives) ([LMFlow markdown twin](/tools/optimalscale-lmflow/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/optimalscale-lmflow-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, LMFlow or llmflows? LMFlow: Steady. 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 LMFlow and llmflows? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LMFlow trust report](/tools/optimalscale-lmflow/trust); [llmflows trust report](/tools/stoyan-stoyanov-llmflows/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=optimalscale-lmflow`](/api/graphcanon/graph?tool=optimalscale-lmflow) - 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/_