Comparison
LMFlow vs llmflows
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 a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency.
Markdown twin · LMFlow alternatives · llmflows alternatives
GraphCanon updated Sep 26, 2026
13views this month
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Trust & integrity
| Signal | LMFlow | llmflows |
|---|---|---|
| Maintenance | Steady (46d since push) As of Sep 26, 2026 · github_public_v1 | Dormant (574d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 26, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | Published findings As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LMFlow
- An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
- llmflows
- Simple, Explicit and Transparent LLM Apps
Stars
- LMFlow
- 8.5k
- llmflows
- 708
Forks
- LMFlow
- 822
- llmflows
- 35
Open issues
- LMFlow
- 88
- llmflows
- 19
Language
- LMFlow
- Python
- llmflows
- Python
Adopt for
- LMFlow
- LMFlow is an extensible Python toolkit for fine-tuning and inference on large foundation models with Gradio-based chatbot deployment.
- llmflows
- LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency.
Persona
- LMFlow
- -
- llmflows
- -
Runtime
- LMFlow
- -
- llmflows
- -
License
- LMFlow
- Apache-2.0
- llmflows
- LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution.
Last pushed
- LMFlow
- Aug 10, 2026
- llmflows
- Feb 20, 2025
Categories
- LMFlow
- Inference & Serving, LLM Frameworks
- llmflows
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- LMFlow
- Steady (60%)
- llmflows
- Dormant (18%)
Days since push
- LMFlow
- 46d
- llmflows
- 574d
Open issues (now)
- LMFlow
- 88
- llmflows
- 19
Owner type
- LMFlow
- Organization
- llmflows
- User
Full report
- LMFlow
- Trust report
- llmflows
- Trust report
Shared compatibility
- Python · LMFlow: Python runtime · llmflows: Python runtime
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.
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.
Choose llmflows if…
- License: llmflows is MIT, LMFlow 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: ai, 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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (OptimalScale/LMFlow) · observed Sep 26, 2026
- GitHub forks (OptimalScale/LMFlow) · observed Sep 26, 2026
- Last push (OptimalScale/LMFlow) · observed Aug 10, 2026
- License file (Apache-2.0) · observed Sep 26, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (stoyan-stoyanov/llmflows) · observed Sep 20, 2026
- GitHub forks (stoyan-stoyanov/llmflows) · observed Sep 20, 2026
- Last push (stoyan-stoyanov/llmflows) · observed Feb 20, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: LMFlow 8.5k · llmflows 708 (synced Sep 26, 2026).
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 and 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; 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: ai, 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 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?
- 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 LMFlow or llmflows more popular on GitHub?
- LMFlow has more GitHub stars (8,487 vs 708). 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 and llmflows alternatives (LMFlow markdown twin, llmflows markdown twin), 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 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; llmflows trust report.