Comparison
keras vs LMFlow
Verdict
Pick keras when tags unique to keras: data-science, neural-networks, machine-learning, tensorflow; pick LMFlow when tags unique to LMFlow: pretrained-models, chatgpt, transformer, language-model.
Markdown twin · keras alternatives · LMFlow alternatives
GraphCanon updated today
Trust & integrity
| Signal | keras | LMFlow |
|---|---|---|
| Maintenance | Very active (4d since push) As of today · github_public_v1 | Steady (50d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No criticals As of today · osv@v1 | 74 low (74 low) As of today · osv@v1 |
Tagline
- keras
- Deep Learning for humans
- LMFlow
- An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
Stars
- keras
- 64k
- LMFlow
- 8.5k
Forks
- keras
- 20k
- LMFlow
- 828
Open issues
- keras
- 228
- LMFlow
- 87
Language
- keras
- Python
- LMFlow
- Python
Adopt for
- keras
- -
- LMFlow
- -
Persona
- keras
- -
- LMFlow
- -
Runtime
- keras
- -
- LMFlow
- -
License
- keras
- Apache-2.0
- LMFlow
- Apache-2.0
Last pushed
- keras
- Jul 7, 2026
- LMFlow
- May 22, 2026
Categories
- keras
- Model Training, Inference & Serving
- LMFlow
- LLM Frameworks, Model Training, Inference & Serving
Trust and health
Maintenance
- keras
- Very active (96%)
- LMFlow
- Steady (60%)
Days since push
- keras
- 4d
- LMFlow
- 50d
Open issues (now)
- keras
- 228
- LMFlow
- 87
Security scan
- keras
- No criticals
- LMFlow
- 74 low (74 low)
Full report
- keras
- Trust report
- LMFlow
- Trust report
Shared compatibility
- Python · keras: Python runtime · LMFlow: Python runtime
Choose keras if…
- Tags unique to keras: data-science, neural-networks, machine-learning, tensorflow.
- More GitHub stars (64k vs 8.5k) - visibility, not fit.
When NOT to use keras
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Choose LMFlow if…
- Tags unique to LMFlow: pretrained-models, chatgpt, transformer, language-model.
- Also covers LLM Frameworks.
- Leaner open-issue backlog (87).
When NOT to use LMFlow
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (keras-team/keras) · observed Jul 11, 2026
- GitHub forks (keras-team/keras) · observed Jul 11, 2026
- Last push (keras-team/keras) · observed Jul 7, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (OptimalScale/LMFlow) · observed Jul 11, 2026
- GitHub forks (OptimalScale/LMFlow) · observed Jul 11, 2026
- Last push (OptimalScale/LMFlow) · observed May 22, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: keras 64k · LMFlow 8.5k (synced Jul 11, 2026).
Common questions
- What is the difference between keras and LMFlow?
- keras: Deep Learning for humans. LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.. See the comparison table for live GitHub stats and shared categories.
- When should I choose keras over LMFlow?
- Choose keras over LMFlow when Tags unique to keras: data-science, neural-networks, machine-learning, tensorflow; More GitHub stars (64k vs 8.5k) - visibility, not fit.
- When should I choose LMFlow over keras?
- Choose LMFlow over keras when Tags unique to LMFlow: pretrained-models, chatgpt, transformer, language-model; Also covers LLM Frameworks; Leaner open-issue backlog (87).
- When should I avoid keras?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- When should I avoid LMFlow?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is keras or LMFlow more popular on GitHub?
- keras has more GitHub stars (64,191 vs 8,483). Stars measure visibility, not whether either tool fits your constraints.
- Are keras and LMFlow open source?
- Yes - both are open-source projects on GitHub (keras: Apache-2.0, LMFlow: Apache-2.0).
- Where can I find alternatives to keras or LMFlow?
- GraphCanon lists graph-backed alternatives at keras alternatives and LMFlow alternatives (keras markdown twin, LMFlow 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, keras or LMFlow?
- keras: Very active. LMFlow: Steady. 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 keras and LMFlow?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: keras trust report; LMFlow trust report.