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Comparison

llm vs llmflows

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.

Markdown twin · llm alternatives · llmflows alternatives

GraphCanon updated Sep 20, 2026

9views this month

llm logo

llm

simonw/llm

★ 12kpushed Sep 2, 2026
vs
llmflows logo

llmflows

stoyan-stoyanov/llmflows

★ 708pushed Feb 20, 2025

Trust & integrity

Signalllmllmflows
Maintenance
Very active (4d since push)
As of Sep 7, 2026 · github_public_v1
Dormant (574d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 7, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
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
llm trust reportllmflows trust reportTrust methodology

Tagline

llm
Access large language models from the command-line
llmflows
Simple, Explicit and Transparent LLM Apps

Stars

llm
12k
llmflows
708

Forks

llm
978
llmflows
35

Open issues

llm
689
llmflows
19

Language

llm
Python
llmflows
Python

Adopt for

llm
Decision-critical facts for 'llm'
llmflows
LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency.

Persona

llm
-
llmflows
-

Runtime

llm
-
llmflows
-

License

llm
Apache-2.0
llmflows
LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution.

Last pushed

llm
Sep 2, 2026
llmflows
Feb 20, 2025

Categories

llm
Inference & Serving, LLM Frameworks
llmflows
Inference & Serving, LLM Frameworks

Trust and health

Maintenance

llm
Very active (96%)
llmflows
Dormant (18%)

Days since push

llm
4d
llmflows
574d

Open issues (now)

llm
689
llmflows
19

Stars delta

llm
+149 (30d)
llmflows
+1 (30d)

Open issues delta

llm
+25 (30d)
llmflows
0 (30d)

OSV dependency advisories

llm
No lockfile (source not queried)
llmflows
Published findings

Full report

llm
Trust report
llmflows
Trust report

Shared compatibility

  • Python · llm: Python runtime · llmflows: Python runtime

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.

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.
GitHubDetails

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 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
GitHubDetails

Explore

llm trust report →llmflows trust report →Inference & Serving category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: llm 12k · llmflows 708 (synced Sep 20, 2026).

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 and llmflows alternatives (llm 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, 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; llmflows trust report.

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