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Comparison

llmflows vs LLMStack

llmflows (LLMFlows - Simple, Explicit and Transparent LLM Apps) vs LLMStack (No-code multi-agent framework for building LLM Agents and applications) - live GitHub stats and typed graph relationships, not marketing.

Markdown twin · llmflows alternatives · LLMStack alternatives

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llmflows

stoyan-stoyanov/llmflows

★ 706pushed Feb 20, 2025

LLMStack

trypromptly/LLMStack

★ 2.3kpushed Dec 11, 2024

Tagline

llmflows: LLMFlows - Simple, Explicit and Transparent LLM Apps
LLMStack: No-code multi-agent framework for building LLM Agents and applications

Stars

llmflows: 706
LLMStack: 2.3k

Forks

llmflows: 35
LLMStack: 347

Open issues

llmflows: 19
LLMStack: 23

Language

llmflows: Python
LLMStack: Python

Adopt for

llmflows: -
LLMStack: LLMStack is a no-code multi-agent framework for building and deploying generative AI applications, chatbots, and workflows that integrate with your data and business processes through a simple visual interface.

Persona

llmflows: -
LLMStack: -

Runtime

llmflows: -
LLMStack: -

License

llmflows: MIT
LLMStack: Other

Last pushed

llmflows: Feb 20, 2025
LLMStack: Dec 11, 2024

Categories

llmflows: Inference & Serving, Developer Tools
LLMStack: AI Agents, LLM Frameworks

Trust and health

Days since push

llmflows: 502d
LLMStack: 573d

Open issues (now)

llmflows: 19
LLMStack: 23

Owner type

llmflows: User
LLMStack: Organization

Security scan

llmflows: 17 low (17 low)
LLMStack: No lockfile

Full report

llmflows: Trust report
LLMStack: Trust report

Typed relationship

llmflows alternative LLMStackLLMStack is a no-code platform for building generative AI agents and applications, while LLMFlows provides a framework for building simple and transparent LLM apps. Both tools address the need to create applications using large language models but with different approaches—one no-code and the other code-based.

Shared compatibility

Python · llmflows: Python runtime · LLMStack: Python runtime

Trust and health
	llmflows	LLMStack
Tagline	LLMFlows - Simple, Explicit and Transparent LLM Apps	No-code multi-agent framework for building LLM Agents and applications
Stars	706	2.3k
Forks	35	347
Open issues	19	23
Language	Python	Python
Adopt for	-	LLMStack is a no-code multi-agent framework for building and deploying generative AI applications, chatbots, and workflows that integrate with your data and business processes through a simple visual interface.
Persona	-	-
Runtime	-	-
License	MIT	Other
Last pushed	Feb 20, 2025	Dec 11, 2024
Categories	Inference & Serving, Developer Tools	AI Agents, LLM Frameworks
Days since push	502d	573d
Open issues (now)	19	23
Owner type	User	Organization
Security scan	17 low (17 low)	No lockfile
Full report	Trust report	Trust report
Typed relationship	llmflows alternative LLMStackLLMStack is a no-code platform for building generative AI agents and applications, while LLMFlows provides a framework for building simple and transparent LLM apps. Both tools address the need to create applications using large language models but with different approaches—one no-code and the other code-based.
Shared compatibility	Python · llmflows: Python runtime · LLMStack: Python runtime

Choose llmflows if…

License: llmflows is MIT, LLMStack is Other.
LLMStack is a no-code platform for building generative AI agents and applications, while LLMFlows provides a framework for building simple and transparent LLM apps. Both tools address the need to create applications using large language models but with different approaches—one no-code and the other code-based.
Tags unique to llmflows: llmops, vector-database, ai, python.
Also covers Inference & Serving, Developer Tools.

When NOT to use llmflows

Last GitHub push was 504 days ago (dormant maintenance, Feb 20, 2025). Validate activity before betting a new project on llmflows.
Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Developer Tools: A gateway is overkill when you're pinned to a single provider and model.

GitHub Details

Choose LLMStack if…

License: LLMStack is Other, llmflows is MIT.
Pricing: Pricing details are not provided in the repository data. You can check LLMStack's official site or contact their sales for more details..
Requirements: Min 4 GB RAM; Requires Docker; Docker is required to run jobs within LLMStack.; For Windows users, WSL2 (Windows Subsystem for Linux) must be installed..
LLMStack is a no-code platform for building generative AI agents and applications, while LLMFlows provides a framework for building simple and transparent LLM apps. Both tools address the need to create applications using large language models but with different approaches—one no-code and the other code-based.
Tags unique to LLMStack: platform, agents, generative-ai, ai-agents-framework.
Also covers AI Agents, LLM Frameworks.
You need to create complex generative AI agents or workflows and want to avoid coding.

When NOT to use LLMStack

You require extensive customization that goes beyond the no-code capabilities of LLMStack.
Your organization enforces strict security practices that do not allow for cloud deployments or third-party services integration without thorough scrutiny.
The need for real-time, high-throughput data processing where latency could be introduced by using a no-code solution.

GitHub Details

Explore

llmflows trust report →LLMStack trust report →Inference & Serving category →Developer Tools category →AI Agents category →LLM Frameworks category →All comparisons Stack workflows Trending tools

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Common questions

What is the difference between llmflows and LLMStack?: llmflows: LLMFlows - Simple, Explicit and Transparent LLM Apps. LLMStack: No-code multi-agent framework for building LLM Agents and applications. See the comparison table for live GitHub stats and shared categories.
When should I choose llmflows over LLMStack?: Choose llmflows over LLMStack when License: llmflows is MIT, LLMStack is Other; LLMStack is a no-code platform for building generative AI agents and applications, while LLMFlows provides a framework for building simple and transparent LLM apps. Both tools address the need to create applications using large language models but with different approaches—one no-code and the other code-based; Tags unique to llmflows: llmops, vector-database, ai, python; Also covers Inference & Serving, Developer Tools.
When should I choose LLMStack over llmflows?: Choose LLMStack over llmflows when License: LLMStack is Other, llmflows is MIT; Pricing: Pricing details are not provided in the repository data. You can check LLMStack's official site or contact their sales for more details.; Requirements: Min 4 GB RAM; Requires Docker; Docker is required to run jobs within LLMStack.; For Windows users, WSL2 (Windows Subsystem for Linux) must be installed.; LLMStack is a no-code platform for building generative AI agents and applications, while LLMFlows provides a framework for building simple and transparent LLM apps. Both tools address the need to create applications using large language models but with different approaches—one no-code and the other code-based; Tags unique to LLMStack: platform, agents, generative-ai, ai-agents-framework; Also covers AI Agents, LLM Frameworks; You need to create complex generative AI agents or workflows and want to avoid coding.
When should I avoid llmflows?: Last GitHub push was 504 days ago (dormant maintenance, Feb 20, 2025). Validate activity before betting a new project on llmflows. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
When should I avoid LLMStack?: You require extensive customization that goes beyond the no-code capabilities of LLMStack. Your organization enforces strict security practices that do not allow for cloud deployments or third-party services integration without thorough scrutiny. The need for real-time, high-throughput data processing where latency could be introduced by using a no-code solution.
Is llmflows or LLMStack more popular on GitHub?: LLMStack has more GitHub stars (2,304 vs 706). Stars measure visibility, not whether either tool fits your constraints.
Are llmflows and LLMStack open source?: Yes - both are open-source projects on GitHub (llmflows: MIT, LLMStack: Other).
Where can I find alternatives to llmflows or LLMStack?: GraphCanon lists graph-backed alternatives at /tools/stoyan-stoyanov-llmflows/alternatives and /tools/trypromptly-llmstack/alternatives (/tools/stoyan-stoyanov-llmflows/alternatives.md, /tools/trypromptly-llmstack/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 /compare/stoyan-stoyanov-llmflows-vs-trypromptly-llmstack.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, llmflows or LLMStack?: llmflows: Dormant. LLMStack: 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 llmflows and LLMStack?: GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llmflows: /tools/stoyan-stoyanov-llmflows/trust; LLMStack: /tools/trypromptly-llmstack/trust.