Comparison
llmflows vs LLMStack
Verdict
Pick llmflows if lLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency; pick LLMStack if lLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise.
Markdown twin · llmflows alternatives · LLMStack alternatives
GraphCanon updated Sep 20, 2026
20views this month
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Trust & integrity
| Signal | llmflows | LLMStack |
|---|---|---|
| Maintenance | Dormant (574d since push) As of Sep 18, 2026 · github_public_v1 | Dormant (612d since push) As of Aug 16, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 16, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- llmflows
- Simple, Explicit and Transparent LLM Apps
- LLMStack
- No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Stars
- llmflows
- 708
- LLMStack
- 2.3k
Forks
- llmflows
- 35
- LLMStack
- 347
Open issues
- llmflows
- 19
- LLMStack
- 23
Language
- llmflows
- Python
- LLMStack
- Python
Adopt for
- llmflows
- LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency.
- LLMStack
- LLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise.
Persona
- llmflows
- -
- LLMStack
- -
Runtime
- llmflows
- -
- LLMStack
- -
License
- llmflows
- LLMFlows is distributed under the MIT license, allowing for free use, modification, and distribution.
- LLMStack
- Other
Last pushed
- llmflows
- Feb 20, 2025
- LLMStack
- Dec 11, 2024
Categories
- llmflows
- Inference & Serving, LLM Frameworks
- LLMStack
- AI Agents, LLM Frameworks
Trust and health
Days since push
- llmflows
- 574d
- LLMStack
- 612d
Open issues (now)
- llmflows
- 19
- LLMStack
- 23
Stars delta
- llmflows
- +1 (30d)
- LLMStack
- +2 (30d)
Open issues delta
- llmflows
- 0 (30d)
- LLMStack
- -1 (30d)
Owner type
- llmflows
- User
- LLMStack
- Organization
OSV dependency advisories
- llmflows
- Published findings
- LLMStack
- No lockfile (source not queried)
Full report
- llmflows
- Trust report
- LLMStack
- Trust report
Typed relationship
llmflows alternative LLMStackLLMStack is a no-code platform for creating generative AI agents and applications. LLMFlows, on the other hand, offers a framework for developing straightforward and clear LLM apps. Both tools cater to building applications with large language models but differ in approach, with one being 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.
- Pricing: The library is free to use, but additional services or support might incur costs..
- Requirements: Min 2 GB RAM; Python environment is required..
- LLMStack is a no-code platform for creating generative AI agents and applications. LLMFlows, on the other hand, offers a framework for developing straightforward and clear LLM apps. Both tools cater to building applications with large language models but differ in approach, with one being no-code and the other code-based.
- Tags unique to llmflows: ai, chatgpt, gpt-4, llm.
- Also covers Inference & Serving.
- 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
Choose LLMStack if…
- License: LLMStack is Other, llmflows is MIT.
- LLMStack is a no-code platform for creating generative AI agents and applications. LLMFlows, on the other hand, offers a framework for developing straightforward and clear LLM apps. Both tools cater to building applications with large language models but differ in approach, with one being no-code and the other code-based.
- Tags unique to LLMStack: agents, ai-agents-framework, generative-ai, llm-agents.
- Also covers AI Agents.
- Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models.
When NOT to use LLMStack
- Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives.
- Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (trypromptly/LLMStack) · observed Sep 20, 2026
- GitHub forks (trypromptly/LLMStack) · observed Sep 20, 2026
- Last push (trypromptly/LLMStack) · observed Dec 11, 2024
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: llmflows 708 · LLMStack 2.3k (synced Sep 20, 2026).
Common questions
- What is the difference between llmflows and LLMStack?
- llmflows: Simple, Explicit and Transparent LLM Apps. LLMStack: No-code multi-agent framework to build LLM Agents, workflows and applications with your data. 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; Pricing: The library is free to use, but additional services or support might incur costs.; Requirements: Min 2 GB RAM; Python environment is required.; LLMStack is a no-code platform for creating generative AI agents and applications. LLMFlows, on the other hand, offers a framework for developing straightforward and clear LLM apps. Both tools cater to building applications with large language models but differ in approach, with one being no-code and the other code-based; Tags unique to llmflows: ai, chatgpt, gpt-4, llm; Also covers Inference & Serving; 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 choose LLMStack over llmflows?
- Choose LLMStack over llmflows when License: LLMStack is Other, llmflows is MIT; LLMStack is a no-code platform for creating generative AI agents and applications. LLMFlows, on the other hand, offers a framework for developing straightforward and clear LLM apps. Both tools cater to building applications with large language models but differ in approach, with one being no-code and the other code-based; Tags unique to LLMStack: agents, ai-agents-framework, generative-ai, llm-agents; Also covers AI Agents; Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language 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
- When should I avoid LLMStack?
- Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives. Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures.
- Is llmflows or LLMStack more popular on GitHub?
- LLMStack has more GitHub stars (2,309 vs 708). 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 llmflows alternatives and LLMStack alternatives (llmflows markdown twin, LLMStack 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, 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 trust report; LLMStack trust report.