--- title: "llmflows vs LLMStack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/stoyan-stoyanov-llmflows-vs-trypromptly-llmstack" tools: ["stoyan-stoyanov-llmflows", "trypromptly-llmstack"] --- # llmflows vs LLMStack Neutral, constraint-first comparison with live GitHub stats. | | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Tagline | LLMFlows - Simple, Explicit and Transparent LLM Apps | No-code multi-agent framework for building LLM Agents and applications | | Stars | 706 | 2,304 | | 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 | | Categories | Inference & Serving, Developer Tools | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llmflows](/tools/stoyan-stoyanov-llmflows.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | 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](/tools/stoyan-stoyanov-llmflows/trust.md) | [trust report](/tools/trypromptly-llmstack/trust.md) | **Typed relationship:** llmflows _(alternative)_ LLMStack 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. ## Shared compatibility - **Python**: [llmflows](/tools/stoyan-stoyanov-llmflows.md) - Python runtime; [LLMStack](/tools/trypromptly-llmstack.md) - Python runtime ## Decision facts: LLMStack - **Pricing:** unknown - 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. - **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. ## Choose when ### 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. ### 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 LLM Frameworks, AI Agents. - You need to create complex generative AI agents or workflows and want to avoid coding. ## 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. ## 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. ## 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 LLM Frameworks, AI Agents; 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. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=stoyan-stoyanov-llmflows`](/api/graphcanon/graph?tool=stoyan-stoyanov-llmflows) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_