--- title: "lobehub vs LLMStack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lobehub-lobehub-vs-trypromptly-llmstack" tools: ["lobehub-lobehub", "trypromptly-llmstack"] --- # lobehub vs LLMStack Neutral, constraint-first comparison with live GitHub stats. | | [lobehub](/tools/lobehub-lobehub.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Tagline | Your Chief Agent Operator for organizing agents into continuous operations | No-code multi-agent framework for building LLM Agents and applications | | Stars | 79,597 | 2,304 | | Forks | 15,565 | 347 | | Open issues | 586 | 23 | | Language | TypeScript | Python | | Adopt for | LobeHub is designed as a Chief Agent Operator, focusing on orchestrating AI agents into continuous operations through tasks such as hiring, scheduling, and reporting. | 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 | Other | Other | | Categories | AI Agents | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lobehub](/tools/lobehub-lobehub.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 573d | | Open issues (now) | 586 | 23 | | Security scan | No MCP manifest | Not scanned | | Full report | [trust report](/tools/lobehub-lobehub/trust.md) | [trust report](/tools/trypromptly-llmstack/trust.md) | **Typed relationship:** lobehub _(alternative)_ LLMStack LLMStack and lobeHub both aim to provide a platform for organizing AI agents into continuous operations. LLMStack particularly stands out with its no-code framework, which contrasts with the more operational focus of lobeHub. ## Decision facts: lobehub - **Requirements:** LobeHub supports deployment via Vercel, Zeabur, Sealos, or Alibaba Cloud. It can also be deployed using Docker. - **Adopt for:** LobeHub is designed as a Chief Agent Operator, focusing on orchestrating AI agents into continuous operations through tasks such as hiring, scheduling, and reporting. - **Runtime:** unknown ## 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 lobehub if… - lobehub is primarily TypeScript; LLMStack is Python. - Requirements: LobeHub supports deployment via Vercel, Zeabur, Sealos, or Alibaba Cloud. It can also be deployed using Docker.. - LLMStack and lobeHub both aim to provide a platform for organizing AI agents into continuous operations. LLMStack particularly stands out with its no-code framework, which contrasts with the more operational focus of lobeHub. - Tags unique to lobehub: agent-collaboration, ai, chief-agent-operator, agent. - lobehub ships Docker support for self-hosted deployment. - lobehub ships an MCP server manifest. - If you need around-the-clock management of your AI team. ### Choose LLMStack if… - LLMStack is primarily Python; lobehub is TypeScript. - 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 and lobeHub both aim to provide a platform for organizing AI agents into continuous operations. LLMStack particularly stands out with its no-code framework, which contrasts with the more operational focus of lobeHub. - Tags unique to LLMStack: platform, agents, generative-ai, ai-agents-framework. - Also covers LLM Frameworks. - You need to create complex generative AI agents or workflows and want to avoid coding. ## When NOT to use lobehub - If only single agent management is needed; LobeHub's strength lies in orchestrating multiple agents. - When the operational environment does not require continuous (24/7) operations of AI agents as LobeHub focuses on providing that constant availability. - For users who prefer open-source alternatives with extensive community support, given its license isn’t explicitly marked as open-source. ## 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 lobehub and LLMStack? lobehub: Your Chief Agent Operator for organizing agents into continuous operations. 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 lobehub over LLMStack? Choose lobehub over LLMStack when lobehub is primarily TypeScript; LLMStack is Python; Requirements: LobeHub supports deployment via Vercel, Zeabur, Sealos, or Alibaba Cloud. It can also be deployed using Docker.; LLMStack and lobeHub both aim to provide a platform for organizing AI agents into continuous operations. LLMStack particularly stands out with its no-code framework, which contrasts with the more operational focus of lobeHub; Tags unique to lobehub: agent-collaboration, ai, chief-agent-operator, agent; lobehub ships Docker support for self-hosted deployment; lobehub ships an MCP server manifest; If you need around-the-clock management of your AI team. ### When should I choose LLMStack over lobehub? Choose LLMStack over lobehub when LLMStack is primarily Python; lobehub is TypeScript; 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 and lobeHub both aim to provide a platform for organizing AI agents into continuous operations. LLMStack particularly stands out with its no-code framework, which contrasts with the more operational focus of lobeHub; Tags unique to LLMStack: platform, agents, generative-ai, ai-agents-framework; Also covers LLM Frameworks; You need to create complex generative AI agents or workflows and want to avoid coding. ### When should I avoid lobehub? If only single agent management is needed; LobeHub's strength lies in orchestrating multiple agents. When the operational environment does not require continuous (24/7) operations of AI agents as LobeHub focuses on providing that constant availability. For users who prefer open-source alternatives with extensive community support, given its license isn’t explicitly marked as open-source. ### 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 lobehub or LLMStack more popular on GitHub? lobehub has more GitHub stars (79,597 vs 2,304). Stars measure visibility, not whether either tool fits your constraints. ### Are lobehub and LLMStack open source? Yes - both are open-source projects on GitHub (lobehub: Other, LLMStack: Other). ### Where can I find alternatives to lobehub or LLMStack? GraphCanon lists graph-backed alternatives at /tools/lobehub-lobehub/alternatives and /tools/trypromptly-llmstack/alternatives (/tools/lobehub-lobehub/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/lobehub-lobehub-vs-trypromptly-llmstack.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lobehub or LLMStack? lobehub: Very active. 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 lobehub and LLMStack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lobehub: /tools/lobehub-lobehub/trust; LLMStack: /tools/trypromptly-llmstack/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lobehub-lobehub`](/api/graphcanon/graph?tool=lobehub-lobehub) - 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/_