--- title: "agentset vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentset-ai-agentset-vs-shubhamsaboo-awesome-llm-apps" tools: ["agentset-ai-agentset", "shubhamsaboo-awesome-llm-apps"] --- # agentset vs awesome-llm-apps *GraphCanon updated Aug 22, 2026* ## Verdict Pick agentset if agentSet is a Retrieval-Augmented Generation (RAG) platform emphasizing built-in citations and support for deep research. It's designed to handle diverse file formats while ensuring effective memory management; pick awesome-llm-apps if awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases. [agentset](https://agentset.ai) reports 2.1k GitHub stars, 185 forks, and 14 open issues, last pushed Jul 16, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 131k stars, 19k forks, and 13 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [agentset's repository](https://github.com/agentset-ai/agentset) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [agentset](/tools/agentset-ai-agentset.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | The open-source RAG platform with built-in citations and support for deep research | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | | Stars | 2,066 | 131,230 | | Forks | 185 | 19,346 | | Open issues | 14 | 13 | | Language | TypeScript | Python | | Adopt for | AgentSet is a Retrieval-Augmented Generation (RAG) platform emphasizing built-in citations and support for deep research. It's designed to handle diverse file formats while ensuring effective memory management. | awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. | | Persona | - | - | | Runtime | - | - | | License | AgentSet operates under the MIT License, allowing for broad usage and modification rights. | The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentset](/tools/agentset-ai-agentset.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 36d | 4d | | Open issues (now) | 14 | 13 | | Stars delta | +31 (30d) | +14k (30d) | | Open issues delta | +1 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/agentset-ai-agentset/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Decision facts: agentset - **Pricing:** freemium - Free to use as it is open-source. - **Requirements:** Primarily developed in TypeScript.; Best used with an understanding of Retrieval-Augmented Generation and AI agent functionalities. - **Adopt for:** AgentSet is a Retrieval-Augmented Generation (RAG) platform emphasizing built-in citations and support for deep research. It's designed to handle diverse file formats while ensuring effective memory management. - **License detail:** AgentSet operates under the MIT License, allowing for broad usage and modification rights. ## Decision facts: awesome-llm-apps - **Pricing:** freemium - Free with open-source licensing, but commercial exploitation is allowed. - **Adopt for:** awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. - **License detail:** The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. ## Choose when ### Choose agentset if… - agentset is primarily TypeScript; awesome-llm-apps is Python. - License: agentset is MIT, awesome-llm-apps is Apache-2.0. - Pricing: Free to use as it is open-source.. - Requirements: Primarily developed in TypeScript.; Best used with an understanding of Retrieval-Augmented Generation and AI agent functionalities.. - Tags unique to agentset: agentic-rag, ai-agents, embeddings, memory-management. - - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### Choose awesome-llm-apps if… - awesome-llm-apps is primarily Python; agentset is TypeScript. - License: awesome-llm-apps is Apache-2.0, agentset is MIT. - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: agents, applications, customizable, deployable. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ## When NOT to use agentset - - Avoid selecting AgentSet if your application does not benefit from or necessitate support for a wide array of file types, as its complexity might overwhelm simpler use-cases. - - If seamless integration with third-party citation services is more preferred, another tool might be better suited since AgentSet focuses on built-in citation capabilities. ## When NOT to use awesome-llm-apps - If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. - When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ## Common questions ### What is the difference between agentset and awesome-llm-apps? agentset: The open-source RAG platform with built-in citations and support for deep research. awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentset over awesome-llm-apps? Choose agentset over awesome-llm-apps when agentset is primarily TypeScript; awesome-llm-apps is Python; License: agentset is MIT, awesome-llm-apps is Apache-2.0; Pricing: Free to use as it is open-source.; Requirements: Primarily developed in TypeScript.; Best used with an understanding of Retrieval-Augmented Generation and AI agent functionalities.; Tags unique to agentset: agentic-rag, ai-agents, embeddings, memory-management; - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### When should I choose awesome-llm-apps over agentset? Choose awesome-llm-apps over agentset when awesome-llm-apps is primarily Python; agentset is TypeScript; License: awesome-llm-apps is Apache-2.0, agentset is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### When should I avoid agentset? - Avoid selecting AgentSet if your application does not benefit from or necessitate support for a wide array of file types, as its complexity might overwhelm simpler use-cases. - If seamless integration with third-party citation services is more preferred, another tool might be better suited since AgentSet focuses on built-in citation capabilities. ### When should I avoid awesome-llm-apps? If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ### Is agentset or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (131,230 vs 2,066). Stars measure visibility, not whether either tool fits your constraints. ### Are agentset and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (agentset: MIT, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to agentset or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [agentset alternatives](/tools/agentset-ai-agentset/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([agentset markdown twin](/tools/agentset-ai-agentset/alternatives.md), [awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/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 [this comparison](/compare/agentset-ai-agentset-vs-shubhamsaboo-awesome-llm-apps.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentset or awesome-llm-apps? agentset: Steady. awesome-llm-apps: Very active. 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 agentset and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentset trust report](/tools/agentset-ai-agentset/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentset-ai-agentset`](/api/graphcanon/graph?tool=agentset-ai-agentset) - 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/_