--- title: "dagster vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dagster-io-dagster-vs-shubhamsaboo-awesome-llm-apps" tools: ["dagster-io-dagster", "shubhamsaboo-awesome-llm-apps"] --- # dagster vs awesome-llm-apps *GraphCanon updated Jul 15, 2026* ## Verdict Pick dagster when tags unique to dagster: analytics, dagster, data-engineering, data-integration; pick awesome-llm-apps when pricing: Free with open-source licensing, but commercial exploitation is allowed.. [dagster](https://dagster.io) reports 16k GitHub stars, 2.2k forks, and 2.6k open issues, last pushed Jul 15, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 120k stars, 18k forks, and 17 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [dagster's repository](https://github.com/dagster-io/dagster) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [dagster](/tools/dagster-io-dagster.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | An orchestration platform for the development, production, and observation of data assets. | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | | Stars | 15,840 | 119,936 | | Forks | 2,195 | 17,799 | | Open issues | 2,580 | 17 | | Language | Python | Python | | 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. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | 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, Developer Tools | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dagster](/tools/dagster-io-dagster.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Days since push | 0d | 3d | | Open issues (now) | 2.6k | 17 | | Owner type | Organization | User | | Full report | [trust report](/tools/dagster-io-dagster/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Shared compatibility - **Python**: [dagster](/tools/dagster-io-dagster.md) - Python runtime; [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - Python runtime ## 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 dagster if… - Tags unique to dagster: analytics, dagster, data-engineering, data-integration. - Also covers Developer Tools. - More recently updated (last pushed Jul 15, 2026). ### Choose awesome-llm-apps if… - 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 dagster - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## 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 dagster and awesome-llm-apps? dagster: An orchestration platform for the development, production, and observation of data assets.. 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 dagster over awesome-llm-apps? Choose dagster over awesome-llm-apps when Tags unique to dagster: analytics, dagster, data-engineering, data-integration; Also covers Developer Tools; More recently updated (last pushed Jul 15, 2026). ### When should I choose awesome-llm-apps over dagster? Choose awesome-llm-apps over dagster when 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 dagster? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### 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 dagster or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (119,936 vs 15,840). Stars measure visibility, not whether either tool fits your constraints. ### Are dagster and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (dagster: Apache-2.0, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to dagster or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [dagster alternatives](/tools/dagster-io-dagster/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([dagster markdown twin](/tools/dagster-io-dagster/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/dagster-io-dagster-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, dagster or awesome-llm-apps? dagster: Very active. 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 dagster and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dagster trust report](/tools/dagster-io-dagster/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dagster-io-dagster`](/api/graphcanon/graph?tool=dagster-io-dagster) - 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/_