--- title: "kedro vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kedro-org-kedro-vs-shubhamsaboo-awesome-llm-apps" tools: ["kedro-org-kedro", "shubhamsaboo-awesome-llm-apps"] --- # kedro vs awesome-llm-apps *GraphCanon updated Jul 11, 2026* ## Verdict Pick kedro when tags unique to kedro: machine-learning, agentic-workflow, hacktoberfest, agentic-ai; pick awesome-llm-apps when pricing: Free with open-source licensing, but commercial exploitation is allowed.. [kedro](https://kedro.org) reports 11k GitHub stars, 1.1k forks, and 161 open issues, last pushed Jul 9, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 118k stars, 17k forks, and 6 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [kedro's repository](https://github.com/kedro-org/kedro) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [kedro](/tools/kedro-org-kedro.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, an | 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. | | Stars | 10,911 | 117,774 | | Forks | 1,050 | 17,498 | | Open issues | 161 | 6 | | 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 | Data & Retrieval, AI Agents | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [kedro](/tools/kedro-org-kedro.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 161 | 6 | | Owner type | Organization | User | | Full report | [trust report](/tools/kedro-org-kedro/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Shared compatibility - **Python**: [kedro](/tools/kedro-org-kedro.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 kedro if… - Tags unique to kedro: machine-learning, agentic-workflow, hacktoberfest, agentic-ai. ### Choose awesome-llm-apps if… - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: llms, deployable, applications, agents. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ## When NOT to use kedro - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## 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 kedro and awesome-llm-apps? kedro: Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, an. awesome-llm-apps: 100+ AI Agent & RAG apps you can actually run — clone, customize, ship.. See the comparison table for live GitHub stats and shared categories. ### When should I choose kedro over awesome-llm-apps? Choose kedro over awesome-llm-apps when Tags unique to kedro: machine-learning, agentic-workflow, hacktoberfest, agentic-ai. ### When should I choose awesome-llm-apps over kedro? Choose awesome-llm-apps over kedro when Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: llms, deployable, applications, agents; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### When should I avoid kedro? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### 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 kedro or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (117,774 vs 10,911). Stars measure visibility, not whether either tool fits your constraints. ### Are kedro and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (kedro: Apache-2.0, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to kedro or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [kedro alternatives](/tools/kedro-org-kedro/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([kedro markdown twin](/tools/kedro-org-kedro/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/kedro-org-kedro-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, kedro or awesome-llm-apps? kedro: 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 kedro and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [kedro trust report](/tools/kedro-org-kedro/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kedro-org-kedro`](/api/graphcanon/graph?tool=kedro-org-kedro) - 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/_