--- title: "awesome-llm-apps vs raglite" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shubhamsaboo-awesome-llm-apps-vs-superlinear-ai-raglite" tools: ["shubhamsaboo-awesome-llm-apps", "superlinear-ai-raglite"] --- # awesome-llm-apps vs raglite *GraphCanon updated Jul 12, 2026* ## Verdict 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 in Python; pick raglite if rAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. [awesome-llm-apps](https://www.theunwindai.com) reports 118k GitHub stars, 17k forks, and 6 open issues, last pushed Jul 11, 2026. [raglite](https://github.com/superlinear-ai/raglite) has 1.2k stars, 108 forks, and 13 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps) and [raglite's repository](https://github.com/superlinear-ai/raglite). | | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Tagline | 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. | Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL | | Stars | 117,774 | 1,194 | | Forks | 17,498 | 108 | | Open issues | 6 | 13 | | 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. | RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. | | Persona | - | - | | Runtime | - | - | | License | 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. | MPL-2.0 | | Categories | AI Agents, Data & Retrieval | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 6 | 13 | | Owner type | User | Organization | | Full report | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | [trust report](/tools/superlinear-ai-raglite/trust.md) | ## 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. ## Decision facts: raglite - **Adopt for:** RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. ## Choose when ### Choose awesome-llm-apps if… - License: awesome-llm-apps is Apache-2.0, raglite is MPL-2.0. - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: agents, applications, customizable, deployable. - Also covers AI Agents. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ### Choose raglite if… - License: raglite is MPL-2.0, awesome-llm-apps is Apache-2.0. - Tags unique to raglite: chainlit, colbert, duckdb, evals. - Also covers Model Training. - raglite ships Docker support for self-hosted deployment. - - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ## 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. ## When NOT to use raglite - - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options. - - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL. ## Common questions ### What is the difference between awesome-llm-apps and raglite? awesome-llm-apps: 100+ AI Agent & RAG apps you can actually run — clone, customize, ship.. raglite: Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-llm-apps over raglite? Choose awesome-llm-apps over raglite when License: awesome-llm-apps is Apache-2.0, raglite is MPL-2.0; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; Also covers AI Agents; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### When should I choose raglite over awesome-llm-apps? Choose raglite over awesome-llm-apps when License: raglite is MPL-2.0, awesome-llm-apps is Apache-2.0; Tags unique to raglite: chainlit, colbert, duckdb, evals; Also covers Model Training; raglite ships Docker support for self-hosted deployment; - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ### 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. ### When should I avoid raglite? - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options. - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL. ### Is awesome-llm-apps or raglite more popular on GitHub? awesome-llm-apps has more GitHub stars (117,774 vs 1,194). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llm-apps and raglite open source? Yes - both are open-source projects on GitHub (awesome-llm-apps: Apache-2.0, raglite: MPL-2.0). ### Where can I find alternatives to awesome-llm-apps or raglite? GraphCanon lists graph-backed alternatives at [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) and [raglite alternatives](/tools/superlinear-ai-raglite/alternatives) ([awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/alternatives.md), [raglite markdown twin](/tools/superlinear-ai-raglite/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/shubhamsaboo-awesome-llm-apps-vs-superlinear-ai-raglite.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-llm-apps or raglite? awesome-llm-apps: Very active. raglite: 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 awesome-llm-apps and raglite? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust); [raglite trust report](/tools/superlinear-ai-raglite/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=shubhamsaboo-awesome-llm-apps`](/api/graphcanon/graph?tool=shubhamsaboo-awesome-llm-apps) - 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/_