--- title: "faraday vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/infobyte-faraday-vs-shubhamsaboo-awesome-llm-apps" tools: ["infobyte-faraday", "shubhamsaboo-awesome-llm-apps"] --- # faraday vs awesome-llm-apps *GraphCanon updated Jul 15, 2026* ## Verdict Pick faraday when license: faraday is GPL-3.0, awesome-llm-apps is Apache-2.0; pick awesome-llm-apps when license: awesome-llm-apps is Apache-2.0, faraday is GPL-3.0. [faraday](https://www.faradaysec.com) reports 6.7k GitHub stars, 1.1k forks, and 22 open issues, last pushed Jun 23, 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 [faraday's repository](https://github.com/infobyte/faraday) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [faraday](/tools/infobyte-faraday.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | Open Source Vulnerability Management Platform | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | | Stars | 6,651 | 119,936 | | Forks | 1,065 | 17,799 | | Open issues | 22 | 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 | GPL-3.0 | 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, Vector Databases | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [faraday](/tools/infobyte-faraday.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 21d | 3d | | Open issues (now) | 22 | 17 | | Owner type | Organization | User | | Full report | [trust report](/tools/infobyte-faraday/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/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. ## Choose when ### Choose faraday if… - License: faraday is GPL-3.0, awesome-llm-apps is Apache-2.0. - Tags unique to faraday: appsec, burpsuite, collaboration, continuous-scanning. - Also covers Vector Databases. - faraday ships Docker support for self-hosted deployment. ### Choose awesome-llm-apps if… - License: awesome-llm-apps is Apache-2.0, faraday is GPL-3.0. - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: agents, applications, customizable, deployable. - Also covers Data & Retrieval. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ## When NOT to use faraday - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 faraday and awesome-llm-apps? faraday: Open Source Vulnerability Management Platform. 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 faraday over awesome-llm-apps? Choose faraday over awesome-llm-apps when License: faraday is GPL-3.0, awesome-llm-apps is Apache-2.0; Tags unique to faraday: appsec, burpsuite, collaboration, continuous-scanning; Also covers Vector Databases; faraday ships Docker support for self-hosted deployment. ### When should I choose awesome-llm-apps over faraday? Choose awesome-llm-apps over faraday when License: awesome-llm-apps is Apache-2.0, faraday is GPL-3.0; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; Also covers Data & Retrieval; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### When should I avoid faraday? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 faraday or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (119,936 vs 6,651). Stars measure visibility, not whether either tool fits your constraints. ### Are faraday and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (faraday: GPL-3.0, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to faraday or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [faraday alternatives](/tools/infobyte-faraday/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([faraday markdown twin](/tools/infobyte-faraday/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/infobyte-faraday-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, faraday or awesome-llm-apps? faraday: 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 faraday and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [faraday trust report](/tools/infobyte-faraday/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=infobyte-faraday`](/api/graphcanon/graph?tool=infobyte-faraday) - 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/_