--- title: "awesome-llm-apps vs agent-toolkit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shubhamsaboo-awesome-llm-apps-vs-softaworks-agent-toolkit" tools: ["shubhamsaboo-awesome-llm-apps", "softaworks-agent-toolkit"] --- # awesome-llm-apps vs agent-toolkit *GraphCanon updated Jul 15, 2026* ## Verdict Pick awesome-llm-apps when license: awesome-llm-apps is Apache-2.0, agent-toolkit is MIT; pick agent-toolkit when license: agent-toolkit is MIT, awesome-llm-apps is Apache-2.0. [awesome-llm-apps](https://www.theunwindai.com) reports 120k GitHub stars, 18k forks, and 17 open issues, last pushed Jul 11, 2026. [agent-toolkit](https://github.com/softaworks/agent-toolkit) has 2.2k stars, 210 forks, and 18 open issues, last pushed Mar 5, 2026. Figures are from public GitHub metadata via [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps) and [agent-toolkit's repository](https://github.com/softaworks/agent-toolkit). | | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | [agent-toolkit](/tools/softaworks-agent-toolkit.md) | | --- | --- | --- | | Tagline | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | A curated collection of skills for AI coding agents. Skills are packaged instructions and scripts that extend agent capabilities across development, documentation, planning, and professional workflows | | Stars | 119,936 | 2,190 | | Forks | 17,799 | 210 | | Open issues | 17 | 18 | | 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 | 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. | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | [agent-toolkit](/tools/softaworks-agent-toolkit.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 3d | 131d | | Open issues (now) | 17 | 18 | | Owner type | User | Organization | | Full report | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | [trust report](/tools/softaworks-agent-toolkit/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 awesome-llm-apps if… - License: awesome-llm-apps is Apache-2.0, agent-toolkit is MIT. - 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. ### Choose agent-toolkit if… - License: agent-toolkit is MIT, awesome-llm-apps is Apache-2.0. - Tags unique to agent-toolkit: agent-skills, ai, automation, claude. - Also covers Vector 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 agent-toolkit - Last GitHub push was 132 days ago (slowing maintenance, Mar 5, 2026). Validate activity before betting a new project on agent-toolkit. - 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. ## Common questions ### What is the difference between awesome-llm-apps and agent-toolkit? awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. agent-toolkit: A curated collection of skills for AI coding agents. Skills are packaged instructions and scripts that extend agent capabilities across development, documentation, planning, and professional workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-llm-apps over agent-toolkit? Choose awesome-llm-apps over agent-toolkit when License: awesome-llm-apps is Apache-2.0, agent-toolkit is MIT; 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 choose agent-toolkit over awesome-llm-apps? Choose agent-toolkit over awesome-llm-apps when License: agent-toolkit is MIT, awesome-llm-apps is Apache-2.0; Tags unique to agent-toolkit: agent-skills, ai, automation, claude; Also covers Vector 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 agent-toolkit? Last GitHub push was 132 days ago (slowing maintenance, Mar 5, 2026). Validate activity before betting a new project on agent-toolkit. 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. ### Is awesome-llm-apps or agent-toolkit more popular on GitHub? awesome-llm-apps has more GitHub stars (119,936 vs 2,190). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llm-apps and agent-toolkit open source? Yes - both are open-source projects on GitHub (awesome-llm-apps: Apache-2.0, agent-toolkit: MIT). ### Where can I find alternatives to awesome-llm-apps or agent-toolkit? GraphCanon lists graph-backed alternatives at [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) and [agent-toolkit alternatives](/tools/softaworks-agent-toolkit/alternatives) ([awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/alternatives.md), [agent-toolkit markdown twin](/tools/softaworks-agent-toolkit/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-softaworks-agent-toolkit.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 agent-toolkit? awesome-llm-apps: Very active. agent-toolkit: Slowing. 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 agent-toolkit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust); [agent-toolkit trust report](/tools/softaworks-agent-toolkit/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/_