--- title: "NExT-GPT vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/next-gpt-next-gpt-vs-wangrongsheng-awesome-llm-resources" tools: ["next-gpt-next-gpt", "wangrongsheng-awesome-llm-resources"] --- # NExT-GPT vs awesome-LLM-resources *GraphCanon updated Aug 18, 2026* ## Verdict Pick NExT-GPT if nExT-GPT is focused on multimodal capabilities and instruction tuning for a large language model, targeting researchers and developers interested in multimodal applications; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a. [NExT-GPT](https://next-gpt.github.io/) reports 3.6k GitHub stars, 359 forks, and 81 open issues, last pushed May 13, 2025. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [NExT-GPT's repository](https://github.com/NExT-GPT/NExT-GPT) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [NExT-GPT](/tools/next-gpt-next-gpt.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Code and models for ICML 2024 paper on multimodal large language model | Summary of the world's best LLM resources. | | Stars | 3,637 | 8,845 | | Forks | 359 | 950 | | Open issues | 81 | 23 | | Language | Python | - | | Adopt for | NExT-GPT is focused on multimodal capabilities and instruction tuning for a large language model, targeting researchers and developers interested in multimodal applications. | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | LLM Frameworks, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [NExT-GPT](/tools/next-gpt-next-gpt.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 461d | 2d | | Open issues (now) | 81 | 23 | | Stars delta | -1 (30d) | +142 (30d) | | Open issues delta | 0 (30d) | -13 (30d) | | Full report | [trust report](/tools/next-gpt-next-gpt/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: NExT-GPT - **Pricing:** freemium - NExT-GPT is open-source under BSD-3-Clause license, indicating a free but restricted-for-commercial-use model without associated direct monetary cost. - **Requirements:** Min 8 GB RAM; - The repository notes that the code and models are intended for non-commercial use only and must not be used in any illegal or harmful contexts.; - Potential commercial users should seek approval from the authors, making it unsuitable without prior authorization if commercial application is considered. - **Adopt for:** NExT-GPT is focused on multimodal capabilities and instruction tuning for a large language model, targeting researchers and developers interested in multimodal applications. ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose NExT-GPT if… - License: NExT-GPT is BSD-3-Clause, awesome-LLM-resources is Apache-2.0. - Pricing: NExT-GPT is open-source under BSD-3-Clause license, indicating a free but restricted-for-commercial-use model without associated direct monetary cost.. - Requirements: Min 8 GB RAM; - The repository notes that the code and models are intended for non-commercial use only and must not be used in any illegal or harmful contexts.; - Potential commercial users should seek approval from the authors, making it unsuitable without prior authorization if commercial application is considered.. - Tags unique to NExT-GPT: chatgpt, foundation-models, instruction-tuning, multimodal. - - If you are conducting research specifically centered around multimodal interactions (combining text with visual elements) aligning with the scope of NExT-GPT. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, NExT-GPT is BSD-3-Clause. - Tags unique to awesome-LLM-resources: awesome-list, book, course, llama. - Also covers AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use NExT-GPT - - When your project necessitates a production-ready solution, as NExT-GPT is positioned purely for research and non-commercial use. - - If your application requires the model to be used in contexts like illegal, harmful, violent, racist, or sexual purposes, since its usage guidelines explicitly prohibit such applications. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between NExT-GPT and awesome-LLM-resources? NExT-GPT: Code and models for ICML 2024 paper on multimodal large language model. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose NExT-GPT over awesome-LLM-resources? Choose NExT-GPT over awesome-LLM-resources when License: NExT-GPT is BSD-3-Clause, awesome-LLM-resources is Apache-2.0; Pricing: NExT-GPT is open-source under BSD-3-Clause license, indicating a free but restricted-for-commercial-use model without associated direct monetary cost.; Requirements: Min 8 GB RAM; - The repository notes that the code and models are intended for non-commercial use only and must not be used in any illegal or harmful contexts.; - Potential commercial users should seek approval from the authors, making it unsuitable without prior authorization if commercial application is considered.; Tags unique to NExT-GPT: chatgpt, foundation-models, instruction-tuning, multimodal; - If you are conducting research specifically centered around multimodal interactions (combining text with visual elements) aligning with the scope of NExT-GPT. ### When should I choose awesome-LLM-resources over NExT-GPT? Choose awesome-LLM-resources over NExT-GPT when License: awesome-LLM-resources is Apache-2.0, NExT-GPT is BSD-3-Clause; Tags unique to awesome-LLM-resources: awesome-list, book, course, llama; Also covers AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid NExT-GPT? - When your project necessitates a production-ready solution, as NExT-GPT is positioned purely for research and non-commercial use. - If your application requires the model to be used in contexts like illegal, harmful, violent, racist, or sexual purposes, since its usage guidelines explicitly prohibit such applications. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is NExT-GPT or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 3,637). Stars measure visibility, not whether either tool fits your constraints. ### Are NExT-GPT and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (NExT-GPT: BSD-3-Clause, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to NExT-GPT or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [NExT-GPT alternatives](/tools/next-gpt-next-gpt/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([NExT-GPT markdown twin](/tools/next-gpt-next-gpt/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/next-gpt-next-gpt-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, NExT-GPT or awesome-LLM-resources? NExT-GPT: Dormant. awesome-LLM-resources: 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 NExT-GPT and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [NExT-GPT trust report](/tools/next-gpt-next-gpt/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=next-gpt-next-gpt`](/api/graphcanon/graph?tool=next-gpt-next-gpt) - 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/_