--- title: "graph-of-thoughts vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/spcl-graph-of-thoughts-vs-wangrongsheng-awesome-llm-resources" tools: ["spcl-graph-of-thoughts", "wangrongsheng-awesome-llm-resources"] --- # graph-of-thoughts vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick graph-of-thoughts if the Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems; 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. [graph-of-thoughts](https://arxiv.org/pdf/2308.09687.pdf) reports 2.8k GitHub stars, 217 forks, and 7 open issues, last pushed Mar 24, 2026. [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 [graph-of-thoughts's repository](https://github.com/spcl/graph-of-thoughts) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Implementation of Graph of Thoughts for large language models problem-solving | Summary of the world's best LLM resources. | | Stars | 2,826 | 8,845 | | Forks | 217 | 950 | | Open issues | 7 | 23 | | Language | Python | - | | Adopt for | The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. | 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 | Other | 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._ | | [graph-of-thoughts](/tools/spcl-graph-of-thoughts.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 125d | 2d | | Open issues (now) | 7 | 23 | | Stars delta | Unknown | +142 (30d) | | Open issues delta | Unknown | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/spcl-graph-of-thoughts/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: graph-of-thoughts - **Pricing:** freemium - **Requirements:** Min 8 GB RAM - **Adopt for:** The Graph of Thoughts tool is designed for leveraging large language models and graph structures to solve elaborate problems. - **License detail:** Other ## 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 graph-of-thoughts if… - License: graph-of-thoughts is Other, awesome-LLM-resources is Apache-2.0. - Requirements: Min 8 GB RAM. - Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering. - Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition capabilities. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, graph-of-thoughts is Other. - 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 graph-of-thoughts - Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. - Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs. ## 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 graph-of-thoughts and awesome-LLM-resources? graph-of-thoughts: Implementation of Graph of Thoughts for large language models problem-solving. 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 graph-of-thoughts over awesome-LLM-resources? Choose graph-of-thoughts over awesome-LLM-resources when License: graph-of-thoughts is Other, awesome-LLM-resources is Apache-2.0; Requirements: Min 8 GB RAM; Tags unique to graph-of-thoughts: graph-of-thoughts, graph-structures, prompt-engineering; Use when you need to solve complex problem scenarios that require the interplay between advanced language understanding and structured problem decomposition capabilities. ### When should I choose awesome-LLM-resources over graph-of-thoughts? Choose awesome-LLM-resources over graph-of-thoughts when License: awesome-LLM-resources is Apache-2.0, graph-of-thoughts is Other; 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 graph-of-thoughts? Avoid using Graph of Thoughts for simpler, straightforward queries or when real-time performance is critical because it may introduce overhead due to its complex graph processing. Do not use this tool where privacy and data security are paramount concerns if the official license does not sufficiently protect your needs. ### 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 graph-of-thoughts or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 2,826). Stars measure visibility, not whether either tool fits your constraints. ### Are graph-of-thoughts and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (graph-of-thoughts: Other, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to graph-of-thoughts or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [graph-of-thoughts alternatives](/tools/spcl-graph-of-thoughts/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([graph-of-thoughts markdown twin](/tools/spcl-graph-of-thoughts/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/spcl-graph-of-thoughts-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, graph-of-thoughts or awesome-LLM-resources? graph-of-thoughts: Slowing. 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 graph-of-thoughts and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graph-of-thoughts trust report](/tools/spcl-graph-of-thoughts/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=spcl-graph-of-thoughts`](/api/graphcanon/graph?tool=spcl-graph-of-thoughts) - 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/_