--- title: "Awesome-LLM-Reasoning vs ThoughtSource" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/atfortes-awesome-llm-reasoning-vs-openbiolink-thoughtsource" tools: ["atfortes-awesome-llm-reasoning", "openbiolink-thoughtsource"] --- # Awesome-LLM-Reasoning vs ThoughtSource *GraphCanon updated Aug 15, 2026* ## Verdict Pick Awesome-LLM-Reasoning if awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning; pick ThoughtSource if thoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools. [Awesome-LLM-Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) reports 3.7k GitHub stars, 212 forks, and 26 open issues, last pushed Apr 20, 2026. [ThoughtSource](https://github.com/OpenBioLink/ThoughtSource) has 1.0k stars, 81 forks, and 15 open issues, last pushed Dec 16, 2024. Figures are from public GitHub metadata via [Awesome-LLM-Reasoning's repository](https://github.com/atfortes/Awesome-LLM-Reasoning) and [ThoughtSource's repository](https://github.com/OpenBioLink/ThoughtSource). | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [ThoughtSource](/tools/openbiolink-thoughtsource.md) | | --- | --- | --- | | Tagline | Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1. | Central resource for data and tools related to chain-of-thought reasoning in LLMs | | Stars | 3,657 | 1,015 | | Forks | 212 | 81 | | Open issues | 26 | 15 | | Language | - | Jupyter Notebook | | Adopt for | Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning. | ThoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License allows free use, modification, and distribution of the project's source code under its terms and conditions without any cost. | | Categories | LLM Frameworks, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [ThoughtSource](/tools/openbiolink-thoughtsource.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 99d | 606d | | Open issues (now) | 26 | 15 | | Stars delta | Unknown | 0 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/atfortes-awesome-llm-reasoning/trust.md) | [trust report](/tools/openbiolink-thoughtsource/trust.md) | ## Decision facts: Awesome-LLM-Reasoning - **Pricing:** freemium - Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models. - **Adopt for:** Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning. ## Decision facts: ThoughtSource - **Adopt for:** ThoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools. - **License detail:** MIT License allows free use, modification, and distribution of the project's source code under its terms and conditions without any cost. ## Choose when ### Choose Awesome-LLM-Reasoning if… - Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.. - Tags unique to Awesome-LLM-Reasoning: chain-of-thought, chatgpt, cot, deepseek-r1. - Also covers LLM Frameworks. - Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository. ### Choose ThoughtSource if… - Tags unique to ThoughtSource: dataset, machine-learning, natural-language-processing, question-answering. - You need focused resources on chain-of-thought reasoning techniques. - Leaner open-issue backlog (15). ## When NOT to use Awesome-LLM-Reasoning - Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models. - Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities. ## When NOT to use ThoughtSource - Looking for a comprehensive general-purpose AI development environment. - Prefer tools with multi-language support beyond Jupyter Notebooks. ## Common questions ### What is the difference between Awesome-LLM-Reasoning and ThoughtSource? Awesome-LLM-Reasoning: Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.. ThoughtSource: Central resource for data and tools related to chain-of-thought reasoning in LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Reasoning over ThoughtSource? Choose Awesome-LLM-Reasoning over ThoughtSource when Pricing: Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.; Tags unique to Awesome-LLM-Reasoning: chain-of-thought, chatgpt, cot, deepseek-r1; Also covers LLM Frameworks; Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository. ### When should I choose ThoughtSource over Awesome-LLM-Reasoning? Choose ThoughtSource over Awesome-LLM-Reasoning when Tags unique to ThoughtSource: dataset, machine-learning, natural-language-processing, question-answering; You need focused resources on chain-of-thought reasoning techniques; Leaner open-issue backlog (15). ### When should I avoid Awesome-LLM-Reasoning? Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models. Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities. ### When should I avoid ThoughtSource? Looking for a comprehensive general-purpose AI development environment. Prefer tools with multi-language support beyond Jupyter Notebooks. ### Is Awesome-LLM-Reasoning or ThoughtSource more popular on GitHub? Awesome-LLM-Reasoning has more GitHub stars (3,657 vs 1,015). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Reasoning and ThoughtSource open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, ThoughtSource: MIT). ### Where can I find alternatives to Awesome-LLM-Reasoning or ThoughtSource? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Reasoning alternatives](/tools/atfortes-awesome-llm-reasoning/alternatives) and [ThoughtSource alternatives](/tools/openbiolink-thoughtsource/alternatives) ([Awesome-LLM-Reasoning markdown twin](/tools/atfortes-awesome-llm-reasoning/alternatives.md), [ThoughtSource markdown twin](/tools/openbiolink-thoughtsource/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/atfortes-awesome-llm-reasoning-vs-openbiolink-thoughtsource.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Reasoning or ThoughtSource? Awesome-LLM-Reasoning: Slowing. ThoughtSource: Dormant. 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-Reasoning and ThoughtSource? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Reasoning trust report](/tools/atfortes-awesome-llm-reasoning/trust); [ThoughtSource trust report](/tools/openbiolink-thoughtsource/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=atfortes-awesome-llm-reasoning`](/api/graphcanon/graph?tool=atfortes-awesome-llm-reasoning) - 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/_