--- title: "Awesome-LLM-Reasoning vs HRM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/atfortes-awesome-llm-reasoning-vs-sapientinc-hrm" tools: ["atfortes-awesome-llm-reasoning", "sapientinc-hrm"] --- # Awesome-LLM-Reasoning vs HRM *GraphCanon updated Sep 20, 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 HRM if hRM is a brain-inspired AI model for reasoning tasks, requiring CUDA 12.6 and PyTorch installation. [Awesome-LLM-Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) reports 3.7k GitHub stars, 213 forks, and 27 open issues, last pushed Apr 20, 2026. [HRM](https://sapient.inc) has 13k stars, 1.8k forks, and 75 open issues, last pushed Mar 31, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Reasoning's repository](https://github.com/atfortes/Awesome-LLM-Reasoning) and [HRM's repository](https://github.com/sapientinc/HRM). | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [HRM](/tools/sapientinc-hrm.md) | | --- | --- | --- | | Tagline | Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1. | Hierarchical Reasoning Model Official Release | | Stars | 3,685 | 12,641 | | Forks | 213 | 1,823 | | Open issues | 27 | 75 | | Language | - | Python | | 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. | HRM is a brain-inspired AI model for reasoning tasks, requiring CUDA 12.6 and PyTorch installation. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Reasoning](/tools/atfortes-awesome-llm-reasoning.md) | [HRM](/tools/sapientinc-hrm.md) | | --- | --- | --- | | Days since push | 153d | 170d | | Open issues (now) | 27 | 75 | | Open issues delta | +1 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/atfortes-awesome-llm-reasoning/trust.md) | [trust report](/tools/sapientinc-hrm/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: HRM - **Requirements:** HRM requires CUDA 12.6 and PyTorch installation.; Ensure you have the necessary permissions to install CUDA and PyTorch in your environment. - **Adopt for:** HRM is a brain-inspired AI model for reasoning tasks, requiring CUDA 12.6 and PyTorch installation. ## Choose when ### Choose Awesome-LLM-Reasoning if… - License: Awesome-LLM-Reasoning is MIT, HRM is Apache-2.0. - 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. - 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 HRM if… - License: HRM is Apache-2.0, Awesome-LLM-Reasoning is MIT. - Requirements: HRM requires CUDA 12.6 and PyTorch installation.; Ensure you have the necessary permissions to install CUDA and PyTorch in your environment.. - Tags unique to HRM: brain-inspired-ai, cuda, deep-learning, large-language-models. - Use HRM when you need a brain-inspired AI model for reasoning tasks that can leverage the latest CUDA 12.6 for optimal performance. ## 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 HRM - Avoid HRM if your project does not require CUDA 12.6, as it may not be compatible with other CUDA versions. - Do not use HRM if your development environment cannot support PyTorch, as this is a critical dependency for the model. ## Common questions ### What is the difference between Awesome-LLM-Reasoning and HRM? Awesome-LLM-Reasoning: Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.. HRM: Hierarchical Reasoning Model Official Release. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Reasoning over HRM? Choose Awesome-LLM-Reasoning over HRM when License: Awesome-LLM-Reasoning is MIT, HRM is Apache-2.0; 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; 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 HRM over Awesome-LLM-Reasoning? Choose HRM over Awesome-LLM-Reasoning when License: HRM is Apache-2.0, Awesome-LLM-Reasoning is MIT; Requirements: HRM requires CUDA 12.6 and PyTorch installation.; Ensure you have the necessary permissions to install CUDA and PyTorch in your environment.; Tags unique to HRM: brain-inspired-ai, cuda, deep-learning, large-language-models; Use HRM when you need a brain-inspired AI model for reasoning tasks that can leverage the latest CUDA 12.6 for optimal performance. ### 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 HRM? Avoid HRM if your project does not require CUDA 12.6, as it may not be compatible with other CUDA versions. Do not use HRM if your development environment cannot support PyTorch, as this is a critical dependency for the model. ### Is Awesome-LLM-Reasoning or HRM more popular on GitHub? HRM has more GitHub stars (12,641 vs 3,685). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Reasoning and HRM open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Reasoning: MIT, HRM: Apache-2.0). ### Where can I find alternatives to Awesome-LLM-Reasoning or HRM? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Reasoning alternatives](/tools/atfortes-awesome-llm-reasoning/alternatives) and [HRM alternatives](/tools/sapientinc-hrm/alternatives) ([Awesome-LLM-Reasoning markdown twin](/tools/atfortes-awesome-llm-reasoning/alternatives.md), [HRM markdown twin](/tools/sapientinc-hrm/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-sapientinc-hrm.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 HRM? Awesome-LLM-Reasoning: Slowing. HRM: 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-Reasoning and HRM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Reasoning trust report](/tools/atfortes-awesome-llm-reasoning/trust); [HRM trust report](/tools/sapientinc-hrm/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/_