--- title: "Awesome-Code-LLM vs LLMsPracticalGuide" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-mooler0410-llmspracticalguide" tools: ["huybery-awesome-code-llm", "mooler0410-llmspracticalguide"] --- # Awesome-Code-LLM vs LLMsPracticalGuide *GraphCanon updated Aug 6, 2026* ## Verdict Pick Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers; pick LLMsPracticalGuide if lLMsPracticalGuide is a curated list of practical guide resources focused on Large Language Models (LLMs), including an evolutionary tree and licensing details. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) reports 1.3k GitHub stars, 74 forks, and 4 open issues, last pushed Dec 10, 2024. [LLMsPracticalGuide](https://arxiv.org/abs/2304.13712v2) has 10k stars, 788 forks, and 17 open issues, last pushed Apr 8, 2026. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [LLMsPracticalGuide's repository](https://github.com/Mooler0410/LLMsPracticalGuide). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [LLMsPracticalGuide](/tools/mooler0410-llmspracticalguide.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | A curated list of practical guide resources of LLMs | | Stars | 1,291 | 10,196 | | Forks | 74 | 788 | | Open issues | 4 | 17 | | Language | - | - | | Adopt for | Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. | LLMsPracticalGuide is a curated list of practical guide resources focused on Large Language Models (LLMs), including an evolutionary tree and licensing details. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | - | | Categories | Evaluation & Observability, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [LLMsPracticalGuide](/tools/mooler0410-llmspracticalguide.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 604d | 119d | | Open issues (now) | 4 | 17 | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/mooler0410-llmspracticalguide/trust.md) | ## Decision facts: Awesome-Code-LLM - **Requirements:** No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs. - **Adopt for:** Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. - **License detail:** MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. ## Decision facts: LLMsPracticalGuide - **Adopt for:** LLMsPracticalGuide is a curated list of practical guide resources focused on Large Language Models (LLMs), including an evolutionary tree and licensing details. ## Choose when ### Choose Awesome-Code-LLM if… - Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.. - Tags unique to Awesome-Code-LLM: awesome, code generation. - Also covers Evaluation & Observability. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### Choose LLMsPracticalGuide if… - Tags unique to LLMsPracticalGuide: natural-language-processing, nlp, survey. - - If you are looking for a resource to navigate the landscape of LLMs, this repository provides a comprehensive list of practical guides and resources. - More GitHub stars (10k vs 1.3k) - visibility, not fit. ## When NOT to use Awesome-Code-LLM - When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. - If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. - In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ## When NOT to use LLMsPracticalGuide - - Avoid using it if your project does not require a detailed overview or is focused on very specific aspects of LLM implementation where deep, specialized guides are preferred. - - If you prefer hands-on tutorials over curated lists and practical guides, another tool in the same category that offers more interactive content might be better suited. - - This repository may lack real-time updates or ongoing active contributions if other resources provide daily curated inputs from a broader community of contributors. ## Common questions ### What is the difference between Awesome-Code-LLM and LLMsPracticalGuide? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. LLMsPracticalGuide: A curated list of practical guide resources of LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over LLMsPracticalGuide? Choose Awesome-Code-LLM over LLMsPracticalGuide when Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, code generation; Also covers Evaluation & Observability; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I choose LLMsPracticalGuide over Awesome-Code-LLM? Choose LLMsPracticalGuide over Awesome-Code-LLM when Tags unique to LLMsPracticalGuide: natural-language-processing, nlp, survey; - If you are looking for a resource to navigate the landscape of LLMs, this repository provides a comprehensive list of practical guides and resources; More GitHub stars (10k vs 1.3k) - visibility, not fit. ### When should I avoid Awesome-Code-LLM? When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ### When should I avoid LLMsPracticalGuide? - Avoid using it if your project does not require a detailed overview or is focused on very specific aspects of LLM implementation where deep, specialized guides are preferred. - If you prefer hands-on tutorials over curated lists and practical guides, another tool in the same category that offers more interactive content might be better suited. - This repository may lack real-time updates or ongoing active contributions if other resources provide daily curated inputs from a broader community of contributors. ### Is Awesome-Code-LLM or LLMsPracticalGuide more popular on GitHub? LLMsPracticalGuide has more GitHub stars (10,196 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and LLMsPracticalGuide open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Code-LLM or LLMsPracticalGuide? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [LLMsPracticalGuide alternatives](/tools/mooler0410-llmspracticalguide/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [LLMsPracticalGuide markdown twin](/tools/mooler0410-llmspracticalguide/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/huybery-awesome-code-llm-vs-mooler0410-llmspracticalguide.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Code-LLM or LLMsPracticalGuide? Awesome-Code-LLM: Dormant. LLMsPracticalGuide: 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-Code-LLM and LLMsPracticalGuide? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [LLMsPracticalGuide trust report](/tools/mooler0410-llmspracticalguide/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huybery-awesome-code-llm`](/api/graphcanon/graph?tool=huybery-awesome-code-llm) - 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/_