--- title: "HLCE vs MGDebugger" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/humanity-s-last-code-exam-hlce-vs-yerbapage-mgdebugger" tools: ["humanity-s-last-code-exam-hlce", "yerbapage-mgdebugger"] --- # HLCE vs MGDebugger *GraphCanon updated Aug 8, 2026* ## Verdict Pick HLCE if hLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes; pick MGDebugger if mGDebugger offers hierarchical debugging for various levels of code granularity, emphasizing efficient error resolution and improved debug accuracy. [HLCE](https://humanity-s-last-code-exam.github.io/website/) reports 96 GitHub stars, 8 forks, and 1 open issues, last pushed Aug 21, 2025. [MGDebugger](https://github.com/YerbaPage/MGDebugger) has 101 stars, 10 forks, and 0 open issues, last pushed Jul 6, 2025. Figures are from public GitHub metadata via [HLCE's repository](https://github.com/Humanity-s-Last-Code-Exam/HLCE) and [MGDebugger's repository](https://github.com/YerbaPage/MGDebugger). | | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | [MGDebugger](/tools/yerbapage-mgdebugger.md) | | --- | --- | --- | | Tagline | Source Evaluation scripts for Humanity's Last Code Exam | Multi-Granularity LLM Debugger | | Stars | 96 | 101 | | Forks | 8 | 10 | | Open issues | 1 | 0 | | Language | Python | Python | | Adopt for | HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. | MGDebugger offers hierarchical debugging for various levels of code granularity, emphasizing efficient error resolution and improved debug accuracy. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | [MGDebugger](/tools/yerbapage-mgdebugger.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 352d | 395d | | Open issues (now) | 1 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/humanity-s-last-code-exam-hlce/trust.md) | [trust report](/tools/yerbapage-mgdebugger/trust.md) | ## Decision facts: HLCE - **Adopt for:** HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. ## Decision facts: MGDebugger - **Pricing:** freemium - MGDebugger is free to use under MIT license but may require users to manage model hosting costs and dependencies. - **Requirements:** Min 4 GB RAM; Requires Python version 3.8 or later; vLLM version 0.6.0 or later must be installed for model inference - **Adopt for:** MGDebugger offers hierarchical debugging for various levels of code granularity, emphasizing efficient error resolution and improved debug accuracy. ## Choose when ### Choose HLCE if… - Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation. - When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively. - More recently updated (last pushed Aug 21, 2025). ### Choose MGDebugger if… - Pricing: MGDebugger is free to use under MIT license but may require users to manage model hosting costs and dependencies.. - Requirements: Min 4 GB RAM; Requires Python version 3.8 or later; vLLM version 0.6.0 or later must be installed for model inference. - Tags unique to MGDebugger: automatic-program-repair, code generation, debugger, large language models. - When you need to perform granular analysis on complex codes, progressing from subfunctions to the whole system to ensure precise error detection and correction. ## When NOT to use HLCE - If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. - When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ## When NOT to use MGDebugger - Avoid using MGDebugger if you operate primarily on Mac systems and do not require support for quantized models (as some essential dependencies are unsupported on MacOS). - If your model does not align well with the DeepSeek-Coder-V2-Lite-Instruct or similar models, since the effectiveness of MGDebugger might vary without support for those particular frameworks. ## Common questions ### What is the difference between HLCE and MGDebugger? HLCE: Source Evaluation scripts for Humanity's Last Code Exam. MGDebugger: Multi-Granularity LLM Debugger. See the comparison table for live GitHub stats and shared categories. ### When should I choose HLCE over MGDebugger? Choose HLCE over MGDebugger when Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation; When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively; More recently updated (last pushed Aug 21, 2025). ### When should I choose MGDebugger over HLCE? Choose MGDebugger over HLCE when Pricing: MGDebugger is free to use under MIT license but may require users to manage model hosting costs and dependencies.; Requirements: Min 4 GB RAM; Requires Python version 3.8 or later; vLLM version 0.6.0 or later must be installed for model inference; Tags unique to MGDebugger: automatic-program-repair, code generation, debugger, large language models; When you need to perform granular analysis on complex codes, progressing from subfunctions to the whole system to ensure precise error detection and correction. ### When should I avoid HLCE? If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only. ### When should I avoid MGDebugger? Avoid using MGDebugger if you operate primarily on Mac systems and do not require support for quantized models (as some essential dependencies are unsupported on MacOS). If your model does not align well with the DeepSeek-Coder-V2-Lite-Instruct or similar models, since the effectiveness of MGDebugger might vary without support for those particular frameworks. ### Is HLCE or MGDebugger more popular on GitHub? MGDebugger has more GitHub stars (101 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are HLCE and MGDebugger open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to HLCE or MGDebugger? GraphCanon lists graph-backed alternatives at [HLCE alternatives](/tools/humanity-s-last-code-exam-hlce/alternatives) and [MGDebugger alternatives](/tools/yerbapage-mgdebugger/alternatives) ([HLCE markdown twin](/tools/humanity-s-last-code-exam-hlce/alternatives.md), [MGDebugger markdown twin](/tools/yerbapage-mgdebugger/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/humanity-s-last-code-exam-hlce-vs-yerbapage-mgdebugger.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HLCE or MGDebugger? HLCE: Slowing. MGDebugger: 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 HLCE and MGDebugger? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HLCE trust report](/tools/humanity-s-last-code-exam-hlce/trust); [MGDebugger trust report](/tools/yerbapage-mgdebugger/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=humanity-s-last-code-exam-hlce`](/api/graphcanon/graph?tool=humanity-s-last-code-exam-hlce) - 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/_