--- title: "bigcode-evaluation-harness vs LLMDebugger" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-floridsleeves-llmdebugger" tools: ["bigcode-project-bigcode-evaluation-harness", "floridsleeves-llmdebugger"] --- # bigcode-evaluation-harness vs LLMDebugger *GraphCanon updated Aug 5, 2026* ## Verdict Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick LLMDebugger if lLMDebugger offers step-by-step verification of runtime execution for large language models. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 261 forks, and 96 open issues, last pushed Jul 22, 2025. [LLMDebugger](https://github.com/FloridSleeves/LLMDebugger) has 587 stars, 56 forks, and 5 open issues, last pushed Sep 10, 2024. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [LLMDebugger's repository](https://github.com/FloridSleeves/LLMDebugger). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [LLMDebugger](/tools/floridsleeves-llmdebugger.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | A Large Language Model Debugger verifying runtime execution step by step | | Stars | 1,055 | 587 | | Forks | 261 | 56 | | Open issues | 96 | 5 | | Language | Python | Python | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | LLMDebugger offers step-by-step verification of runtime execution for large language models. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | The LLMDebugger is distributed under the Apache-2.0 license. | | Categories | Evaluation & Observability | Developer Tools, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [LLMDebugger](/tools/floridsleeves-llmdebugger.md) | | --- | --- | --- | | Days since push | 378d | 693d | | Open issues (now) | 96 | 5 | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/floridsleeves-llmdebugger/trust.md) | ## Decision facts: bigcode-evaluation-harness - **Requirements:** Users must have Docker installed to leverage the isolated execution environments for model output evaluation. - **Adopt for:** bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. - **License detail:** bigcode-evaluation-harness is distributed under the Apache-2.0 license. ## Decision facts: LLMDebugger - **Pricing:** freemium - Free for use, based on its open-source nature with an Apache-2.0 license. - **Adopt for:** LLMDebugger offers step-by-step verification of runtime execution for large language models. - **License detail:** The LLMDebugger is distributed under the Apache-2.0 license. ## Choose when ### Choose bigcode-evaluation-harness if… - Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.. - Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python. - bigcode-evaluation-harness ships Docker support for self-hosted deployment. - When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### Choose LLMDebugger if… - Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.. - Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification. - Also covers Developer Tools. - When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase. ## When NOT to use bigcode-evaluation-harness - When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. - If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ## When NOT to use LLMDebugger - Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. - Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment. ## Common questions ### What is the difference between bigcode-evaluation-harness and LLMDebugger? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. LLMDebugger: A Large Language Model Debugger verifying runtime execution step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over LLMDebugger? Choose bigcode-evaluation-harness over LLMDebugger when Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### When should I choose LLMDebugger over bigcode-evaluation-harness? Choose LLMDebugger over bigcode-evaluation-harness when Pricing: Free for use, based on its open-source nature with an Apache-2.0 license.; Tags unique to LLMDebugger: acl'24, llm debugging, python debugger for ai, runtime verification; Also covers Developer Tools; When detailed step-by-step inspection of the runtime behavior of large language models is required, LLMDebugger can provide precise insights into each execution phase. ### When should I avoid bigcode-evaluation-harness? When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ### When should I avoid LLMDebugger? Avoid using if you are only interested in higher-level performance metrics rather than the intricate details of runtime behavior, as LLMDebugger emphasizes step-by-step execution. Not recommended for teams lacking experience with Python or specific to this tool's installation and usage workflow that involves setting up a Conda environment. ### Is bigcode-evaluation-harness or LLMDebugger more popular on GitHub? bigcode-evaluation-harness has more GitHub stars (1,055 vs 587). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and LLMDebugger open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, LLMDebugger: Apache-2.0). ### Where can I find alternatives to bigcode-evaluation-harness or LLMDebugger? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [LLMDebugger alternatives](/tools/floridsleeves-llmdebugger/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [LLMDebugger markdown twin](/tools/floridsleeves-llmdebugger/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/bigcode-project-bigcode-evaluation-harness-vs-floridsleeves-llmdebugger.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bigcode-evaluation-harness or LLMDebugger? bigcode-evaluation-harness: Dormant. LLMDebugger: 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 bigcode-evaluation-harness and LLMDebugger? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust); [LLMDebugger trust report](/tools/floridsleeves-llmdebugger/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness`](/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness) - 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/_