--- title: "bigcode-evaluation-harness vs llm-lobbyist" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-johnnay-llm-lobbyist" tools: ["bigcode-project-bigcode-evaluation-harness", "johnnay-llm-lobbyist"] --- # bigcode-evaluation-harness vs llm-lobbyist *GraphCanon updated Oct 6, 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 llm-lobbyist if the llm-lobbyist tool specializes in evaluating large language models' efficiency in conducting corporate lobbying activities using Jupyter Notebook and the `text-davinci-003` model. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 262 forks, and 99 open issues, last pushed Jul 22, 2025. [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist) has 174 stars, 14 forks, and 0 open issues, last pushed Jan 13, 2023. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [llm-lobbyist's repository](https://github.com/JohnNay/llm-lobbyist). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | Code for research on large language models conducting corporate lobbying activities. | | Stars | 1,062 | 174 | | Forks | 262 | 14 | | Open issues | 99 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | The llm-lobbyist tool specializes in evaluating large language models' efficiency in conducting corporate lobbying activities using Jupyter Notebook and the `text-davinci-003` model. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | - | | Categories | Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | | --- | --- | --- | | Days since push | 440d | 1346d | | Open issues (now) | 99 | 0 | | Stars delta | +4 (30d) | 0 (30d) | | Open issues delta | +3 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/johnnay-llm-lobbyist/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: llm-lobbyist - **Adopt for:** The llm-lobbyist tool specializes in evaluating large language models' efficiency in conducting corporate lobbying activities using Jupyter Notebook and the `text-davinci-003` model. ## Choose when ### Choose bigcode-evaluation-harness if… - bigcode-evaluation-harness is primarily Python; llm-lobbyist is Jupyter Notebook. - 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 llm-lobbyist if… - llm-lobbyist is primarily Jupyter Notebook; bigcode-evaluation-harness is Python. - Tags unique to llm-lobbyist: corporate lobbying, llm-evaluation, text davinci 003. - Also covers Model Training. - When you need to assess how well automated systems can determine if legislative proposals are relevant for particular public companies. ## 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 llm-lobbyist - If your scope of work does not involve evaluating or training large language models in a legal policy context. - When you require a tool to analyze or generate content unrelated to legislative relevance, such as technical documentation or creative writing. ## Common questions ### What is the difference between bigcode-evaluation-harness and llm-lobbyist? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. llm-lobbyist: Code for research on large language models conducting corporate lobbying activities.. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over llm-lobbyist? Choose bigcode-evaluation-harness over llm-lobbyist when bigcode-evaluation-harness is primarily Python; llm-lobbyist is Jupyter Notebook; 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 llm-lobbyist over bigcode-evaluation-harness? Choose llm-lobbyist over bigcode-evaluation-harness when llm-lobbyist is primarily Jupyter Notebook; bigcode-evaluation-harness is Python; Tags unique to llm-lobbyist: corporate lobbying, llm-evaluation, text davinci 003; Also covers Model Training; When you need to assess how well automated systems can determine if legislative proposals are relevant for particular public companies. ### 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 llm-lobbyist? If your scope of work does not involve evaluating or training large language models in a legal policy context. When you require a tool to analyze or generate content unrelated to legislative relevance, such as technical documentation or creative writing. ### Is bigcode-evaluation-harness or llm-lobbyist more popular on GitHub? bigcode-evaluation-harness has more GitHub stars (1,062 vs 174). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and llm-lobbyist open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to bigcode-evaluation-harness or llm-lobbyist? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [llm-lobbyist alternatives](/tools/johnnay-llm-lobbyist/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [llm-lobbyist markdown twin](/tools/johnnay-llm-lobbyist/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-johnnay-llm-lobbyist.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 llm-lobbyist? bigcode-evaluation-harness: Dormant. llm-lobbyist: 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 llm-lobbyist? 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); [llm-lobbyist trust report](/tools/johnnay-llm-lobbyist/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/_