--- title: "llm-lobbyist vs autoguardrails" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/johnnay-llm-lobbyist-vs-santanderai-autoguardrails" tools: ["johnnay-llm-lobbyist", "santanderai-autoguardrails"] --- # llm-lobbyist vs autoguardrails *GraphCanon updated Aug 15, 2026* ## Verdict 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; pick autoguardrails if autoguardrails is an evaluation and development framework for AI policy creation and review. It enables the iterative adjustment and testing of guardrail policies in alignment research through a controlled workflow. [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist) reports 174 GitHub stars, 14 forks, and 0 open issues, last pushed Jan 13, 2023. [autoguardrails](https://github.com/SantanderAI) has 128 stars, 35 forks, and 2 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [llm-lobbyist's repository](https://github.com/JohnNay/llm-lobbyist) and [autoguardrails's repository](https://github.com/SantanderAI/autoguardrails). | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [autoguardrails](/tools/santanderai-autoguardrails.md) | | --- | --- | --- | | Tagline | Code for research on large language models conducting corporate lobbying activities. | Alignment-research scaffold for LLM guardrails involving policy evaluation and content moderation | | Stars | 174 | 128 | | Forks | 14 | 35 | | Open issues | 0 | 2 | | Language | Jupyter Notebook | Python | | 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. | Autoguardrails is an evaluation and development framework for AI policy creation and review. It enables the iterative adjustment and testing of guardrail policies in alignment research through a controlled workflow. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [autoguardrails](/tools/santanderai-autoguardrails.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1310d | 8d | | Open issues (now) | 0 | 2 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/johnnay-llm-lobbyist/trust.md) | [trust report](/tools/santanderai-autoguardrails/trust.md) | ## 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. ## Decision facts: autoguardrails - **Requirements:** Requires Python 3.10 or higher.; No third-party runtimes; it is built completely on the standard Python library. - **Adopt for:** Autoguardrails is an evaluation and development framework for AI policy creation and review. It enables the iterative adjustment and testing of guardrail policies in alignment research through a controlled workflow. ## Choose when ### Choose llm-lobbyist if… - llm-lobbyist is primarily Jupyter Notebook; autoguardrails 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. ### Choose autoguardrails if… - autoguardrails is primarily Python; llm-lobbyist is Jupyter Notebook. - Requirements: Requires Python 3.10 or higher.; No third-party runtimes; it is built completely on the standard Python library.. - Tags unique to autoguardrails: ai safety, alignment, autoresearch, content-moderation. - Also covers LLM Frameworks. - When you are conducting alignment research that requires systematic iteration on LLM safeguard policies. ## 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. ## When NOT to use autoguardrails - Autoguardrails may not suit needs requiring real-time or dynamic policy adjustments outside its autoresearch workflow. - Avoid using Autoguardrails if you cannot accept offline operation as it is built on the Python standard library and runs without third-party runtime dependencies. ## Common questions ### What is the difference between llm-lobbyist and autoguardrails? llm-lobbyist: Code for research on large language models conducting corporate lobbying activities.. autoguardrails: Alignment-research scaffold for LLM guardrails involving policy evaluation and content moderation. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-lobbyist over autoguardrails? Choose llm-lobbyist over autoguardrails when llm-lobbyist is primarily Jupyter Notebook; autoguardrails 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 choose autoguardrails over llm-lobbyist? Choose autoguardrails over llm-lobbyist when autoguardrails is primarily Python; llm-lobbyist is Jupyter Notebook; Requirements: Requires Python 3.10 or higher.; No third-party runtimes; it is built completely on the standard Python library.; Tags unique to autoguardrails: ai safety, alignment, autoresearch, content-moderation; Also covers LLM Frameworks; When you are conducting alignment research that requires systematic iteration on LLM safeguard policies. ### 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. ### When should I avoid autoguardrails? Autoguardrails may not suit needs requiring real-time or dynamic policy adjustments outside its autoresearch workflow. Avoid using Autoguardrails if you cannot accept offline operation as it is built on the Python standard library and runs without third-party runtime dependencies. ### Is llm-lobbyist or autoguardrails more popular on GitHub? llm-lobbyist has more GitHub stars (174 vs 128). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-lobbyist and autoguardrails open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-lobbyist or autoguardrails? GraphCanon lists graph-backed alternatives at [llm-lobbyist alternatives](/tools/johnnay-llm-lobbyist/alternatives) and [autoguardrails alternatives](/tools/santanderai-autoguardrails/alternatives) ([llm-lobbyist markdown twin](/tools/johnnay-llm-lobbyist/alternatives.md), [autoguardrails markdown twin](/tools/santanderai-autoguardrails/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/johnnay-llm-lobbyist-vs-santanderai-autoguardrails.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-lobbyist or autoguardrails? llm-lobbyist: Dormant. autoguardrails: Active. 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 llm-lobbyist and autoguardrails? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-lobbyist trust report](/tools/johnnay-llm-lobbyist/trust); [autoguardrails trust report](/tools/santanderai-autoguardrails/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=johnnay-llm-lobbyist`](/api/graphcanon/graph?tool=johnnay-llm-lobbyist) - 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/_