Comparison
llm-lobbyist vs autoguardrails
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.
Markdown twin · llm-lobbyist alternatives · autoguardrails alternatives
GraphCanon updated 1w
Trust & integrity
| Signal | llm-lobbyist | autoguardrails |
|---|---|---|
| Maintenance | Dormant (1310d since push) As of 1w · github_public_v1 | Active (8d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- llm-lobbyist
- 174
- autoguardrails
- 128
Forks
- llm-lobbyist
- 14
- autoguardrails
- 35
Open issues
- llm-lobbyist
- 0
- autoguardrails
- 2
Language
- llm-lobbyist
- Jupyter Notebook
- autoguardrails
- Python
Adopt for
- llm-lobbyist
- 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
- 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
- llm-lobbyist
- -
- autoguardrails
- -
Runtime
- llm-lobbyist
- -
- autoguardrails
- -
License
- llm-lobbyist
- -
- autoguardrails
- Apache-2.0
Last pushed
- llm-lobbyist
- Jan 13, 2023
- autoguardrails
- Aug 1, 2026
Categories
- llm-lobbyist
- Evaluation & Observability, Model Training
- autoguardrails
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- llm-lobbyist
- Dormant (18%)
- autoguardrails
- Active (82%)
Days since push
- llm-lobbyist
- 1310d
- autoguardrails
- 8d
Open issues (now)
- llm-lobbyist
- 0
- autoguardrails
- 2
Stars delta
- llm-lobbyist
- 0 (30d)
- autoguardrails
- Unknown
Open issues delta
- llm-lobbyist
- 0 (30d)
- autoguardrails
- Unknown
Owner type
- llm-lobbyist
- User
- autoguardrails
- Organization
Full report
- llm-lobbyist
- Trust report
- autoguardrails
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (JohnNay/llm-lobbyist) · observed Aug 15, 2026
- GitHub forks (JohnNay/llm-lobbyist) · observed Aug 15, 2026
- Last push (JohnNay/llm-lobbyist) · observed Jan 13, 2023
- License file (unknown) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (SantanderAI/autoguardrails) · observed Aug 9, 2026
- GitHub forks (SantanderAI/autoguardrails) · observed Aug 9, 2026
- Last push (SantanderAI/autoguardrails) · observed Aug 1, 2026
- License file (Apache-2.0) · observed Aug 9, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: llm-lobbyist 174 · autoguardrails 128 (synced Aug 15, 2026).
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 and autoguardrails alternatives (llm-lobbyist markdown twin, autoguardrails markdown twin), 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 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; autoguardrails trust report.