Home/Compare/llm-lobbyist vs autoguardrails

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

llm-lobbyist logo

llm-lobbyist

JohnNay/llm-lobbyist

174pushed Jan 13, 2023
vs
autoguardrails logo

autoguardrails

SantanderAI/autoguardrails

128pushed Aug 1, 2026

Trust & integrity

Signalllm-lobbyistautoguardrails
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 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.

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