Home/Compare/vigil-llm vs autoguardrails

Comparison

vigil-llm vs autoguardrails

Verdict

Pick vigil-llm if vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs; 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 · vigil-llm alternatives · autoguardrails alternatives

GraphCanon updated today

vigil-llm logo

vigil-llm

deadbits/vigil-llm

496pushed Jan 31, 2024
vs
autoguardrails logo

autoguardrails

SantanderAI/autoguardrails

128pushed Aug 1, 2026

Trust & integrity

Signalvigil-llmautoguardrails
Maintenance
Dormant (932d since push)
As of today · github_public_v1
Active (8d since push)
As of 1w · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of 1w · 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

vigil-llm
Detect prompt injections and other risky inputs in LLMs
autoguardrails
Alignment-research scaffold for LLM guardrails involving policy evaluation and content moderation

Stars

vigil-llm
496
autoguardrails
128

Forks

vigil-llm
56
autoguardrails
35

Open issues

vigil-llm
16
autoguardrails
2

Language

vigil-llm
Python
autoguardrails
Python

Adopt for

vigil-llm
Vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs.
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

vigil-llm
-
autoguardrails
-

Runtime

vigil-llm
-
autoguardrails
-

License

vigil-llm
Apache-2.0
autoguardrails
Apache-2.0

Last pushed

vigil-llm
Jan 31, 2024
autoguardrails
Aug 1, 2026

Categories

vigil-llm
Evaluation & Observability
autoguardrails
Evaluation & Observability, LLM Frameworks

Trust and health

Maintenance

vigil-llm
Dormant (18%)
autoguardrails
Active (82%)

Days since push

vigil-llm
932d
autoguardrails
8d

Open issues (now)

vigil-llm
16
autoguardrails
2

Stars delta

vigil-llm
+5 (30d)
autoguardrails
Unknown

Open issues delta

vigil-llm
0 (30d)
autoguardrails
Unknown

Owner type

vigil-llm
User
autoguardrails
Organization

Full report

vigil-llm
Trust report
autoguardrails
Trust report

Shared compatibility

  • Python · vigil-llm: Python runtime · autoguardrails: Python runtime

Choose vigil-llm if…

  • Tags unique to vigil-llm: adversarial-attacks, large language models, llm security, prompt-injection.
  • vigil-llm ships Docker support for self-hosted deployment.
  • When deploying large language models that require high levels of input security, vigil-llm can be employed to detect maliciously crafted inputs intended to manipulate model behavior.

When NOT to use vigil-llm

  • If your application does not require high security against malicious inputs or if the risks of prompt injection are minimal due to controlled input sources, vigil-llm might be unnecessary.
  • For projects that focus on optimizing output speed rather than input robustness, other tools might be more appropriate as vigil-llm could add significant processing overhead.

Choose autoguardrails if…

  • 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: vigil-llm 496 · autoguardrails 128 (synced Aug 21, 2026).

Common questions

What is the difference between vigil-llm and autoguardrails?
vigil-llm: Detect prompt injections and other risky inputs in LLMs. 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 vigil-llm over autoguardrails?
Choose vigil-llm over autoguardrails when Tags unique to vigil-llm: adversarial-attacks, large language models, llm security, prompt-injection; vigil-llm ships Docker support for self-hosted deployment; When deploying large language models that require high levels of input security, vigil-llm can be employed to detect maliciously crafted inputs intended to manipulate model behavior.
When should I choose autoguardrails over vigil-llm?
Choose autoguardrails over vigil-llm when 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 vigil-llm?
If your application does not require high security against malicious inputs or if the risks of prompt injection are minimal due to controlled input sources, vigil-llm might be unnecessary. For projects that focus on optimizing output speed rather than input robustness, other tools might be more appropriate as vigil-llm could add significant processing overhead.
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 vigil-llm or autoguardrails more popular on GitHub?
vigil-llm has more GitHub stars (496 vs 128). Stars measure visibility, not whether either tool fits your constraints.
Are vigil-llm and autoguardrails open source?
Yes - both are open-source projects on GitHub (vigil-llm: Apache-2.0, autoguardrails: Apache-2.0).
Where can I find alternatives to vigil-llm or autoguardrails?
GraphCanon lists graph-backed alternatives at vigil-llm alternatives and autoguardrails alternatives (vigil-llm 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, vigil-llm or autoguardrails?
vigil-llm: 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 vigil-llm and autoguardrails?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: vigil-llm trust report; autoguardrails trust report.

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