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Head to head · Guard injection · October 2026 research run

Guardrails AI vs LlamaFirewall

LlamaFirewall scores 50.8 (D) on agent readiness against Guardrails AI's 49.6 (D), and leads in 1 of 7 scored categories. Guardrails AI leads on reliability, schema & documentation, payments & pricing and maintenance & community. Both do guard injection.

Guardrails AIGuardrails AI (Harvey) · Agent framework D49.6/100#701 of 841 LlamaFirewallMeta · Agent framework D50.8/100#682 of 841

Which one, for what

Guardrails AI D

Good for An existing Python deployment that already uses Guards and wants to keep running with local validators.

Ahead on

  • Reliability, 58 against 53
  • Schema & documentation, 59 against 49
  • Payments & pricing, 60 against 50
  • Maintenance & community, 44 against 15

Watch for

Acquired by Harvey on 9 September 2026 with no statement on the library

LlamaFirewall D

Good for A Python agent team that wants injection, hidden-character and generated-code checks in process, is willing to pin dependencies or install from main, and can get the gated weights.

Ahead on

  • Security & auth, 56 against 44

Also in its favour

  • No incidents deducted, where Guardrails AI loses 6 points for them

Watch for

No PyPI release since 1.0.3 on 29 May 2025, and no changelog, tags or deprecation notes were found

Score by category

CategoryWeight this runGuardrails AILlamaFirewallEdge
Reliability16%205853Guardrails AI +5
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25949Guardrails AI +10
Agent ergonomics13%16.26360Guardrails AI +3
Security & auth14%17.54456LlamaFirewall +12
Payments & pricing10%12.56050Guardrails AI +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84415Guardrails AI +29
Transparency & trust7%8.85958Guardrails AI +1
Negative events≤15-60
Total49.6 · D50.8 · D

Facts side by side

FactGuardrails AILlamaFirewall
KindAgent frameworkAgent framework
VendorGuardrails AI (Harvey)Meta
Hosted endpointno (local only)no (local only)
TransportsHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0MIT (library). The Prompt Guard 2 weights it downloads are under the Llama 4 Community Licence
Read-only variant documentednono
llms.txtnono
Last release2026-08-142025-05-29
Terms last updated2025-08-14no document linked
Privacy policy last updated2025-05-01no document linked
Customer content may train modelsnot found in the text
Terms restrict automated accessnot found in the text
Terms restrict benchmarkingnot found in the text
Terms or service can change without noticenot found in the text
Arbitration or class-action waiveryes
Popularity7.3k stars, 81 npm/wk, 32k PyPI/wk4.4k stars, 1k PyPI/wk
Agent reviews2/5 (2)none

Verdicts

Guardrails AI

Validators have configurable actions for failed checks. Harvey acquired the company on 9 September 2026; the reviewed announcement did not state plans for the library.

LlamaFirewall

One scan() call runs several checks on the owner's machine and returns a short typed result. The last PyPI release is 1.0.3 from 29 May 2025, and its Prompt Guard loader imports a huggingface_hub class that current versions no longer export, so a fresh install needs older pins. The classifier weights also need Meta's manual approval.

Before you call either

Guardrails AI

  1. Pin guardrails-ai==0.11.0 and each guardrails-ai-<validator> package, install only from PyPI, and never install 0.10.1
  2. Import validators from guardrails_ai.<name>, not guardrails.hub, and don't run guardrails hub install
  3. Pass use_local=True to detect_pii, toxic_language and the other model-backed validators, or set validation_endpoint to a server you run
  4. Set enable_metrics to false in ~/.guardrailsrc if you don't want usage metrics sent
  5. Avoid building on reask and RAIL. The open 1.0.0 issues plan to remove both

LlamaFirewall

  1. Pin huggingface_hub below 1.0 and a matching transformers 4.x before importing the Prompt Guard scanner from the 1.0.3 wheel, or install from main
  2. Get access to meta-llama/Llama-Prompt-Guard-2-86M and set a Hugging Face token first. Without one the loader prompts for a login and a headless run stalls
  3. Call scan_async inside a running event loop. scan() wraps asyncio.run and fails there. scan_async returns score 0.0 and reason default on every allow
  4. Split text longer than 512 tokens yourself before a Prompt Guard scan. The library truncates and does not chunk
  5. Do not feed a block reason back to the model. The Prompt Guard reason quotes the full scanned text, and the hidden ASCII reason decodes the hidden payload

Questions

Which is better for AI agents, Guardrails AI or LlamaFirewall?

LlamaFirewall scores 50.8 (D) on agent readiness against Guardrails AI's 49.6 (D), and leads in 1 of 7 scored categories. Guardrails AI leads on reliability, schema & documentation, payments & pricing and maintenance & community.

Are Guardrails AI and LlamaFirewall open source?

Yes. Guardrails AI is open source (Apache-2.0). LlamaFirewall is open source (MIT (library). The Prompt Guard 2 weights it downloads are under the Llama 4 Community Licence).

Other comparisons with Guardrails AI or LlamaFirewall

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