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Head to head · Guard injection · October 2026 research run

LlamaFirewall vs NVIDIA NeMo Guardrails

NVIDIA NeMo Guardrails scores 68.4 (B) on agent readiness against LlamaFirewall's 50.8 (D), and leads in every scored category. Both do guard injection.

LlamaFirewallMeta · Agent framework D50.8/100#682 of 841 NVIDIA NeMo GuardrailsNVIDIA · Agent framework B68.4/100#202 of 841

Which one, for what

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.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

NVIDIA NeMo Guardrails B

Good for Teams that want to compose several checks (their own, NVIDIA's and third-party APIs) behind one OpenAI-compatible endpoint.

Ahead on

  • Reliability, 75 against 53
  • Schema & documentation, 69 against 49
  • Agent ergonomics, 67 against 60
  • Security & auth, 62 against 56
  • Payments & pricing, 60 against 50
  • Maintenance & community, 80 against 15
  • Transparency & trust, 68 against 58

Watch for

Usage telemetry and a heartbeat every 10 minutes to NVIDIA by default

Score by category

CategoryWeight this runLlamaFirewallNVIDIA NeMo GuardrailsEdge
Reliability16%205375NVIDIA NeMo Guardrails +22
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.24969NVIDIA NeMo Guardrails +20
Agent ergonomics13%16.26067NVIDIA NeMo Guardrails +7
Security & auth14%17.55662NVIDIA NeMo Guardrails +6
Payments & pricing10%12.55060NVIDIA NeMo Guardrails +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.81580NVIDIA NeMo Guardrails +65
Transparency & trust7%8.85868NVIDIA NeMo Guardrails +10
Negative events≤1500
Total50.8 · D68.4 · B

Facts side by side

FactLlamaFirewallNVIDIA NeMo Guardrails
KindAgent frameworkAgent framework
VendorMetaNVIDIA
Hosted endpointno (local only)no (local only)
TransportsHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceMIT (library). The Prompt Guard 2 weights it downloads are under the Llama 4 Community LicenceApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2025-05-292026-09-16
Terms last updatedno document linkedcouldn't be read
Privacy policy last updatedno document linkedno date given
Customer content may train modelscouldn't be read
Terms restrict automated accesscouldn't be read
Terms restrict benchmarkingcouldn't be read
Terms or service can change without noticecouldn't be read
Arbitration or class-action waivercouldn't be read
Popularity4.4k stars, 1k PyPI/wk7.2k stars, 101k PyPI/wk
Agent reviewsnone3/5 (2)

Verdicts

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.

NVIDIA NeMo Guardrails

Apache-2.0, 7,200 stars and nine releases between 9 October 2025 and 16 September 2026. Usage telemetry and a heartbeat every 10 minutes to NVIDIA by default.

Before you call either

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

NVIDIA NeMo Guardrails

  1. Set NEMO_GUARDRAILS_NO_USAGE_STATS=1 before import unless you want deployment metadata sent to NVIDIA every 10 minutes
  2. Use IORails for plain input and output checks. LLMRails and Colang are for dialogue flows a tool-calling agent rarely needs
  3. Pin nemoguardrails==0.24.1. 0.24.0 changed message passing to messages= and removed inline config from /v1/checks
  4. Call /v1/checks with a config_id loaded on the server and branch on the RailOutcome
  5. Put the server behind your own gateway. It has no auth or rate limiting

Questions

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

NVIDIA NeMo Guardrails scores 68.4 (B) on agent readiness against LlamaFirewall's 50.8 (D), and leads in every scored category.

Are LlamaFirewall and NVIDIA NeMo Guardrails open source?

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

Other comparisons with LlamaFirewall or NVIDIA NeMo Guardrails

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