NeMo Guardrails vs LlamaFirewall: which fits your LLM and agent stack
If you are putting runtime guardrails in front of an LLM app or a set of agents, you are probably weighing two open-source options: NVIDIA's Apache-2.0 NeMo Guardrails and Meta's MIT-licensed LlamaFirewall. They solve different parts of the problem. NeMo Guardrails fits teams that need programmable dialog and topic policy written in Colang, and it fits teams that want a guardrails server in front of existing chat clients through its /v1/chat/completions endpoint. Its five rail types (input, dialog, retrieval, execution and output) let you control what the bot discusses, which tools it may call and what it may return. Expect policy authoring to be the main ongoing work, since the rules live in config.yml, optional Python actions and Colang files that your team must maintain.
LlamaFirewall fits teams whose main exposure is agents. It layers PromptGuard 2 for direct prompt injection, AlignmentCheck for auditing an agent's reasoning against its task, CodeShield for insecure LLM-generated code across 8 languages, and regex or custom scanners. Those components map to specific failure modes, such as an injected instruction in a retrieved web page or an agent writing a vulnerable snippet. Its demos cover OpenAI Agents and LangChain, so check that your agent framework is supported before you commit. Note that LlamaFirewall is a Python framework you embed in your code, not a standalone server like NeMo's, so your engineers own the integration points.
The decision turns on three things. First, agent protection needs: if your risk is tool misuse and goal hijacking, AlignmentCheck is the component to test, and if your risk is conversational scope, NeMo's rails are the closer match. Second, your latency budget, since each scanner or model call adds time to every request, and you should measure both stacks on your own prompts. Third, what you must run. AlignmentCheck requires a Together API key, so confirm with your security and procurement teams whether sending agent reasoning to that external service is acceptable, and check the PromptGuard 2 model license terms under the Llama 4 Community License Agreement before you deploy.
Which platform fits which scenario
This table maps five common LLM deployment scenarios to the platform that fits better and the condition that decides it. Read each row as a starting point for a pilot, since both tools can be combined in one stack and the right answer depends on your architecture and team skills.
| Scenario | Better fit | Condition that makes it so |
|---|---|---|
| Customer-facing chatbot needing policy and topic control | NeMo Guardrails | Your team wants to define allowed topics, dialog flows and refusals in Colang and maintain them as versioned policy files, and can own that configuration over time. |
| Agent with tool calls needing reasoning audits | LlamaFirewall | Your agent takes actions through tools and you need an AlignmentCheck-style audit of its reasoning, with the caveat that AlignmentCheck requires a Together API key and you should confirm that dependency fits your data rules. |
| RAG app needing retrieval rails | NeMo Guardrails | You need checks on retrieved chunks before they reach the model, which NeMo's retrieval rails address directly. LlamaFirewall's published scanners focus on prompts, agent reasoning and generated code, so confirm any retrieval-specific coverage with its maintainers. |
| Team generating code with an LLM | LlamaFirewall | Generated code is the main risk and you want static analysis of it. CodeShield covers 8 languages using Semgrep and regex rules, so confirm that your languages are included before relying on it. |
| Organization that needs a managed server image | NeMo Guardrails | NVIDIA offers a NeMo Guardrails microservice packaged for Kubernetes with Helm, built on the same configuration model. Confirm licensing and support terms with NVIDIA, since the open-source library alone is self-run. |
How each is built and what that means for operations
NeMo Guardrails and LlamaFirewall solve overlapping problems with very different packaging. One is a configuration-driven toolkit that can run as a library or a server, the other is a Python framework you import into your own code path. The cards below set out what each one holds, where it runs and what an upgrade touches.
Configuration files and Colang rails
NeMo Guardrails reads a config.yml file, Colang files and optional Python actions from a config folder. Policy lives in those files, so rules change by editing text that your team reviews and versions. Colang 1.0 is the default and Colang 2.0 is also supported, so check which dialect your existing configurations use before you upgrade. NVIDIA documents a migration path with the nemoguardrails convert tool and says converted configurations still need manual review.
Python API and HTTP server
NeMo Guardrails can be called in-process through its Python API or run as a guardrails server that loads configurations from a folder and exposes an HTTP API, including a /v1/chat/completions endpoint. A Dockerfile is provided to build a server image. Your application team decides which mode fits: in-process keeps the guardrail inside the application's trust boundary, while a shared server centralizes policy but adds a network hop you must measure.
Microservice on Kubernetes with Helm
NVIDIA also offers the NeMo Guardrails microservice, a production container image built on the same configuration model and deployed on Kubernetes with Helm. Confirm with NVIDIA which features, support terms and platform dependencies apply to the microservice compared with the open-source library, because the open-source project does not provide that deployment package. Plan for cluster operations, image pinning and configuration rollout as ongoing work.
LlamaFirewall as a pip-installed framework
LlamaFirewall installs with pip install llamafirewall and needs Python 3.10 or later. It is a framework you call from your code, layering PromptGuard 2 for direct prompt injection, AlignmentCheck for auditing agent reasoning, CodeShield for insecure generated code, and regex or custom scanners across chat models and agents. Because it runs inside your agent loop, scanner placement and failure handling are your engineering decisions, not configuration.
External services and model state
LlamaFirewall's AlignmentCheck scanner requires a Together API key, so that dependency and its data flow must be approved before deployment. Confirm which scanners call external services and which run fully locally in your environment. NeMo Guardrails can call models you configure, so confirm where each model runs and where prompts and logs are stored in your own infrastructure.
Upgrades and version pinning
NeMo Guardrails releases move quickly, and v0.24.1 was published on September 16, 2026, so pin the version and retest policies after each upgrade. LlamaFirewall is pinned through pip, and its scanner set and model weights are versioned separately, so an upgrade may change classifier behavior even when the package version looks minor. Keep a regression set of attack prompts and benign prompts in CI and rerun it before each change to either layer.
Capability, license and runtime requirements side by side
This table sets NeMo Guardrails and LlamaFirewall against the facts that shape a runtime decision: license, language support, what each one inspects and what each one needs to run. Rows marked as items to confirm depend on your own traffic, model and network, so measure them in your environment before you commit.
| Capability | NeMo Guardrails | LlamaFirewall |
|---|---|---|
| License | Apache-2.0, developed in the NVIDIA-NeMo/Guardrails repository | MIT for the framework, with PromptGuard 2 under the Llama 4 Community License Agreement |
| Python support | Python 3.10, 3.11, 3.12 and 3.13 | Python 3.10 or later, installed with pip install llamafirewall |
| Latest release | v0.24.1, published September 16, 2026 | 1.0.3 is the latest on PyPI (confirm the current release before adoption) |
| Input and output checks | Input, dialog, output, retrieval and execution rails configured in Colang, plus self-check and jailbreak detection | PromptGuard 2 scans direct prompt injection on inputs, with regex or custom scanners available |
| Agent reasoning audit | Not applicable | AlignmentCheck audits agent reasoning, but requires a Together API key |
| Generated code checks | Not listed as a built-in scanner in the supplied facts (confirm with your own rail design) | CodeShield statically analyzes LLM-generated code in 8 languages using Semgrep and regex rules |
| Configuration language | config.yml, optional Python actions and Colang 1.0 (default) or Colang 2.0 | Python scanners configured in code, with demos for OpenAI Agents and LangChain |
| Framework integration | LangChain integration is opt-in via NEMOGUARDRAILS_LLM_FRAMEWORK=langchain | Demos ship for OpenAI Agents and LangChain (confirm your framework fit) |
| Latency under your load | Confirm with a load test using your rails, models and policy count | Confirm with a load test, including the AlignmentCheck call path and any external API round trip |
What each license and edition includes and costs
The NeMo Guardrails library is licensed under Apache 2.0 and developed in the NVIDIA-NeMo/Guardrails repository. The license lets you run the library, including its server, inside your own services. Your cost is mainly engineering time: writing Colang and config.yml files, hosting the server, testing policies and maintaining them through upgrades.
- License: Apache 2.0 covers the library. Have counsel review the LICENSE.md before you ship a modified copy.
- Release: v0.24.1 was released on September 16, 2026, and the library supports Python 3.10, 3.11, 3.12 and 3.13.
- Colang migration: Colang 1.0 is the default. NVIDIA documents a move to 2.x using the
nemoguardrails converttool, and converted configurations still need manual review, so budget that work in any upgrade plan. - Optional integrations: LangChain integration is opt-in through the
NEMOGUARDRAILS_LLM_FRAMEWORK=langchainenvironment variable.
Confirm with NVIDIA whether any commercial support, indemnity or add-on applies to the library, since none is stated in this comparison.
The NeMo Guardrails microservice is a separate NVIDIA offering. It is a production container image built on the same configuration model as the library and deployed on Kubernetes with Helm. Its packaging, support and terms are not the Apache 2.0 terms of the library, so treat it as its own purchase decision with its own total cost. It also adds platform dependency, because you run NVIDIA's packaging and release cadence inside your cluster.
- Configuration carry-over: Configurations written for the library should map to the microservice, but test them against the exact container version you deploy.
- Packaging: Helm deployment on Kubernetes suits a platform team that already operates clusters and image pipelines.
- Platform dependency: Ask which NVIDIA components the microservice requires and what upgrades those components force on your cluster.
- Terms: Request the license, support matrix and any fee schedule in writing before you compare the microservice to the free library.
Ask NVIDIA for the current license and support terms directly, and confirm who carries the cost of cluster operations, upgrades and on-call support under your chosen model.
LlamaFirewall is an MIT-licensed framework from Meta's PurpleLlama project. It layers PromptGuard 2 for direct prompt injection, AlignmentCheck for auditing agent reasoning, CodeShield for insecure generated code, and regex or custom scanners. The framework license carries no fee in the terms supplied for this comparison. The cost sits in engineering time to integrate scanners, tune them and maintain them, plus the separate terms covering model weights and external services.
- Framework license: MIT, as stated in the LlamaFirewall LICENSE file. Confirm the license still applies to any fork or vendored copy you build.
- Install:
pip install llamafirewallrequires Python 3.10 or later, and version 1.0.3 is the latest release on PyPI. - CodeShield: It statically analyzes LLM-generated code in 8 languages using Semgrep and regex rules. This fits CI well, but it adds scanner runtime and rule upkeep.
- Demos: Demos ship for OpenAI Agents and LangChain. Treat them as starting points rather than production configuration.
Check the version you pin against the PyPI release history and your own dependency scanning before you adopt the framework.
Two parts of the LlamaFirewall stack carry terms outside the MIT license. The Llama Prompt Guard 2 model weights ship under the Llama 4 Community License Agreement, and AlignmentCheck requires a Together API key. Both affect cost and legal review in ways the framework license does not, so treat them as separate line items in your approval process.
- Prompt Guard 2 weights: The Llama 4 Community License Agreement applies, according to the LICENSE files in the PurpleLlama repository. Have legal review it for your intended use and for any redistribution of the weights.
- Model sizes: The Prompt Guard 2 model card lists 86M and 22M parameter versions with a 512-token context window. It says the 22M version cuts latency and compute costs by 75% with minimal performance trade-offs, and the 86M version is multilingual.
- AlignmentCheck key: AlignmentCheck requires a Together API key, so its usage terms, data handling and charges apply to your deployment.
- Other safety models: If you add Llama Guard 4 from the same project, it also ships under the Llama 4 Community License Agreement.
Ask Together for its current pricing and data terms directly, and confirm them before AlignmentCheck enters a production path.
Which one protects agents better, and under what conditions
LlamaFirewall is the stronger fit when the risk is an agent's reasoning drifting away from the user's goal during multi-step tool use. Its AlignmentCheck scanner audits the agent's reasoning trace, which is a control that NeMo Guardrails does not provide as a built-in rail. LlamaFirewall also layers PromptGuard 2 for direct prompt injection and CodeShield for insecure generated code, and its README ships demos for OpenAI Agents and LangChain, so a team building on those frameworks has a working starting point. Confirm one practical limit before you rely on it: AlignmentCheck requires a Together API key, so you must decide whether sending agent reasoning to that service is acceptable under your data handling rules, and what it does to latency and cost on every agent step.
NeMo Guardrails is the stronger fit when you need to govern exactly which tools an agent may call and how the conversation may flow. Its execution rails apply to custom actions and tools, and you write the policy in Colang, which lets a security or product team express rules such as which actions require confirmation, which arguments are blocked, and which dialog paths are off limits. Those rules are deterministic and readable, but they only cover the actions and flows you anticipate. An agent that reasons its way to an unlisted tool, or that is steered by injected content in retrieved documents, will not be caught by a rail you did not write. Plan to test each execution rail against your real tool inventory and to review Colang changes as you would review code.
Most regulated teams will need both layers, and the decision is about which gap you cannot accept. If your agents hold broad permissions across systems, the reasoning audit and the tool-level policy address different failures, so you should map each agent's tools to the risks you must block before choosing. Neither product, on its own, replaces authorization controls in the systems the agent calls, and neither should be described to auditors as a complete agent defense. Ask both vendors and your own test team to confirm the exact agent frameworks, versions and hosting model you will run, since the supplied facts describe LlamaFirewall's demos but not a full integration matrix for every agent stack.
Moving between the two without rewriting every policy
This sequence lets a team move guardrail coverage from one layer to the other in controlled stages, keeping the policies that transfer and rebuilding only the ones that do not. Each step ends with evidence you can check before traffic moves.
Inventory every current rail and scanner
The platform or security lead lists each rail, scanner, validator and custom check in production, along with the route it guards, the model it sits in front of and the team that owns the policy. Include application-side filters that were written in code rather than in a guardrail framework, because those are easy to miss. The step is complete when every guarded route appears in one inventory with an owner and a plain-language statement of what each control blocks.
Map each policy to its new home
For each inventory item, decide whether it becomes a NeMo Guardrails Colang flow or action, a LlamaFirewall scanner such as PromptGuard 2, AlignmentCheck or CodeShield, a custom regex or scanner inside LlamaFirewall, or application code that should stay where it is. Dialog and topic policies usually map to Colang, while direct prompt injection checks map more naturally to a classifier scanner. Record the mapping in a table that a reviewer signs off on. The step is done when every policy has one target and every gap is named.
Carry over the test prompts and attack corpora
The security engineer exports the existing regression prompts, known-bad examples and any red team results into a format both stacks can replay, and keeps the expected outcome for each case. Attack corpora and benign-traffic samples are the assets most likely to transfer unchanged, because they describe the attacks and the business traffic rather than the framework. The step is complete when a single test set runs against the current layer and reproduces its recorded results.
Rebuild flows and custom scanners
The engineering team rewrites Colang flows and Python actions for the target framework and recreates any custom scanners as LlamaFirewall scanners or application code. Colang 1.0 to 2.x conversion is the only automated path that NVIDIA documents, through the nemoguardrails convert tool, and the migration guide says converted configurations still need manual review. Moving between NeMo and LlamaFirewall is a rebuild, so budget reviewer time for every flow. The step closes when the rebuilt policies pass the carried-over test set and a reviewer has read each change.
Run both layers in shadow mode
The team sends recorded production traffic through the new layer in shadow mode, so it produces verdicts without blocking users, and compares those verdicts with what the existing layer decided. Each disagreement is classified as a missed block, an unwanted block or an acceptable difference, and the latency of the new path is measured on the same traffic. Ask the vendor or your own benchmarks for latency figures, because neither project's supplied material gives a number for your hardware. The step is complete when disagreements have an owner and a decision.
Cut over one route at a time
The platform team moves one route, such as a single application or a single tenant, to the new layer while the old one stays available for rollback. Monitoring tracks blocked requests, false positives reported by users and any change in latency on that route. Expand to the next route only after the previous one has run through a full business cycle without a policy regression. The step is done when every route is served by the new layer and the rollback path has been tested.
Retire the old layer and keep the evidence
Once every route is cut over, the team removes the old configuration from the runtime path, archives its inventory, mappings and shadow-mode results, and updates the runbooks and the owner list. Keep the test set under version control so it runs in CI on future policy changes. The step closes when the old layer is no longer in production, its configuration is archived and the evidence bundle is stored where auditors and the next owner can find it.
What drives running cost for each option
Running cost for both platforms comes from the same places: the hosts that run the code, the people who write and maintain policies, the external services they call, and the work of moving to new versions. The cards below separate the one-time setup from the costs that recur, and list what to ask for in a written quote.
Compute for runtime checks
NeMo Guardrails runs as a Python library or as a server that loads configurations from a folder, and it can be built into a container image from the provided Dockerfile. LlamaFirewall runs its scanners inside the application process, and PromptGuard 2 is a classifier model whose size depends on the 86M or 22M version you choose. Ask for the CPU, memory and GPU sizing each option needs at your peak request rate, and confirm which models load locally and which are called over a network.
Colang and Python skills
NeMo Guardrails policies are written in Colang, with Colang 1.0 as the default and 2.0 also supported, and custom behavior lives in optional Python actions. LlamaFirewall policies are mostly Python configuration with regex or custom scanners. Budget engineering time for writing and reviewing policies, and for the migration effort when moving between Colang versions, since NVIDIA says converted configurations still need manual review.
External services and model calls
AlignmentCheck requires a Together API key, so its per-call charges and data terms are a recurring cost and a data-handling decision that sits outside your hosts. NeMo Guardrails can call hosted or local LLMs for self-check, fact-checking and hallucination rails, which adds model inference to every request. Ask each vendor for the billing unit, the rate limits and whether prompts leave your network.
Upgrades across versions
NeMo Guardrails v0.24.1 was released on September 16, 2026, and LlamaFirewall 1.0.3 is the latest release on PyPI, so both move on their own schedules. Each upgrade means rerunning your rail tests, checking Colang or API changes and updating pinned dependencies. Plan a recurring window for this work and ask how long each project's release notes cover breaking changes before you commit.
Ongoing support options
DeployOpen offers fixed-scope implementation and retainers for ongoing support, upgrades and continuous testing, and it does not publish a price list, so the cost follows from the written scope. NVIDIA's NeMo Guardrails microservice is a production container deployed on Kubernetes with Helm, which shifts operations to a platform you may need to license and run. Ask whether each path includes upgrade testing, incident response hours and documentation of your rail configuration.
Testing rails in CI
Every policy change needs regression tests against attack prompts and benign traffic, and these tests run on each build and each upgrade. NeMo rails need test conversations that exercise the Colang flows, while LlamaFirewall needs labeled prompt sets for its classifiers and scanners. Ask for the compute and engineering hours that this test suite will consume each month, since it recurs for the life of the system.
Where implementation projects usually run long
Most delay in a NeMo Guardrails versus LlamaFirewall project comes from the NeMo side, because Colang flows are the largest unknown in the work. Writing dialog and topic rails in Colang is a new skill for most application teams, and the flows must be tested against the conversations your users will actually attempt, not only the happy path. Plan time for a test set of benign and hostile prompts that exercises each input, dialog and output rail. If your team is still on Colang 1.0 (the default), check whether the migration guide's nemoguardrails convert path applies to your configurations. NVIDIA says converted configurations still need manual review, so budget a review pass for every converted file before it reaches production.
On the LlamaFirewall side, the usual slip is agent wiring. Confirm which agent framework you will use and whether its demos (OpenAI Agents or LangChain) match your code, because a scanner placed at the wrong point in the tool loop can miss injected instructions. AlignmentCheck also depends on a Together API key, so confirm with your procurement and security owners how that external call is approved before design starts. Classifier threshold tuning is the other common sink. PromptGuard 2 will produce false positives on legitimate prompts, and tuning them against your own traffic takes repeated rounds with product owners who must accept some blocked requests. Ask the vendor or project for guidance on acceptable false-positive settings for your use case.
Finally, securing the server image consumes time in regulated environments. NeMo Guardrails provides a Dockerfile for building a server image, and the server exposes an HTTP API, so the image, its base layers, its dependencies and its network exposure all need review under your hardening baseline. Confirm who owns image scanning, signing and patching, and whether the Python versions you need (3.10 through 3.13 are supported) match your approved base images. The microservice option in the NVIDIA platform is a different deployment with its own Helm-based install, so decide early which path your security team will assess, because switching late forces a second review.
Questions buyers ask before choosing or hiring help
Which is cheaper to run, NeMo Guardrails or LlamaFirewall?
Neither license carries a fee: NeMo Guardrails is Apache-2.0 and LlamaFirewall is MIT-licensed, so your running cost comes from the infrastructure, the engineering time and any third-party services each one calls. NeMo Guardrails runs as a Python library or a server you host, and its configuration, actions and Colang files need upkeep as policies change. LlamaFirewall's PromptGuard 2 models run locally, but its AlignmentCheck scanner requires a Together API key, so you pay that provider per use. Ask any quote to model the model hosting, the inference calls per request and the engineering hours for policy maintenance separately.
How much effort does each one take to implement?
Effort depends on how much of your agent you need to control. NeMo Guardrails asks you to write a config.yml, optional Python actions and Colang files, and Colang 1.0 is the default with 2.x also supported. If you move between Colang versions, NVIDIA's migration guide says converted configurations still need manual review. LlamaFirewall installs with pip install llamafirewall and needs Python 3.10 or later, which makes a first integration quick, but tuning scanners and wiring AlignmentCheck into your agent loop takes more work. Scope the integration against your real tool calls before you estimate.
Can our own team implement this without outside help?
A team with strong Python and LLM application experience can usually run a first integration of either tool in-house, especially using the demos and documentation each project publishes. Outside help adds the most value where policies must be mapped to your business rules, where the guardrails sit in front of production agents with access to sensitive tools, and where evidence must be produced for an assessment. DeployOpen plans, implements, integrates, hardens and documents self-hosted platforms in your own infrastructure under a fixed scope agreed in writing, and retainers can cover ongoing upgrades and testing.
What does the work need from our engineers?
Plan for engineers who can write and test Python, read Colang or a YAML configuration, and reason about agent tool permissions. For NeMo Guardrails, someone must own the Colang rails, the custom actions and the server configuration. For LlamaFirewall, someone must decide which scanners run on which messages, understand what a PromptGuard 2 label means for your traffic, and handle the Together API key for AlignmentCheck. Both need a named owner for updates, because each project ships new versions regularly and your policies must be re-tested after upgrades.
Can we use NeMo Guardrails and LlamaFirewall together?
Yes, they operate at different points and can be layered, but this is an architecture decision you must test rather than assume. NeMo Guardrails covers input, dialog, retrieval, execution and output rails, while LlamaFirewall adds PromptGuard 2 for direct injection, AlignmentCheck for agent reasoning and CodeShield for generated code. Running both adds latency and another dependency to the request path, and it creates two policy sets to maintain. Test the combined path with your own traffic, and confirm that each layer's blocking decisions are logged in a way your security team can audit.
What should we confirm about licensing and the Llama terms?
NeMo Guardrails is Apache-2.0 and LlamaFirewall is MIT-licensed, but the models around them are different. Llama Guard 4 and Llama Prompt Guard 2 ship under the Llama 4 Community License Agreement, and Purple Llama's models generally follow the matching Llama Community License. Ask legal to review the model license terms that apply to the specific model files you deploy, the attribution and usage conditions, and any acceptable use limits. Confirm the current license file in each repository, because terms can change between versions.
Does a guardrail layer replace security testing or compliance work?
No. A runtime layer blocks or flags requests it recognizes, but it does not prove your application is secure, and it does not issue audit reports or certifications. Pair either tool with pre-release testing of your application and agents, and keep evidence of the controls you configured. DeployOpen prepares systems, controls and evidence for compliance assessments, while independent auditors and certification bodies issue the actual conclusions.
How DeployOpen helps you choose, implement and migrate
DeployOpen plans, implements, customizes, integrates, hardens and documents self-hosted open-source platforms inside your own infrastructure. For NeMo Guardrails vs LlamaFirewall, we start from the agent and chat paths you need to protect, then build and hand over the runtime layer that fits your team's skills and hosting environment.
How we scope
- We confirm which chat, RAG and agent paths need runtime checks and where each layer would sit in the request flow.
- We document which model endpoints, classifier services and API keys each option would require in your environment.
- Engagements are fixed-scope, with scope and deliverables agreed in writing before any build work starts.
How we build
- We implement the chosen runtime layer, whether Colang rails with the server or LlamaFirewall scanners, inside your cloud account or data center.
- We test blocked and allowed cases against your own prompts, tool calls and generated code, and record the results.
- We review licensing of each model and component so that license terms are checked against your intended use before rollout.
How we hand over
- We deliver configuration, runbooks and a written record of rails or scanner policies, with the reasoning behind each decision.
- We document the upgrade path for the chosen library, including how Colang versions or PyPI releases would be tested before adoption.
- We walk your platform and security teams through operation so that they can maintain policies after the engagement ends.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers after handover.
- Retainers can cover re-testing of guardrail policies when models, prompts or agent tools change in your environment.
- Ask any provider to confirm which versions, licenses and hosting dependencies they will track on your behalf.
