{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"nemo-guardrails","slug":"nemo-guardrails","name":"NeMo Guardrails","type":"framework","url":"https://github.com/NVIDIA/NeMo-Guardrails","page_url":"https://unfragile.ai/nemo-guardrails","categories":["code-review-security"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"nemo-guardrails__cap_0","uri":"capability://planning.reasoning.colang.based.dialog.flow.definition.and.state.machine.execution","name":"colang-based dialog flow definition and state machine execution","description":"Defines conversational flows using Colang, a domain-specific language that compiles to state machines for managing dialog turns, branching logic, and context transitions. The Colang 2.x runtime executes these flows as event-driven state machines, processing user messages through defined states and triggering actions based on flow conditions. This enables declarative specification of multi-turn conversations without imperative control flow.","intents":["Define complex multi-turn dialog flows with branching logic without writing Python state machines","Manage conversation context and state transitions across multiple user turns","Compose reusable dialog patterns and flows from a standard library","Execute flows with runtime event processing and action triggering"],"best_for":["Teams building conversational AI with complex dialog requirements","Developers who prefer declarative flow definition over imperative code","Organizations needing version-controlled, auditable conversation logic"],"limitations":["Colang 1.0 and 2.x have breaking differences; migration requires rewriting flows","State machine execution adds latency for deeply nested flows with many state transitions","Limited debugging visibility into state machine internals during runtime","No built-in persistence for long-running conversations across server restarts"],"requires":["Python 3.8+","Colang language knowledge (learning curve for new users)","LLM provider API key (OpenAI, Anthropic, or compatible)"],"input_types":["Colang flow definitions (.co files)","User messages (text)","Context variables (structured data)"],"output_types":["Dialog responses (text)","Action invocations (function calls)","State machine events (internal)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_1","uri":"capability://safety.moderation.multi.stage.input.output.dialog.retrieval.tool.rails.pipeline","name":"multi-stage input/output/dialog/retrieval/tool rails pipeline","description":"Implements a configurable pipeline of safety and constraint enforcement layers that process requests before LLM invocation (input rails), after LLM generation (output rails), during dialog turns (dialog rails), before retrieval operations (retrieval rails), and around tool calls (tool rails). Each rail stage can apply custom validators, filters, and transformations using Python actions or LLM-based checks, enabling fine-grained control over what enters and exits the LLM.","intents":["Block harmful, off-topic, or policy-violating user inputs before they reach the LLM","Filter, sanitize, or reject LLM outputs that violate safety policies or contain hallucinations","Enforce topic boundaries and conversation constraints at dialog level","Validate and filter retrieval results before they're used in RAG pipelines","Intercept and validate tool calls before execution"],"best_for":["Enterprise teams deploying LLMs in regulated industries (finance, healthcare, government)","Applications requiring strict content safety and policy enforcement","Teams building RAG systems with quality gates on retrieved documents","Developers needing granular control over LLM input/output without modifying core logic"],"limitations":["Each rail stage adds latency; deep pipelines can add 200-500ms per request","LLM-based rails (using self-check mechanisms) require additional LLM calls, increasing costs","No built-in rail composition or reuse across multiple guardrail configs","Debugging which rail rejected a request requires verbose logging configuration"],"requires":["Python 3.8+","LLM provider API key for self-check mechanisms","Custom action implementations in Python for domain-specific rails"],"input_types":["User messages (text)","LLM outputs (text)","Tool call specifications (structured data)","Retrieved documents (text)"],"output_types":["Filtered/sanitized messages (text)","Rejection decisions (boolean + reason)","Modified tool calls (structured data)","Approved retrieval results (text)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_10","uri":"capability://memory.knowledge.embeddings.and.vector.store.integration.for.rag.and.semantic.search","name":"embeddings and vector store integration for rag and semantic search","description":"Integrates with embedding models (OpenAI, Hugging Face, local models) and vector stores (Chroma, Pinecone, FAISS) to support semantic search and retrieval-augmented generation (RAG). Handles embedding generation, vector storage, similarity search, and result ranking. Supports both in-memory and persistent vector stores, enabling guardrails to retrieve relevant context for fact-checking, topic validation, and knowledge-based responses.","intents":["Retrieve relevant documents/facts from a knowledge base for RAG pipelines","Validate LLM outputs against retrieved facts for hallucination detection","Implement semantic search for topic-based guardrails","Build knowledge-aware guardrails that understand domain-specific information"],"best_for":["Teams building RAG systems with guardrails on retrieved content","Applications requiring fact-checking against a knowledge base","Organizations with large document collections needing semantic search","Developers wanting to ground LLM responses in retrieved facts"],"limitations":["Embedding quality depends on model; poor embeddings lead to irrelevant retrievals","Vector store scaling requires careful indexing and partitioning for large datasets","Similarity search adds latency (50-500ms depending on dataset size)","No built-in deduplication or ranking of retrieved results","Requires maintaining embeddings in sync with source documents"],"requires":["Embedding model API key or local model","Vector store (Chroma, Pinecone, FAISS, etc.)","Document collection to embed and index"],"input_types":["Query text (for semantic search)","Documents (for embedding and indexing)","Embedding model configuration"],"output_types":["Retrieved documents (text + similarity scores)","Embeddings (vectors)","Search results (ranked list)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_11","uri":"capability://automation.workflow.observability.and.tracing.with.span.management.and.llm.call.tracking","name":"observability and tracing with span management and llm call tracking","description":"Provides built-in observability through span-based tracing that tracks request flow, LLM calls, action execution, and rail decisions. Integrates with OpenTelemetry for distributed tracing, logs detailed execution traces, and supports exporting traces to external systems (Datadog, Jaeger, etc.). Enables debugging of complex guardrail flows and performance monitoring of LLM calls.","intents":["Debug guardrail flows by tracing execution path and seeing which rails rejected requests","Monitor LLM API usage and costs by tracking all LLM calls","Identify performance bottlenecks in guardrail pipelines","Export traces to external observability platforms for centralized monitoring"],"best_for":["Teams operating guardrails in production and needing visibility into behavior","Developers debugging complex guardrail flows with many rails and actions","Organizations tracking LLM API costs and usage patterns","Applications requiring compliance audit trails of guardrail decisions"],"limitations":["Tracing adds overhead (~5-10% latency) even when not actively used","Detailed traces can be verbose and expensive to store long-term","No built-in alerting on trace anomalies; requires external monitoring setup","Trace sampling can miss rare edge cases","Exporting traces to external systems requires additional configuration"],"requires":["Python 3.8+","OpenTelemetry SDK (optional, for external trace export)","External observability platform (Datadog, Jaeger, etc.) for centralized traces"],"input_types":["Execution events (internal)","LLM call metadata (tokens, latency, cost)","Rail decision logs (structured)"],"output_types":["Trace spans (structured)","Execution logs (text)","Performance metrics (latency, token counts)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_12","uri":"capability://tool.use.integration.langchain.integration.with.custom.chain.and.agent.support","name":"langchain integration with custom chain and agent support","description":"Provides seamless integration with LangChain chains and agents, allowing guardrails to wrap LangChain components or be wrapped by them. Supports using LangChain tools within guardrails, integrating guardrails into LangChain agent loops, and sharing context between guardrails and chains. Enables building complex agentic systems with guardrails applied at multiple points in the execution flow.","intents":["Add guardrails to existing LangChain chains without rewriting them","Use LangChain tools (web search, calculators, etc.) within guardrail flows","Build agentic systems where guardrails validate agent decisions and tool calls","Integrate guardrails into LangChain agent loops for safety-aware agents"],"best_for":["Teams already using LangChain and wanting to add guardrails","Developers building complex agents with multiple tools and guardrails","Applications requiring guardrails at multiple points in agent execution","Organizations wanting to leverage LangChain ecosystem with safety controls"],"limitations":["Integration requires understanding both LangChain and NeMo Guardrails APIs","Context passing between guardrails and chains can be error-prone","No automatic conflict resolution if guardrails and chains have different assumptions","Performance overhead from wrapping/unwrapping between frameworks","Limited documentation on advanced integration patterns"],"requires":["LangChain library installed","Python 3.8+","Understanding of LangChain chains and agents"],"input_types":["LangChain chain inputs","Tool definitions (LangChain format)","Agent state (dict)"],"output_types":["LangChain chain outputs","Guardrail-validated results","Tool call decisions (approved/rejected)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_13","uri":"capability://automation.workflow.cli.tools.for.configuration.validation.testing.and.deployment","name":"cli tools for configuration validation, testing, and deployment","description":"Provides command-line tools for validating guardrail configurations, running tests, generating documentation, and deploying guardrails. Includes commands for checking YAML syntax, validating Colang flows, running test suites, and generating API documentation. Enables CI/CD integration and local development workflows without requiring Python code.","intents":["Validate guardrail configurations before deployment to catch errors early","Run automated tests on guardrail flows to ensure behavior is correct","Generate documentation from guardrail configurations","Integrate guardrails into CI/CD pipelines for automated testing"],"best_for":["Teams with CI/CD pipelines wanting to validate guardrails automatically","Developers testing guardrail changes locally before committing","Organizations requiring documentation of guardrail policies","DevOps teams deploying guardrails as part of application deployment"],"limitations":["CLI tools are limited to basic validation; complex testing requires Python code","No built-in integration with popular CI/CD platforms (GitHub Actions, GitLab CI, etc.)","Documentation generation is basic; requires manual customization for complex policies","No support for guardrail versioning or rollback via CLI","Limited error messages for complex validation failures"],"requires":["Python 3.8+","NeMo Guardrails CLI installed","Guardrail configuration files (YAML)"],"input_types":["Configuration files (YAML)","Test specifications (Python or YAML)","Colang flow files"],"output_types":["Validation results (pass/fail + errors)","Test results (pass/fail + coverage)","Generated documentation (Markdown or HTML)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_2","uri":"capability://safety.moderation.llm.based.self.check.mechanisms.for.hallucination.and.jailbreak.detection","name":"llm-based self-check mechanisms for hallucination and jailbreak detection","description":"Uses secondary LLM calls to validate outputs and detect attacks through structured prompting. Implements jailbreak detection by analyzing user inputs against known attack patterns, and hallucination detection by having the LLM verify its own outputs against retrieved facts or user context. These checks run asynchronously or synchronously depending on configuration, using the same LLM provider or a separate safety-focused model.","intents":["Detect and block jailbreak attempts before they influence the main LLM response","Verify LLM outputs don't contain factual hallucinations by cross-checking against knowledge bases","Use a specialized safety model (e.g., Nemotron Safety Guard) to validate general outputs","Implement reasoning-based content safety checks that understand context and intent"],"best_for":["Teams deploying LLMs in high-stakes domains where hallucinations are costly","Applications facing active adversarial users attempting jailbreaks","Organizations with budget for additional LLM API calls for safety validation","Systems requiring explainable safety decisions (reasoning traces)"],"limitations":["Doubles or triples LLM API costs due to additional validation calls","Adds 500ms-2s latency per request for synchronous self-checks","Self-checks can be fooled by sophisticated attacks that also fool the validation model","Requires careful prompt engineering to avoid false positives/negatives","No guarantee that validation model is more robust than main model"],"requires":["API keys for LLM provider(s) used for validation","Sufficient budget for 2-3x LLM API calls per user request","Knowledge base or context for hallucination fact-checking (optional)"],"input_types":["User messages (text)","LLM outputs (text)","Retrieved context/facts (text)","Conversation history (text)"],"output_types":["Jailbreak detection result (boolean + confidence)","Hallucination detection result (boolean + specific claims flagged)","Safety reasoning trace (text explanation)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_3","uri":"capability://safety.moderation.topic.control.and.content.safety.classification.with.embeddings","name":"topic control and content safety classification with embeddings","description":"Uses semantic embeddings (via configurable embedding models) to classify user messages and LLM outputs against allowed topics and content categories. Compares input/output embeddings against a knowledge base of topic examples or safety categories, using cosine similarity thresholds to determine if content is on-topic or violates safety policies. This enables semantic understanding beyond keyword matching, supporting nuanced topic boundaries and content policies.","intents":["Restrict conversations to specific topics (e.g., customer service bot only answers product questions)","Detect off-topic requests and politely redirect or refuse","Classify content safety violations (hate speech, violence, etc.) using semantic similarity","Build topic-aware guardrails without manual rule writing"],"best_for":["Teams building domain-specific chatbots with strict topic boundaries","Applications needing semantic content classification without labeled training data","Organizations wanting to avoid keyword-based filtering (which misses context)","Systems with diverse content safety requirements across multiple categories"],"limitations":["Embedding quality depends on chosen model; poor embeddings lead to false positives/negatives","Requires curating representative examples for each topic/category (manual effort)","Similarity thresholds must be tuned per topic; no universal threshold works across domains","Semantic similarity can be fooled by paraphrasing or indirect requests","Embedding lookups add 50-200ms latency per request"],"requires":["Embedding model API key (OpenAI, Hugging Face, or local model)","Knowledge base of topic/category examples (curated list or vector store)","Similarity threshold tuning for each topic/category"],"input_types":["User messages (text)","LLM outputs (text)","Topic/category examples (text)"],"output_types":["Topic classification (category name + confidence)","Content safety classification (category + similarity score)","On-topic/off-topic decision (boolean)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_4","uri":"capability://safety.moderation.sensitive.data.detection.and.redaction.with.pattern.matching.and.llm.based.recognition","name":"sensitive data detection and redaction with pattern matching and llm-based recognition","description":"Detects and redacts sensitive information (PII, credentials, secrets) in user inputs and LLM outputs using a hybrid approach: pattern-based detection (regex for credit cards, SSNs, API keys) combined with LLM-based recognition for context-dependent sensitive data (names, addresses). Detected sensitive data can be redacted, masked, or flagged for manual review, with configurable handling per data type.","intents":["Prevent users from accidentally sharing PII, credentials, or secrets in conversations","Redact sensitive data from LLM outputs before returning to users","Detect and flag context-dependent sensitive information (e.g., person names in certain contexts)","Maintain audit logs of detected sensitive data without storing the actual data"],"best_for":["Applications handling user PII (healthcare, financial services, HR systems)","Teams operating in regulated industries requiring data protection (GDPR, HIPAA, PCI-DSS)","Systems where accidental credential exposure is a high-risk event","Organizations needing to demonstrate data protection compliance"],"limitations":["Pattern-based detection has high false negatives for obfuscated or non-standard formats","LLM-based detection adds latency and cost; can produce false positives on benign data","Redaction can break LLM reasoning if sensitive data is critical to the response","No built-in support for domain-specific sensitive data types (requires custom patterns)","Redacted data cannot be recovered; may complicate debugging or user support"],"requires":["Python 3.8+","LLM provider API key for LLM-based detection (optional)","Custom regex patterns for domain-specific sensitive data types"],"input_types":["User messages (text)","LLM outputs (text)","Custom pattern definitions (regex)"],"output_types":["Redacted/masked text (text)","Detected sensitive data metadata (type, location, confidence)","Audit log entries (structured data)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_5","uri":"capability://tool.use.integration.action.system.with.python.function.binding.and.async.execution","name":"action system with python function binding and async execution","description":"Provides a framework for binding Python functions as actions that can be invoked from Colang flows or rails. Actions are registered in a central registry, support async/sync execution, accept typed parameters, and can access the current conversation context. The action system handles parameter marshaling, error handling, and result propagation back to flows, enabling seamless integration of custom business logic into guardrails.","intents":["Execute custom Python functions from Colang flows (e.g., database lookups, API calls)","Integrate external systems (CRM, knowledge bases, payment processors) into guardrails","Implement domain-specific validation or transformation logic as reusable actions","Handle async operations (API calls, database queries) without blocking the dialog flow"],"best_for":["Teams building guardrails that need to integrate with existing business systems","Developers comfortable writing Python and wanting to extend guardrails with custom logic","Applications requiring real-time data lookups or external API calls during conversation","Organizations with complex business rules that can't be expressed in Colang alone"],"limitations":["Action execution is synchronous by default; async actions require explicit async/await syntax","No built-in retry logic or circuit breaker for failing external API calls","Action errors can crash the entire flow if not properly handled","Limited visibility into action execution time; no built-in performance monitoring","Actions must be registered at startup; dynamic registration requires framework modifications"],"requires":["Python 3.8+","Knowledge of Python async/await for async actions","Access to external systems/APIs that actions will call"],"input_types":["Typed parameters (any Python type)","Conversation context (dict)","User message history (list)"],"output_types":["Action results (any Python type)","Exceptions/errors (propagated to flow)","Side effects (external API calls, database writes)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_6","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support.and.streaming","name":"llm provider abstraction with multi-provider support and streaming","description":"Abstracts LLM provider differences (OpenAI, Anthropic, Ollama, Azure, etc.) behind a unified interface, handling provider-specific API formats, authentication, and parameter mapping. Supports streaming responses with configurable chunk handling, token counting, and caching. The provider system allows swapping LLM backends without changing guardrail logic, and supports using different providers for different tasks (main generation vs. safety checks).","intents":["Use different LLM providers (OpenAI, Anthropic, local Ollama) interchangeably without code changes","Stream LLM responses for real-time user feedback without waiting for full generation","Cache LLM responses to reduce API costs and latency for repeated queries","Use specialized models for different tasks (fast model for main response, safety model for validation)"],"best_for":["Teams wanting to avoid vendor lock-in with a single LLM provider","Applications requiring real-time streaming responses for user experience","Organizations optimizing for cost by using cheaper models for non-critical tasks","Developers testing guardrails with multiple LLM backends"],"limitations":["Provider abstraction adds ~50ms overhead per LLM call due to parameter mapping","Not all providers support identical features (e.g., function calling, vision); feature gaps require fallbacks","Streaming adds complexity; some providers have different streaming semantics","Caching is provider-agnostic but requires external cache backend (Redis, etc.)","Token counting varies by provider; estimates may be inaccurate"],"requires":["API keys for at least one LLM provider","Python 3.8+","Redis or similar for caching (optional but recommended)"],"input_types":["Provider configuration (name, API key, model name)","Prompt/messages (text)","Generation parameters (temperature, max_tokens, etc.)"],"output_types":["LLM responses (text)","Streaming chunks (text)","Token usage metadata (structured data)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_7","uri":"capability://automation.workflow.railsconfig.yaml.based.configuration.with.validation.and.schema.enforcement","name":"railsconfig yaml-based configuration with validation and schema enforcement","description":"Defines guardrail configurations in YAML format with a strict schema that validates structure, required fields, and parameter types at load time. The RailsConfig parser converts YAML into Python objects, performs validation, and raises clear errors for misconfigurations. Supports configuration inheritance, variable substitution, and environment-based overrides, enabling version-controlled, auditable guardrail definitions.","intents":["Define guardrail policies in human-readable YAML without writing Python code","Version control guardrail configurations alongside application code","Validate configurations at startup to catch errors early","Override guardrail settings per environment (dev/staging/prod) via environment variables"],"best_for":["Teams wanting non-technical stakeholders to review/approve guardrail policies","Organizations requiring audit trails of guardrail configuration changes","Applications with environment-specific guardrail settings","Developers preferring declarative configuration over imperative code"],"limitations":["YAML schema is rigid; complex conditional logic requires Python actions instead","No built-in support for configuration versioning or rollback","Large configurations become unwieldy; no modularization beyond file includes","Validation errors can be cryptic for deeply nested configurations","No hot-reloading; configuration changes require server restart"],"requires":["YAML syntax knowledge","Understanding of NeMo Guardrails configuration schema","Environment variables for sensitive data (API keys, etc.)"],"input_types":["YAML configuration files","Environment variables","Python objects (for programmatic config)"],"output_types":["RailsConfig Python object","Validation errors (structured)","Merged configuration (with overrides applied)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_8","uri":"capability://tool.use.integration.http.server.api.with.event.based.and.request.response.interfaces","name":"http server api with event-based and request-response interfaces","description":"Exposes guardrails as an HTTP server with two interaction modes: request-response (single turn) and event-based (streaming, multi-turn). The server handles request parsing, context management, streaming response chunking, and error serialization. Supports both REST endpoints and WebSocket connections for real-time streaming, enabling integration with web frontends, mobile apps, and other HTTP clients.","intents":["Deploy guardrails as a microservice that other applications can call via HTTP","Stream LLM responses to web frontends in real-time without waiting for full generation","Manage conversation context across multiple HTTP requests in a stateless manner","Integrate guardrails with existing REST API architectures"],"best_for":["Teams deploying guardrails as a separate microservice","Web applications requiring real-time streaming responses","Organizations with polyglot tech stacks (non-Python clients)","Applications needing to scale guardrails independently from main service"],"limitations":["HTTP adds latency (network round-trip) compared to in-process usage","Stateless design requires clients to manage conversation context and pass it with each request","Streaming over HTTP requires WebSocket or Server-Sent Events; not all clients support these","No built-in authentication/authorization; requires external API gateway","Scaling requires load balancing and session affinity for stateful flows"],"requires":["Python 3.8+","HTTP client library (requests, httpx, etc.) for calling the server","Network connectivity between client and guardrails server"],"input_types":["HTTP POST requests with JSON body (user message, context)","WebSocket messages (for streaming)","Query parameters (for configuration overrides)"],"output_types":["HTTP JSON responses (LLM response, metadata)","Streaming chunks (Server-Sent Events or WebSocket)","HTTP error responses (with error codes and messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__cap_9","uri":"capability://text.generation.language.prompt.system.with.templating.filters.and.context.injection","name":"prompt system with templating, filters, and context injection","description":"Manages prompts as templates with variable substitution, filters for formatting/transformation, and automatic context injection. Prompts are defined in YAML or Python, support Jinja2-style templating, and can reference conversation history, user context, and retrieved documents. The prompt system handles prompt composition (system + user messages), token counting, and parameter passing to LLMs.","intents":["Define reusable prompt templates with variable placeholders for context injection","Apply formatting filters to prompt variables (e.g., truncate long context, format dates)","Compose multi-part prompts (system message + user message + context) dynamically","Manage different prompts for different tasks (main generation, safety checks, etc.)"],"best_for":["Teams managing many prompts and wanting to avoid duplication","Applications with dynamic context that needs to be injected into prompts","Developers wanting to separate prompt logic from guardrail logic","Organizations requiring prompt versioning and A/B testing"],"limitations":["Templating adds complexity; Jinja2 syntax can be error-prone","No built-in prompt versioning or A/B testing framework","Large context injection can exceed token limits; requires manual truncation","Filters are limited to built-in set; custom filters require Python code","No optimization for prompt efficiency (e.g., removing redundant context)"],"requires":["Jinja2 templating knowledge","Understanding of prompt composition and LLM token limits","Context data in structured format (dict)"],"input_types":["Prompt templates (YAML or Python)","Context variables (dict)","Conversation history (list of messages)"],"output_types":["Composed prompts (text)","Token count estimates (integer)","Formatted messages (list of dicts for LLM API)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nemo-guardrails__headline","uri":"capability://safety.moderation.programmable.guardrails.for.llm.applications","name":"programmable guardrails for llm applications","description":"NeMo Guardrails is an open-source toolkit that allows developers to implement programmable guardrails for large language model applications, ensuring safety and control over dialog flows and content generation.","intents":["best LLM guardrails","programmable guardrails for conversational AI","how to prevent LLM hallucinations","tools for content safety in AI","dialog management frameworks for LLMs"],"best_for":["developers building LLM applications","teams focusing on AI safety"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Colang language knowledge (learning curve for new users)","LLM provider API key (OpenAI, Anthropic, or compatible)","LLM provider API key for self-check mechanisms","Custom action implementations in Python for domain-specific rails","Embedding model API key or local model","Vector store (Chroma, Pinecone, FAISS, etc.)","Document collection to embed and index","OpenTelemetry SDK (optional, for external trace export)","External observability platform (Datadog, Jaeger, etc.) for centralized traces"],"failure_modes":["Colang 1.0 and 2.x have breaking differences; migration requires rewriting flows","State machine execution adds latency for deeply nested flows with many state transitions","Limited debugging visibility into state machine internals during runtime","No built-in persistence for long-running conversations across server restarts","Each rail stage adds latency; deep pipelines can add 200-500ms per request","LLM-based rails (using self-check mechanisms) require additional LLM calls, increasing costs","No built-in rail composition or reuse across multiple guardrail configs","Debugging which rail rejected a request requires verbose logging configuration","Embedding quality depends on model; poor embeddings lead to irrelevant retrievals","Vector store scaling requires careful indexing and partitioning for large datasets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nemo-guardrails","compare_url":"https://unfragile.ai/compare?artifact=nemo-guardrails"}},"signature":"W/VxKXa+aOD77N/fAQr+jEU96nsnCXihDIWPZVAKz9Prw+NyWagMhhTKXorPmLDzoEsvRdevPDNfQz5SMD2NAg==","signedAt":"2026-06-22T07:56:15.723Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nemo-guardrails","artifact":"https://unfragile.ai/nemo-guardrails","verify":"https://unfragile.ai/api/v1/verify?slug=nemo-guardrails","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}