{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"guardrails-ai","slug":"guardrails-ai","name":"Guardrails AI","type":"framework","url":"https://github.com/guardrails-ai/guardrails","page_url":"https://unfragile.ai/guardrails-ai","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"guardrails-ai__cap_0","uri":"capability://safety.moderation.composable.validation.pipeline.with.multi.strategy.failure.handling","name":"composable validation pipeline with multi-strategy failure handling","description":"Orchestrates a chain of validators through the Guard class that execute sequentially against LLM outputs, with each validator implementing a validate() method and specifying OnFailAction strategies (exception, reask, fix, filter, noop, refrain). The framework automatically routes validation failures to appropriate handlers—reask re-prompts the LLM with context about the failure, fix applies corrective transformations, filter removes invalid content, and exception halts execution. This enables declarative composition of validation logic without imperative error handling.","intents":["I want to chain multiple validators (PII detection, toxicity, schema compliance) and automatically re-prompt the LLM if any fail","I need to define what happens when validation fails—should we retry, fix the output, or reject it entirely","I want to compose validators from different sources (Hub, custom) into a single reusable Guard"],"best_for":["Teams building production LLM applications requiring deterministic output validation","Developers implementing multi-stage validation workflows with automatic remediation","Organizations needing audit trails of validation decisions and re-prompting attempts"],"limitations":["Re-asking adds latency proportional to LLM response time—no built-in timeout controls per re-ask iteration","Validator composition order matters; early validators that filter content may prevent downstream validators from executing","OnFailAction strategies are validator-level, not pipeline-level—cannot easily apply conditional logic across multiple validators"],"requires":["Python >=3.10","At least one LLM provider configured (OpenAI, Anthropic, LiteLLM, HuggingFace)","Validators installed from Guardrails Hub or custom validators registered via @register_validator decorator"],"input_types":["unstructured text from LLM","partially structured outputs (JSON strings, markdown)","streaming token sequences"],"output_types":["validated text","structured data (Pydantic models, JSON)","validation metadata (pass/fail, error messages, re-ask history)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_1","uri":"capability://data.processing.analysis.schema.driven.structured.output.generation.with.rail.pydantic.and.json.schema","name":"schema-driven structured output generation with rail, pydantic, and json schema","description":"Converts unstructured LLM outputs into validated, typed data structures by accepting schema definitions in three formats: RAIL (Guardrails' XML-based specification language), Pydantic models, or JSON Schema. The framework maintains a type registry that maps schema definitions to Python types, automatically generating validators for type constraints and field requirements. When the LLM output is parsed, it's coerced into the target schema with validation applied at parse time, ensuring type safety and structural correctness without manual deserialization code.","intents":["I want to define a schema once (as Pydantic or JSON Schema) and have Guardrails automatically validate LLM outputs against it","I need to extract structured data from LLM responses and guarantee it matches my application's data model","I want to use OpenAI function calling or similar structured output APIs but add additional validation layers on top"],"best_for":["Data extraction pipelines requiring guaranteed schema compliance","Teams already using Pydantic models who want to extend validation to LLM outputs","Applications integrating with OpenAI function calling or similar structured generation APIs"],"limitations":["RAIL syntax is Guardrails-specific and requires learning a custom XML format; no automatic conversion from Pydantic to RAIL","Type coercion is best-effort—complex nested types or union types may require custom validators","JSON Schema support is read-only for validation; modifications to schema require re-instantiation of the Guard"],"requires":["Python >=3.10","Pydantic >=2.0 (for Pydantic model integration) OR valid JSON Schema OR RAIL specification file","LLM provider configured to support structured output (OpenAI, Anthropic with function calling)"],"input_types":["RAIL XML specification files","Pydantic model classes","JSON Schema objects","LLM text output (raw or streaming)"],"output_types":["Pydantic model instances","Python dictionaries matching schema","Typed dataclass instances","Validation error metadata"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_10","uri":"capability://automation.workflow.guardrails.server.deployment.with.rest.api.and.remote.validation","name":"guardrails server deployment with rest api and remote validation","description":"Provides a standalone server mode (guardrails server) that exposes Guards as REST API endpoints, enabling remote validation without embedding Guardrails in the application. The server handles authentication, request routing, and response serialization. Clients can invoke validation by sending HTTP requests to the server, which executes the Guard and returns validation results. This enables centralized validation infrastructure shared across multiple applications.","intents":["I want to deploy Guardrails as a separate service that multiple applications can call via REST API","I need to centralize validation logic so all teams use the same validators and rules","I want to scale validation independently from my application servers"],"best_for":["Organizations with multiple applications needing consistent validation","Teams wanting to centralize validation infrastructure","Scenarios where validation is computationally expensive and should be scaled separately"],"limitations":["Network latency added for each validation call; not suitable for ultra-low-latency applications","Server deployment adds operational complexity (monitoring, scaling, authentication)","Remote validation cannot access application-local context; all context must be passed in the request","Server authentication and authorization must be configured manually; no built-in multi-tenant support"],"requires":["Python >=3.10","Guardrails server installed (`pip install guardrails-ai[server]`)","HTTP client library for remote validation calls","Server configuration (port, authentication, Guards to expose)"],"input_types":["HTTP requests with LLM output and validation parameters","Guard configuration files to expose as endpoints"],"output_types":["HTTP responses with validation results","JSON-serialized validation metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_11","uri":"capability://automation.workflow.cli.tools.for.validator.management.and.guard.configuration","name":"cli tools for validator management and guard configuration","description":"Provides command-line tools for managing validators (install, update, remove), configuring authentication, and deploying the Guardrails server. The CLI supports commands like `guardrails hub install`, `guardrails hub list`, `guardrails configure`, and `guardrails server start`. Configuration is stored in a credentials file that can be shared across projects. The CLI enables non-developers to manage validators and configure Guardrails without writing code.","intents":["I want to install validators from the Hub via command line without writing Python code","I need to configure authentication for LLM providers and the Guardrails Hub","I want to start the Guardrails server with a single command"],"best_for":["DevOps teams managing Guardrails deployments","Non-technical users installing validators","CI/CD pipelines automating validator installation and server deployment"],"limitations":["CLI is Python-specific; requires Python installation and pip","Configuration is stored in plaintext files; no built-in encryption for credentials","CLI commands are synchronous; no support for background operations or job queues","Error messages may be cryptic for non-technical users"],"requires":["Python >=3.10","Guardrails CLI installed (`pip install guardrails-ai`)","Internet connectivity for Hub operations","Appropriate permissions to write configuration files"],"input_types":["CLI command arguments (validator names, configuration parameters)","credentials (API keys, Hub tokens)"],"output_types":["installed validators","configuration files","server process"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_12","uri":"capability://data.processing.analysis.pydantic.model.integration.with.automatic.validator.generation","name":"pydantic model integration with automatic validator generation","description":"Integrates with Pydantic models by automatically generating validators from Pydantic field definitions (type annotations, constraints, validators). When a Guard is instantiated from a Pydantic model, the framework extracts field metadata and creates validators for type checking, required fields, and custom Pydantic validators. LLM outputs are parsed into Pydantic model instances with validation applied automatically, ensuring type safety and constraint compliance.","intents":["I want to use my existing Pydantic models as validation schemas for LLM outputs","I need to automatically generate validators from Pydantic field definitions without writing custom validation code","I want to ensure LLM outputs can be safely deserialized into Pydantic model instances"],"best_for":["Teams already using Pydantic for data validation","Applications with complex data models that need LLM output validation","Projects wanting to reuse existing Pydantic schemas for LLM validation"],"limitations":["Pydantic integration requires Pydantic >=2.0; older versions are not supported","Custom Pydantic validators are not automatically converted to Guardrails validators; manual mapping may be needed","Pydantic model changes require Guard re-instantiation; no dynamic schema updates"],"requires":["Python >=3.10","Pydantic >=2.0","Pydantic model class definitions"],"input_types":["Pydantic model classes","LLM output text to parse into Pydantic models"],"output_types":["Pydantic model instances","validation errors from Pydantic"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_13","uri":"capability://tool.use.integration.json.schema.and.openai.function.calling.integration","name":"json schema and openai function calling integration","description":"Integrates with JSON Schema and OpenAI's function calling API by accepting JSON Schema definitions and automatically converting them to OpenAI function schemas. The framework can invoke OpenAI's function calling mode with the schema, ensuring the LLM generates structured output that matches the schema. Validation is applied to the function call result, and re-asking is supported if validation fails.","intents":["I want to use OpenAI function calling with Guardrails validation to ensure structured outputs","I need to define schemas in JSON Schema format and have Guardrails validate against them","I want to leverage OpenAI's native function calling while adding additional validation layers"],"best_for":["Teams using OpenAI's function calling API who want additional validation","Applications requiring structured outputs with guaranteed schema compliance","Projects already using JSON Schema for other purposes"],"limitations":["OpenAI function calling integration is specific to OpenAI models; other providers have different structured output mechanisms","JSON Schema support is read-only for validation; schema modifications require Guard re-instantiation","Function calling adds latency compared to text generation; not suitable for ultra-low-latency applications"],"requires":["Python >=3.10","OpenAI API key and access to function calling models (gpt-4, gpt-3.5-turbo)","Valid JSON Schema definition","LiteLLM or OpenAI Python client library"],"input_types":["JSON Schema objects","OpenAI function calling responses"],"output_types":["validated function call results","structured data matching JSON Schema"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_2","uri":"capability://tool.use.integration.hub.based.validator.ecosystem.with.registry.and.dependency.management","name":"hub-based validator ecosystem with registry and dependency management","description":"Provides a centralized marketplace (Guardrails Hub) of pre-built validators for common use cases (PII detection, toxicity, bias, hallucination, regex matching, etc.) that can be installed via CLI commands like `guardrails hub install hub://guardrails/regex_match`. The framework maintains a validator registry that maps validator names to implementations, supports versioning and dependency resolution, and allows validators to be imported declaratively in RAIL specifications or programmatically via @register_validator decorators. Custom validators can be published back to the Hub, creating a community-driven ecosystem.","intents":["I want to use pre-built validators for common risks (PII, toxicity) without writing validation code from scratch","I need to install and manage validator versions across my team without manual dependency tracking","I want to publish custom validators to the Hub so other teams can reuse them"],"best_for":["Teams building LLM applications who want to leverage community validators","Organizations with multiple projects needing consistent validation rules across codebases","Developers contributing to the open-source validator ecosystem"],"limitations":["Hub validators are community-maintained with varying quality and update frequency; no SLA on security patches","Validator versioning is semantic but not enforced—breaking changes in minor versions can occur","Hub installation requires network access and authentication; offline validator management is not supported"],"requires":["Python >=3.10","Guardrails CLI installed (`pip install guardrails-ai`)","Hub authentication token (optional for public validators, required for private/custom validators)","Internet connectivity for Hub operations"],"input_types":["validator names (e.g., `hub://guardrails/pii_check`)","custom validator Python code with @register_validator decorator","RAIL specifications referencing Hub validators"],"output_types":["installed validator modules in local cache","validator metadata (version, dependencies, description)","validation results from Hub validators"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_3","uri":"capability://automation.workflow.synchronous.and.asynchronous.execution.with.streaming.validation.support","name":"synchronous and asynchronous execution with streaming validation support","description":"Supports four execution patterns through Guard and AsyncGuard classes: synchronous blocking (Guard.__call__()), asynchronous non-blocking (AsyncGuard.__call__()), synchronous streaming (Guard.__call__(stream=True)), and asynchronous streaming (AsyncGuard.__call__(stream=True)). Streaming validation processes LLM output tokens incrementally, applying validators to partial outputs and enabling early rejection or correction before the full response is generated. This architecture allows the same Guard definition to be used across different execution contexts without code duplication.","intents":["I want to validate LLM outputs as they stream in, not wait for the full response","I need to support both sync and async code paths in my application without duplicating validation logic","I want to cancel or correct LLM generation early if streaming validation detects a problem"],"best_for":["Real-time applications (chatbots, live transcription) requiring low-latency validation","Async-first frameworks (FastAPI, aiohttp) where blocking I/O is unacceptable","Applications with long-running LLM generations where early termination saves compute costs"],"limitations":["Streaming validation cannot validate complete schema constraints until the full output is received—partial validation only checks token-level rules","AsyncGuard requires async/await syntax throughout the call chain; mixing sync and async code requires additional orchestration","Streaming adds complexity to error handling—validation failures mid-stream may leave partial outputs that need cleanup"],"requires":["Python >=3.10","For async: Python asyncio event loop running","For streaming: LLM provider supporting streaming responses (OpenAI, Anthropic, LiteLLM)","Validators compatible with streaming (some validators require full context and cannot stream)"],"input_types":["LLM streaming responses (token iterators)","async iterables of tokens","full text responses (for non-streaming modes)"],"output_types":["validated text (streaming or complete)","structured data with streaming metadata","async iterables of validation events"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_4","uri":"capability://planning.reasoning.automatic.re.prompting.with.validation.context.and.iteration.management","name":"automatic re-prompting with validation context and iteration management","description":"When a validator fails with OnFailAction.reask, the Guard automatically constructs a corrective prompt that includes the original LLM output, the validation error message, and instructions to fix the issue, then re-invokes the LLM. The framework tracks re-asking history (number of attempts, error messages, corrected outputs) and enforces configurable iteration limits to prevent infinite loops. Re-ask prompts are customizable via templates, allowing teams to define domain-specific correction instructions.","intents":["I want the LLM to automatically fix its output if validation fails, without manual retry logic","I need to track how many times we've re-asked and stop after N attempts to avoid wasting tokens","I want to customize the re-ask prompt to include domain-specific instructions for fixing common errors"],"best_for":["Applications where LLM output quality is critical and re-generation is cheaper than manual correction","Teams with domain-specific validation rules that benefit from LLM self-correction","Scenarios where validation failures are often fixable (e.g., JSON formatting, missing fields)"],"limitations":["Re-asking adds latency proportional to LLM response time; no built-in timeout per re-ask iteration","LLM may not understand or be able to fix the validation error, leading to repeated failures and token waste","Re-ask templates are global per Guard; cannot vary correction instructions based on which validator failed","History tracking adds memory overhead for long re-ask chains; no built-in persistence to external storage"],"requires":["Python >=3.10","LLM provider with sufficient context window to include original output + error + correction instructions","At least one validator configured with OnFailAction.reask","Iteration limit configured (default typically 3-5 attempts)"],"input_types":["LLM output that failed validation","validation error messages","re-ask prompt templates (optional)"],"output_types":["corrected LLM output","re-ask history (attempt count, errors, outputs)","final validation result after all re-ask attempts"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.unified.interface","name":"multi-provider llm integration with unified interface","description":"Abstracts over multiple LLM providers (OpenAI, Anthropic, LiteLLM, HuggingFace) through a unified Guard interface, allowing the same validation logic to work with different model backends without code changes. The framework handles provider-specific details like API authentication, request formatting, streaming protocol differences, and function calling conventions. LLM provider selection is configured at Guard instantiation time via the `llm_api` parameter, supporting both remote APIs and local models through LiteLLM.","intents":["I want to switch between OpenAI, Anthropic, and local models without rewriting validation code","I need to use provider-specific features like OpenAI function calling or Anthropic's tool_use while maintaining consistent validation","I want to support multiple LLM providers in my application for redundancy or cost optimization"],"best_for":["Teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","Applications requiring fallback to alternative providers if primary provider is unavailable","Organizations with cost-sensitive workloads wanting to switch between expensive and cheaper models"],"limitations":["Provider-specific features (function calling, tool use) require conditional code; not all features are available across all providers","Streaming protocol differences mean streaming validation may behave differently across providers","Authentication management is provider-specific; no unified credential store across all providers","Model-specific behavior (response quality, token counting) is not abstracted—validation may need tuning per model"],"requires":["Python >=3.10","API keys or credentials for at least one LLM provider (OpenAI, Anthropic, HuggingFace, etc.)","LiteLLM installed for provider abstraction (`pip install litellm`)","Network connectivity to remote LLM APIs (unless using local models)"],"input_types":["LLM provider name (e.g., 'openai', 'anthropic', 'huggingface')","model identifier (e.g., 'gpt-4', 'claude-3-opus')","provider-specific configuration (API key, endpoint, parameters)"],"output_types":["LLM responses from any supported provider","provider-specific metadata (token counts, finish reasons)","unified validation results across providers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_6","uri":"capability://tool.use.integration.custom.validator.registration.and.lifecycle.management","name":"custom validator registration and lifecycle management","description":"Enables developers to define custom validators using the @register_validator decorator, which registers the validator in a global registry with metadata (name, description, on_fail_action, etc.). Custom validators implement a validate() method that receives the value to validate and returns a ValidationResult. The framework manages validator lifecycle including initialization, dependency injection, and cleanup. Validators can be registered programmatically or discovered from Python modules, and can be composed into Guards alongside Hub validators.","intents":["I want to write custom validation logic for domain-specific rules (e.g., business logic validation, custom PII patterns)","I need to register my custom validators so they can be referenced in RAIL specs or imported by other code","I want to share custom validators across multiple Guards without duplicating code"],"best_for":["Teams with domain-specific validation requirements not covered by Hub validators","Organizations building internal validator libraries for reuse across projects","Developers extending Guardrails with custom validation logic"],"limitations":["Custom validators must be registered before Guard instantiation; dynamic registration at runtime is not supported","Validator dependencies must be manually managed; no automatic dependency injection framework","Custom validators are not automatically versioned or published to the Hub; manual packaging required","Validator discovery is module-based; validators in unimported modules are not registered"],"requires":["Python >=3.10","Understanding of Guardrails validator interface (validate() method, ValidationResult return type)","@register_validator decorator imported from guardrails","Validators must be imported before Guard instantiation to be available"],"input_types":["Python function or class implementing validate() method","validator metadata (name, description, on_fail_action)","value to validate (any type)"],"output_types":["ValidationResult object (pass/fail with error message)","registered validator in global registry","validator metadata for introspection"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_7","uri":"capability://memory.knowledge.validation.history.and.call.tracking.with.telemetry","name":"validation history and call tracking with telemetry","description":"Maintains detailed execution history for each Guard call, including input prompts, LLM responses, validation results, re-ask attempts, and final outputs. The framework provides telemetry and tracing capabilities that log validation decisions, error messages, and performance metrics. History can be accessed programmatically via the Guard's call history API and exported for analysis. Telemetry integrates with external observability platforms for production monitoring.","intents":["I want to audit what validation rules were applied and why outputs were accepted or rejected","I need to debug validation failures by reviewing the full execution trace including re-ask attempts","I want to monitor validation performance and error rates in production"],"best_for":["Production applications requiring audit trails for compliance (healthcare, finance)","Teams debugging validation issues and needing detailed execution traces","Organizations monitoring LLM application quality and cost metrics"],"limitations":["History tracking adds memory overhead; long execution traces can consume significant RAM","No built-in persistence to external storage; history is lost when the Guard is garbage collected","Telemetry export requires manual configuration; no automatic integration with observability platforms","History API is read-only; cannot modify or delete historical records"],"requires":["Python >=3.10","Optional: external observability platform (Datadog, New Relic, etc.) for telemetry export","Telemetry utilities imported from guardrails.utils.telemetry_utils"],"input_types":["Guard execution context","validation results and error messages","LLM request/response data"],"output_types":["call history objects with full execution trace","telemetry events for external platforms","performance metrics (latency, token counts)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_8","uri":"capability://automation.workflow.context.management.and.state.isolation.for.concurrent.validations","name":"context management and state isolation for concurrent validations","description":"Provides context management utilities (guardrails/stores/context.py) that isolate state across concurrent Guard executions, preventing validation context from one request from leaking into another. The framework uses thread-local and async-local storage to maintain separate validation state per execution context. This enables safe concurrent use of the same Guard instance across multiple threads or async tasks without race conditions.","intents":["I want to use the same Guard instance across multiple concurrent requests without state leakage","I need to ensure validation context (re-ask history, validation results) is isolated per request","I want to deploy Guardrails in a multi-threaded or async web application safely"],"best_for":["Web applications (FastAPI, Flask) handling concurrent requests","Async applications with multiple concurrent tasks","High-throughput systems where creating new Guard instances per request is expensive"],"limitations":["Context isolation is automatic but not transparent; developers must understand thread-local/async-local semantics","Context cleanup is automatic but may fail if exceptions occur; manual cleanup may be needed in error cases","Context isolation does not apply to shared resources (LLM API connections, validator caches); those require separate synchronization"],"requires":["Python >=3.10","For async: Python asyncio event loop","For threading: Python threading module (standard library)","Understanding of thread-local and async-local storage patterns"],"input_types":["Guard instance","concurrent execution contexts (threads, async tasks)"],"output_types":["isolated validation state per context","context-local storage for validation metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__cap_9","uri":"capability://data.processing.analysis.rail.specification.language.for.declarative.validation.schemas","name":"rail specification language for declarative validation schemas","description":"Provides RAIL (Reliable AI Markup Language), an XML-based domain-specific language for declaratively defining validation schemas, validators, and failure handling strategies. RAIL specifications can define data structures, field constraints, validators to apply, and re-ask behavior in a single file. Guards can be instantiated directly from RAIL files using Guard.from_rail(), making validation logic portable and version-controllable. RAIL specifications are human-readable and can be edited without code changes.","intents":["I want to define validation schemas and rules in a declarative format that's version-controllable and human-readable","I need to specify complex validation logic (multiple validators, conditional rules) without writing Python code","I want to share validation specifications across teams and projects without code duplication"],"best_for":["Teams preferring declarative configuration over imperative code","Organizations with non-technical stakeholders who need to review validation rules","Projects requiring version control of validation logic separate from application code"],"limitations":["RAIL syntax is Guardrails-specific and requires learning a custom XML format","RAIL specifications cannot express arbitrary Python logic; complex validation requires custom validators","No IDE support or schema validation for RAIL files; syntax errors are discovered at runtime","RAIL is less flexible than Pydantic models for complex type hierarchies"],"requires":["Python >=3.10","Valid RAIL specification file (XML format)","Validators referenced in RAIL must be installed or registered before Guard instantiation"],"input_types":["RAIL XML specification files","LLM output to validate against RAIL schema"],"output_types":["Guard instance instantiated from RAIL","validated data matching RAIL schema","validation errors with RAIL-specific error messages"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guardrails-ai__headline","uri":"capability://testing.quality.llm.output.validation.framework","name":"llm output validation framework","description":"An open-source framework designed to enhance the reliability of Large Language Model applications by providing structured validation and quality guarantees for LLM outputs, including PII detection and bias mitigation.","intents":["best LLM output validation framework","LLM validation for ensuring quality","open-source framework for validating AI outputs","how to validate LLM outputs effectively","tools for ensuring LLM output reliability"],"best_for":["developers working with LLMs","teams needing output quality assurance"],"limitations":[],"requires":["Python 3.10 or higher"],"input_types":["unstructured LLM outputs"],"output_types":["validated, structured data"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python >=3.10","At least one LLM provider configured (OpenAI, Anthropic, LiteLLM, HuggingFace)","Validators installed from Guardrails Hub or custom validators registered via @register_validator decorator","Pydantic >=2.0 (for Pydantic model integration) OR valid JSON Schema OR RAIL specification file","LLM provider configured to support structured output (OpenAI, Anthropic with function calling)","Guardrails server installed (`pip install guardrails-ai[server]`)","HTTP client library for remote validation calls","Server configuration (port, authentication, Guards to expose)","Guardrails CLI installed (`pip install guardrails-ai`)","Internet connectivity for Hub operations"],"failure_modes":["Re-asking adds latency proportional to LLM response time—no built-in timeout controls per re-ask iteration","Validator composition order matters; early validators that filter content may prevent downstream validators from executing","OnFailAction strategies are validator-level, not pipeline-level—cannot easily apply conditional logic across multiple validators","RAIL syntax is Guardrails-specific and requires learning a custom XML format; no automatic conversion from Pydantic to RAIL","Type coercion is best-effort—complex nested types or union types may require custom validators","JSON Schema support is read-only for validation; modifications to schema require re-instantiation of the Guard","Network latency added for each validation call; not suitable for ultra-low-latency applications","Server deployment adds operational complexity (monitoring, scaling, authentication)","Remote validation cannot access application-local context; all context must be passed in the request","Server authentication and authorization must be configured manually; no built-in multi-tenant support","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":"2026-06-17T09:51:04.691Z","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=guardrails-ai","compare_url":"https://unfragile.ai/compare?artifact=guardrails-ai"}},"signature":"EuAWaBbPBDlG7BEr2jAWcAPEsUzCRqOSaRVPO8MnoLf3Ru3NqgeELUz+CQ6yLQ9u/CHPfXHNPQjg71wlaw5RCw==","signedAt":"2026-06-21T22:58:40.971Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/guardrails-ai","artifact":"https://unfragile.ai/guardrails-ai","verify":"https://unfragile.ai/api/v1/verify?slug=guardrails-ai","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"}}