{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-guardrails-ai","slug":"pypi-guardrails-ai","name":"guardrails-ai","type":"framework","url":"https://pypi.org/project/guardrails-ai/","page_url":"https://unfragile.ai/pypi-guardrails-ai","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-guardrails-ai__cap_0","uri":"capability://safety.moderation.declarative.output.validation.with.schema.based.guardrails","name":"declarative output validation with schema-based guardrails","description":"Validates LLM outputs against developer-defined schemas and constraints using a declarative YAML/JSON configuration system. Guardrails-ai parses output specifications (Pydantic models, JSON schemas, or custom validators) and enforces them through a validation pipeline that intercepts model responses before returning to the application. The system supports both synchronous validation and asynchronous correction loops where invalid outputs trigger re-prompting or structured repair.","intents":["I need to ensure LLM outputs always match a specific JSON schema or data structure","I want to validate that generated code, SQL, or structured data meets my constraints before using it","I need to automatically correct or re-generate outputs that fail validation without manual intervention"],"best_for":["teams building production LLM applications requiring deterministic output formats","developers integrating LLMs into data pipelines where schema compliance is mandatory","enterprises needing audit trails of validation failures and corrections"],"limitations":["Re-prompting on validation failure increases latency and token consumption proportionally to failure rate","Complex nested schemas with many interdependent fields may require multiple validation passes","Custom validators must be implemented in Python; no native support for arbitrary DSLs","Validation overhead adds ~50-200ms per request depending on schema complexity"],"requires":["Python 3.8+","Pydantic v1 or v2 for schema definition (or raw JSON Schema)","LLM API key (OpenAI, Anthropic, Cohere, or local model via Ollama/vLLM)"],"input_types":["Pydantic model definitions","JSON Schema objects","Python dataclass definitions","Custom validator functions","LLM text responses"],"output_types":["validated structured data (dict, Pydantic instance)","corrected/repaired output","validation error reports with failure reasons"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_1","uri":"capability://planning.reasoning.corrective.re.prompting.with.iterative.refinement","name":"corrective re-prompting with iterative refinement","description":"Implements an automatic feedback loop where validation failures trigger structured re-prompting of the LLM with detailed error messages and correction instructions. The system maintains context across iterations, appending validation failure reasons to the prompt and optionally providing examples of valid outputs. This enables the LLM to self-correct without requiring external intervention or manual prompt engineering.","intents":["I want the LLM to automatically fix its output if it doesn't match my schema instead of failing","I need to provide the model with specific feedback about what went wrong so it can improve","I want to set a maximum number of correction attempts before giving up"],"best_for":["applications where occasional validation failures are acceptable if auto-corrected","teams without strict latency requirements who prioritize correctness over speed","scenarios with high-value outputs where re-generation cost is justified"],"limitations":["Each correction attempt consumes additional tokens, increasing cost by 2-5x for failing outputs","No guarantee that re-prompting will succeed; may exhaust max retries without valid output","Correction quality depends on LLM capability; weaker models may loop indefinitely","Adds variable latency (100ms-5s per retry depending on model and output length)"],"requires":["Python 3.8+","guardrails-ai library with correction module enabled","LLM with sufficient reasoning capability (GPT-3.5+ or equivalent)"],"input_types":["validation error messages","original LLM output","schema/constraint definitions","optional correction examples"],"output_types":["corrected LLM output","validation success/failure status","iteration count and token usage metrics"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Provides a provider-agnostic wrapper around multiple LLM APIs (OpenAI, Anthropic, Cohere, Azure, local models via Ollama/vLLM) with a unified Python interface. Guardrails-ai normalizes request/response formats, handles provider-specific quirks (token limits, function calling schemas, streaming behavior), and enables seamless switching between providers without code changes. The abstraction layer manages authentication, rate limiting, and error handling across heterogeneous APIs.","intents":["I want to switch between OpenAI, Anthropic, and local models without rewriting my validation code","I need to compare outputs from different LLM providers on the same task","I want to fall back to a secondary provider if the primary one fails or is rate-limited"],"best_for":["teams evaluating multiple LLM providers for production use","applications requiring provider redundancy or cost optimization","developers building LLM-agnostic frameworks or libraries"],"limitations":["Not all providers support identical feature sets (e.g., function calling syntax differs)","Abstraction adds ~10-50ms overhead per request due to normalization","Provider-specific optimizations (e.g., vision capabilities, tool use) may not be fully exposed","Streaming behavior varies by provider; unified streaming interface may mask provider differences"],"requires":["Python 3.8+","API keys for desired providers (OpenAI, Anthropic, etc.) OR local model server (Ollama, vLLM)","guardrails-ai with provider-specific dependencies installed"],"input_types":["provider name (string identifier)","model name (provider-specific)","API credentials (keys, endpoints)","unified request parameters (messages, temperature, max_tokens)"],"output_types":["normalized LLM response object","streaming token iterator (if streaming enabled)","provider-agnostic error objects"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_3","uri":"capability://safety.moderation.semantic.constraint.validation.with.llm.based.checks","name":"semantic constraint validation with llm-based checks","description":"Extends schema validation with semantic guardrails that use the LLM itself to verify outputs against natural language constraints (e.g., 'output must be appropriate for children', 'response must cite sources'). These checks run after structural validation and invoke the LLM to evaluate semantic properties that cannot be expressed as regex or schema rules. The system caches semantic validation results to avoid redundant LLM calls for identical outputs.","intents":["I need to ensure generated content is appropriate, factual, or aligned with specific guidelines beyond schema validation","I want to check if the output contains citations, follows a tone, or meets semantic requirements","I need to validate outputs that require reasoning or judgment, not just structural rules"],"best_for":["content moderation and safety-critical applications","teams requiring domain-specific semantic validation (medical, legal, financial)","applications where output quality depends on subjective criteria"],"limitations":["Semantic validation adds 500ms-2s latency per output (requires additional LLM call)","Semantic validators are non-deterministic; same output may pass/fail on different runs","Requires careful prompt engineering to define semantic rules clearly","Cost multiplies with each semantic check (2-3 additional API calls per validation)"],"requires":["Python 3.8+","guardrails-ai with semantic validator module","LLM API access (same provider as main generation or separate)","Well-defined semantic constraint descriptions"],"input_types":["LLM output text","semantic constraint descriptions (natural language or structured)","optional context or reference materials for validation"],"output_types":["semantic validation pass/fail status","confidence score (if provided by validator)","explanation of validation failure"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_4","uri":"capability://tool.use.integration.structured.function.calling.with.schema.based.routing","name":"structured function calling with schema-based routing","description":"Enables LLMs to invoke external functions or APIs by defining a schema of available functions and letting the model choose which to call based on the task. Guardrails-ai converts function definitions into provider-native function calling formats (OpenAI function calling, Anthropic tool_use, etc.) and routes the LLM's function call decisions to actual Python functions or HTTP endpoints. The system validates function arguments against the schema before execution and handles return values.","intents":["I want the LLM to decide when to call external APIs or tools based on the user's request","I need to expose a set of functions to the LLM and have it choose the right one with correct arguments","I want to validate that function arguments match my schema before executing them"],"best_for":["agentic applications where LLMs orchestrate multiple tools","teams building LLM-powered assistants with access to external APIs","applications requiring deterministic function argument validation"],"limitations":["Function calling success depends on LLM's ability to understand schema; complex schemas may confuse weaker models","No built-in retry logic if function execution fails; requires manual error handling","Provider-specific function calling formats may not support all schema features (e.g., complex nested types)","Adds latency for schema conversion and argument validation (~50-100ms per call)"],"requires":["Python 3.8+","guardrails-ai with function calling module","LLM provider with native function calling support (OpenAI, Anthropic, Cohere)","Function definitions as Python functions or HTTP endpoints"],"input_types":["function definitions (Python functions with type hints or JSON schemas)","user query/prompt","optional function execution context"],"output_types":["function call decision (function name + arguments)","function execution result","validated return value"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_5","uri":"capability://safety.moderation.streaming.output.validation.with.incremental.parsing","name":"streaming output validation with incremental parsing","description":"Validates LLM outputs in real-time as they stream token-by-token, performing incremental parsing and validation without waiting for the complete response. The system buffers tokens into logical chunks (e.g., JSON objects, code blocks) and validates each chunk as it arrives, enabling early error detection and correction before the full output is generated. This reduces latency for streaming applications and enables cancellation of invalid outputs mid-generation.","intents":["I want to validate streaming outputs as they arrive instead of waiting for the full response","I need to detect and stop generation early if the output is going in an invalid direction","I want to provide real-time feedback to users while the LLM is still generating"],"best_for":["real-time chat and interactive applications where latency is critical","streaming APIs where early error detection saves bandwidth and tokens","applications with strict output format requirements (JSON, code) where partial validation is possible"],"limitations":["Incremental validation is only possible for certain output formats (JSON, XML, code); unstructured text is harder to validate mid-stream","Early stopping may interrupt valid outputs if validation logic is too strict","Buffering and parsing overhead can negate latency benefits for small outputs","Requires careful state management to handle partial tokens and incomplete structures"],"requires":["Python 3.8+","guardrails-ai with streaming validation module","LLM provider with streaming API support (OpenAI, Anthropic, etc.)","Structured output format (JSON, XML, code) for effective incremental validation"],"input_types":["streaming token iterator from LLM","schema/validation rules","optional buffering strategy configuration"],"output_types":["streaming validated token iterator","incremental validation status updates","early termination signal if validation fails"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_6","uri":"capability://automation.workflow.guardrail.composition.and.chaining.with.execution.pipelines","name":"guardrail composition and chaining with execution pipelines","description":"Allows developers to compose multiple guardrails (validators, correctors, semantic checks) into reusable pipelines that execute in sequence or parallel. Each guardrail is a modular component with defined inputs/outputs, and the system orchestrates their execution, passing outputs from one guardrail as inputs to the next. Pipelines can be defined declaratively in YAML/JSON or programmatically in Python, enabling complex validation workflows without custom code.","intents":["I want to apply multiple validation rules in sequence (e.g., schema validation, then semantic check, then format correction)","I need to reuse the same set of guardrails across multiple LLM calls","I want to define complex validation logic declaratively without writing custom Python"],"best_for":["teams with complex validation requirements across multiple LLM calls","applications requiring consistent guardrail policies across different models or endpoints","organizations wanting to version and audit guardrail configurations"],"limitations":["Pipeline execution is sequential by default; parallel execution requires explicit configuration and may have ordering dependencies","Debugging complex pipelines can be difficult; error attribution across multiple guardrails requires detailed logging","YAML/JSON configuration syntax may be verbose for complex conditional logic","Pipeline overhead adds ~100-300ms per execution depending on number of guardrails"],"requires":["Python 3.8+","guardrails-ai with pipeline module","YAML or JSON configuration files (optional; can also use Python API)"],"input_types":["guardrail definitions (validators, correctors, semantic checks)","pipeline configuration (YAML/JSON or Python objects)","LLM output to validate"],"output_types":["final validated/corrected output","pipeline execution trace (which guardrails ran, results)","detailed error report if any guardrail fails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_7","uri":"capability://automation.workflow.observability.and.validation.metrics.with.structured.logging","name":"observability and validation metrics with structured logging","description":"Provides comprehensive logging and metrics collection for all validation operations, including execution time, token usage, validation pass/fail rates, and correction attempts. Guardrails-ai exports structured logs in JSON format and integrates with observability platforms (Datadog, New Relic, etc.) to enable monitoring of guardrail performance in production. The system tracks validation failures by type and provides dashboards for identifying problematic outputs or guardrails.","intents":["I want to monitor how often my guardrails are failing and which types of outputs are problematic","I need to track token usage and latency impact of validation and correction","I want to audit which guardrails were applied to each LLM output for compliance"],"best_for":["production applications requiring observability into validation performance","teams needing compliance audit trails for regulated industries","organizations optimizing guardrail configurations based on real-world failure patterns"],"limitations":["Structured logging adds ~10-50ms overhead per validation operation","Exporting metrics to external platforms requires network calls and may introduce latency","High-volume applications may generate excessive log data; requires filtering/sampling strategy","Observability integration is optional; basic logging is always enabled"],"requires":["Python 3.8+","guardrails-ai with observability module","optional: observability platform (Datadog, New Relic, etc.) and API credentials"],"input_types":["validation operation metadata (guardrail type, input, output, result)","LLM call metadata (model, tokens, latency)","optional: custom tags or context"],"output_types":["structured JSON logs","metrics (counters, histograms, gauges)","optional: exported events to observability platform"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_8","uri":"capability://tool.use.integration.custom.validator.framework.with.plugin.architecture","name":"custom validator framework with plugin architecture","description":"Enables developers to implement custom validators as Python classes or functions that integrate seamlessly into the guardrails system. The framework provides base classes and decorators for defining validators with standard interfaces (validate method, error handling, caching), and a plugin registry for discovering and loading custom validators at runtime. Validators can be synchronous or asynchronous and can access external services (APIs, databases) for validation logic.","intents":["I want to implement domain-specific validation logic that isn't covered by built-in validators","I need to integrate custom business logic or external services into the validation pipeline","I want to package my validators as reusable plugins for other teams"],"best_for":["teams with domain-specific validation requirements (medical, legal, financial)","organizations building internal validator libraries for reuse","developers extending guardrails-ai with custom functionality"],"limitations":["Custom validators must be implemented in Python; no support for other languages","Async validators add complexity; synchronous validators are simpler but may block","Custom validators are not automatically optimized; developers must handle caching and performance","Plugin discovery requires explicit registration or configuration; no auto-discovery"],"requires":["Python 3.8+","guardrails-ai with plugin framework","knowledge of guardrails-ai validator interface and base classes"],"input_types":["validator class definition (inheriting from BaseValidator)","validation input (LLM output, schema, context)","optional: external service credentials or configuration"],"output_types":["validation result (pass/fail)","optional: error message or correction suggestion","optional: metadata (confidence, execution time)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guardrails-ai__cap_9","uri":"capability://automation.workflow.batch.validation.and.correction.with.cost.optimization","name":"batch validation and correction with cost optimization","description":"Processes multiple LLM outputs in batch mode, applying guardrails to all outputs before returning results. The system deduplicates validation work (e.g., if multiple outputs are identical, validation runs once), batches LLM calls for semantic validation to reduce API overhead, and provides cost/latency tradeoffs (e.g., validate all outputs vs. sample-based validation). Batch mode is optimized for throughput rather than latency.","intents":["I want to validate many LLM outputs efficiently without making individual API calls for each","I need to reduce the cost of semantic validation by batching LLM calls","I want to apply the same guardrails to a dataset of outputs and get a summary report"],"best_for":["batch processing pipelines where latency is not critical","cost-sensitive applications validating large volumes of outputs","data analysis and quality assurance workflows"],"limitations":["Batch mode introduces latency (all outputs must be collected before processing starts)","Deduplication only works for identical outputs; similar outputs are validated separately","Batching semantic validation may reduce accuracy if context is lost across outputs","Requires sufficient memory to hold all outputs in memory; very large batches may require streaming"],"requires":["Python 3.8+","guardrails-ai with batch processing module","collection of LLM outputs to validate"],"input_types":["list of LLM outputs","guardrail configuration","optional: batch size and deduplication settings"],"output_types":["list of validated/corrected outputs","batch summary report (pass rate, correction rate, cost)","optional: detailed per-output validation results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Pydantic v1 or v2 for schema definition (or raw JSON Schema)","LLM API key (OpenAI, Anthropic, Cohere, or local model via Ollama/vLLM)","guardrails-ai library with correction module enabled","LLM with sufficient reasoning capability (GPT-3.5+ or equivalent)","API keys for desired providers (OpenAI, Anthropic, etc.) OR local model server (Ollama, vLLM)","guardrails-ai with provider-specific dependencies installed","guardrails-ai with semantic validator module","LLM API access (same provider as main generation or separate)","Well-defined semantic constraint descriptions"],"failure_modes":["Re-prompting on validation failure increases latency and token consumption proportionally to failure rate","Complex nested schemas with many interdependent fields may require multiple validation passes","Custom validators must be implemented in Python; no native support for arbitrary DSLs","Validation overhead adds ~50-200ms per request depending on schema complexity","Each correction attempt consumes additional tokens, increasing cost by 2-5x for failing outputs","No guarantee that re-prompting will succeed; may exhaust max retries without valid output","Correction quality depends on LLM capability; weaker models may loop indefinitely","Adds variable latency (100ms-5s per retry depending on model and output length)","Not all providers support identical feature sets (e.g., function calling syntax differs)","Abstraction adds ~10-50ms overhead per request due to normalization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:13.887Z","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=pypi-guardrails-ai","compare_url":"https://unfragile.ai/compare?artifact=pypi-guardrails-ai"}},"signature":"eCbhXK4+9XcCKVpwF+kM2jc248WL9d0mzNH6OlueU5E80GaxL22w0TfZf2Nm1nWk2vDo5LeAIv2JwLnLWFeHAw==","signedAt":"2026-06-21T09:30:45.299Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-guardrails-ai","artifact":"https://unfragile.ai/pypi-guardrails-ai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}