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| Pricing | Free | Free |
| Capabilities | 14 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Uses a declarative OnFailAction enum (exception, reask, fix, filter, noop, refrain) bound to individual validators rather than global error handlers, enabling fine-grained control over remediation strategy per validation rule. The reask mechanism integrates directly with the Guard's LLM interaction loop, automatically constructing corrective prompts with validation context.
vs alternatives: More flexible than simple output validation (e.g., Pydantic validators) because it can automatically retry LLM generation with corrective prompts rather than just rejecting invalid outputs; more structured than ad-hoc try-catch patterns because failure strategies are declarative and composable.
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.
Unique: Maintains a unified type registry that bridges RAIL, Pydantic, and JSON Schema formats, allowing schema definitions to be swapped at runtime without code changes. The framework automatically generates validators from schema constraints (required fields, type annotations, regex patterns) and applies them during parsing, eliminating the need for separate validation logic.
vs alternatives: More comprehensive than Pydantic alone because it adds re-prompting and fix strategies when schema validation fails; more flexible than OpenAI function calling because it supports multiple schema formats and can layer additional custom validators on top of structural validation.
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.
Unique: Provides a standalone server mode that exposes Guards as REST API endpoints, enabling remote validation without embedding Guardrails in the application. The server abstracts away Guard instantiation and management, allowing clients to invoke validation via simple HTTP requests.
vs alternatives: More scalable than embedded validation because the server can be scaled independently; more centralized than distributed validation because all validation logic is in one place.
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.
Unique: Provides a comprehensive CLI that abstracts validator installation, authentication configuration, and server deployment, enabling non-developers to manage Guardrails without writing code. Configuration is centralized in a credentials file that can be shared across projects.
vs alternatives: More user-friendly than manual Python code because CLI commands are simple and discoverable; more portable than hardcoded configuration because credentials are stored in a centralized file.
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.
Unique: Automatically extracts validators from Pydantic field definitions (type annotations, constraints, custom validators) and applies them to LLM outputs without requiring explicit validator registration. This enables seamless integration with existing Pydantic-based codebases.
vs alternatives: More convenient than manual validator definition because validators are automatically generated from Pydantic models; more type-safe than unvalidated JSON parsing because Pydantic ensures type correctness.
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.
Unique: Integrates with OpenAI's native function calling API by converting JSON Schema to OpenAI function schemas and validating the resulting function calls. This enables leveraging OpenAI's structured output capabilities while adding Guardrails' validation and re-asking logic.
vs alternatives: More efficient than text-based parsing because OpenAI function calling guarantees structured output; more flexible than raw function calling because Guardrails adds validation and re-asking on top.
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.
Unique: Implements a decentralized validator registry where validators are identified by URIs (hub://guardrails/validator_name) and can be installed, versioned, and updated independently. The framework supports both Hub-hosted validators and locally-registered custom validators through a unified import mechanism, enabling seamless composition of community and proprietary validation logic.
vs alternatives: More modular than monolithic validation libraries because validators are independently versioned and installable; more discoverable than custom validation code because the Hub provides a searchable marketplace with documentation and examples.
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.
Unique: Provides a unified Guard API that abstracts over four execution modes (sync, async, sync-streaming, async-streaming) through method overloads and class variants, allowing the same validation logic to be deployed in different runtime contexts. Streaming validation integrates with the re-asking mechanism to enable mid-stream correction without waiting for full LLM output.
vs alternatives: More flexible than single-mode validators because the same Guard works in sync, async, and streaming contexts; more efficient than post-hoc validation because streaming mode can detect and correct problems before the full response is generated.
+6 more capabilities
Generates augmented test suites for MBPP problems by creating 35x more test cases than the original benchmark through systematic edge-case and boundary-condition generation. The system maintains structured metadata for each problem including base_input (original tests), plus_input (extended tests), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth), and entry_point (function name). This architectural separation enables rigorous detection of fragile solutions that pass shallow tests but fail on edge cases, addressing the fundamental limitation that original MBPP's ~3 tests per task miss correctness issues.
Unique: Provides 35x test case multiplier specifically for MBPP (378 tasks) with structured metadata separation (base_input vs plus_input) and input validation contracts, enabling systematic edge-case coverage that original MBPP's ~3 tests per task cannot achieve. Uses canonical_solution ground truth execution to dynamically calibrate timeouts and floating-point tolerances per problem.
vs alternatives: Significantly more rigorous than original MBPP (3→105 tests per task average) and HumanEval+ (80x multiplier) while maintaining Python-specific focus; catches correctness issues that shallow benchmarks miss but requires more computational resources for evaluation.
Executes arbitrary Python code generated by LLMs in isolated processes with enforced resource limits and system call restrictions to prevent malicious or buggy code from crashing the evaluation framework. The untrusted_check function spawns separate processes via multiprocessing with shared memory IPC, applies memory limits (default 4GB via EVALPLUS_MAX_MEMORY_BYTES environment variable), dynamically calculated time limits based on ground truth execution time, I/O suppression via swallow_io to prevent output pollution, and reliability_guard to disable dangerous system calls. This architecture prevents code injection, infinite loops, memory exhaustion, and filesystem access while maintaining execution fidelity for correctness evaluation.
Implements multi-layer isolation using process-level separation (multiprocessing), memory limits (EVALPLUS_MAX_MEMORY_BYTES), dynamic timeout calculation from canonical_solution execution, I/O suppression (swallow_io), and system call restrictions (reliability_guard). This combination prevents both accidental crashes and intentional attacks while maintaining execution fidelity for correctness evaluation.
MBPP+ scores higher at 64/100 vs Guardrails AI at 56/100.
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vs alternatives: More robust than simple try-catch approaches because it uses OS-level process isolation rather than Python-level exception handling; prevents infinite loops and memory exhaustion that would crash a single-process evaluator, though with higher latency than in-process execution.
Preprocesses LLM-generated code to normalize formatting, remove extraneous content, and extract the target function before execution. The sanitize module (evalplus/sanitize.py) handles variable formatting inconsistencies, removes comments and docstrings that may interfere with parsing, extracts the function matching the entry_point name, and validates syntax before execution. This ensures that evaluation results reflect code correctness rather than formatting quirks or LLM hallucinations like extra imports or wrapper code. The sanitization pipeline is essential because different LLMs produce code with different indentation, naming conventions, and structural patterns that would otherwise cause false negatives.
Unique: Implements multi-stage sanitization pipeline that separates formatting normalization (indentation, whitespace) from structural extraction (entry_point function isolation) and validation (syntax checking). Uses AST-based function extraction rather than regex, ensuring robust handling of complex code structures and nested functions.
vs alternatives: More robust than simple regex-based extraction because it uses Python's ast module for structural parsing; handles edge cases like nested functions, decorators, and complex indentation that regex approaches would miss. Enables fair comparison across LLM models with different output conventions.
Provides unified interface to generate code from 8+ LLM backends including vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Ollama. The provider architecture (evalplus/provider/) abstracts backend-specific API details behind a common interface, handling authentication, request formatting, response parsing, and error handling for each provider. This enables researchers to benchmark code generation across different models and providers without rewriting evaluation code. The codegen module (evalplus/codegen.py) orchestrates the generation pipeline: problem specification → prompt formatting → LLM call → response extraction → sanitization → evaluation.
Unique: Implements provider abstraction layer that unifies 8+ LLM backends (vLLM, HuggingFace, OpenAI, Anthropic, Gemini, Bedrock, Ollama) behind a common interface, enabling single-codebase evaluation across local and cloud models. Each provider handles authentication, request formatting, and response parsing independently, allowing researchers to swap backends without modifying evaluation logic.
vs alternatives: More comprehensive than single-provider frameworks (e.g., OpenAI-only evaluators) because it supports both cloud APIs and self-hosted models; enables cost-benefit analysis between providers and avoids vendor lock-in. Abstraction layer reduces code duplication compared to implementing each provider separately.
Computes pass@k metrics by generating multiple code samples per problem and calculating the probability that at least one sample passes all tests. The metric is calculated as: pass@k = 1 - (C(n-c, k) / C(n, k)) where n is total samples, c is passing samples, and k is the sample count. This enables evaluation of model reliability: pass@1 measures single-shot accuracy, while pass@10 or pass@100 measures whether the model can eventually generate correct code. The framework aggregates results across all problems to produce dataset-level pass@k scores, enabling comparison of models' code generation reliability.
Unique: Implements pass@k metric using combinatorial formula (1 - C(n-c,k)/C(n,k)) rather than empirical sampling, enabling exact calculation without Monte Carlo approximation. Supports configurable k values and aggregation across problems, enabling multi-level analysis (per-problem, per-category, dataset-wide).
vs alternatives: More statistically rigorous than simple accuracy metrics because it accounts for sampling variance and model reliability; enables fair comparison between models with different single-shot accuracy but similar pass@k. Combinatorial calculation is faster and more precise than empirical sampling approaches.
Measures code efficiency using CPU instruction counting rather than wall-clock time, enabling reproducible performance evaluation across different hardware. The EvalPerf dataset generates performance-exercising inputs with exponential scaling (2^1 to 2^26 elements) to stress-test algorithmic complexity. The profiling pipeline uses Linux perf counters to measure CPU instructions, filters tasks based on profile size, compute cost, coefficient of variation, and performance clustering to select representative benchmarks. This approach isolates algorithmic efficiency from hardware variance, enabling rigorous comparison of code quality across models and implementations.
Unique: Uses CPU instruction counting via Linux perf counters rather than wall-clock time, enabling reproducible performance evaluation independent of hardware variance. Generates performance-exercising inputs with exponential scaling (2^1 to 2^26) to stress-test algorithmic complexity, and filters tasks based on profile size, compute cost, and coefficient of variation to select representative benchmarks.
vs alternatives: More reproducible than wall-clock timing because instruction counts are hardware-independent; enables fair comparison across different machines and cloud environments. Exponential input scaling reveals algorithmic complexity issues that constant-size inputs would miss, providing deeper insight into code quality.
Organizes MBPP+ problems as structured JSON with metadata fields: base_input (original test cases), plus_input (extended test cases), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth implementation), and entry_point (function name). The dataset management system (evalplus/data/) loads problems from JSON, validates metadata consistency, and provides programmatic access to test cases and solutions. This structured approach enables systematic evaluation: problems can be filtered by category, difficulty, or test coverage; test cases can be aggregated across base and plus inputs; and metadata enables reproducible evaluation across different tools and frameworks.
Unique: Implements structured JSON-based dataset organization with explicit separation of base_input (original tests) and plus_input (extended tests), enabling selective evaluation and test coverage analysis. Metadata includes contract (input validation), atol (floating-point tolerance), canonical_solution, and entry_point, providing complete problem specification for reproducible evaluation.
vs alternatives: More structured than flat test files because metadata is explicitly organized and queryable; enables filtering, aggregation, and analysis that would be difficult with unstructured test data. JSON format is human-readable and tool-agnostic, supporting integration with external evaluation frameworks.
Provides CLI tools (evalplus.evaluate, evalplus.codegen, evalplus.evalperf, evalplus.sanitize) that orchestrate the complete evaluation workflow: code generation → sanitization → correctness evaluation → optional performance evaluation. The evaluate command executes generated code against MBPP+ test suites with configurable timeouts and memory limits, producing pass@k metrics and detailed result logs. The codegen command generates code from specified LLM providers. The evalperf command measures performance via instruction counting. The sanitize command preprocesses code before evaluation. This modular CLI design enables researchers to run evaluation pipelines without writing custom code, supporting reproducible benchmarking and result sharing.
Unique: Implements modular CLI tools (evaluate, codegen, evalperf, sanitize) that can be chained together or run independently, enabling flexible evaluation workflows. Each tool handles a specific stage of the pipeline (generation, sanitization, evaluation, performance measurement), allowing users to customize workflows without writing code.
vs alternatives: More user-friendly than programmatic APIs for researchers who prefer command-line tools; enables reproducible evaluation without custom code. Modular design allows selective use of components (e.g., evaluate without codegen) for flexibility.
+2 more capabilities