Ragas
FrameworkFreeRAG evaluation framework — faithfulness, relevancy, context precision/recall metrics.
Capabilities13 decomposed
llm-based rag faithfulness evaluation with reference-free scoring
Medium confidenceEvaluates whether generated answers are factually grounded in retrieved context using an LLM-as-judge approach without requiring reference answers. Implements a PydanticPrompt-based evaluation pipeline that sends the question, context, and answer to a configurable LLM (via the LLM factory pattern supporting OpenAI, Anthropic, Ollama, etc.) which returns a faithfulness score (0-1) and reasoning. Uses structured output adapters (Instructor, LiteLLM) to parse LLM responses into typed Pydantic models, enabling reliable extraction of scores and explanations.
Uses PydanticPrompt architecture with pluggable LLM adapters (Instructor, LiteLLM) to enable structured output parsing across heterogeneous LLM providers, rather than regex-based or template-based scoring. Supports provider-agnostic evaluation through the LLM factory pattern, allowing users to swap evaluation models without code changes.
More flexible than static rubric-based systems because it leverages LLM reasoning to detect context-answer misalignment; more cost-efficient than reference-based metrics because it requires only questions and generated outputs, not labeled ground truth answers.
multi-metric rag evaluation pipeline with async batch processing
Medium confidenceOrchestrates parallel evaluation of multiple metrics (faithfulness, answer relevancy, context precision, context recall, etc.) across a dataset using an async executor pattern. The evaluate() and aevaluate() functions accept a list of samples (questions, answers, contexts) and a list of metric objects, then distributes metric computation across async workers with configurable concurrency. Implements callback hooks for progress tracking, cost accumulation, and result streaming. Uses RunConfig to control execution parameters (timeout, retries, LLM provider selection) globally across all metrics in a run.
Implements a metric-agnostic executor that treats metrics as pluggable Metric subclasses with a standardized interface (compute() method), enabling users to mix built-in metrics with custom metrics without pipeline modification. Uses async/await throughout to enable true parallelization across metric computations, not just sequential execution.
More efficient than sequential evaluation because it parallelizes metric computation across async workers; more flexible than monolithic evaluation tools because metrics are composable and can be added/removed without framework changes.
async execution with configurable concurrency and timeout handling
Medium confidenceImplements async/await throughout the evaluation pipeline (aevaluate function) to enable non-blocking execution of LLM calls and metric computations. Uses an Executor pattern with configurable concurrency limits (max_workers) to control parallelism and prevent overwhelming LLM APIs. Supports timeout configuration via RunConfig to abort long-running evaluations and implements exponential backoff retry logic for transient failures. Async execution is transparent to users — metrics can be written synchronously and the framework handles async wrapping automatically.
Provides transparent async execution where synchronous metric code is automatically wrapped in async contexts via the Executor pattern. Concurrency is controlled globally via RunConfig, allowing users to tune parallelism without modifying metric code.
More efficient than sequential evaluation because it parallelizes metric computations; more user-friendly than manual async code because the framework handles async wrapping automatically.
dataset schema validation and sample type enforcement
Medium confidenceDefines standardized dataset schemas (EvaluationSample, TestsetSample) as Pydantic models that enforce required fields (question, answer, context) and optional fields (ground_truth, metadata). Validates datasets at load time to catch schema violations early. Supports multiple sample types (single-turn, multi-turn, agent traces) with type-specific validation. The schema system enables type-safe dataset manipulation and ensures metrics receive correctly-formatted inputs without defensive coding.
Uses Pydantic models to define dataset schemas with built-in validation, enabling type-safe dataset handling and early error detection. Supports multiple sample types (single-turn, multi-turn, agent traces) with type-specific validation rules.
More robust than manual validation because Pydantic enforces schema at the type level; more flexible than fixed schemas because sample types can be extended with custom fields.
integration with observability platforms for tracing and monitoring
Medium confidenceIntegrates with observability platforms (Langfuse, etc.) via a tracing adapter pattern that logs evaluation events (metric computations, LLM calls, results) to external systems. Metrics can emit structured events that are automatically captured and sent to configured observability backends. Enables real-time monitoring of evaluation runs, cost tracking across multiple evaluations, and debugging of metric behavior through detailed trace logs. Integration is optional and transparent — evaluation works without observability configuration.
Implements observability as an optional, pluggable adapter that doesn't require code changes to enable. Metrics emit structured events that are automatically captured and routed to configured backends, enabling transparent monitoring.
More flexible than built-in logging because it supports multiple observability platforms; more transparent than manual instrumentation because the framework handles event emission automatically.
synthetic test data generation for rag with llm-based question synthesis
Medium confidenceGenerates synthetic evaluation datasets (questions, answers, contexts) from raw documents using a TestsetGenerator that applies a series of LLM-based transformations. The generator accepts a knowledge graph (built from documents via extractors) and applies synthesizers (e.g., QuestionGenerator, AnswerGenerator) that use PydanticPrompt templates to generate diverse question types (simple, multi-hop, conditional) and corresponding answers. Supports filtering and validation of generated samples via a Validator component. Outputs a Testset object with schema-validated samples ready for evaluation.
Uses a composable transformer pipeline (knowledge graph → synthesizers → validators) where each stage is independently configurable, allowing users to swap synthesizers (e.g., use different question generation strategies) without modifying the core generator. Implements schema-based validation via Pydantic to ensure generated samples conform to evaluation requirements.
More flexible than template-based data generation because it uses LLM reasoning to create contextually relevant questions; more scalable than manual annotation because it automates question generation at the cost of potential quality variance.
context precision and recall metrics with retrieval-aware scoring
Medium confidenceMeasures retrieval quality by evaluating whether retrieved context chunks are relevant (precision) and whether all necessary information is present (recall). Context precision uses an LLM to identify which retrieved chunks are actually relevant to answering the question, then computes the ratio of relevant chunks to total retrieved chunks. Context recall requires ground truth answers and uses semantic similarity (embedding-based) or LLM-based comparison to determine if the retrieved context contains information needed to generate the ground truth answer. Both metrics integrate with the embedding_factory to support multiple embedding models (OpenAI, HuggingFace, Ollama).
Decouples retrieval evaluation from generation by treating context as a first-class evaluation target. Uses dual-path evaluation: LLM-based relevance judgment for precision (no ground truth needed) and embedding-based semantic matching for recall (ground truth required), allowing partial evaluation even with incomplete labels.
More granular than end-to-end RAG metrics because it isolates retrieval quality; more practical than recall-only metrics because precision can be computed without ground truth, enabling evaluation of retrieval in production systems.
answer relevancy metric with question-answer semantic alignment
Medium confidenceEvaluates whether generated answers directly address the user's question using semantic similarity between the question and answer embeddings. The metric generates multiple re-phrasings of the original question using an LLM (via PydanticPrompt), then computes embedding-based cosine similarity between each rephrasing and the answer. Returns the mean similarity score as a measure of relevancy. This approach captures whether the answer content aligns with question intent, independent of factual correctness. Integrates with embedding_factory for model selection and supports batch embedding computation for efficiency.
Uses question rephrasing as a proxy for semantic robustness — instead of comparing question to answer directly, it generates multiple question variants and averages their similarity to the answer, reducing sensitivity to specific question wording. This multi-variant approach is more robust than single-comparison metrics.
More nuanced than keyword-matching approaches because it captures semantic intent; more practical than reference-based metrics because it requires only the question and answer, not labeled ground truth.
custom metric definition with rubric-based evaluation
Medium confidenceEnables users to define custom evaluation metrics by subclassing the Metric base class and implementing a compute() method that accepts evaluation samples and returns scores. Supports rubric-based evaluation where users define scoring criteria as Pydantic models and pass them to an LLM evaluator (via PydanticPrompt) which applies the rubric to generate structured scores. The framework handles LLM integration, output parsing, and result aggregation automatically. Metrics can be composed with built-in metrics in the same evaluation pipeline without modification.
Provides a standardized Metric base class with a compute() interface that allows custom metrics to be plugged into the evaluation pipeline without framework modification. Uses PydanticPrompt for rubric definition, enabling type-safe, structured evaluation criteria that can be versioned and shared.
More flexible than fixed metric sets because users can define arbitrary evaluation logic; more maintainable than ad-hoc evaluation scripts because metrics are composable and reusable across projects.
llm provider abstraction with multi-provider support and adapter pattern
Medium confidenceAbstracts LLM interactions behind a unified interface (BaseLLM) that supports multiple providers (OpenAI, Anthropic, Ollama, LiteLLM, etc.) without changing evaluation code. Uses the adapter pattern with structured output adapters (Instructor for Pydantic validation, LiteLLM for provider routing) to handle provider-specific API differences. The LLM factory pattern allows users to configure a default LLM via RunConfig, and metrics automatically use the configured provider. Supports async/await for non-blocking LLM calls and implements retry logic with exponential backoff for transient failures.
Implements a two-layer abstraction: BaseLLM for provider interface and structured output adapters (Instructor, LiteLLM) for output parsing. This allows metrics to request structured outputs (Pydantic models) without knowing provider implementation details, enabling seamless provider swapping.
More flexible than provider-specific SDKs because it abstracts away provider differences; more reliable than direct API calls because it includes retry logic and error handling built-in.
embedding model integration with semantic similarity computation
Medium confidenceProvides a unified interface (BaseEmbedding) for embedding models across providers (OpenAI, HuggingFace, Ollama, etc.) via the embedding_factory pattern. Metrics that require semantic similarity (answer relevancy, context recall) use this abstraction to compute embeddings without provider-specific code. Supports batch embedding for efficiency (computing multiple embeddings in a single API call) and caching of embeddings to avoid redundant computation. Integrates with RunConfig to allow global embedding model selection across all metrics.
Abstracts embedding models behind a factory pattern (embedding_factory) that allows users to swap models globally via RunConfig without modifying metric code. Supports batch embedding and in-memory caching to optimize repeated evaluations on the same data.
More flexible than hardcoded embedding models because it supports provider-agnostic selection; more efficient than per-metric embedding calls because it enables caching and batching across metrics.
evaluation results aggregation with cost tracking and callbacks
Medium confidenceAggregates evaluation results across all samples and metrics into an EvaluationResults object that provides per-metric statistics (mean, std, min, max), per-sample scores, and cost breakdown (tokens, API calls, estimated cost). Implements a callback system (via Callback base class) that fires events at key pipeline stages (on_sample_start, on_sample_end, on_evaluation_end) enabling real-time progress tracking, result streaming, and custom logging. Cost tracking integrates with LLM and embedding providers to accumulate token counts and estimate monetary costs based on provider pricing.
Combines results aggregation with cost tracking and callback-based event streaming in a single Results object. Callbacks enable real-time monitoring and custom integrations (e.g., logging to external systems) without modifying the evaluation pipeline.
More comprehensive than simple result dictionaries because it includes cost tracking and statistical aggregation; more extensible than hardcoded logging because callbacks allow arbitrary custom behavior.
prompt management and adaptation with pydanticprompt architecture
Medium confidenceImplements a prompt system (PydanticPrompt) where evaluation prompts are defined as Pydantic models with template variables, enabling type-safe, version-controlled prompts. Prompts can be adapted via PromptMixin for localization (multiple languages), domain customization, or model-specific optimization. The system supports prompt composition (combining multiple prompts) and automatic output parsing via structured output adapters. Metrics inherit from PromptMixin to expose their prompts for inspection and customization, allowing users to view and modify evaluation criteria without code changes.
Uses Pydantic models to define prompts as typed data structures rather than strings, enabling validation, composition, and automatic output parsing. PromptMixin allows metrics to expose their prompts for inspection and customization without modifying metric code.
More maintainable than string-based prompts because Pydantic provides type safety and validation; more flexible than hardcoded prompts because users can customize evaluation criteria without code changes.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Ragas, ranked by overlap. Discovered automatically through the match graph.
AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
ragas
Evaluation framework for RAG and LLM applications
deepeval
The LLM Evaluation Framework
haystack-ai
LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data.
LlamaIndex
Data framework for LLM applications — advanced RAG, indexing, and data connectors.
Langfuse
Open-source LLM observability — tracing, prompt management, evaluation, cost tracking, self-hosted.
Best For
- ✓RAG system builders validating answer quality without labeled datasets
- ✓Teams iterating on retrieval and generation components
- ✓Production monitoring of LLM-based QA systems
- ✓Data scientists benchmarking RAG systems against multiple quality dimensions
- ✓Teams running nightly evaluation jobs on production RAG outputs
- ✓Researchers comparing different retrieval or generation strategies
- ✓Large-scale evaluation jobs where parallelization is critical
- ✓Applications with strict latency requirements
Known Limitations
- ⚠Depends on LLM quality — weaker models (GPT-3.5) may miss subtle hallucinations
- ⚠Requires API calls per evaluation sample, adding latency (~1-3s per sample with cloud LLMs)
- ⚠No built-in caching of evaluations — repeated evaluations on same data incur duplicate costs
- ⚠Scoring can be inconsistent across different LLM versions or temperature settings
- ⚠Async execution adds complexity for synchronous-only environments (requires event loop setup)
- ⚠No built-in result persistence — outputs must be manually saved to database or file
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Evaluation framework specifically for RAG pipelines. Metrics: faithfulness, answer relevancy, context precision, context recall. Requires only questions and ground truth answers. Widely adopted for RAG quality measurement.
Categories
Alternatives to Ragas
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
Compare →Amplication brings order to the chaos of large-scale software development by creating Golden Paths for developers - streamlined workflows that drive consistency, enable high-quality code practices, simplify onboarding, and accelerate standardized delivery across teams.
Compare →Are you the builder of Ragas?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →