{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"deepeval","slug":"deepeval","name":"DeepEval","type":"framework","url":"https://github.com/confident-ai/deepeval","page_url":"https://unfragile.ai/deepeval","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"deepeval__cap_0","uri":"capability://safety.moderation.llm.as.judge.metric.evaluation.with.multi.provider.abstraction","name":"llm-as-judge metric evaluation with multi-provider abstraction","description":"Executes evaluation metrics using any LLM provider (OpenAI, Anthropic, Ollama, local models) as a judge through a unified model abstraction layer. DeepEval abstracts provider-specific APIs into a common interface, routing metric prompts to the configured LLM and parsing structured outputs (scores, reasoning) via schema-based deserialization. Supports both synchronous and asynchronous evaluation with built-in retry logic and token counting for cost tracking.","intents":["Evaluate LLM outputs using different judge models without rewriting metric logic","Switch between cloud and local LLM judges for cost/privacy tradeoffs","Track token usage and costs across evaluation runs","Run evaluations asynchronously to parallelize metric computation"],"best_for":["Teams evaluating RAG systems and LLM agents at scale","Developers building custom metrics that need flexible LLM backends","Organizations with privacy constraints requiring local model judges"],"limitations":["Judge model quality directly impacts metric reliability — weak judges produce unreliable scores","Latency scales with judge model response time; local models may be slower than cloud APIs","Requires valid API credentials or local model deployment for each provider used","No built-in caching across different judge models — same test case re-evaluated if judge changes"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama/vLLM instance","Network access to provider APIs or local model server"],"input_types":["LLMTestCase with input, actual_output, expected_output fields","Metric configuration with judge model name and parameters"],"output_types":["Structured metric score (float 0-1)","Reasoning explanation (string)","Token usage metadata (input_tokens, output_tokens)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_1","uri":"capability://safety.moderation.research.backed.metric.library.with.50.implementations","name":"research-backed metric library with 50+ implementations","description":"Provides 50+ pre-built evaluation metrics including faithfulness, answer relevancy, contextual recall, hallucination detection, bias, toxicity, and RAG-specific metrics (retrieval precision, context utilization). Each metric inherits from a BaseMetric class defining the measure() interface and is implemented using LLM-as-judge prompts (G-Eval style), statistical methods (ROUGE, BERTScore), or specialized NLP models (toxicity classifiers). Metrics are composable and can be combined into evaluation suites.","intents":["Evaluate RAG systems for retrieval quality and answer grounding","Detect hallucinations and factual inconsistencies in LLM outputs","Measure bias, toxicity, and safety properties of generated text","Assess conversation quality in multi-turn dialogue systems","Use research-validated metrics without implementing from scratch"],"best_for":["Data scientists building RAG evaluation pipelines","Teams implementing LLM safety and compliance checks","Researchers comparing LLM outputs against published benchmarks","Developers needing quick evaluation without metric engineering"],"limitations":["Metric quality depends on underlying judge model — LLM-based metrics can be inconsistent with weak judges","Some metrics require specific input structure (e.g., contextual recall needs retrieval context); mismatched inputs produce invalid scores","Statistical metrics (ROUGE, BERTScore) may not capture semantic nuances that LLM judges detect","No built-in metric calibration — scores are not normalized across different metric implementations","Toxicity and bias metrics use pre-trained models with inherent dataset biases"],"requires":["Python 3.9+","LLM provider API key for LLM-as-judge metrics (OpenAI, Anthropic, etc.)","For statistical metrics: transformers library (HuggingFace) for BERTScore, rouge-score for ROUGE","For toxicity/bias: detoxify or perspective-api credentials"],"input_types":["LLMTestCase or ConversationalTestCase with input, actual_output, expected_output, retrieval_context","Metric-specific parameters (e.g., threshold for hallucination, model name for judge)"],"output_types":["Metric score (float 0-1 or 0-100 depending on metric)","Pass/fail boolean (if threshold configured)","Reasoning explanation (for LLM-based metrics)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_10","uri":"capability://data.processing.analysis.benchmark.comparison.and.model.evaluation","name":"benchmark comparison and model evaluation","description":"Provides benchmark functionality to compare LLM model performance across evaluation datasets using standardized metrics. Benchmarks define a set of models, datasets, and metrics to evaluate, and produce comparison reports showing performance differences. Supports benchmarking against published datasets (MMLU, HellaSwag, etc.) and custom datasets. Results are tracked over time, enabling trend analysis and regression detection. Benchmark reports include statistical significance testing and visualization of performance differences.","intents":["Compare performance of different LLM models on standardized benchmarks","Track model performance improvements over time","Detect performance regressions when updating models or prompts","Publish benchmark results for model comparison and selection","Validate that model updates improve evaluation metrics"],"best_for":["Teams evaluating multiple LLM models for production deployment","Researchers publishing model comparisons and benchmarks","Organizations tracking model performance over time","Developers validating that model updates improve quality"],"limitations":["Benchmark results are specific to the chosen metrics and datasets — different metrics may show different rankings","Published benchmarks (MMLU, etc.) may not reflect real-world application performance","No automatic statistical significance testing — differences may be due to noise","Benchmark execution is expensive (multiple models × multiple datasets × multiple metrics)","No built-in handling of model versioning — tracking which model version produced which result requires manual management"],"requires":["Python 3.9+","API keys for all models being benchmarked","Evaluation dataset (custom or published)"],"input_types":["Benchmark configuration (models, datasets, metrics)","Evaluation dataset"],"output_types":["Benchmark report with performance metrics per model","Comparison table showing ranking and performance differences","Trend analysis showing performance over time"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_11","uri":"capability://planning.reasoning.prompt.optimization.and.a.b.testing","name":"prompt optimization and a/b testing","description":"Provides prompt optimization capabilities to iteratively improve LLM prompts based on evaluation metrics. Supports A/B testing of different prompt variants against the same evaluation dataset, measuring performance differences using metrics like answer relevancy and hallucination. Optimization strategies include prompt template variation, few-shot example selection, and instruction refinement. Results are tracked and compared, enabling data-driven prompt engineering. Optimized prompts can be versioned and deployed to production.","intents":["Improve LLM output quality by testing different prompt formulations","A/B test prompt variants to identify high-performing versions","Systematically optimize prompts based on evaluation metrics","Track prompt versions and their performance over time","Deploy optimized prompts to production with confidence"],"best_for":["Teams optimizing LLM prompts for production systems","Developers iterating on prompt design based on evaluation feedback","Organizations running A/B tests on prompt variants","Researchers studying prompt engineering techniques"],"limitations":["Prompt optimization is computationally expensive — each variant requires full evaluation","Optimization results may not generalize to different datasets or use cases","No automatic prompt generation — variants must be manually created or generated by LLM","Optimization is greedy — may converge to local optima rather than global optimum","No built-in handling of prompt versioning and deployment"],"requires":["Python 3.9+","Evaluation dataset for testing prompt variants","LLM provider API keys for prompt evaluation"],"input_types":["Prompt variants (different formulations of the same prompt)","Evaluation dataset","Metrics to optimize for"],"output_types":["A/B test results comparing prompt variants","Performance metrics per variant","Recommendation for best-performing prompt"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_12","uri":"capability://automation.workflow.test.run.management.and.result.persistence","name":"test run management and result persistence","description":"Manages test run lifecycle including execution, result storage, and historical tracking. Each test run captures metadata (timestamp, model version, dataset version, metrics evaluated, pass rate) and individual test results (metric scores, pass/fail status). Test runs are persisted locally (JSON/SQLite) or in Confident AI cloud backend, enabling historical comparison and regression detection. Supports filtering and querying test runs by date, model, dataset, or metric. Test run reports can be exported for analysis or shared with stakeholders.","intents":["Track evaluation results over time to detect performance regressions","Compare test runs across different model versions or dataset versions","Generate reports showing evaluation trends and improvements","Archive evaluation results for compliance and audit purposes","Share evaluation results with team members and stakeholders"],"best_for":["Teams running frequent evaluation iterations and needing historical tracking","Organizations with compliance requirements for evaluation audit trails","DevOps engineers monitoring LLM application quality over time","Developers debugging performance regressions across model versions"],"limitations":["Test run storage grows unbounded — no automatic archival or cleanup of old runs","Local storage (JSON/SQLite) is not suitable for distributed teams; cloud storage requires Confident AI account","No built-in data retention policies or compliance controls","Querying large test run histories can be slow due to lack of indexing","No automatic anomaly detection — regressions must be manually identified"],"requires":["Python 3.9+","SQLite (built-in) for local storage or Confident AI account for cloud storage","Sufficient disk space for test run history"],"input_types":["Test run configuration (model, dataset, metrics)","Individual test results (metric scores, pass/fail status)"],"output_types":["Test run metadata (timestamp, pass rate, metrics evaluated)","Test run report (summary and detailed results)","Historical comparison across test runs"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_13","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.model.configuration","name":"multi-provider llm abstraction with model configuration","description":"Provides a unified Model abstraction layer (deepeval/models/base.py) that normalizes APIs across 10+ LLM providers (OpenAI, Anthropic, Ollama, vLLM, Azure, Bedrock, etc.). Each provider has a concrete implementation that translates DeepEval's generic model interface (generate(), generate_async()) to provider-specific APIs. Model configuration is centralized, supporting environment variables, config files, and programmatic initialization. Supports model-specific features (temperature, max_tokens, system prompts) while maintaining a consistent interface.","intents":["Use different LLM providers for metrics without changing metric code","Switch between cloud and local models for cost/privacy optimization","Configure model parameters (temperature, max_tokens) per metric","Support new LLM providers by implementing a single provider adapter","Track token usage and costs across different providers"],"best_for":["Teams using multiple LLM providers and wanting unified evaluation","Organizations with privacy requirements needing local model support","Developers building provider-agnostic LLM applications","Cost-conscious teams optimizing provider selection"],"limitations":["Provider abstraction adds ~5-10ms latency per LLM call due to translation overhead","Not all provider features are exposed through the abstraction — advanced features require provider-specific code","Model configuration is not automatically validated — invalid configs fail at runtime","No built-in provider fallback — if primary provider fails, evaluation fails","Token counting is approximate for some providers; actual usage may differ"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, etc.) OR local model deployment (Ollama, vLLM)","Network access to provider APIs or local model server"],"input_types":["Model name and provider (e.g., 'gpt-4', 'claude-3-opus', 'ollama/llama2')","Model configuration (temperature, max_tokens, system prompt)"],"output_types":["Model response (text)","Token usage metadata (input_tokens, output_tokens, total_cost)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_14","uri":"capability://automation.workflow.cli.and.configuration.management.for.evaluation.workflows","name":"cli and configuration management for evaluation workflows","description":"Provides command-line interface (CLI) for running evaluations, managing datasets, and configuring projects without writing Python code. CLI commands support test execution (deepeval test), dataset operations (deepeval dataset), and cloud integration (deepeval login). Configuration is managed through YAML files (deepeval.yaml) and environment variables, enabling reproducible evaluation workflows and CI/CD integration. CLI output includes human-readable result summaries and machine-readable JSON export for integration with external tools.","intents":["Run evaluations from command line without writing Python code","Configure evaluation projects using YAML files for reproducibility","Integrate evaluation into CI/CD pipelines using standard CLI commands","Export evaluation results in machine-readable format for external processing"],"best_for":["DevOps engineers integrating evaluation into CI/CD pipelines","Non-Python developers running evaluations","Teams requiring reproducible evaluation workflows via configuration files"],"limitations":["CLI is limited to standard evaluation workflows — complex scenarios require Python API","Configuration is YAML-based — no validation of configuration syntax","CLI output is text-based — limited visualization capabilities vs. web dashboards","No interactive CLI mode — all configuration must be specified upfront"],"requires":["Python 3.9+","DeepEval installed and in PATH"],"input_types":["YAML configuration files","Environment variables","Command-line arguments"],"output_types":["Human-readable evaluation results","JSON export of results","Exit codes for CI/CD integration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_2","uri":"capability://automation.workflow.pytest.integrated.test.execution.with.ci.cd.automation","name":"pytest-integrated test execution with ci/cd automation","description":"Integrates DeepEval metrics into pytest test discovery and execution via a pytest plugin (deepeval/plugins/pytest_plugin.py). Test cases are defined as pytest test functions decorated with @pytest.mark.deepeval, and metrics are asserted using standard pytest assertions. The plugin captures test results, manages test runs, and exports results to the Confident AI platform or local storage. Supports parallel test execution, test filtering, and integration with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).","intents":["Run LLM evaluations as part of standard pytest test suites","Integrate evaluation into CI/CD pipelines to gate deployments on metric thresholds","Track evaluation results over time and compare against baseline runs","Use familiar pytest syntax and tooling for LLM testing"],"best_for":["Teams already using pytest for unit/integration testing","DevOps engineers automating LLM quality gates in CI/CD","Organizations wanting to treat LLM evaluation as first-class testing","Projects needing historical evaluation tracking and regression detection"],"limitations":["Pytest plugin adds ~50-100ms overhead per test due to result capture and serialization","Parallel test execution (-n flag) requires careful handling of shared LLM API rate limits","Test discovery only works for functions matching pytest naming conventions (test_*.py)","No built-in test result visualization in pytest output — requires Confident AI dashboard for rich reporting"],"requires":["pytest 7.0+","Python 3.9+","Confident AI account (optional, for cloud result storage) or local file system for result persistence"],"input_types":["pytest test functions with LLMTestCase or ConversationalTestCase","Metric assertions using standard pytest assert syntax"],"output_types":["pytest test results (passed/failed/skipped)","Test run metadata (duration, metrics evaluated, pass rate)","JSON/CSV export of results for CI/CD integration"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_3","uri":"capability://data.processing.analysis.evaluation.dataset.management.with.golden.records.and.versioning","name":"evaluation dataset management with golden records and versioning","description":"Provides a dataset abstraction (EvaluationDataset class) for managing collections of test cases with version control, persistence, and synthetic data generation. Golden records are curated test cases stored in JSON/CSV format with input, expected output, and optional metadata. Datasets support CRUD operations, filtering, and export to multiple formats. Integrates with Confident AI platform for cloud-based dataset versioning and collaboration, enabling teams to maintain evaluation datasets across model iterations.","intents":["Create and maintain curated evaluation datasets for consistent benchmarking","Version evaluation datasets to track changes and enable reproducibility","Generate synthetic test cases from templates to expand evaluation coverage","Share evaluation datasets across team members and CI/CD pipelines","Track dataset lineage and understand which test cases changed between runs"],"best_for":["Teams building production LLM systems requiring reproducible evaluation","Data scientists managing evaluation datasets across multiple model versions","Organizations needing audit trails for evaluation data (compliance, governance)","Collaborative teams sharing evaluation datasets across regions/departments"],"limitations":["No built-in data validation — malformed test cases (missing fields) are not caught until evaluation time","Synthetic data generation quality depends on template quality; poor templates produce low-quality test cases","Cloud dataset versioning requires Confident AI account; local-only usage has no version control","Large datasets (>10k test cases) may have slow load times due to JSON parsing overhead","No built-in deduplication — duplicate test cases are not automatically detected or merged"],"requires":["Python 3.9+","JSON or CSV files for dataset import/export","Confident AI account (optional, for cloud versioning) or local file system","For synthetic generation: LLM provider API key (OpenAI, Anthropic, etc.)"],"input_types":["JSON/CSV files with test case records","EvaluationDataset objects with list of LLMTestCase instances","Synthetic data generation templates (prompt + expected output patterns)"],"output_types":["EvaluationDataset object (in-memory)","JSON/CSV export of dataset","Dataset metadata (version, creation date, test case count, schema)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_4","uri":"capability://automation.workflow.tracing.and.observability.with.observe.decorator.and.span.hierarchy","name":"tracing and observability with @observe decorator and span hierarchy","description":"Provides distributed tracing capabilities via an @observe decorator that instruments LLM application code to capture execution spans (function calls, LLM invocations, tool calls). Spans form a hierarchical tree structure with parent-child relationships, enabling visualization of complex LLM workflows. Integrates with OpenTelemetry for standards-based tracing and exports spans to Confident AI dashboard or external observability platforms. Captures latency, token usage, errors, and custom attributes per span.","intents":["Trace execution flow through multi-step LLM agents and RAG pipelines","Identify performance bottlenecks in LLM application components","Correlate evaluation metrics with specific execution paths (e.g., which retrieval step caused low relevancy)","Debug LLM application failures by inspecting span logs and error traces","Export traces to external observability platforms (Datadog, New Relic, etc.) via OpenTelemetry"],"best_for":["Teams building complex LLM agents with multiple components","DevOps engineers monitoring LLM application performance in production","Developers debugging multi-turn conversations and RAG retrieval issues","Organizations integrating LLM observability into existing monitoring stacks"],"limitations":["@observe decorator adds ~5-10ms overhead per decorated function due to span creation and serialization","Span hierarchy is limited to single-threaded execution; async/concurrent spans may have ordering issues","No automatic span sampling — all spans are captured, which can create large trace volumes at scale","OpenTelemetry export requires additional configuration; default behavior stores spans in-memory only","Custom attributes must be manually added to spans; no automatic context propagation from function arguments"],"requires":["Python 3.9+","Confident AI account (optional, for cloud trace storage) or local file system","For OpenTelemetry export: opentelemetry-api and exporter package (e.g., opentelemetry-exporter-jaeger)"],"input_types":["Python functions decorated with @observe","Custom span attributes (key-value pairs)","LLM invocations and tool calls within decorated functions"],"output_types":["Span objects with metadata (name, duration, status, attributes)","Trace tree visualization (in Confident AI dashboard)","OpenTelemetry-compatible span exports (JSON, protobuf)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_5","uri":"capability://code.generation.editing.custom.metric.definition.with.schema.based.validation","name":"custom metric definition with schema-based validation","description":"Allows developers to define custom metrics by subclassing BaseMetric and implementing a measure() method that accepts an LLMTestCase and returns a MetricResult. Custom metrics can use any evaluation logic (LLM-as-judge, statistical, ML models) and are validated against a schema defining required inputs (input, actual_output, expected_output, retrieval_context). The framework provides template prompts and helper functions for common patterns (LLM-as-judge via G-Eval, reference-based scoring). Custom metrics integrate seamlessly with the evaluation pipeline and can be combined with built-in metrics.","intents":["Implement domain-specific evaluation metrics tailored to application requirements","Reuse metric implementations across multiple evaluation runs and projects","Combine custom and built-in metrics into evaluation suites","Define metrics that require proprietary scoring logic or external APIs"],"best_for":["Teams with domain-specific evaluation needs not covered by built-in metrics","Researchers implementing novel evaluation approaches","Organizations with proprietary scoring logic (e.g., business rule-based evaluation)","Developers extending DeepEval for specialized use cases (medical LLMs, legal AI, etc.)"],"limitations":["Custom metrics must implement the measure() interface; no partial implementations or mixins","Schema validation is loose — required fields are checked but no type validation on nested objects","No built-in caching for custom metrics; expensive computations are re-run on each evaluation","Debugging custom metrics requires manual logging; no built-in profiling or error reporting","Custom metrics are not automatically discoverable; must be explicitly imported and registered"],"requires":["Python 3.9+","Understanding of BaseMetric interface and MetricResult structure","For LLM-based custom metrics: LLM provider API key"],"input_types":["LLMTestCase or ConversationalTestCase","Custom parameters passed to metric constructor"],"output_types":["MetricResult object with score (float), pass (bool), and reason (string)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_6","uri":"capability://data.processing.analysis.caching.system.for.metric.evaluation.results","name":"caching system for metric evaluation results","description":"Implements a caching layer (deepeval/cache.py) that stores metric evaluation results keyed by test case hash and metric configuration, avoiding redundant evaluations of identical inputs. Cache is stored locally (SQLite) or in Confident AI cloud backend. Supports cache invalidation by metric version, test case modification, or explicit clearing. Caching is transparent to users — metrics check cache before execution and store results after completion.","intents":["Reduce evaluation latency by reusing cached metric results for unchanged test cases","Lower LLM API costs by avoiding redundant judge invocations","Enable faster iteration during development by caching expensive metrics","Track which test cases have been evaluated and which are new"],"best_for":["Teams running frequent evaluation iterations with overlapping test cases","Cost-conscious organizations evaluating large datasets with expensive LLM judges","Development workflows requiring rapid feedback on metric changes","Projects with stable test cases that are re-evaluated across model versions"],"limitations":["Cache key is based on test case content hash — any change to input/output invalidates cache, even minor whitespace changes","No built-in cache warming or precomputation — cache is populated on-demand","Cache size grows unbounded; no automatic eviction policy or size limits","Cache invalidation is manual or version-based; no automatic invalidation when metric logic changes","SQLite cache is not suitable for distributed evaluation across multiple machines; cloud cache requires Confident AI account"],"requires":["Python 3.9+","SQLite (built-in) for local caching or Confident AI account for cloud caching","Sufficient disk space for cache storage (depends on dataset size and metric count)"],"input_types":["LLMTestCase or ConversationalTestCase","Metric configuration (metric name, judge model, parameters)"],"output_types":["Cached MetricResult (if cache hit) or newly computed result (if cache miss)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_7","uri":"capability://data.processing.analysis.conversation.simulation.for.multi.turn.dialogue.evaluation","name":"conversation simulation for multi-turn dialogue evaluation","description":"Provides a ConversationSimulator that generates multi-turn dialogue datasets by simulating conversations between user and assistant LLMs. The simulator takes a conversation template (initial prompt, turn count, evaluation criteria) and generates realistic dialogue sequences. Supports different conversation styles (question-answering, task-oriented, open-ended) and can evaluate conversation quality using metrics like turn relevancy and coherence. Generated conversations are stored as ConversationalTestCase objects compatible with the evaluation pipeline.","intents":["Generate diverse multi-turn dialogue datasets for evaluating conversational AI systems","Simulate user interactions to test chatbot robustness and consistency","Create evaluation datasets for dialogue-specific metrics (turn relevancy, coherence)","Reduce manual effort in creating multi-turn test cases"],"best_for":["Teams building conversational AI systems (chatbots, dialogue agents)","Researchers evaluating multi-turn dialogue quality","Organizations needing diverse conversation datasets for stress testing","Developers prototyping dialogue evaluation metrics"],"limitations":["Simulated conversations may not reflect real user behavior — generated dialogues can be overly formal or unrealistic","Conversation quality depends on the quality of the user and assistant LLMs used for simulation","No built-in diversity control — generated conversations may be repetitive or lack edge cases","Simulator requires two LLM API calls per turn (user + assistant), making dataset generation expensive","No automatic validation of conversation coherence — incoherent or contradictory dialogues may be generated"],"requires":["Python 3.9+","LLM provider API keys for both user and assistant simulation (OpenAI, Anthropic, etc.)","Conversation template defining initial prompt and turn count"],"input_types":["Conversation template with initial prompt, turn count, and evaluation criteria","LLM configuration for user and assistant simulators"],"output_types":["ConversationalTestCase objects with multi-turn dialogue history","Conversation metadata (turn count, total tokens, simulation duration)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_8","uri":"capability://safety.moderation.red.teaming.and.adversarial.test.case.generation","name":"red teaming and adversarial test case generation","description":"Provides red teaming capabilities to generate adversarial test cases designed to expose weaknesses in LLM applications. Red teaming strategies include prompt injection, jailbreak attempts, edge case generation, and bias probing. The framework uses LLM-as-judge to generate adversarial inputs and evaluates system robustness using safety metrics (toxicity, bias, hallucination). Red teaming results are tracked separately from standard evaluation and can be used to identify failure modes and improve system resilience.","intents":["Identify adversarial inputs that cause LLM failures or unsafe outputs","Test robustness of LLM applications against prompt injection and jailbreak attempts","Generate edge cases and corner cases for comprehensive evaluation","Measure bias and toxicity exposure in LLM outputs","Build adversarial test suites for continuous safety monitoring"],"best_for":["Security-conscious teams deploying LLMs in production","Organizations subject to regulatory requirements (AI Act, responsible AI policies)","Researchers studying LLM robustness and adversarial vulnerabilities","Teams building safety-critical LLM applications (healthcare, finance, legal)"],"limitations":["Red teaming is adversarial by nature — generated attacks may not reflect real-world threat models","LLM-based red teaming can be expensive (multiple LLM calls per adversarial case generation)","No guarantee that generated adversarial cases will actually expose vulnerabilities","Red teaming results are subjective — different judges may rate the same adversarial input differently","No built-in prioritization of adversarial cases by severity or likelihood"],"requires":["Python 3.9+","LLM provider API keys for red teaming and evaluation (OpenAI, Anthropic, etc.)","Understanding of adversarial attack patterns and safety metrics"],"input_types":["Original test cases or application prompts","Red teaming strategy configuration (injection, jailbreak, bias probing, etc.)","Safety metric thresholds for evaluation"],"output_types":["Adversarial test cases (LLMTestCase with attack inputs)","Red teaming results with safety metric scores","Vulnerability report identifying failure modes"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__cap_9","uri":"capability://safety.moderation.guardrails.for.llm.output.validation.and.filtering","name":"guardrails for llm output validation and filtering","description":"Provides guardrails (deepeval/guardrails.py) that validate and filter LLM outputs against user-defined rules before they reach end users. Guardrails can enforce constraints like output length, content filtering (toxicity, PII), format validation (JSON schema, regex), and custom business logic. Guardrails are composable and can be chained together. When a guardrail violation is detected, the system can reject the output, retry with a modified prompt, or flag for human review. Guardrails integrate with the evaluation pipeline to measure compliance.","intents":["Prevent unsafe or inappropriate LLM outputs from reaching users","Enforce output format requirements (JSON, structured data)","Filter PII and sensitive information from LLM responses","Implement business logic constraints (e.g., max response length, required fields)","Track guardrail violations for safety monitoring and compliance"],"best_for":["Teams deploying LLMs in regulated industries (healthcare, finance, legal)","Organizations with strict content moderation requirements","Developers building LLM APIs with output format guarantees","Teams implementing responsible AI practices and safety controls"],"limitations":["Guardrails are reactive — they filter outputs after generation, not preventing unsafe generation","Complex guardrails (custom business logic) require manual implementation and testing","No built-in learning from guardrail violations — patterns are not automatically detected","Guardrail violations may require expensive retry loops with modified prompts","No prioritization of guardrail violations by severity; all violations treated equally"],"requires":["Python 3.9+","Custom guardrail implementations for domain-specific logic","For PII filtering: external PII detection library (e.g., presidio)"],"input_types":["LLM output (string or structured data)","Guardrail configuration (rules, thresholds, actions)"],"output_types":["Validated/filtered output (if guardrail passes)","Guardrail violation report (if guardrail fails)","Retry prompt (if retry action configured)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepeval__headline","uri":"capability://testing.quality.llm.evaluation.framework","name":"llm evaluation framework","description":"DeepEval is an open-source framework designed for systematically evaluating Large Language Model (LLM) applications using a variety of metrics and test cases, similar to unit testing in traditional software development.","intents":["best LLM evaluation framework","LLM evaluation for production","open-source tools for LLM testing","how to evaluate LLM outputs","metrics for LLM evaluation","CI/CD integration for LLM testing"],"best_for":["developers testing LLMs","teams integrating LLMs into production"],"limitations":[],"requires":[],"input_types":["LLM outputs","evaluation datasets"],"output_types":["evaluation metrics","test results"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama/vLLM instance","Network access to provider APIs or local model server","LLM provider API key for LLM-as-judge metrics (OpenAI, Anthropic, etc.)","For statistical metrics: transformers library (HuggingFace) for BERTScore, rouge-score for ROUGE","For toxicity/bias: detoxify or perspective-api credentials","API keys for all models being benchmarked","Evaluation dataset (custom or published)","Evaluation dataset for testing prompt variants","LLM provider API keys for prompt evaluation"],"failure_modes":["Judge model quality directly impacts metric reliability — weak judges produce unreliable scores","Latency scales with judge model response time; 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