{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-deepeval","slug":"pypi-deepeval","name":"deepeval","type":"benchmark","url":"https://github.com/confident-ai/deepeval","page_url":"https://unfragile.ai/pypi-deepeval","categories":["testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-deepeval__cap_0","uri":"capability://data.processing.analysis.llm.as.judge.metric.evaluation.with.multi.provider.support","name":"llm-as-judge metric evaluation with multi-provider support","description":"Executes evaluation metrics using LLMs as judges by constructing structured prompts with evaluation schemas and routing them to any LLM provider (OpenAI, Anthropic, Ollama, etc.). Implements the G-Eval pattern with research-backed scoring templates that normalize outputs to 0-1 scales. The metric execution pipeline handles provider abstraction, caching of LLM responses, and deterministic scoring through configurable model selection and temperature control.","intents":["I need to evaluate whether my LLM output is relevant to the user query using an LLM-as-judge approach","I want to run the same evaluation metric across different LLM providers to compare judge consistency","I need to cache evaluation results to avoid redundant LLM calls during iterative testing"],"best_for":["teams building RAG systems who need relevance/hallucination scoring","LLM application developers evaluating output quality at scale","researchers comparing metric implementations across different judge models"],"limitations":["LLM-as-judge metrics inherit the non-determinism of the underlying judge model; same input may produce different scores across runs","Requires API credentials for external LLM providers or local model setup; adds latency (typically 1-5 seconds per metric evaluation)","Caching system is in-memory by default; no built-in distributed cache for multi-process evaluation"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama instance","Network access to LLM provider endpoints"],"input_types":["LLMTestCase (input, actual_output, expected_output, context)","ConversationalTestCase (conversation history with multiple turns)"],"output_types":["MetricResult (score: float 0-1, reason: string, success: bool)"],"categories":["data-processing-analysis","evaluation-metrics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_1","uri":"capability://data.processing.analysis.research.backed.metric.library.with.domain.specific.evaluations","name":"research-backed metric library with domain-specific evaluations","description":"Provides 50+ pre-built metrics covering general LLM quality (relevance, coherence, faithfulness), RAG-specific concerns (retrieval precision, context relevance), and conversation quality (turn-level relevance, conversation coherence). Each metric is implemented as a subclass of the Metric base class with built-in scoring logic that can use LLM-as-judge, statistical methods, or local NLP models. Metrics are composable and can be mixed in test runs to evaluate multiple dimensions simultaneously.","intents":["I want to evaluate my RAG system's retrieval quality without building custom metrics from scratch","I need to measure hallucination, faithfulness, and relevance across my LLM outputs","I want to evaluate multi-turn conversations at the turn level to identify where quality degrades"],"best_for":["RAG system builders evaluating retrieval and context relevance","LLM application teams needing standard quality metrics without custom development","researchers benchmarking LLM outputs against established evaluation criteria"],"limitations":["Pre-built metrics assume English text; multilingual support is limited","Some metrics (e.g., hallucination detection) rely on LLM-as-judge and inherit judge model limitations","Metrics are optimized for text; limited support for multimodal evaluation (images, audio)"],"requires":["Python 3.9+","For LLM-based metrics: API key for judge model provider","For NLP-based metrics: automatic download of model weights (e.g., BERT for embeddings)"],"input_types":["LLMTestCase with input, actual_output, expected_output, context","ConversationalTestCase with conversation history"],"output_types":["MetricResult (score: float 0-1, reason: string, success: bool, metadata: dict)"],"categories":["data-processing-analysis","evaluation-metrics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_10","uri":"capability://safety.moderation.guardrails.and.safety.evaluation.for.llm.outputs","name":"guardrails and safety evaluation for llm outputs","description":"Provides guardrail metrics to evaluate safety and compliance of LLM outputs, including toxicity detection, PII redaction, prompt injection detection, and bias assessment. Guardrails can be applied as pre-generation filters or post-generation validators. Integrates with external safety APIs (e.g., OpenAI Moderation) and local NLP models for offline evaluation.","intents":["I need to detect and filter toxic or harmful outputs from my LLM application","I want to ensure my LLM doesn't leak personally identifiable information (PII)","I need to evaluate whether my LLM is vulnerable to prompt injection attacks"],"best_for":["teams deploying LLM applications in production with safety requirements","developers building guardrails for customer-facing LLM systems","teams evaluating LLM safety and compliance"],"limitations":["Guardrail effectiveness depends on the underlying detection model; no guardrail is 100% effective","External safety APIs (e.g., OpenAI Moderation) add latency and cost; local models are slower but cheaper","PII detection is language-specific and may miss domain-specific sensitive information"],"requires":["Python 3.9+","Optional: API key for external safety services (OpenAI Moderation, etc.)","Optional: Local NLP models for offline evaluation"],"input_types":["LLM output text"],"output_types":["GuardrailResult (passed: bool, violations: list, reason: string)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_11","uri":"capability://data.processing.analysis.red.teaming.and.adversarial.test.case.generation","name":"red teaming and adversarial test case generation","description":"Generates adversarial test cases designed to expose weaknesses in LLM applications through systematic perturbation of inputs (e.g., typos, paraphrasing, edge cases). Red teaming metrics evaluate robustness by measuring how outputs change under adversarial conditions. Supports both automated generation and manual specification of adversarial scenarios.","intents":["I want to test whether my LLM application is robust to typos and misspellings in user input","I need to find edge cases and adversarial inputs that cause my LLM to fail","I want to measure how sensitive my LLM outputs are to small changes in input"],"best_for":["teams building production LLM systems who need robustness testing","security-focused teams evaluating LLM vulnerability to adversarial inputs","researchers studying LLM robustness"],"limitations":["Automated adversarial generation may not cover all relevant perturbation types; manual specification is often necessary","Robustness evaluation is expensive (requires multiple evaluations per test case); scales poorly with dataset size","Adversarial inputs may not reflect real user behavior; synthetic perturbations may not be representative"],"requires":["Python 3.9+","Base test cases to perturb","Metrics to evaluate robustness"],"input_types":["LLMTestCase instances"],"output_types":["List of adversarial LLMTestCase instances with perturbation metadata"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_12","uri":"capability://automation.workflow.prompt.optimization.and.a.b.testing.framework","name":"prompt optimization and a/b testing framework","description":"Provides utilities for systematic prompt optimization by running evaluations across multiple prompt variants and comparing results. Supports A/B testing of prompts, model versions, and hyperparameters. Results are aggregated and compared to identify the best-performing variant. Integrates with the Confident AI platform for historical tracking of prompt iterations.","intents":["I want to test 5 different prompt variations and see which one produces the best evaluation scores","I need to A/B test my current prompt against a new variant to ensure it's an improvement","I want to track the history of prompt iterations and their evaluation results"],"best_for":["teams iterating on prompts to improve LLM output quality","developers optimizing prompts for specific tasks","teams running continuous prompt optimization"],"limitations":["Prompt optimization is expensive (requires multiple evaluations per variant); scales poorly with number of variants","Evaluation metrics may not capture all dimensions of prompt quality; manual review is often necessary","Optimal prompts may be task-specific and not generalize to other domains"],"requires":["Python 3.9+","Multiple prompt variants to compare","Evaluation metrics and test cases"],"input_types":["List of prompt variants","Test cases and metrics"],"output_types":["Comparison results with scores for each variant and statistical significance"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_13","uri":"capability://automation.workflow.cli.and.configuration.management.for.evaluation.workflows","name":"cli and configuration management for evaluation workflows","description":"Provides a command-line interface (deepeval CLI) for running evaluations, managing datasets, and configuring projects. Supports configuration files (deepeval.json) for project settings, environment variables for API keys, and provider configuration management. CLI commands enable running evaluations without writing Python code, making it accessible to non-developers.","intents":["I want to run evaluations from the command line without writing Python code","I need to configure my project settings (API keys, model versions) in a config file","I want to manage evaluation datasets and test runs via CLI commands"],"best_for":["teams with non-technical stakeholders who need to run evaluations","developers integrating evaluations into shell scripts or CI/CD pipelines","teams managing multiple evaluation projects with shared configuration"],"limitations":["CLI is limited to basic operations; complex evaluation workflows require Python code","Configuration management is file-based; no support for dynamic configuration or environment-specific overrides","CLI output is text-based; detailed results require parsing or integration with other tools"],"requires":["Python 3.9+","deepeval package installed","Optional: deepeval.json configuration file"],"input_types":["CLI arguments and configuration files"],"output_types":["Text output with evaluation results and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_2","uri":"capability://data.processing.analysis.test.case.definition.and.management.with.structured.data.models","name":"test case definition and management with structured data models","description":"Defines evaluation test cases as structured Python dataclasses (LLMTestCase, ConversationalTestCase) that capture input, expected output, actual output, and context. The framework provides schema validation, serialization to JSON/CSV, and dataset-level operations (filtering, splitting, versioning). Test cases can be created manually, loaded from files, or generated synthetically using LLM-based data generation.","intents":["I need to organize my evaluation test cases in a structured format that integrates with my evaluation pipeline","I want to load test cases from CSV/JSON files and validate them before running evaluations","I need to version and track changes to my evaluation datasets over time"],"best_for":["teams managing large evaluation datasets (100s to 1000s of test cases)","LLM application developers who want to version control their evaluation data","data scientists building evaluation datasets incrementally"],"limitations":["Test case schema is fixed (input, actual_output, expected_output, context); custom fields require subclassing","No built-in support for test case versioning or branching; requires external version control","Serialization to CSV loses nested structure (e.g., conversation history); JSON is recommended for complex cases"],"requires":["Python 3.9+","Pandas for CSV operations (optional but recommended)"],"input_types":["Python dict or dataclass instances","CSV files with columns: input, actual_output, expected_output, context","JSON files with test case objects"],"output_types":["LLMTestCase or ConversationalTestCase instances","Serialized JSON/CSV for storage or sharing"],"categories":["data-processing-analysis","test-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_3","uri":"capability://automation.workflow.evaluation.execution.and.test.run.orchestration","name":"evaluation execution and test run orchestration","description":"Orchestrates the execution of test cases against metrics using the evaluate() function, which handles parallel metric execution, result aggregation, and test run persistence. The execution engine manages metric scheduling, error handling, and result caching. Test runs are tracked with metadata (timestamp, model version, dataset version) and can be compared across iterations to detect regressions.","intents":["I want to run 100 test cases against 5 metrics in parallel and get aggregated results","I need to track evaluation results over time to detect when my LLM output quality degrades","I want to run evaluations in CI/CD pipelines with pass/fail thresholds"],"best_for":["teams running evaluation suites in CI/CD pipelines","LLM developers iterating on prompts and models with continuous evaluation","teams tracking evaluation metrics across model versions"],"limitations":["Parallel execution is limited by the number of available threads; no distributed execution across machines","Test run persistence requires Confident AI platform integration; local-only runs are not persisted by default","Error handling is basic; individual metric failures don't stop the entire test run but may produce incomplete results"],"requires":["Python 3.9+","Test cases and metrics defined","Optional: Confident AI API key for test run persistence"],"input_types":["List of LLMTestCase or ConversationalTestCase instances","List of Metric instances to evaluate"],"output_types":["TestRunResult (test_cases: list, metrics_results: dict, summary: dict)","Persisted test run metadata in Confident AI platform"],"categories":["automation-workflow","test-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_4","uri":"capability://data.processing.analysis.synthetic.test.case.generation.using.llm.based.data.synthesis","name":"synthetic test case generation using llm-based data synthesis","description":"Generates synthetic test cases by prompting an LLM to create realistic input-output pairs based on seed data or templates. The synthesis engine uses configurable prompts to control the diversity and quality of generated cases. Generated cases are validated against the test case schema and can be filtered or augmented before being added to evaluation datasets.","intents":["I have 10 golden test cases but need 100 for comprehensive evaluation; I want to generate more synthetically","I want to create edge case test cases (e.g., adversarial inputs) to stress-test my LLM application","I need to expand my evaluation dataset without manual annotation"],"best_for":["teams with limited labeled evaluation data who want to bootstrap larger datasets","LLM developers creating edge case test suites","researchers generating benchmark datasets"],"limitations":["Synthetic data quality depends on the seed data and generation prompt; garbage in, garbage out","Generated cases may have distribution shift compared to real user inputs; not a substitute for real evaluation data","Synthesis is slow (1-5 seconds per case); generating 1000 cases requires significant time and API costs"],"requires":["Python 3.9+","API key for LLM provider (OpenAI, Anthropic, etc.)","Seed data or templates to guide synthesis"],"input_types":["Seed LLMTestCase instances or templates","Generation prompts (optional; defaults provided)"],"output_types":["List of generated LLMTestCase instances"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_5","uri":"capability://code.generation.editing.custom.metric.implementation.with.geval.base.class","name":"custom metric implementation with geval base class","description":"Allows developers to define custom metrics by subclassing the Metric or GEval base class and implementing a measure() method. Custom metrics can use LLM-as-judge, statistical methods, or external APIs for scoring. The framework provides utilities for prompt templating, response parsing, and score normalization. Custom metrics integrate seamlessly with the evaluation pipeline and can be composed with built-in metrics.","intents":["I have a domain-specific evaluation criterion that isn't covered by built-in metrics; I want to implement it","I want to use my own LLM-as-judge prompt instead of the default templates","I need to integrate an external evaluation service (e.g., a proprietary scoring API) into my evaluation pipeline"],"best_for":["teams with specialized evaluation requirements","researchers implementing novel evaluation metrics","developers integrating proprietary evaluation services"],"limitations":["Custom metrics must implement the Metric interface; no automatic schema inference","Error handling is the responsibility of the metric implementer; framework provides limited debugging support","Custom metrics don't benefit from built-in caching unless explicitly implemented"],"requires":["Python 3.9+","Understanding of the Metric base class interface","For LLM-based metrics: API key for judge model provider"],"input_types":["LLMTestCase or ConversationalTestCase instances"],"output_types":["MetricResult (score: float 0-1, reason: string, success: bool)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_6","uri":"capability://automation.workflow.component.level.tracing.and.observability.with.observe.decorator","name":"component-level tracing and observability with @observe decorator","description":"Provides the @observe decorator to instrument individual functions within an LLM application, capturing inputs, outputs, and execution metadata as spans in a trace hierarchy. Traces are collected by the TraceManager and can be exported to OpenTelemetry or persisted to the Confident AI platform. Enables visibility into which components contribute to evaluation failures and supports production monitoring of LLM systems.","intents":["I want to trace which components in my LLM pipeline are causing low evaluation scores","I need to monitor my LLM application in production and correlate traces with evaluation metrics","I want to export traces to OpenTelemetry for integration with my observability stack"],"best_for":["teams building complex LLM systems with multiple components (retrieval, generation, ranking)","developers debugging evaluation failures by tracing component outputs","teams running LLM applications in production who need observability"],"limitations":["Tracing adds overhead (typically 10-50ms per span); not suitable for latency-critical applications without sampling","Trace storage is in-memory by default; requires Confident AI platform integration for persistence","OpenTelemetry export requires additional configuration; no built-in support for other observability platforms"],"requires":["Python 3.9+","Optional: Confident AI API key for trace persistence","Optional: OpenTelemetry SDK for exporting traces"],"input_types":["Function inputs (any type)"],"output_types":["Trace spans with metadata (function name, inputs, outputs, duration, status)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_7","uri":"capability://automation.workflow.pytest.plugin.integration.for.test.driven.llm.development","name":"pytest plugin integration for test-driven llm development","description":"Integrates with pytest to allow evaluation metrics to be run as test assertions using the @test_case decorator. Test cases are discovered and executed by pytest, enabling LLM evaluations to be part of the standard testing workflow. Supports pytest fixtures, parametrization, and reporting. Failed evaluations are reported as test failures with detailed metrics output.","intents":["I want to run my LLM evaluations as part of my pytest test suite","I need to fail CI/CD pipelines when evaluation metrics drop below thresholds","I want to use pytest parametrization to run the same evaluation across multiple test cases"],"best_for":["teams already using pytest for traditional software testing","developers integrating LLM evaluation into existing CI/CD pipelines","teams wanting test-driven development for LLM applications"],"limitations":["Pytest integration is limited to test discovery and reporting; advanced pytest features (fixtures, plugins) have limited support","Test results are reported as pass/fail; detailed metric scores require custom reporting","Parallel execution is limited by pytest's worker model; not as efficient as native parallel evaluation"],"requires":["Python 3.9+","pytest 7.0+","Test cases and metrics defined"],"input_types":["Pytest test functions decorated with @test_case"],"output_types":["Pytest test results (pass/fail) with metric details in output"],"categories":["automation-workflow","test-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_8","uri":"capability://data.processing.analysis.multi.turn.conversation.evaluation.with.turn.level.metrics","name":"multi-turn conversation evaluation with turn-level metrics","description":"Supports evaluation of multi-turn conversations through the ConversationalTestCase data structure, which captures conversation history with turn-level metadata. Metrics can be evaluated at the conversation level (overall coherence) or turn level (individual response quality). The conversation simulator can generate synthetic multi-turn conversations for testing dialogue systems.","intents":["I need to evaluate my chatbot's responses at each turn, not just the final output","I want to measure conversation coherence and track where quality degrades across turns","I need to generate synthetic multi-turn conversations to test my dialogue system"],"best_for":["teams building chatbots and conversational AI systems","developers evaluating dialogue quality across multiple turns","researchers benchmarking conversational models"],"limitations":["Turn-level metrics require more LLM calls than single-turn evaluation; significantly increases evaluation time and cost","Conversation context grows with each turn; very long conversations may exceed LLM context windows","Synthetic conversation generation may not capture realistic user behavior patterns"],"requires":["Python 3.9+","ConversationalTestCase instances with conversation history","For LLM-based metrics: API key for judge model provider"],"input_types":["ConversationalTestCase with conversation history (list of turns)"],"output_types":["MetricResult for conversation level or list of MetricResult for turn level"],"categories":["data-processing-analysis","evaluation-metrics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-deepeval__cap_9","uri":"capability://automation.workflow.confident.ai.platform.integration.for.test.run.persistence.and.comparison","name":"confident ai platform integration for test run persistence and comparison","description":"Integrates with the Confident AI platform to persist test runs, compare results across iterations, and track evaluation metrics over time. Test runs are uploaded with full metadata (model version, dataset version, timestamp) and can be queried via the platform dashboard. Enables regression detection and historical analysis of evaluation trends.","intents":["I want to track how my evaluation metrics change as I iterate on my LLM application","I need to detect when a new model version regresses on evaluation metrics","I want to compare evaluation results across different model versions or prompts"],"best_for":["teams running continuous evaluation in production","developers iterating on models and prompts with metric tracking","teams needing historical analysis of evaluation trends"],"limitations":["Requires Confident AI account and API key; adds external dependency","Test run persistence is asynchronous; results may not be immediately available in the platform","Platform features (comparison, regression detection) are limited to the Confident AI UI; no programmatic API for advanced analysis"],"requires":["Python 3.9+","Confident AI API key","Network access to Confident AI platform"],"input_types":["TestRunResult from evaluation execution"],"output_types":["Persisted test run in Confident AI platform with metadata and results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, etc.) OR local Ollama instance","Network access to LLM provider endpoints","For LLM-based metrics: API key for judge model provider","For NLP-based metrics: automatic download of model weights (e.g., BERT for embeddings)","Optional: API key for external safety services (OpenAI Moderation, etc.)","Optional: Local NLP models for offline evaluation","Base test cases to perturb","Metrics to evaluate robustness","Multiple prompt variants to compare"],"failure_modes":["LLM-as-judge metrics inherit the non-determinism of the underlying judge model; same input may produce different scores across runs","Requires API credentials for external LLM providers or local model setup; adds latency (typically 1-5 seconds per metric evaluation)","Caching system is in-memory by default; no built-in distributed cache for multi-process evaluation","Pre-built metrics assume English text; multilingual support is limited","Some metrics (e.g., hallucination detection) rely on LLM-as-judge and inherit judge model limitations","Metrics are optimized for text; limited support for multimodal evaluation (images, audio)","Guardrail effectiveness depends on the underlying detection model; no guardrail is 100% effective","External safety APIs (e.g., OpenAI Moderation) add latency and cost; local models are slower but cheaper","PII detection is language-specific and may miss domain-specific sensitive information","Automated adversarial generation may not cover all relevant perturbation types; manual specification is often necessary","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"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":"2026-05-03T15:20:18.280Z","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-deepeval","compare_url":"https://unfragile.ai/compare?artifact=pypi-deepeval"}},"signature":"M51nYGbRHFKWXDNPbxyH7bLnFKY19X4LPNxbWPbzGkotfFNhSYVqJMUY74wgASlzR2lVnEuDg1NkPa/KzuwACQ==","signedAt":"2026-06-19T19:58:41.652Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-deepeval","artifact":"https://unfragile.ai/pypi-deepeval","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-deepeval","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"}}