{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"promptfoo","slug":"promptfoo","name":"promptfoo","type":"cli","url":"https://github.com/promptfoo/promptfoo","page_url":"https://unfragile.ai/promptfoo","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"promptfoo__cap_0","uri":"capability://automation.workflow.multi.provider.prompt.evaluation.engine","name":"multi-provider prompt evaluation engine","description":"Executes the same prompt across multiple LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Ollama, local models) in parallel, collecting structured outputs with metadata (latency, token counts, cost). Uses a provider registry pattern with pluggable provider implementations that normalize API differences into a unified interface, enabling side-by-side comparison of model behavior on identical inputs.","intents":["Compare how different models respond to the same prompt to find the best fit for my use case","Benchmark model performance and cost across providers before committing to one","Test prompt changes against multiple models simultaneously to catch regressions"],"best_for":["teams evaluating which LLM provider to use for production","prompt engineers optimizing prompts across model families","organizations with multi-model strategies needing unified testing"],"limitations":["Parallel execution speed limited by slowest provider (no timeout per provider by default)","Cost accumulates across all providers — testing 10 prompts × 5 models = 50 API calls","Provider API rate limits may throttle concurrent requests; no built-in backoff strategy per provider"],"requires":["API keys for at least one provider (OpenAI, Anthropic, Google, AWS, etc.)","Node.js 18+ for CLI, or Node.js 16+ for library usage","Network access to provider APIs or local model endpoint (Ollama, LocalAI)"],"input_types":["prompt templates with variable substitution ({{variable}} syntax)","test cases as JSON/YAML with inputs and expected outputs","provider configuration objects specifying model, temperature, max_tokens"],"output_types":["structured evaluation results with model outputs, latency, token counts","cost breakdown per provider and test case","JSON/CSV export for further analysis"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_1","uri":"capability://safety.moderation.assertion.based.test.grading.with.custom.evaluators","name":"assertion-based test grading with custom evaluators","description":"Defines test assertions (exact match, similarity, regex, LLM-based grading) that automatically evaluate whether model outputs meet criteria. Supports custom evaluator functions (JavaScript, Python, HTTP webhooks) that receive the prompt, output, and test case metadata, returning a pass/fail score and optional details. Assertions are composable and can be chained to create complex evaluation logic without writing test harnesses.","intents":["Automatically grade model outputs against expected behavior without manual review","Define quality thresholds (e.g., 'response must contain keyword X' or 'similarity to reference > 0.8')","Use another LLM to evaluate subjective qualities like tone, accuracy, or helpfulness"],"best_for":["QA engineers building automated test suites for LLM applications","teams with domain-specific grading logic (e.g., SQL correctness, code compilation)","organizations needing reproducible, version-controlled evaluation criteria"],"limitations":["LLM-based graders add latency (~1-5s per test) and cost (additional API calls)","Custom evaluators must be synchronous; async operations require wrapping in promises","No built-in support for probabilistic or threshold-based grading (e.g., 'pass if 70% of evaluators agree')"],"requires":["Test configuration file (YAML/JSON) with assertions defined","For LLM graders: API key for grading model (OpenAI, Anthropic, etc.)","For custom evaluators: Node.js runtime or Python 3.7+ with script execution enabled"],"input_types":["assertion objects with type (exact-match, similarity, regex, llm-rubric, custom-function)","test case outputs from model evaluation","reference/expected outputs for comparison"],"output_types":["boolean pass/fail per assertion","numeric score (0-1) for similarity/rubric-based assertions","detailed failure reasons and assertion metadata"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_10","uri":"capability://data.processing.analysis.evaluation.result.persistence.and.historical.tracking","name":"evaluation result persistence and historical tracking","description":"Stores evaluation results in local SQLite database or cloud storage (AWS S3, Google Cloud Storage, etc.), enabling historical tracking of prompt quality over time. Results include full metadata (prompt, model, variables, outputs, scores, latency, cost). Enables trend analysis (e.g., 'pass rate improved 5% over last month') and regression detection by comparing against previous baselines.","intents":["Track how prompt quality changes over time as I iterate on prompts","Compare current evaluation results against historical baselines to detect regressions","Analyze trends in model performance, cost, and latency across multiple evaluations"],"best_for":["teams iterating on prompts over weeks/months and needing trend analysis","organizations with compliance requirements needing audit trails of prompt changes","teams using CI/CD pipelines requiring baseline comparison for regression detection"],"limitations":["Local SQLite storage requires manual backup; no built-in replication or disaster recovery","Cloud storage integration requires additional configuration (AWS credentials, bucket setup)","Large result sets (millions of test cases) may be slow to query and analyze","No built-in data retention policies; results accumulate indefinitely unless manually pruned"],"requires":["Local file system (for SQLite) or cloud storage credentials (AWS S3, GCS)","Sufficient disk space for storing evaluation results"],"input_types":["evaluation results JSON from promptfoo evaluator","metadata (timestamp, prompt version, model, etc.)"],"output_types":["stored evaluation results with full metadata","historical trend data (pass rate, latency, cost over time)","baseline results for regression detection"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_11","uri":"capability://tool.use.integration.aws.bedrock.and.cloud.provider.integration","name":"aws bedrock and cloud provider integration","description":"Provides native integration with AWS Bedrock (Claude, Llama, Mistral models), Google Vertex AI, Azure OpenAI, and other cloud providers. Handles authentication (IAM roles, API keys), model selection, and parameter mapping. Enables teams to test against cloud-hosted models without writing custom provider code. Supports streaming responses for real-time output evaluation.","intents":["Test against AWS Bedrock models (Claude, Llama, Mistral) without writing custom integration code","Evaluate Google Vertex AI and Azure OpenAI models alongside open-source alternatives","Use cloud provider authentication (IAM roles) instead of API keys for better security"],"best_for":["teams using AWS, Google Cloud, or Azure as primary cloud provider","organizations with existing cloud infrastructure and IAM policies","teams evaluating cloud-hosted models (Bedrock, Vertex) as alternatives to OpenAI"],"limitations":["Bedrock integration requires AWS account and model access; not available in all regions","Cloud provider authentication (IAM roles) requires proper AWS/GCP/Azure setup; API keys are simpler for testing","Streaming responses add complexity; not all models support streaming","Cost tracking for cloud providers may lag behind actual billing (depends on provider API)"],"requires":["AWS account with Bedrock access (for Bedrock provider)","AWS credentials (IAM role or API key) configured in environment","Model access enabled in AWS Bedrock console","For Vertex AI: Google Cloud project with Vertex AI API enabled","For Azure: Azure subscription with OpenAI service deployed"],"input_types":["provider configuration specifying cloud provider, model name, region","prompt and variables"],"output_types":["model output from cloud provider","latency and cost metrics","streaming responses (if supported)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_12","uri":"capability://tool.use.integration.python.and.node.js.script.provider.execution","name":"python and node.js script provider execution","description":"Executes Python scripts (3.7+) and Node.js scripts (18+) as providers, passing prompt and variables as command-line arguments or stdin. Scripts can implement arbitrary logic (e.g., calling local models, preprocessing inputs, routing to multiple models). Output is captured from stdout and parsed as JSON or plain text. Enables teams to test custom inference logic without modifying promptfoo.","intents":["Test custom inference logic (preprocessing, routing, fallback) without writing a custom provider","Integrate local models (Ollama, LLaMA.cpp) via Python/Node.js scripts","Test LLM applications that use multiple models or custom logic"],"best_for":["teams with custom inference logic that doesn't fit standard provider APIs","organizations using local models (Ollama, LLaMA.cpp) via Python/Node.js wrappers","projects requiring complex preprocessing or routing logic"],"limitations":["Script execution is synchronous; long-running scripts block evaluation","Scripts must handle their own error handling and logging","No built-in timeout for scripts; long-running scripts can hang evaluation","Scripts must be executable and have proper shebang lines (for shell scripts)"],"requires":["Python 3.7+ or Node.js 18+ installed and in PATH","Script file with proper permissions (executable)","Script must accept prompt and variables as arguments or stdin"],"input_types":["command-line arguments: prompt, variables (as JSON)","stdin: JSON object with prompt and variables"],"output_types":["stdout: model output (JSON or plain text)","exit code: 0 for success, non-zero for failure"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_13","uri":"capability://tool.use.integration.ollama.and.local.model.integration","name":"ollama and local model integration","description":"Provides native integration with Ollama (local LLM inference engine) and compatible local model servers (LLaMA.cpp, LocalAI). Connects to local HTTP endpoints, enabling teams to test open-source models (Llama, Mistral, etc.) without cloud API costs or latency. Supports model selection, parameter tuning, and streaming responses.","intents":["Test open-source models (Llama, Mistral, Phi) locally without cloud API costs","Evaluate models on private data without sending to external APIs","Benchmark local models against cloud providers to optimize cost-performance"],"best_for":["teams with privacy requirements preventing cloud API usage","organizations optimizing cost by using local open-source models","teams evaluating open-source models before committing to cloud providers"],"limitations":["Requires local hardware (GPU recommended) to run models; inference is slower than cloud providers","Model quality varies significantly; smaller models may not match cloud provider quality","Ollama setup and model download can be time-consuming (models are 5-50GB)","No built-in model management; teams must manually download and manage models"],"requires":["Ollama installed and running locally (or compatible server like LLaMA.cpp)","Model downloaded and available in Ollama (e.g., 'ollama pull llama2')","Local HTTP endpoint (default: http://localhost:11434)"],"input_types":["provider configuration specifying Ollama endpoint and model name","prompt and variables"],"output_types":["model output from local Ollama instance","latency metrics (local inference time)","no cost metrics (local execution)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_14","uri":"capability://search.retrieval.evaluation.result.filtering.and.search","name":"evaluation result filtering and search","description":"Provides CLI and web UI search/filtering capabilities to navigate large evaluation result sets. Supports filtering by test case name, provider, model, pass/fail status, and custom metadata. Search uses full-text indexing for fast queries. Enables teams to quickly find specific test cases or failure patterns without manually reviewing all results.","intents":["Find specific test cases or failure patterns in large evaluation result sets","Filter results by provider or model to compare specific comparisons","Search for test cases matching specific criteria (e.g., 'all failed tests')"],"best_for":["teams with large test suites (1000+ test cases) needing efficient result navigation","organizations analyzing evaluation results to identify patterns or trends","teams collaborating on prompt optimization and needing to share specific result subsets"],"limitations":["Full-text search requires indexing; large result sets may be slow to index","Search syntax is limited; no support for complex boolean queries","Filtering is performed in-memory; very large result sets may cause browser slowdown"],"requires":["Evaluation results stored in promptfoo database or JSON file","Web UI or CLI access to search/filter results"],"input_types":["evaluation results with metadata (test case name, provider, model, status)","search query or filter criteria"],"output_types":["filtered result subset matching search criteria","count of matching results"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_2","uri":"capability://safety.moderation.automated.red.team.vulnerability.scanning","name":"automated red-team vulnerability scanning","description":"Generates adversarial test cases using attack strategies (jailbreaks, prompt injection, prompt leaking, toxicity, bias) to probe LLM vulnerabilities. Uses a plugin-based attack provider system where each strategy (e.g., 'crescendo jailbreak', 'SQL injection') generates variations of inputs designed to trigger unsafe behavior. Results are graded using guardrails (safety checks) to identify which attacks succeeded, producing a vulnerability report.","intents":["Automatically discover security and safety vulnerabilities in my LLM application before production","Generate adversarial test cases to stress-test guardrails and safety measures","Identify which attack vectors (jailbreaks, injections, etc.) my model is vulnerable to"],"best_for":["security teams responsible for LLM application safety","teams building customer-facing LLM products needing pre-launch vulnerability assessment","organizations with compliance requirements (e.g., financial, healthcare) needing documented red-team results"],"limitations":["Attack generation is heuristic-based; may miss novel attack vectors not in strategy library","Grading relies on pattern matching or LLM-based detection; sophisticated attacks may evade detection","Running full red-team suite against all strategies can be expensive (100+ API calls per test case)","No guarantee that passing red-team tests means the system is safe — only that known attacks are mitigated"],"requires":["API key for attack provider (OpenAI, Anthropic, or local model via Ollama)","API key for grading/guardrails model (may be same as attack provider)","Red-team configuration specifying strategies, num_tests, and grading criteria"],"input_types":["base prompt or system prompt to attack","red-team configuration with strategy names and parameters","guardrail definitions (patterns, LLM-based checks) for grading"],"output_types":["list of generated adversarial test cases with attack strategy metadata","pass/fail results for each test case against guardrails","vulnerability report summarizing which strategies succeeded"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_3","uri":"capability://automation.workflow.test.configuration.and.variable.substitution","name":"test configuration and variable substitution","description":"Defines test suites as YAML/JSON files with templated prompts, test cases, and variables. Supports variable substitution using {{variable}} syntax, allowing a single prompt template to be tested against multiple input combinations. Test cases can include expected outputs, assertions, and metadata. Configuration is declarative and version-controllable, enabling teams to track prompt changes over time.","intents":["Define reusable prompt templates with variables so I can test multiple scenarios without duplicating prompts","Organize test cases in a human-readable format that non-technical stakeholders can understand","Version-control prompt configurations alongside code to track prompt evolution"],"best_for":["teams using version control (Git) to track prompt changes","organizations with non-technical stakeholders (product managers, domain experts) who need to review test cases","projects requiring reproducible, documented evaluation criteria"],"limitations":["YAML/JSON syntax can be verbose for complex test suites with many variables","No built-in support for conditional logic in test definitions (e.g., 'if model == GPT-4, use assertion X')","Large test suites (1000+ test cases) may be slow to parse and execute"],"requires":["YAML or JSON test configuration file","Prompt template file(s) with {{variable}} placeholders","Node.js 18+ to parse and execute configuration"],"input_types":["YAML/JSON test suite definition with tests array","prompt template files with {{variable}} syntax","variable values (strings, numbers, objects) to substitute"],"output_types":["expanded test cases with variables substituted","evaluation results per test case","summary statistics (pass rate, average latency, cost)"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_4","uri":"capability://automation.workflow.ci.cd.pipeline.integration.with.regression.detection","name":"ci/cd pipeline integration with regression detection","description":"Integrates evaluation results into CI/CD workflows via GitHub Actions, GitLab CI, or generic webhook triggers. Compares current evaluation results against baseline results to detect regressions (e.g., 'pass rate dropped from 95% to 90%'). Fails CI builds if regressions exceed configured thresholds, preventing degraded prompts from being merged. Results can be stored locally or uploaded to cloud storage for historical tracking.","intents":["Automatically run prompt tests on every commit to catch regressions before merging","Prevent prompt changes that degrade model quality from being deployed","Track prompt quality metrics over time to identify trends and improvements"],"best_for":["teams using Git-based workflows with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)","organizations with strict quality gates requiring automated testing before deployment","teams iterating rapidly on prompts and needing fast feedback loops"],"limitations":["Baseline comparison requires storing previous evaluation results; no built-in version control for baselines","Regression detection is threshold-based; may produce false positives if thresholds are too strict","CI/CD integration adds latency to build pipeline (evaluation time scales with number of tests and providers)","No built-in support for A/B testing or gradual rollout of prompt changes"],"requires":["CI/CD platform (GitHub Actions, GitLab CI, Jenkins, etc.) with webhook support","promptfoo CLI installed in CI environment (Node.js 18+)","Baseline evaluation results stored (locally or in cloud storage)","API keys for LLM providers available in CI environment (via secrets)"],"input_types":["test configuration and prompts from Git repository","baseline evaluation results (JSON) for comparison","regression thresholds (pass rate, latency, cost limits)"],"output_types":["CI build status (pass/fail) based on regression detection","detailed regression report (metrics that changed, by how much)","evaluation results artifact for historical tracking"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_5","uri":"capability://automation.workflow.web.based.results.viewer.and.comparison.ui","name":"web-based results viewer and comparison ui","description":"Provides a local web interface (React-based frontend) for visualizing evaluation results, filtering by test case or provider, and comparing model outputs side-by-side. Results can be shared via shareable URLs (with optional cloud storage backend) or self-hosted. The UI supports real-time updates when new evaluation results are available, and includes search/filtering to navigate large result sets.","intents":["Visually compare model outputs side-by-side to understand differences in quality and style","Filter and search evaluation results to find specific test cases or failure patterns","Share evaluation results with teammates or stakeholders via a shareable link"],"best_for":["teams collaborating on prompt optimization who need visual comparison tools","stakeholders (product managers, domain experts) reviewing evaluation results without CLI access","organizations sharing evaluation results with external partners or auditors"],"limitations":["Web UI requires local server or cloud deployment; no static HTML export","Shareable URLs require cloud backend (promptfoo cloud or self-hosted); local-only results cannot be easily shared","Real-time updates require WebSocket connection; may not work behind corporate proxies","Large result sets (10,000+ test cases) may be slow to load and filter in browser"],"requires":["Node.js 18+ to run web server","Modern web browser (Chrome, Firefox, Safari, Edge)","For sharing: cloud account (promptfoo cloud) or self-hosted deployment"],"input_types":["evaluation results JSON from promptfoo evaluator","test configuration metadata (prompt, variables, assertions)"],"output_types":["interactive HTML/React UI with side-by-side comparison","shareable URL (if cloud backend enabled)","filtered/searched result subsets"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_6","uri":"capability://tool.use.integration.provider.agnostic.http.and.script.execution","name":"provider-agnostic http and script execution","description":"Supports custom providers via HTTP endpoints (POST requests with prompt/variables, returns output) and script execution (Python, Node.js, shell scripts). Allows teams to test against proprietary models, internal APIs, or custom inference servers without modifying promptfoo code. Scripts receive prompt and variables as arguments, execute locally, and return output to be graded.","intents":["Test against proprietary or internal models not supported by promptfoo's built-in providers","Integrate custom inference servers or fine-tuned models into evaluation pipeline","Test LLM applications that use multiple models or custom logic (e.g., routing, fallback)"],"best_for":["teams with proprietary models or custom inference infrastructure","organizations using fine-tuned models deployed on internal servers","projects requiring integration with legacy systems or custom APIs"],"limitations":["HTTP provider requires running external server; no built-in server provided","Script execution is synchronous; long-running scripts block evaluation","No built-in error handling or retry logic for failed HTTP requests","Scripts must handle their own authentication and error reporting"],"requires":["For HTTP provider: running HTTP server that accepts POST requests","For script provider: Python 3.7+, Node.js 18+, or shell interpreter","Provider configuration specifying endpoint URL or script path"],"input_types":["HTTP POST request with JSON body containing prompt and variables","command-line arguments passed to script (prompt, variables as JSON)"],"output_types":["HTTP response with JSON body containing model output","script stdout containing model output (JSON or plain text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_7","uri":"capability://data.processing.analysis.cost.and.latency.tracking.across.providers","name":"cost and latency tracking across providers","description":"Automatically tracks API costs per provider using model-specific pricing tables (OpenAI, Anthropic, Google, AWS, etc.), and measures latency for each API call. Aggregates costs and latency by provider, test case, and overall suite. Enables cost-benefit analysis (e.g., 'GPT-4 is 10x more expensive but only 5% more accurate'). Pricing tables are updated with each release to reflect current API costs.","intents":["Compare cost-effectiveness of different models to optimize spending","Identify which test cases or providers are most expensive","Track latency to ensure models meet performance requirements"],"best_for":["teams optimizing LLM spend across multiple providers","organizations with strict cost budgets needing ROI analysis","teams evaluating models for production where latency is critical"],"limitations":["Cost tracking relies on model-specific pricing tables; custom pricing (e.g., enterprise discounts) not supported","Latency includes network overhead; does not isolate model inference time","Pricing tables may lag behind actual provider price changes (updated per release, not real-time)","No built-in cost forecasting or budget alerts"],"requires":["Provider API keys to enable cost tracking","Model names must match promptfoo's pricing table (e.g., 'gpt-4-turbo', 'claude-3-opus')"],"input_types":["evaluation results with token counts and latency per API call","provider and model names"],"output_types":["cost breakdown per provider, test case, and overall suite","latency statistics (min, max, average, p95)","cost-per-test and cost-per-token metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_8","uri":"capability://text.generation.language.prompt.template.processing.with.variable.expansion","name":"prompt template processing with variable expansion","description":"Processes prompt templates with {{variable}} syntax, supporting variable substitution, array expansion (cartesian product of multiple variable values), and nested variable references. Allows a single prompt template to generate multiple test cases by expanding variables. Supports both simple string substitution and complex variable structures (objects, arrays).","intents":["Create reusable prompt templates that can be tested against multiple input combinations without duplication","Generate test cases by expanding variables (e.g., test 10 different user queries with same prompt)","Support nested or complex variable structures in prompts"],"best_for":["teams with many similar test cases that differ only in variable values","prompt engineers creating parameterized prompt templates","organizations testing prompts against diverse input scenarios"],"limitations":["Variable expansion uses cartesian product; large variable sets can generate exponential test cases","No built-in support for conditional variable substitution (e.g., 'use variable X if condition Y')","Complex nested variables may be difficult to debug if substitution fails"],"requires":["Prompt template with {{variable}} syntax","Test configuration specifying variable values"],"input_types":["prompt template string with {{variable}} placeholders","variable values (strings, numbers, objects, arrays)"],"output_types":["expanded prompt strings with variables substituted","list of generated test cases (one per variable combination)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__cap_9","uri":"capability://safety.moderation.llm.based.grading.with.custom.rubrics","name":"llm-based grading with custom rubrics","description":"Uses another LLM (OpenAI, Anthropic, Google, etc.) to grade model outputs against custom rubrics. Rubrics are defined as text descriptions of evaluation criteria (e.g., 'Is the response accurate? Is it helpful? Is it concise?'). The grading LLM receives the prompt, output, and rubric, and returns a score (0-1) and reasoning. Enables subjective quality evaluation without manual review.","intents":["Automatically evaluate subjective qualities (tone, accuracy, helpfulness) without manual review","Use domain-specific rubrics to grade outputs according to custom criteria","Scale evaluation to large test suites by automating subjective grading"],"best_for":["teams evaluating subjective qualities (tone, style, accuracy) that can't be checked with regex","organizations with domain-specific grading criteria (e.g., medical accuracy, legal compliance)","projects needing to scale evaluation beyond manual review"],"limitations":["LLM-based grading adds latency (~1-5s per test) and cost (additional API calls)","Grading quality depends on rubric clarity and grading model capability; vague rubrics produce inconsistent results","No built-in inter-rater reliability measurement (e.g., agreement between multiple grading models)","Grading results may vary between runs due to LLM non-determinism"],"requires":["API key for grading model (OpenAI, Anthropic, Google, etc.)","Custom rubric text describing evaluation criteria"],"input_types":["prompt (for context)","model output to grade","custom rubric text describing evaluation criteria"],"output_types":["numeric score (0-1)","reasoning/explanation from grading LLM","pass/fail based on score threshold"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"promptfoo__headline","uri":"capability://testing.quality.llm.evaluation.and.red.teaming.toolkit","name":"llm evaluation and red-teaming toolkit","description":"Promptfoo is an open-source CLI and library designed for testing and evaluating LLM prompts, enabling users to run structured test suites, compare outputs across models, and integrate evaluations into CI/CD pipelines.","intents":["best LLM evaluation tool","red teaming toolkit for LLMs","how to test LLM prompts","evaluate AI model responses","automated testing for language models"],"best_for":["developers testing LLMs","teams integrating AI in CI/CD"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one provider (OpenAI, Anthropic, Google, AWS, etc.)","Node.js 18+ for CLI, or Node.js 16+ for library usage","Network access to provider APIs or local model endpoint (Ollama, LocalAI)","Test configuration file (YAML/JSON) with assertions defined","For LLM graders: API key for grading model (OpenAI, Anthropic, etc.)","For custom evaluators: Node.js runtime or Python 3.7+ with script execution enabled","Local file system (for SQLite) or cloud storage credentials (AWS S3, GCS)","Sufficient disk space for storing evaluation results","AWS account with Bedrock access (for Bedrock provider)","AWS credentials (IAM role or API key) configured in environment"],"failure_modes":["Parallel execution speed limited by slowest provider (no timeout per provider by default)","Cost accumulates across all providers — testing 10 prompts × 5 models = 50 API calls","Provider API rate limits may throttle concurrent requests; no built-in backoff strategy per provider","LLM-based graders add latency (~1-5s per test) and cost (additional API calls)","Custom evaluators must be synchronous; async operations require wrapping in promises","No built-in support for probabilistic or threshold-based grading (e.g., 'pass if 70% of evaluators agree')","Local SQLite storage requires manual backup; no built-in replication or disaster recovery","Cloud storage integration requires additional configuration (AWS credentials, bucket setup)","Large result sets (millions of test cases) may be slow to query and analyze","No built-in data retention policies; results accumulate indefinitely unless manually pruned","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":null,"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=promptfoo","compare_url":"https://unfragile.ai/compare?artifact=promptfoo"}},"signature":"+Kf3UXDAH1TffT1gb5SvtuQ9F9n5SjUuTZfa5bMYHpd+Cqfmaar9CGTJ+NjQkhcG1EhDDdV8Zaw5SbgsbebjAA==","signedAt":"2026-06-21T18:41:21.934Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/promptfoo","artifact":"https://unfragile.ai/promptfoo","verify":"https://unfragile.ai/api/v1/verify?slug=promptfoo","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"}}