{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-promptfoo--promptfoo","slug":"promptfoo--promptfoo","name":"promptfoo","type":"cli","url":"https://promptfoo.dev","page_url":"https://unfragile.ai/promptfoo--promptfoo","categories":["testing-quality"],"tags":["ci","ci-cd","cicd","evaluation","evaluation-framework","llm","llm-eval","llm-evaluation","llm-evaluation-framework","llmops","pentesting","prompt-engineering","prompt-testing","prompts","rag","red-teaming","testing","vulnerability-scanners"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-promptfoo--promptfoo__cap_0","uri":"capability://automation.workflow.declarative.test.suite.configuration.and.execution","name":"declarative test suite configuration and execution","description":"Executes structured test suites defined in YAML/JSON config files against LLM prompts, agents, and RAG systems. The evaluator engine (src/evaluator.ts) parses test configurations containing prompts, variables, assertions, and expected outputs, then orchestrates parallel execution across multiple test cases with result aggregation and reporting. Supports dynamic variable substitution, conditional assertions, and multi-step test chains.","intents":["I want to define a set of test cases for my prompt in a simple config file and run them all at once","I need to test my LLM application with different input variables and verify outputs against expected results","I want to run the same test suite repeatedly as part of my development workflow"],"best_for":["prompt engineers and LLM application developers building repeatable test suites","teams integrating LLM evaluation into CI/CD pipelines","developers comparing prompt variations systematically"],"limitations":["Config-driven approach requires upfront test definition; dynamic test generation not built-in","Test execution is sequential by default within a suite; parallel execution across suites requires external orchestration","No built-in persistence of test history — requires external database for trend analysis"],"requires":["Node.js 18+","YAML or JSON config file with test definitions","API keys for target LLM providers (OpenAI, Anthropic, etc.)"],"input_types":["YAML/JSON configuration files","prompt templates with variable placeholders","test case definitions with inputs and expected outputs"],"output_types":["structured test results (pass/fail per test case)","aggregated metrics (success rate, latency)","detailed logs with model responses and assertion details"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_1","uri":"capability://data.processing.analysis.multi.provider.model.comparison.and.benchmarking","name":"multi-provider model comparison and benchmarking","description":"Executes identical test suites against multiple LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Ollama, etc.) and generates side-by-side comparison reports. The provider system (src/providers/) implements a unified interface with provider-specific adapters that handle authentication, request formatting, and response normalization. Results are aggregated with metrics like latency, cost, and quality scores to enable direct model comparison.","intents":["I want to test the same prompt against GPT-4, Claude, and Gemini to see which performs best","I need to compare response quality and latency across different models for cost-benefit analysis","I want to benchmark a new model release against our current production model"],"best_for":["teams evaluating multiple LLM providers for production deployment","researchers comparing model capabilities across vendors","cost-conscious teams optimizing model selection for their use case"],"limitations":["Requires valid API keys for each provider being compared; no free tier aggregation","Response format normalization may lose provider-specific features (e.g., tool use metadata)","Latency measurements include network overhead; not suitable for sub-millisecond precision benchmarking","Cost comparison requires up-to-date pricing data; manual updates needed when providers change rates"],"requires":["API keys for each provider (OpenAI, Anthropic, Google Cloud, AWS, etc.)","Network connectivity to provider endpoints","Provider configuration in promptfoo config file"],"input_types":["test suite configuration with provider list","prompts and test cases (identical across providers)","provider-specific credentials and parameters"],"output_types":["comparison matrix (model vs metric)","response samples from each provider","aggregated metrics (latency, cost, quality scores)","HTML/JSON reports with side-by-side visualization"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_10","uri":"capability://data.processing.analysis.streaming.response.handling.and.token.level.evaluation","name":"streaming response handling and token-level evaluation","description":"Supports streaming responses from LLM providers and enables token-level evaluation via callbacks that process partial responses as they arrive. The provider system handles streaming protocol differences (Server-Sent Events for OpenAI, event streams for Anthropic) and normalizes them into a unified callback interface. Enables measuring time-to-first-token, streaming latency, and token-level quality metrics.","intents":["I want to measure time-to-first-token for my LLM to optimize user experience","I need to evaluate response quality at different token counts (e.g., first 100 tokens vs full response)","I want to detect if my model starts generating incorrect content early in the response"],"best_for":["teams optimizing user-facing LLM applications for latency perception","researchers studying streaming behavior and token-level quality","developers implementing early-stopping or response truncation logic"],"limitations":["Streaming evaluation adds complexity; not all graders support partial responses","Token-level metrics are provider-specific; token boundaries may differ across models","Streaming latency measurements include network jitter; not suitable for precise benchmarking","Callback-based evaluation requires custom grader implementation; no built-in token-level assertions"],"requires":["LLM provider with streaming support (OpenAI, Anthropic, etc.)","Custom grader function that processes streaming callbacks"],"input_types":["streaming response stream (Server-Sent Events or similar)","callback function to process tokens as they arrive"],"output_types":["time-to-first-token metric","token-level quality scores","partial response evaluations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_11","uri":"capability://text.generation.language.dynamic.prompt.templating.with.variable.substitution.and.conditional.logic","name":"dynamic prompt templating with variable substitution and conditional logic","description":"Supports parameterized prompts with variable substitution, conditional blocks, and computed values. The prompt processor (Utilities and Output Generation in DeepWiki) parses template syntax (e.g., `{{variable}}`, `{{#if condition}}...{{/if}}`) and substitutes values from test case inputs or computed expressions. Enables testing prompt variations without duplicating test cases.","intents":["I want to test my prompt with different user inputs without writing separate test cases","I need to conditionally include parts of my prompt based on input parameters","I want to compute derived values (e.g., current date) and inject them into prompts"],"best_for":["prompt engineers testing prompt variations systematically","developers building parameterized prompt templates","teams testing conditional logic in prompts (e.g., different instructions for different user types)"],"limitations":["Template syntax is limited; complex logic should be in custom graders, not prompts","Variable substitution is text-based; no type safety or validation","Computed values require custom functions; no built-in expression language","Template errors may be hard to debug; no syntax validation before execution"],"requires":["Prompt template with variable placeholders","Test case inputs matching variable names","Optional: custom functions for computed values"],"input_types":["prompt template string with {{variable}} syntax","test case inputs (object with variable values)"],"output_types":["rendered prompt with variables substituted","error messages if variables are missing"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_12","uri":"capability://data.processing.analysis.json.schema.validation.and.structured.output.grading","name":"json schema validation and structured output grading","description":"Validates LLM outputs against JSON schemas and grades structured outputs (JSON, YAML) for format compliance and content correctness. The assertion system supports JSON schema validation (via ajv library) and enables grading both schema compliance and semantic content. Supports extracting values from structured outputs for further evaluation.","intents":["I want to ensure my LLM always returns valid JSON matching my expected schema","I need to grade both format correctness and content quality of structured outputs","I want to extract specific fields from JSON responses and evaluate them separately"],"best_for":["teams building LLM APIs that return structured data","developers validating function calling outputs and tool responses","researchers evaluating structured generation tasks (JSON, YAML, etc.)"],"limitations":["JSON schema validation is strict; may fail on minor formatting differences","Schema compliance doesn't guarantee semantic correctness; content still needs grading","Extracted values are text-based; no type coercion or validation","Large schemas may be slow to validate; no schema optimization or caching"],"requires":["JSON schema definition (JSON Schema format)","LLM output in JSON or YAML format"],"input_types":["JSON schema (JSON Schema Draft 7 or later)","LLM output (JSON or YAML string)"],"output_types":["schema validation pass/fail","validation errors (if schema doesn't match)","extracted field values (for further grading)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_13","uri":"capability://data.processing.analysis.cost.estimation.and.token.counting.across.providers","name":"cost estimation and token counting across providers","description":"Estimates API costs for evaluation runs by tracking token usage (input/output tokens) and applying provider-specific pricing. The evaluator aggregates token counts across test cases and providers, then multiplies by current pricing to estimate total cost. Supports both fixed pricing (per-token) and dynamic pricing (e.g., cached tokens in Claude). Enables cost-aware evaluation planning.","intents":["I want to estimate how much my evaluation will cost before running it","I need to track API spending across multiple providers and models","I want to optimize my test suite to reduce evaluation costs"],"best_for":["teams managing LLM API budgets and cost optimization","researchers comparing cost-effectiveness of different models","organizations evaluating large test suites with cost constraints"],"limitations":["Cost estimates are based on published pricing; actual charges may differ due to discounts or usage tiers","Token counting is provider-specific; estimates may be inaccurate if tokenizers differ","Pricing data must be manually updated when providers change rates; no automatic price feed","Cost tracking doesn't account for cached tokens or other provider-specific optimizations"],"requires":["Provider pricing data (configured in promptfoo)","Token counts from LLM provider responses"],"input_types":["test suite configuration with provider list","token usage from provider responses"],"output_types":["cost estimate per test case","total cost for evaluation run","cost breakdown by provider and model"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_2","uri":"capability://safety.moderation.automated.red.team.vulnerability.scanning.and.attack.generation","name":"automated red-team vulnerability scanning and attack generation","description":"Generates adversarial test cases and attack prompts to identify security, safety, and alignment vulnerabilities in LLM applications. The red team system (Red Team Architecture in DeepWiki) uses a plugin-based attack strategy framework with built-in strategies (jailbreak, prompt injection, PII extraction, etc.) and integrates with attack providers that generate targeted adversarial inputs. Results are graded against safety criteria to identify failure modes.","intents":["I want to automatically find security vulnerabilities in my LLM application before deploying to production","I need to test if my chatbot can be jailbroken or manipulated into unsafe behavior","I want to verify my RAG system doesn't leak sensitive information from the knowledge base"],"best_for":["security teams performing LLM pentesting and vulnerability assessment","AI safety researchers studying model robustness and alignment","product teams validating guardrails before production release"],"limitations":["Attack generation is heuristic-based; may miss novel attack vectors not covered by built-in strategies","Requires defining grading criteria for what constitutes a 'failure'; subjective safety judgments need manual review","Red team scans can be expensive (many API calls to generate and evaluate attacks); costs scale with attack count","Results are probabilistic; a single red team run may not find all vulnerabilities"],"requires":["Target LLM application accessible via API or CLI","Grading function or safety classifier to evaluate responses","API keys for attack providers (typically the same LLM being tested)","Defined attack strategies or custom plugin implementations"],"input_types":["target application configuration (prompt, system message, tools)","attack strategy definitions (jailbreak, injection, extraction, etc.)","grading criteria and safety thresholds","optional: custom attack plugins"],"output_types":["list of successful attacks (prompts that triggered unsafe behavior)","failure analysis with categorization (jailbreak, injection, etc.)","vulnerability report with severity scoring","remediation suggestions"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_3","uri":"capability://data.processing.analysis.assertion.based.output.grading.and.evaluation.metrics","name":"assertion-based output grading and evaluation metrics","description":"Evaluates LLM outputs against multiple assertion types (exact match, regex, similarity, custom functions, LLM-based graders) and computes aggregated quality metrics. The assertions system (Assertions and Grading in DeepWiki) supports deterministic checks (string matching, JSON schema validation) and probabilistic graders (semantic similarity, LLM-as-judge). Results are scored and aggregated to produce pass/fail verdicts and quality percentages per test case.","intents":["I want to automatically check if my LLM output matches expected content or format","I need to grade responses using semantic similarity rather than exact string matching","I want to use another LLM as a judge to evaluate quality of generated content"],"best_for":["developers building automated evaluation pipelines for LLM outputs","teams that need both deterministic and probabilistic grading criteria","researchers measuring LLM quality across multiple dimensions"],"limitations":["LLM-based graders add latency and cost; not suitable for real-time evaluation","Semantic similarity metrics (cosine distance, BLEU) may not capture domain-specific quality criteria","Custom grader functions require JavaScript/TypeScript; no Python grader support in core","Assertion results are binary (pass/fail); no fine-grained scoring without custom graders"],"requires":["Expected output definitions or grading criteria","For LLM graders: API key for grading model","For custom graders: JavaScript/TypeScript function implementation"],"input_types":["actual LLM output (text, JSON, structured data)","expected output or reference text","assertion type specification (exact, regex, similarity, custom)","grading function code (optional)"],"output_types":["pass/fail verdict per assertion","quality score (0-1 range)","detailed assertion results with explanation","aggregated metrics across test cases"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_4","uri":"capability://automation.workflow.ci.cd.pipeline.integration.with.automated.test.gating","name":"ci/cd pipeline integration with automated test gating","description":"Integrates LLM evaluation into continuous integration workflows via CLI commands, GitHub Actions, and exit code-based test gating. The CLI system (CLI Architecture in DeepWiki) provides `promptfoo eval` command that runs test suites and returns exit codes indicating pass/fail status. Results can be compared against baseline metrics to gate deployments; integration with version control enables tracking evaluation history per commit.","intents":["I want to automatically run my LLM tests on every commit and block merges if quality degrades","I need to track how prompt changes affect evaluation metrics over time in my CI pipeline","I want to set up a GitHub Action that runs red team scans on pull requests"],"best_for":["teams practicing continuous deployment of LLM applications","organizations requiring automated quality gates before production release","developers integrating LLM evaluation into existing CI/CD workflows (GitHub Actions, GitLab CI, Jenkins)"],"limitations":["Exit code gating is binary (pass/fail); no gradual rollout or canary deployment support","Baseline comparison requires manual setup; no automatic baseline detection from previous runs","CI/CD integration adds latency to build pipelines; evaluation time scales with test suite size","Shared API quotas across CI runs can cause rate limiting; requires careful quota management"],"requires":["CI/CD platform with shell command execution (GitHub Actions, GitLab CI, Jenkins, etc.)","API keys for LLM providers available as CI secrets","promptfoo CLI installed in CI environment","Test configuration file committed to repository"],"input_types":["test configuration file (YAML/JSON)","baseline metrics file (optional, for comparison)","environment variables with API keys and provider config"],"output_types":["exit code (0 for pass, non-zero for fail)","test results JSON/HTML report","comparison report vs baseline (if provided)","CI log output with test summary"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_5","uri":"capability://data.processing.analysis.web.based.results.visualization.and.interactive.exploration","name":"web-based results visualization and interactive exploration","description":"Provides a local web UI (Web Interface in DeepWiki) for exploring evaluation results with interactive filtering, search, and side-by-side comparison views. The frontend (React-based state management) loads test results and enables filtering by provider, assertion type, or test case; the backend server (Backend Server in DeepWiki) serves results and handles real-time updates. Results can be shared via shareable URLs or self-hosted deployments.","intents":["I want to visually compare responses from different models for a specific test case","I need to filter test results by provider or assertion type to find patterns in failures","I want to share evaluation results with my team without exposing API keys or raw data"],"best_for":["teams reviewing evaluation results collaboratively","non-technical stakeholders (product managers, safety reviewers) exploring test results","developers debugging specific test failures with interactive exploration"],"limitations":["Web UI requires local server; no offline viewing of results","Sharing results requires either cloud integration or self-hosted deployment; no simple file-based sharing","Large result sets (1000+ test cases) may have performance issues in browser","Real-time updates require WebSocket connection; not suitable for static result archives"],"requires":["Node.js 18+ for running web server","Test results in promptfoo JSON format","Modern web browser (Chrome, Firefox, Safari, Edge)"],"input_types":["test results JSON files","evaluation metadata (provider names, assertion types, timestamps)"],"output_types":["interactive HTML dashboard","shareable URLs (if cloud integration enabled)","exported result summaries (PDF, JSON)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_6","uri":"capability://tool.use.integration.provider.agnostic.http.api.integration.for.custom.models","name":"provider-agnostic http api integration for custom models","description":"Supports evaluating custom or self-hosted LLM models via HTTP provider abstraction that accepts arbitrary OpenAI-compatible or custom API endpoints. The HTTP provider (HTTP Provider in DeepWiki) handles request/response transformation, enabling integration of models not natively supported by promptfoo (e.g., local Ollama instances, private fine-tuned models, or proprietary APIs). Supports custom request/response mapping via configuration.","intents":["I want to evaluate my locally-hosted Ollama model using the same test suite as cloud models","I need to test a proprietary internal LLM API that's not supported by promptfoo natively","I want to compare my fine-tuned model against OpenAI and Anthropic models"],"best_for":["teams running self-hosted or on-premise LLM deployments","organizations with proprietary model APIs requiring custom integration","researchers comparing custom models against commercial baselines"],"limitations":["HTTP provider requires manual request/response mapping; no automatic schema detection","Custom models may not support all features (streaming, function calling, vision) that cloud models provide","Latency includes network overhead to custom endpoint; not suitable for benchmarking inference speed","No built-in retry logic or circuit breaker for unreliable custom endpoints"],"requires":["HTTP-accessible LLM endpoint (local or remote)","Custom endpoint URL and authentication credentials (if required)","Request/response schema mapping in config (for non-OpenAI-compatible endpoints)"],"input_types":["HTTP endpoint URL","custom request template (JSON with variable placeholders)","response parsing rules (JSON path or regex)"],"output_types":["normalized response format compatible with promptfoo graders","latency and error metrics for custom endpoint"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_7","uri":"capability://tool.use.integration.python.and.shell.script.provider.execution.for.custom.evaluation.logic","name":"python and shell script provider execution for custom evaluation logic","description":"Executes Python scripts or shell commands as LLM providers, enabling integration of custom models, local inference engines, or complex evaluation pipelines. The Python/Script providers (Python and Script Providers in DeepWiki) spawn subprocesses that receive test inputs via stdin/arguments and return outputs via stdout. Supports arbitrary custom logic without requiring native API integration.","intents":["I want to evaluate my custom Python model that doesn't have an HTTP API","I need to test a complex pipeline (retrieval + ranking + generation) as a single provider","I want to integrate my local Hugging Face model into promptfoo evaluation"],"best_for":["researchers and developers with custom Python models or inference code","teams with complex multi-step pipelines that need to be evaluated as a unit","organizations evaluating models that don't expose HTTP APIs"],"limitations":["Subprocess overhead adds latency per test case; slower than native API calls","Script providers require managing dependencies (Python packages, system libraries) in execution environment","No built-in streaming support; scripts must return complete response before promptfoo continues","Error handling is basic; script failures may not provide clear error messages"],"requires":["Python 3.9+ (for Python provider) or shell interpreter (for script provider)","Script file with entry point that accepts input and returns output","All dependencies installed in execution environment"],"input_types":["test input (passed via stdin or command-line arguments)","script path and optional arguments"],"output_types":["script stdout (expected to be model response text)","exit code (0 for success, non-zero for error)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_8","uri":"capability://data.processing.analysis.test.result.persistence.and.historical.comparison","name":"test result persistence and historical comparison","description":"Stores evaluation results in a local database (SQLite by default) and enables comparing current test runs against historical baselines to detect quality regressions. The data models and persistence layer (Data Models and Persistence in DeepWiki) serialize test results with metadata (timestamp, provider, config hash) enabling trend analysis. Supports querying results by date range, provider, or test case to identify when quality degraded.","intents":["I want to track how my prompt quality changes over time as I iterate on it","I need to detect when a model update caused my evaluation metrics to degrade","I want to compare results from today against last week to see if my changes helped"],"best_for":["teams iterating on prompts and needing to track quality trends","organizations monitoring model performance over time","developers detecting regressions caused by prompt or config changes"],"limitations":["No built-in cloud sync; results stored locally and not automatically backed up","Historical comparison requires manual baseline selection; no automatic 'previous best' detection","Database schema is internal; no documented API for querying results programmatically","Large result archives (years of data) may slow down UI performance"],"requires":["Local file system with write access for SQLite database","Consistent test configuration across runs (config hash used for matching)"],"input_types":["test results from evaluation runs","metadata (timestamp, provider, config)"],"output_types":["historical result records","trend analysis (quality over time)","regression detection (current vs baseline)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-promptfoo--promptfoo__cap_9","uri":"capability://tool.use.integration.aws.bedrock.and.cloud.provider.integration.with.unified.authentication","name":"aws bedrock and cloud provider integration with unified authentication","description":"Integrates AWS Bedrock models (Claude, Llama, Mistral, etc.) via unified provider interface with automatic credential handling via AWS SDK. The Bedrock provider (AWS Bedrock Integration in DeepWiki) handles model invocation, streaming, and response parsing. Supports both on-demand and provisioned throughput models with cost tracking. Extends to other cloud providers (Google Vertex AI, Azure OpenAI) via similar adapter patterns.","intents":["I want to evaluate Claude models via AWS Bedrock instead of the Anthropic API","I need to test models available only through cloud provider marketplaces (Bedrock, Vertex AI)","I want to use provisioned throughput for cost optimization in my evaluation pipeline"],"best_for":["AWS-native organizations already using Bedrock for production","teams evaluating models available only through cloud marketplaces","cost-conscious teams using provisioned throughput for predictable pricing"],"limitations":["Requires AWS credentials and IAM permissions; adds authentication complexity vs direct API keys","Bedrock model availability varies by region; may require multi-region setup for all models","Provisioned throughput requires advance capacity planning; not suitable for ad-hoc evaluation","Cost tracking is approximate; actual charges depend on AWS billing details"],"requires":["AWS account with Bedrock access","AWS credentials (IAM user or role with bedrock:InvokeModel permission)","AWS SDK configured in environment or via credentials file"],"input_types":["Bedrock model ID (e.g., 'anthropic.claude-3-sonnet-20240229-v1:0')","prompt and parameters"],"output_types":["model response text","usage metrics (input/output tokens)","cost estimate based on token counts"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","YAML or JSON config file with test definitions","API keys for target LLM providers (OpenAI, Anthropic, etc.)","API keys for each provider (OpenAI, Anthropic, Google Cloud, AWS, etc.)","Network connectivity to provider endpoints","Provider configuration in promptfoo config file","LLM provider with streaming support (OpenAI, Anthropic, etc.)","Custom grader function that processes streaming callbacks","Prompt template with variable placeholders","Test case inputs matching variable names"],"failure_modes":["Config-driven approach requires upfront test definition; dynamic test generation not built-in","Test execution is sequential by default within a suite; parallel execution across suites requires external orchestration","No built-in persistence of test history — requires external database for trend analysis","Requires valid API keys for each provider being compared; no free tier aggregation","Response format normalization may lose provider-specific features (e.g., tool use metadata)","Latency measurements include network overhead; not suitable for sub-millisecond precision benchmarking","Cost comparison requires up-to-date pricing data; manual updates needed when providers change rates","Streaming evaluation adds complexity; not all graders support partial responses","Token-level metrics are provider-specific; token boundaries may differ across models","Streaming latency measurements include network jitter; not suitable for precise benchmarking","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7318551666915629,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:59:50.673Z","last_commit":"2026-05-03T04:06:41Z"},"community":{"stars":20817,"forks":1803,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=promptfoo--promptfoo","compare_url":"https://unfragile.ai/compare?artifact=promptfoo--promptfoo"}},"signature":"NOMK/VKke77zPINf4f2R62yY3LXC6x59dU7ljjBsiCGq31Bct1hbEJOwmPhuPsTApX+iFw3XMFBkJB5ixTLDDg==","signedAt":"2026-06-20T19:49:24.208Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/promptfoo--promptfoo","artifact":"https://unfragile.ai/promptfoo--promptfoo","verify":"https://unfragile.ai/api/v1/verify?slug=promptfoo--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"}}