{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"baserun","slug":"baserun","name":"Baserun","type":"product","url":"https://www.baserun.ai","page_url":"https://unfragile.ai/baserun","categories":["testing-quality","observability"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"baserun__cap_0","uri":"capability://automation.workflow.end.to.end.request.tracing.with.llm.specific.context.capture","name":"end-to-end request tracing with llm-specific context capture","description":"Automatically captures complete execution traces for LLM application requests, including prompt inputs, model outputs, token counts, latency metrics, and intermediate steps across multiple API calls. Uses instrumentation hooks at the SDK level to intercept LLM provider calls (OpenAI, Anthropic, etc.) and structured logging to correlate related operations into unified traces without requiring manual span creation.","intents":["I need to see exactly what prompts were sent to the model and what responses came back for every production request","I want to understand the full execution path of a multi-step LLM workflow including all API calls and their timing","I need to debug why a specific user's request produced an unexpected LLM output by reviewing the complete trace"],"best_for":["LLM application developers building production systems with complex multi-step workflows","teams debugging unexpected model behavior in production","engineers optimizing token usage and latency across LLM chains"],"limitations":["Trace capture requires SDK integration — applications using raw HTTP calls without Baserun SDK will not be automatically instrumented","Trace retention and query performance may degrade with very high-volume applications (>100k requests/day) depending on plan tier","Custom middleware or non-standard LLM provider integrations may require manual instrumentation"],"requires":["Baserun SDK installed (Python, Node.js, or language-specific wrapper)","API key from Baserun dashboard","LLM provider API keys (OpenAI, Anthropic, etc.) already configured in application"],"input_types":["LLM API requests (prompts, parameters, model selection)","Application code execution context","LLM provider responses"],"output_types":["structured trace objects with hierarchical span data","JSON-serialized execution logs","trace visualization in Baserun dashboard"],"categories":["automation-workflow","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_1","uri":"capability://data.processing.analysis.automated.evaluation.framework.with.custom.function.support","name":"automated evaluation framework with custom function support","description":"Executes user-defined evaluation functions against LLM outputs to measure quality, correctness, and safety. Supports both deterministic checks (exact match, regex, schema validation) and LLM-based evaluations (using another model to judge outputs). Evaluations run asynchronously on captured traces and can be parameterized with custom scoring logic, thresholds, and aggregation rules.","intents":["I want to automatically score whether LLM outputs meet my quality standards using custom business logic","I need to run semantic similarity checks or fact-checking evaluations against model outputs without writing infrastructure","I want to define pass/fail criteria for outputs and track evaluation metrics over time"],"best_for":["teams building LLM products who need continuous quality measurement without manual review","developers implementing custom evaluation logic specific to their domain (e.g., medical accuracy, legal compliance)","organizations tracking LLM performance regressions across model versions"],"limitations":["LLM-based evaluations add latency and cost (requires additional API calls to evaluation model)","Custom evaluation functions must be written in supported language (Python/Node.js) — no visual evaluation builder","Evaluation results depend on quality of evaluation function logic — garbage in, garbage out"],"requires":["Baserun SDK with evaluation module","Python 3.8+ or Node.js 14+ for custom evaluation function definitions","API keys for evaluation models if using LLM-based evals (can reuse primary LLM provider)"],"input_types":["LLM output text","reference/expected outputs (optional)","custom evaluation function code","evaluation parameters and thresholds"],"output_types":["numeric scores (0-1 or custom range)","boolean pass/fail results","structured evaluation metadata","aggregated metrics dashboards"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_2","uri":"capability://automation.workflow.regression.testing.with.baseline.comparison.and.ci.cd.integration","name":"regression testing with baseline comparison and ci/cd integration","description":"Automatically compares LLM outputs from new code versions against baseline traces to detect quality regressions. Integrates with CI/CD pipelines (GitHub Actions, GitLab CI, etc.) via webhooks and status checks, allowing tests to block deployments if evaluation scores drop below thresholds. Baselines are established from previous runs and can be manually curated or automatically selected.","intents":["I want to ensure my LLM application doesn't regress in quality when I deploy new code or switch models","I need to automatically fail CI/CD pipelines if evaluation metrics drop below acceptable levels","I want to compare outputs between two model versions to decide which performs better before production rollout"],"best_for":["teams with mature CI/CD pipelines who want LLM-specific quality gates","organizations managing multiple LLM model versions and need data-driven promotion decisions","developers iterating rapidly on prompts and need fast feedback on quality impact"],"limitations":["Regression detection requires establishing baselines — first deployment has no comparison point","Flaky evaluations (non-deterministic scoring) can cause false positives/negatives in regression detection","Baseline selection strategy (latest, best, average) requires manual configuration and can miss subtle regressions"],"requires":["Baserun SDK integrated into application","CI/CD platform with webhook support (GitHub, GitLab, Jenkins, etc.)","Baserun API token with write permissions for status checks","Evaluation functions already defined in Baserun"],"input_types":["new LLM outputs from current code version","baseline traces from previous versions","evaluation function results","threshold configuration"],"output_types":["pass/fail CI/CD status","regression report with metric deltas","comparison visualizations","webhook notifications to Git platforms"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.instrumentation.with.unified.trace.format","name":"multi-provider llm instrumentation with unified trace format","description":"Automatically instruments calls to multiple LLM providers (OpenAI, Anthropic, Cohere, Azure OpenAI, self-hosted models) through a single SDK, normalizing responses into a unified trace schema regardless of provider. Handles provider-specific response formats, streaming responses, and error states transparently, allowing developers to switch providers without changing instrumentation code.","intents":["I want to instrument my application that uses multiple LLM providers without writing provider-specific code","I need to compare outputs and costs across different LLM providers using consistent metrics","I want to switch LLM providers in production without losing observability or changing my application code"],"best_for":["teams using multiple LLM providers for cost optimization or redundancy","developers building provider-agnostic LLM abstractions","organizations evaluating different models and need consistent comparison data"],"limitations":["Normalization may lose provider-specific metadata (e.g., OpenAI's logprobs, Anthropic's stop_reason details)","Streaming responses require buffering to capture complete output — adds latency for streaming-heavy applications","Custom provider implementations or local models require manual instrumentation"],"requires":["Baserun SDK for target language (Python, Node.js, etc.)","API keys for each LLM provider being used","Application code using standard LLM client libraries (openai, anthropic packages) or Baserun's wrapper clients"],"input_types":["LLM API calls to any supported provider","streaming and non-streaming requests","batch requests"],"output_types":["unified trace schema with provider-normalized fields","token count and cost estimates","latency and error metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_4","uri":"capability://data.processing.analysis.cost.tracking.and.token.usage.analytics.across.llm.calls","name":"cost tracking and token usage analytics across llm calls","description":"Automatically extracts token counts and pricing information from LLM provider responses, aggregates costs by model/provider/user/feature, and provides dashboards showing cost trends and per-request breakdowns. Integrates with provider pricing APIs to stay current with rate changes and supports custom pricing configuration for self-hosted models.","intents":["I need to understand how much each feature or user interaction costs in terms of LLM API spend","I want to track token usage trends and identify cost optimization opportunities","I need to allocate LLM costs to different business units or projects for chargeback"],"best_for":["product teams managing LLM application costs and margins","startups optimizing burn rate before scaling","enterprises doing cost allocation across departments"],"limitations":["Cost tracking depends on accurate token counts from providers — some providers don't expose token counts in responses","Pricing data may lag behind provider rate changes by hours or days","Custom model pricing requires manual configuration and won't auto-update"],"requires":["Baserun SDK integrated into application","LLM provider API keys (cost data extracted from provider responses)","Optional: custom pricing configuration for non-standard models"],"input_types":["LLM API responses with token count metadata","provider pricing data (fetched automatically or configured manually)"],"output_types":["per-request cost breakdown","aggregated cost dashboards","cost trends over time","cost attribution by dimension (model, user, feature, etc.)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_5","uri":"capability://data.processing.analysis.dashboard.and.visualization.of.llm.application.behavior","name":"dashboard and visualization of llm application behavior","description":"Provides web-based dashboards displaying traces, evaluation results, cost metrics, and performance trends with filtering, search, and drill-down capabilities. Includes trace timeline visualization showing request flow, latency breakdown by component, and side-by-side output comparison views for regression analysis. Built on time-series data from captured traces.","intents":["I want to visually explore what happened in a specific LLM request without writing queries","I need to see trends in evaluation scores, costs, and latency over time to identify issues","I want to compare outputs from two different model versions side-by-side to evaluate quality"],"best_for":["non-technical stakeholders reviewing LLM application quality metrics","developers debugging specific requests through visual trace exploration","product managers tracking LLM application health and performance"],"limitations":["Dashboard performance may degrade with very large trace volumes (>1M traces) — filtering and aggregation required","Custom visualizations require API access — dashboard is read-only for most users","Real-time updates have latency (typically 5-30 seconds) due to data aggregation"],"requires":["Baserun account with traces captured","Web browser with modern JavaScript support","Appropriate permissions/API key for dashboard access"],"input_types":["trace data from Baserun backend","evaluation results","cost and performance metrics"],"output_types":["interactive web dashboards","trace timeline visualizations","metric charts and graphs","comparison views"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_6","uri":"capability://automation.workflow.webhook.and.alert.notifications.for.quality.cost.anomalies","name":"webhook and alert notifications for quality/cost anomalies","description":"Monitors evaluation scores, cost metrics, and error rates in real-time, triggering webhooks or alerts when values exceed configured thresholds. Supports integration with Slack, PagerDuty, email, and custom webhooks. Alerts include context (affected traces, metric deltas, suggested actions) and can be configured per metric, time window, and alert severity.","intents":["I want to be notified immediately if LLM output quality drops below acceptable levels","I need alerts when token costs spike unexpectedly to catch runaway requests","I want to integrate Baserun alerts into my existing incident management workflow (PagerDuty, Slack, etc.)"],"best_for":["on-call engineers managing production LLM applications","teams with SLAs on LLM application quality or cost","organizations integrating LLM monitoring into broader observability stacks"],"limitations":["Alert latency depends on metric aggregation window — real-time alerts may have 30-60 second delay","Threshold-based alerting can produce false positives if thresholds not tuned carefully","Custom alert logic requires API access — no visual alert builder"],"requires":["Baserun account with traces and evaluations configured","Webhook endpoint or integration credentials (Slack token, PagerDuty API key, etc.)","Alert threshold configuration"],"input_types":["evaluation scores","cost metrics","error rates","threshold configuration"],"output_types":["webhook payloads with alert context","Slack messages","PagerDuty incidents","email notifications"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_7","uri":"capability://automation.workflow.prompt.versioning.and.a.b.testing.framework","name":"prompt versioning and a/b testing framework","description":"Manages multiple versions of prompts with version control, allowing developers to test different prompt variations against the same evaluation suite. Supports A/B testing by routing requests to different prompt versions and comparing evaluation results. Integrates with CI/CD to promote prompts to production based on evaluation metrics.","intents":["I want to test two different prompt versions and see which one produces better outputs according to my evaluations","I need to version control my prompts and track which version is in production","I want to gradually roll out a new prompt version to a percentage of users and monitor quality"],"best_for":["teams iterating on prompt engineering with data-driven decisions","organizations managing multiple prompt variants for different use cases","developers doing continuous prompt optimization"],"limitations":["A/B testing requires sufficient traffic to reach statistical significance — low-traffic applications need longer test windows","Prompt versioning is separate from code versioning — requires discipline to keep in sync","Gradual rollout requires application-level routing logic or Baserun integration for traffic splitting"],"requires":["Baserun SDK with prompt versioning support","Evaluation functions configured for quality comparison","Application code to handle prompt version selection (or Baserun routing)"],"input_types":["prompt text variations","version metadata","A/B test configuration (traffic split, duration)"],"output_types":["prompt version identifiers","A/B test results with statistical comparison","evaluation metric deltas between versions","promotion recommendations"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_8","uri":"capability://data.processing.analysis.dataset.management.and.test.case.curation","name":"dataset management and test case curation","description":"Allows users to create and manage datasets of test cases (input-output pairs) extracted from production traces or uploaded manually. Datasets can be used to run evaluations in batch, establish baselines, or create regression test suites. Supports filtering, tagging, and versioning of datasets.","intents":["I want to extract interesting or problematic cases from production and create a test suite from them","I need to maintain a curated set of test cases to validate my LLM application against","I want to run evaluations in batch against a dataset to measure overall quality"],"best_for":["teams building regression test suites from production data","organizations with domain experts who curate test cases","developers doing batch evaluation and quality measurement"],"limitations":["Dataset curation is manual — no automatic identification of edge cases or problematic patterns","Large datasets (>10k cases) may have slow evaluation runs depending on evaluation function complexity","Dataset versioning is separate from code versioning — requires manual synchronization"],"requires":["Baserun SDK and dashboard access","Production traces to extract from, or manual test case data","Evaluation functions for batch evaluation"],"input_types":["production traces (for extraction)","manual test case uploads (JSON, CSV)","metadata and tags for organization"],"output_types":["dataset objects with versioning","batch evaluation results","quality metrics aggregated across dataset"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__cap_9","uri":"capability://automation.workflow.team.collaboration.with.shared.dashboards.and.reports","name":"team collaboration with shared dashboards and reports","description":"Provides shared dashboards, reports, and insights that teams can access to understand application quality, performance, and costs. Supports role-based access control (read-only, editor, admin) to manage permissions, enables team members to comment on test results and share findings, and generates automated reports (daily, weekly) summarizing key metrics. Enables non-technical stakeholders (product managers, executives) to understand LLM application health without direct access to traces or code.","intents":["I want to share test results and quality metrics with my team without giving everyone access to raw traces","I need to generate weekly reports showing application quality and cost trends for stakeholders","I want to collaborate with my team on debugging issues by sharing traces and annotations","I need to control who can modify test cases and quality gates"],"best_for":["teams collaborating on LLM application development","organizations requiring visibility into LLM application quality for non-technical stakeholders","teams with distributed members needing shared context"],"limitations":["Role-based access control is coarse-grained; no support for fine-grained permissions (e.g., read-only for specific test suites)","Automated reports are generated on fixed schedules; custom report generation requires manual API calls","Collaboration features (comments, annotations) are limited to Baserun platform; no integration with external communication tools","Dashboard customization is limited; teams cannot create fully custom visualizations"],"requires":["Baserun team account with multiple members","role assignments for team members","optional: email configuration for automated reports"],"input_types":["test results and evaluation metrics","performance and cost data","team member roles and permissions"],"output_types":["shared dashboards with real-time metrics","automated reports (PDF, email)","collaboration annotations and comments","role-based access control"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"baserun__headline","uri":"capability://testing.quality.llm.application.testing.and.monitoring.platform","name":"llm application testing and monitoring platform","description":"Baserun is a comprehensive testing and monitoring platform specifically designed for LLM applications, offering end-to-end tracing, automated evaluations, and regression testing to ensure quality and reliability.","intents":["best LLM testing platform","LLM monitoring for CI/CD integration","automated evaluations for LLM applications","end-to-end tracing for LLM testing","regression testing tools for LLMs"],"best_for":["developers working with LLMs","teams integrating LLMs into production"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality","observability"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Baserun SDK installed (Python, Node.js, or language-specific wrapper)","API key from Baserun dashboard","LLM provider API keys (OpenAI, Anthropic, etc.) already configured in application","Baserun SDK with evaluation module","Python 3.8+ or Node.js 14+ for custom evaluation function definitions","API keys for evaluation models if using LLM-based evals (can reuse primary LLM provider)","Baserun SDK integrated into application","CI/CD platform with webhook support (GitHub, GitLab, Jenkins, etc.)","Baserun API token with write permissions for status checks","Evaluation functions already defined in Baserun"],"failure_modes":["Trace capture requires SDK integration — applications using raw HTTP calls without Baserun SDK will not be automatically instrumented","Trace retention and query performance may degrade with very high-volume applications (>100k requests/day) depending on plan tier","Custom middleware or non-standard LLM provider integrations may require manual instrumentation","LLM-based evaluations add latency and cost (requires additional API calls to evaluation model)","Custom evaluation functions must be written in supported language (Python/Node.js) — no visual evaluation builder","Evaluation results depend on quality of evaluation function logic — garbage in, garbage out","Regression detection requires establishing baselines — first deployment has no comparison point","Flaky evaluations (non-deterministic scoring) can cause false positives/negatives in regression detection","Baseline selection strategy (latest, best, average) requires manual configuration and can miss subtle regressions","Normalization may lose provider-specific metadata (e.g., OpenAI's logprobs, Anthropic's stop_reason details)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.013Z","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=baserun","compare_url":"https://unfragile.ai/compare?artifact=baserun"}},"signature":"rCG5pS3bmuxef37Lkrnp3QycmVPBcy+pCwg/0Ti1DUNU48JbLrJBL8Omx+4da18A5NjnTS3FsQ6C4o/p0vX5CQ==","signedAt":"2026-06-22T11:51:10.428Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/baserun","artifact":"https://unfragile.ai/baserun","verify":"https://unfragile.ai/api/v1/verify?slug=baserun","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"}}