{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"galileo","slug":"galileo","name":"Galileo","type":"platform","url":"https://www.rungalileo.io","page_url":"https://unfragile.ai/galileo","categories":["observability"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"galileo__cap_0","uri":"capability://memory.knowledge.trace.based.execution.observability.with.multi.turn.workflow.analysis","name":"trace-based execution observability with multi-turn workflow analysis","description":"Ingests execution traces from external LLM applications (models, prompts, functions, context, datasets) and reconstructs multi-turn agent workflows to surface failure modes, tool selection success rates, and cost breakdowns per interaction. Uses a proprietary trace schema to correlate model outputs with downstream function calls and context usage, enabling post-hoc debugging without code instrumentation.","intents":["I need to understand why my agent failed on a specific user query without re-running the entire workflow","I want to see which tool calls succeeded vs failed and correlate that with model outputs","I need to track cost per conversation turn to optimize my LLM application's economics","I want to identify patterns in agent behavior across hundreds of production traces"],"best_for":["teams operating LLM agents in production who need post-hoc debugging","developers building RAG systems and needing visibility into retrieval + generation steps","enterprises tracking cost and performance across multi-turn conversations"],"limitations":["Trace ingestion is asynchronous — real-time streaming evaluation not mentioned; batch processing only","Trace data schema is proprietary and undocumented — custom trace formats require mapping to Galileo's schema","Trace retention period unknown — no SLA disclosed for how long traces are stored before deletion","No local/offline trace analysis — all traces must be sent to Galileo's hosted platform (except Enterprise VPC/on-prem)"],"requires":["Active LLM application generating execution traces (agent, RAG system, or multi-step workflow)","API key or authentication token for Galileo platform","Ability to instrument application to emit traces in Galileo-compatible format (SDK/API not publicly documented)"],"input_types":["execution traces (model outputs, function calls, context, datasets)","structured metadata (user IDs, session IDs, timestamps)"],"output_types":["interactive trace visualization dashboard","failure mode analysis reports","cost breakdowns per turn","tool selection success rate metrics"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_1","uri":"capability://safety.moderation.pre.built.evaluation.metrics.for.domain.specific.llm.tasks","name":"pre-built evaluation metrics for domain-specific llm tasks","description":"Provides 20+ out-of-the-box evaluators optimized for RAG, agents, safety, and security use cases. Each metric is implemented as a distilled Luna model (proprietary LLM-as-judge variant) that runs at 97% lower cost than full GPT-4o evaluation while maintaining comparable accuracy. Metrics are applied to evaluation datasets in batch mode and scored against ground truth or reference outputs.","intents":["I need to evaluate my RAG system's retrieval quality without writing custom evaluation logic","I want to detect hallucinations in my agent's outputs before deploying to production","I need to run safety and security evaluations on my LLM application at scale","I want to compare evaluation results across multiple model versions using consistent metrics"],"best_for":["teams building RAG systems who need retrieval + generation quality metrics","developers deploying agents and needing hallucination/safety guardrails","enterprises requiring compliance-grade evaluation (safety, security, bias detection)"],"limitations":["Pre-built metrics are domain-specific — no single metric works for all LLM tasks; requires selecting appropriate subset","Luna model distillation process is undocumented — cannot inspect or modify metric logic","Metric accuracy claims lack published benchmarks — '97% cost reduction' is marketing claim without F1/precision/recall data","No offline evaluation mentioned — metrics must be run via Galileo platform API, not locally"],"requires":["Evaluation dataset with inputs and ground truth outputs (or reference outputs for reference-based metrics)","Galileo account (Free tier includes unlimited custom evals but limited traces)","Knowledge of which pre-built metrics apply to your use case (documentation of metric definitions unknown)"],"input_types":["LLM outputs (text)","ground truth or reference outputs (text)","evaluation datasets (structured data with input/output pairs)"],"output_types":["metric scores (numeric: 0-1 or 0-100)","per-sample evaluation results (pass/fail or score per input)","aggregate metric reports (mean, std dev across dataset)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_10","uri":"capability://tool.use.integration.mcp.server.integration.for.model.context.protocol.support","name":"mcp server integration for model context protocol support","description":"Integrates with Model Context Protocol (MCP) servers to ingest context and tool definitions from external systems. Enables Galileo to evaluate LLM applications that use MCP-compatible tools and context sources, allowing evaluation of agent behavior with real-world tool integrations.","intents":["I want to evaluate my LLM agent that uses MCP-compatible tools without mocking or stubbing them","I need to test my agent's behavior with real context from MCP servers (e.g., file systems, databases, APIs)","I want to ensure my agent correctly uses MCP tools and handles tool errors gracefully"],"best_for":["teams building LLM agents with MCP tool integrations","developers wanting to evaluate agent behavior with real-world tool interactions","enterprises using MCP for standardized tool integration across LLM applications"],"limitations":["MCP integration details are undocumented — no specification of which MCP features are supported","No guidance on MCP server setup or configuration — unclear how to connect MCP servers to Galileo","MCP tool evaluation is not explicitly mentioned — unclear if tool success/failure is tracked or evaluated"],"requires":["MCP-compatible servers running and accessible to Galileo platform","LLM application using MCP tools","Galileo account with MCP integration enabled (tier requirements unknown)"],"input_types":["MCP server definitions (tools, context sources)","execution traces from MCP-integrated LLM applications"],"output_types":["evaluation results with MCP tool usage","tool success/failure metrics","context usage analysis"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_11","uri":"capability://safety.moderation.nvidia.nemo.guardrails.integration.for.production.safety.enforcement","name":"nvidia nemo guardrails integration for production safety enforcement","description":"Integrates with NVIDIA NeMo Guardrails via 'Galileo Protect' to enforce guardrails in production. Galileo evaluations (hallucination detection, safety checks) feed into NeMo Guardrails to block or flag unsafe outputs. Enables production deployment of evaluation-driven safety policies without custom guardrail logic.","intents":["I want to enforce safety guardrails in production using Galileo evaluations without building custom safety logic","I need to integrate Galileo hallucination detection with NeMo Guardrails to block hallucinated outputs","I want to use Galileo safety evaluations to gate LLM outputs in production"],"best_for":["teams using NVIDIA NeMo Guardrails who want to integrate Galileo evaluations","enterprises deploying LLM applications in regulated industries requiring production safety enforcement","developers wanting pre-built safety integration without custom guardrail implementation"],"limitations":["Integration details are undocumented — no specification of how Galileo evaluations feed into NeMo Guardrails","Requires both Galileo and NeMo Guardrails — adds operational complexity and dependency management","NeMo Guardrails configuration is separate from Galileo — requires understanding both systems","No guidance on guardrail policy definition or tuning"],"requires":["NVIDIA NeMo Guardrails installed and configured","Galileo account with Protect feature enabled (tier requirements unknown)","LLM application integrated with both Galileo and NeMo Guardrails"],"input_types":["Galileo evaluation results (scores, classifications)","NeMo Guardrails policy definitions"],"output_types":["guardrail actions (allow, flag, block)","audit logs of guardrail enforcement"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_12","uri":"capability://data.processing.analysis.trend.analysis.and.quality.regression.detection","name":"trend analysis and quality regression detection","description":"Tracks evaluation metrics over time and automatically detects regressions (quality drops) in model outputs. Compares current metric values against historical baselines and alerts when metrics fall below configured thresholds. Supports trend visualization and statistical significance testing to distinguish real regressions from noise.","intents":["I want to know immediately when my model quality drops","I need to track how my quality metrics change as I update my prompts or model","I want to detect regressions before they impact users"],"best_for":["teams with continuous deployment pipelines","organizations tracking quality over time","teams needing early warning of quality degradation"],"limitations":["Statistical significance testing methodology not documented","Baseline calculation and update strategy unknown","Alert configuration and notification mechanisms not detailed","No information on how seasonal or expected variations are handled"],"requires":["Historical metric data (requires continuous evaluation)","Configured baseline and threshold values","Pro tier or higher for trend analysis (free tier may have limited history)"],"input_types":["metric values over time","baseline and threshold configurations"],"output_types":["trend visualizations","regression alerts","statistical significance reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_2","uri":"capability://data.processing.analysis.custom.metric.creation.and.auto.tuning.from.production.feedback","name":"custom metric creation and auto-tuning from production feedback","description":"Allows users to define custom evaluation metrics via a framework (implementation details unknown) and automatically tunes metric thresholds based on live production feedback. The platform ingests production traces, correlates metric scores with actual user outcomes or business KPIs, and adjusts metric parameters to improve precision/recall without manual retraining.","intents":["I need to evaluate my LLM application on domain-specific criteria that pre-built metrics don't cover","I want my evaluation metrics to adapt as my application evolves and new failure modes emerge","I need to calibrate metric thresholds to match my actual production performance and user satisfaction","I want to create metrics that correlate with business outcomes (e.g., user retention, task completion)"],"best_for":["teams with domain-specific evaluation needs (e.g., legal document review, medical diagnosis)","enterprises running mature LLM applications that need continuous metric refinement","developers building proprietary LLM applications with custom success criteria"],"limitations":["Custom metric definition framework is undocumented — no public API or DSL provided; implementation approach unknown","Auto-tuning mechanism is a black box — no visibility into how thresholds are adjusted or what feedback signals are used","Auto-tuning requires production data — cannot be used in offline evaluation phase without synthetic feedback","No local metric execution — custom metrics must run on Galileo platform, creating vendor lock-in"],"requires":["Understanding of what constitutes a 'good' output for your use case (ground truth or user feedback)","Production traces with sufficient volume to enable auto-tuning (minimum volume unknown)","Ability to define metric logic (language/framework unknown — likely Python or proprietary DSL)"],"input_types":["custom metric definition (code or DSL format unknown)","evaluation dataset or production traces","feedback signal (user ratings, business KPI, or ground truth)"],"output_types":["metric scores (numeric)","tuned threshold parameters","metric performance report (precision, recall, F1 against feedback signal)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_3","uri":"capability://safety.moderation.hallucination.detection.and.guardrail.enforcement","name":"hallucination detection and guardrail enforcement","description":"Detects when LLM outputs contain factually incorrect or unsupported claims using Luna-based evaluators that analyze output against provided context or ground truth. Integrates with NVIDIA NeMo Guardrails via 'Galileo Protect' to enforce guardrails in production, blocking or flagging hallucinated outputs before they reach users.","intents":["I need to detect when my RAG system generates answers not supported by retrieved documents","I want to prevent my agent from making up tool parameters or function calls","I need to flag hallucinations in production and route them to human review","I want to measure hallucination rate across my LLM application to track quality improvements"],"best_for":["teams building RAG systems where hallucination is a critical failure mode","enterprises deploying LLM agents in high-stakes domains (legal, medical, financial)","developers integrating with NVIDIA NeMo Guardrails for production safety"],"limitations":["Hallucination detection requires context or ground truth — cannot detect hallucinations without reference material","Luna-based detection is a black box — no visibility into how hallucinations are identified or scored","Guardrail enforcement via NeMo Guardrails requires separate integration — not built into Galileo core","No real-time streaming detection mentioned — hallucination detection appears to be batch/trace-based only"],"requires":["LLM outputs to evaluate (text)","Context or ground truth for comparison (retrieved documents, knowledge base, or reference outputs)","For production guardrails: NVIDIA NeMo Guardrails integration (separate tool)"],"input_types":["LLM output (text)","context or ground truth (text or structured data)","execution traces (for production detection)"],"output_types":["hallucination score (0-1 or boolean)","confidence score","explanation or evidence of hallucination (if available)","guardrail action (block, flag, or allow)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_4","uri":"capability://data.processing.analysis.evaluation.dataset.curation.and.synthetic.data.generation","name":"evaluation dataset curation and synthetic data generation","description":"Enables creation and management of evaluation datasets from multiple sources: synthetic data (generated by LLMs), development data (from internal testing), and production data (from live traces). Datasets are versioned and can be used to create ground truth for custom evaluators or to benchmark model versions. Synthetic data generation approach is undocumented but implied to use LLM-based generation.","intents":["I need to create a diverse evaluation dataset without manually writing test cases","I want to version my evaluation datasets to track how metrics change over time","I need to generate synthetic edge cases for my LLM application (e.g., adversarial inputs)","I want to combine production traces with synthetic data to create a comprehensive evaluation set"],"best_for":["teams building LLM applications who need evaluation datasets but lack labeled data","developers iterating on prompts and wanting to track performance across versions","enterprises creating ground truth for domain-specific evaluation metrics"],"limitations":["Synthetic data generation approach is undocumented — no control over generation strategy, diversity, or quality","No published benchmarks on synthetic data quality — unclear how well synthetic data correlates with real-world performance","Dataset versioning is mentioned but no details on version control, branching, or rollback capabilities","No export capability mentioned — datasets may be locked into Galileo platform"],"requires":["Source data (production traces, development test cases, or seed examples for synthetic generation)","Galileo account with dataset management permissions","Understanding of what constitutes good evaluation coverage for your use case"],"input_types":["production traces (for production data)","manual test cases (for development data)","seed examples or prompts (for synthetic data generation)"],"output_types":["versioned evaluation dataset (structured data with inputs and optional ground truth)","dataset statistics (size, diversity metrics, coverage analysis)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_5","uri":"capability://automation.workflow.ci.cd.integration.for.automated.evaluation.gates","name":"ci/cd integration for automated evaluation gates","description":"Enables custom metrics to be integrated into CI/CD pipelines as automated evaluation gates that block deployments if metric thresholds are not met. Evaluation results are reported back to CI/CD systems (webhook or API integration assumed but undocumented) to gate code promotion. Supports offline evaluation of model changes before production deployment.","intents":["I want to automatically evaluate my LLM application on every code change before deploying","I need to prevent regressions in model quality by enforcing metric thresholds in my CI/CD pipeline","I want to compare evaluation results across model versions to decide which version to promote","I need to integrate LLM evaluation into my existing DevOps workflow without manual steps"],"best_for":["teams with mature CI/CD practices who want to extend them to LLM evaluation","enterprises requiring automated quality gates before production deployment","developers iterating on prompts and wanting fast feedback on quality changes"],"limitations":["CI/CD integration details are undocumented — no webhook specifications, API endpoints, or integration examples provided","Evaluation latency unknown — no SLA for how long evaluation takes, which impacts CI/CD cycle time","No local evaluation mentioned — CI/CD integration likely requires sending data to Galileo platform, adding network latency","Threshold configuration is manual — no guidance on how to set appropriate thresholds for different metrics"],"requires":["CI/CD system (GitHub Actions, GitLab CI, Jenkins, etc. — supported systems unknown)","Galileo API key or authentication token","Pre-defined evaluation metrics and threshold values","Evaluation dataset or production traces for comparison"],"input_types":["model version or prompt change (code diff or new model identifier)","evaluation dataset","metric thresholds (numeric values)"],"output_types":["evaluation report (pass/fail per metric)","metric comparison (current vs baseline)","CI/CD gate decision (promote or block)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_6","uri":"capability://planning.reasoning.failure.mode.analysis.and.pattern.detection","name":"failure mode analysis and pattern detection","description":"Analyzes ingested execution traces to identify recurring failure patterns, surface hidden failure modes, and prescribe fixes. Uses an 'insights engine' (implementation unknown) to correlate failures with input characteristics, model outputs, tool selections, and context to identify root causes. Provides actionable recommendations for prompt tuning, tool selection logic, or data augmentation.","intents":["I need to understand why my agent is failing on a specific class of inputs (e.g., complex queries, rare entities)","I want to identify the most common failure modes in my LLM application to prioritize fixes","I need to correlate tool selection failures with model outputs to debug my agent's decision logic","I want recommendations on how to fix identified failure modes (prompt changes, tool logic, data augmentation)"],"best_for":["teams operating LLM agents in production with sufficient failure volume to identify patterns","developers debugging complex multi-step workflows where root causes are non-obvious","enterprises wanting data-driven guidance on LLM application improvements"],"limitations":["Insights engine is a black box — no visibility into how patterns are identified or how recommendations are generated","Requires sufficient failure volume — pattern detection may not work with small trace datasets","Recommendations are prescriptive but not executable — no automated fix application; requires manual implementation","Pattern detection is post-hoc — cannot predict failures before they occur in production"],"requires":["Execution traces with sufficient failure examples (minimum volume unknown)","Traces must include model outputs, tool calls, and context for correlation analysis","Galileo account with insights engine access (tier requirements unknown)"],"input_types":["execution traces (model outputs, function calls, context, failures)"],"output_types":["failure pattern report (grouped failures by root cause)","pattern characteristics (input types, model outputs, tool selections associated with failures)","recommended fixes (prompt changes, tool logic adjustments, data augmentation suggestions)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_7","uri":"capability://data.processing.analysis.cost.tracking.and.optimization.per.interaction","name":"cost tracking and optimization per interaction","description":"Tracks LLM API costs at the granularity of individual trace steps (model calls, tool invocations, context retrievals) and aggregates costs per conversation turn, session, or user. Provides cost breakdowns and identifies high-cost interactions for optimization. Integrates with Luna model cost savings (97% reduction claimed) to show cost impact of using distilled evaluators vs full LLM-as-judge.","intents":["I need to understand the cost breakdown of my LLM application per user interaction","I want to identify which parts of my agent workflow are most expensive (model calls vs tool calls vs context retrieval)","I need to optimize my application's cost-per-interaction to improve unit economics","I want to compare cost impact of different model versions or evaluation approaches"],"best_for":["teams operating LLM applications at scale who need cost visibility","startups optimizing for unit economics and burn rate","enterprises tracking LLM costs for chargeback or cost allocation"],"limitations":["Cost tracking is trace-based — requires ingesting all traces to Galileo platform, adding latency and bandwidth costs","Cost model is proprietary — no visibility into how costs are calculated or what pricing assumptions are used","Cost optimization recommendations are not automated — requires manual analysis and implementation","No cost forecasting mentioned — cannot predict future costs based on usage trends"],"requires":["Execution traces with model call details (model name, token counts, API costs)","Galileo account with cost tracking enabled (tier requirements unknown)","Integration with LLM provider pricing (OpenAI, Anthropic, etc. — supported providers unknown)"],"input_types":["execution traces (model calls, token counts, tool invocations)"],"output_types":["cost breakdown per trace step (model call cost, tool call cost, context retrieval cost)","aggregated costs (per turn, per session, per user)","cost comparison reports (current vs baseline, different model versions)","cost optimization recommendations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_8","uri":"capability://safety.moderation.production.guardrail.deployment.with.luna.models","name":"production guardrail deployment with luna models","description":"Deploys distilled Luna models as production guardrails that run evaluations in real-time on LLM outputs before they reach users. Luna models are optimized for low-latency inference (specific latency SLA unknown) and run at 97% lower cost than LLM-as-judge evaluators. Supports multiple deployment options: Galileo-hosted, customer VPC, or on-premises (Enterprise tier only).","intents":["I need to enforce safety guardrails on my LLM application in production without adding significant latency","I want to detect and block hallucinations or unsafe outputs in real-time before users see them","I need to run evaluation models on-premises or in my VPC for data residency compliance","I want to reduce the cost of production evaluation by using distilled models instead of full LLM-as-judge"],"best_for":["enterprises deploying LLM applications in regulated industries (healthcare, finance, legal) requiring real-time safety checks","teams with strict data residency requirements (on-prem or VPC deployment)","companies optimizing production costs by replacing expensive LLM-as-judge evaluations with Luna models"],"limitations":["Luna model latency is claimed as 'low' but no concrete SLA provided — actual p99 latency unknown","Luna models are proprietary and cannot be inspected or modified — no transparency into evaluation logic","On-premises and VPC deployments are Enterprise tier only — not available on Free/Pro tiers","Luna model distillation process is undocumented — cannot retrain or fine-tune models for custom use cases"],"requires":["Enterprise tier subscription for on-premises or VPC deployment","LLM application infrastructure capable of calling Luna model API or webhook","For on-premises: infrastructure to host Luna model inference (hardware specs unknown)","For VPC: AWS/GCP/Azure VPC with network connectivity to Galileo"],"input_types":["LLM output (text)","optional context or metadata"],"output_types":["evaluation score (0-1 or boolean)","confidence score","guardrail action (allow, flag, block)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__cap_9","uri":"capability://safety.moderation.multi.provider.llm.evaluation.with.pluggable.judge.models","name":"multi-provider llm evaluation with pluggable judge models","description":"Supports multiple LLM providers as evaluation judges (GPT-4o explicitly mentioned; others unknown) and allows users to select which judge to use for each evaluation. Evaluation results can be compared across different judges to assess judge agreement and identify ambiguous cases. Integrates with Luna models as a cost-optimized alternative to full LLM-as-judge evaluation.","intents":["I want to evaluate my LLM application using different judge models to assess consistency","I need to use a specific LLM provider (e.g., GPT-4o) as a judge for regulatory or quality reasons","I want to compare evaluation results across judges to identify ambiguous or contentious cases","I need to switch judges without re-running evaluations (e.g., from GPT-4o to Luna for cost savings)"],"best_for":["teams wanting to validate evaluation results across multiple judges","enterprises with specific LLM provider requirements (e.g., must use OpenAI for compliance)","developers optimizing evaluation cost by comparing judge options"],"limitations":["Supported judge models are undocumented — only GPT-4o explicitly mentioned; unclear if other providers (Anthropic, Gemini, Llama) are supported","Judge selection is manual — no automatic judge selection based on cost, latency, or accuracy","Judge agreement analysis is not mentioned — no built-in tools to compare results across judges","Judge-specific configuration is unknown — unclear how to customize judge behavior or prompts"],"requires":["API keys for selected LLM providers (OpenAI for GPT-4o; others unknown)","Evaluation dataset","Understanding of which judge is appropriate for your use case"],"input_types":["evaluation dataset (inputs and outputs to evaluate)","judge selection (model identifier)"],"output_types":["evaluation results per judge (scores, explanations)","judge comparison report (agreement metrics, divergent cases)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo__headline","uri":"capability://data.processing.analysis.ai.evaluation.and.observability.platform.for.llm.applications","name":"ai evaluation and observability platform for llm applications","description":"Galileo is an AI evaluation and observability platform designed specifically for large language model applications, offering guardrail metrics, hallucination detection, and data-centric debugging to enhance model performance and reliability.","intents":["best AI evaluation platform","observability tools for LLMs","AI metrics for model performance","how to debug LLM applications","evaluation metrics for AI models"],"best_for":["AI developers","data scientists","ML engineers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Active LLM application generating execution traces (agent, RAG system, or multi-step workflow)","API key or authentication token for Galileo platform","Ability to instrument application to emit traces in Galileo-compatible format (SDK/API not publicly documented)","Evaluation dataset with inputs and ground truth outputs (or reference outputs for reference-based metrics)","Galileo account (Free tier includes unlimited custom evals but limited traces)","Knowledge of which pre-built metrics apply to your use case (documentation of metric definitions unknown)","MCP-compatible servers running and accessible to Galileo platform","LLM application using MCP tools","Galileo account with MCP integration enabled (tier requirements unknown)","NVIDIA NeMo Guardrails installed and configured"],"failure_modes":["Trace ingestion is asynchronous — real-time streaming evaluation not mentioned; batch processing only","Trace data schema is proprietary and undocumented — custom trace formats require mapping to Galileo's schema","Trace retention period unknown — no SLA disclosed for how long traces are stored before deletion","No local/offline trace analysis — all traces must be sent to Galileo's hosted platform (except Enterprise VPC/on-prem)","Pre-built metrics are domain-specific — no single metric works for all LLM tasks; requires selecting appropriate subset","Luna model distillation process is undocumented — cannot inspect or modify metric logic","Metric accuracy claims lack published benchmarks — '97% cost reduction' is marketing claim without F1/precision/recall data","No offline evaluation mentioned — metrics must be run via Galileo platform API, not locally","MCP integration details are undocumented — no specification of which MCP features are supported","No guidance on MCP server setup or configuration — unclear how to connect MCP servers to Galileo","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","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=galileo","compare_url":"https://unfragile.ai/compare?artifact=galileo"}},"signature":"NV/Io5A4go70cu9OTcoThSD6nROcLACEdMcsmS5H4k9TK90/xhFUAv+KT+T8Gx9B8aNUwLLfuhsNZYiyOQUbBw==","signedAt":"2026-06-21T08:50:57.271Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/galileo","artifact":"https://unfragile.ai/galileo","verify":"https://unfragile.ai/api/v1/verify?slug=galileo","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"}}