{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"galileo-observe","slug":"galileo-observe","name":"Galileo Observe","type":"product","url":"https://www.rungalileo.io","page_url":"https://unfragile.ai/galileo-observe","categories":["observability","rag-knowledge","deployment-infra","testing-quality"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":"Custom"},"status":"active","verified":false},"capabilities":[{"id":"galileo-observe__cap_0","uri":"capability://safety.moderation.automated.hallucination.detection.in.llm.outputs","name":"automated hallucination detection in llm outputs","description":"Detects factual inconsistencies and fabricated information in LLM-generated responses by analyzing semantic coherence between model outputs and source context. Uses research-backed metrics to identify when models generate plausible-sounding but unsupported claims, with real-time flagging of hallucination patterns across production traffic without requiring manual annotation.","intents":["I need to automatically catch when my RAG system returns answers not grounded in retrieved documents","I want to identify hallucination patterns in production before users encounter them","I need to measure hallucination rates across different model versions or prompts"],"best_for":["teams building RAG applications with strict accuracy requirements","enterprises deploying LLMs in regulated industries (finance, healthcare, legal)","developers iterating on prompt engineering and need quantitative hallucination metrics"],"limitations":["Hallucination detection accuracy not benchmarked in public documentation — claims 'research-backed' but no F1 scores or comparison to baselines provided","Mechanism for detecting hallucinations unclear — likely uses LLM-as-judge or Luna models but specific approach not disclosed","May produce false positives on edge cases like creative writing or speculative reasoning where hallucination is intentional"],"requires":["Active Galileo Observe account (free tier: 5,000 traces/month minimum)","Integration with Galileo trace ingestion API or MCP server","Source context/documents available in trace payload for comparison"],"input_types":["LLM output text","source context/retrieved documents","conversation traces with model, prompt, and context"],"output_types":["hallucination detection score (0-1 range implied)","boolean flag (hallucinated/grounded)","pattern analysis identifying common hallucination modes"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_1","uri":"capability://data.processing.analysis.context.adherence.scoring.for.rag.systems","name":"context adherence scoring for rag systems","description":"Measures how well LLM responses stay grounded in and utilize the retrieved context documents, scoring the degree of semantic alignment between generated answers and source material. Evaluates whether the model is actually using provided context versus relying on parametric knowledge, with scoring that can be customized per use case and tracked across retrieval quality improvements.","intents":["I need to verify my RAG system is actually using retrieved documents instead of hallucinating from training data","I want to measure if context quality improvements translate to better answer grounding","I need to identify when retrievers return irrelevant documents that confuse the generation model"],"best_for":["RAG teams optimizing retriever-to-generator pipelines","product managers tracking RAG quality improvements over time","developers debugging why RAG systems ignore relevant retrieved context"],"limitations":["Scoring mechanism not detailed — unclear if uses embedding similarity, LLM-as-judge, or hybrid approach","No documentation on how context adherence score handles multi-document reasoning or conflicting information in retrieved context","Requires context to be explicitly included in traces — cannot retroactively evaluate systems without context payloads"],"requires":["Galileo Observe account with trace ingestion enabled","RAG pipeline instrumented to include retrieved documents/context in trace payloads","LLM-generated responses paired with source context in same trace"],"input_types":["retrieved documents/context chunks","LLM-generated response text","query/user intent"],"output_types":["adherence score (0-1 range implied)","per-document relevance attribution","trend analysis showing adherence over time"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_10","uri":"capability://planning.reasoning.failure.mode.pattern.detection.and.prescriptive.recommendations","name":"failure mode pattern detection and prescriptive recommendations","description":"Analyzes millions of signals across traces to identify recurring failure patterns (e.g., 'date-based queries fail 40% of the time', 'tool selection fails when context exceeds 5K tokens') and generates prescriptive recommendations for fixes (e.g., 'Add few-shot examples to demonstrate correct tool input'). Uses pattern recognition across models, prompts, functions, context, and datasets to surface hidden issues.","intents":["I need to understand why my LLM/RAG system is failing and what to do about it","I want to identify systemic issues (e.g., certain query types always fail) rather than one-off errors","I need actionable recommendations for improving my system, not just metrics"],"best_for":["teams with large production systems generating millions of traces","developers iterating on prompt/model/retrieval improvements","product managers needing data-driven prioritization of improvements"],"limitations":["Pattern detection mechanism not documented — unclear if uses statistical analysis, clustering, or LLM-based analysis","Recommendation generation not detailed — examples given ('Add few-shot examples') but methodology unknown","Unclear how patterns are ranked/prioritized — which failures get recommendations first?","Recommendations are examples only — no documentation on coverage or accuracy of recommendations"],"requires":["Galileo Observe account with sufficient trace volume (pattern detection likely requires 1000+ traces minimum)","Diverse trace data capturing different failure modes","Optional: ground truth labels for failure classification"],"input_types":["production traces with failures/low scores","trace metadata (query type, context size, model, prompt, etc.)"],"output_types":["failure pattern identification (e.g., 'date-based queries fail 40%')","pattern statistics (frequency, impact, affected user segments)","prescriptive recommendations (e.g., 'Add few-shot examples')"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_11","uri":"capability://automation.workflow.multi.tier.deployment.with.vpc.and.on.premises.options","name":"multi-tier deployment with vpc and on-premises options","description":"Offers deployment flexibility for Enterprise customers with hosted (default), VPC (private cloud), and on-premises deployment options. Enables organizations with strict data residency, compliance, or security requirements to run Galileo observability infrastructure in their own environments while maintaining access to Luna models and evaluation capabilities.","intents":["I need to run Galileo in my VPC for data security and compliance","I want to deploy Galileo on-premises to meet data residency requirements","I need to keep my LLM traces and evaluation data within my infrastructure"],"best_for":["enterprises with strict data residency requirements (GDPR, HIPAA, etc.)","organizations with security policies prohibiting cloud data transfer","teams needing air-gapped or on-premises AI infrastructure"],"limitations":["VPC and on-premises deployment only available on Enterprise tier — free/Pro limited to hosted","Deployment architecture and requirements not documented — unclear what infrastructure is needed","Unclear if Luna models run locally in VPC/on-prem or still call Galileo cloud — if cloud, data still leaves infrastructure","Support model for on-premises deployments not documented — unclear if includes forward-deployed engineering"],"requires":["Enterprise tier Galileo Observe account","For VPC: AWS/GCP/Azure VPC with appropriate networking","For on-premises: infrastructure meeting Galileo requirements (unknown)","Optional: forward-deployed engineering support (mentioned for Enterprise)"],"input_types":["deployment configuration (VPC/on-prem selection)","infrastructure details (for on-premises)"],"output_types":["deployed Galileo instance in customer infrastructure","access to Luna models and evaluation capabilities"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_12","uri":"capability://safety.moderation.real.time.guardrails.with.production.blocking.capability","name":"real-time guardrails with production blocking capability","description":"Blocks unsafe or low-quality LLM outputs in real-time before they reach users, using Luna models and evaluation logic to detect issues and trigger guardrail actions. Available on Enterprise tier with dedicated low-latency inference servers, enabling sub-second evaluation and blocking decisions for production traffic.","intents":["I need to prevent harmful outputs from reaching my users in real-time","I want to block low-quality responses before they're returned to users","I need guardrails that don't add significant latency to my LLM responses"],"best_for":["enterprises deploying LLMs in high-stakes applications (customer-facing, regulated industries)","teams with strict safety/quality SLAs","organizations requiring real-time output filtering"],"limitations":["Real-time guardrails only available on Enterprise tier — free/Pro limited to evaluation/monitoring","Guardrail latency not specified — 'low-latency' is marketing language without SLA","Guardrail actions not documented — unclear if supports blocking, flagging, regeneration, or other actions","Unclear how guardrails handle edge cases — what happens if guardrail evaluation fails or times out?"],"requires":["Enterprise tier Galileo Observe account","Dedicated low-latency inference servers (included with Enterprise)","Integration with LLM application to intercept outputs before returning to users"],"input_types":["LLM output text","source context (optional)","guardrail policy definitions"],"output_types":["guardrail action (block/flag/allow)","evaluation latency (milliseconds)","guardrail decision logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_13","uri":"capability://automation.workflow.enterprise.rbac.and.sso.with.audit.logging","name":"enterprise rbac and sso with audit logging","description":"Provides enterprise-grade access control with role-based access control (RBAC), single sign-on (SSO), and comprehensive audit logging for compliance. Enables organizations to manage user permissions, enforce authentication policies, and maintain audit trails of all evaluation and monitoring activities for regulatory compliance.","intents":["I need to control who can access evaluation results and monitoring dashboards","I want to enforce SSO authentication for my organization","I need audit logs of all evaluation and monitoring activities for compliance"],"best_for":["enterprises with strict access control requirements","organizations subject to regulatory compliance (SOC 2, HIPAA, etc.)","teams with multiple users needing fine-grained permission management"],"limitations":["RBAC and SSO only available on Enterprise tier — free/Pro limited to basic user management","RBAC role definitions not documented — unclear what roles are available or what permissions they grant","Audit logging scope not documented — unclear what events are logged or retention period","No documentation on SSO provider support — unclear if supports Okta, Azure AD, Google Workspace, etc."],"requires":["Enterprise tier Galileo Observe account","For SSO: compatible identity provider (Okta, Azure AD, Google Workspace, etc. — unclear which are supported)"],"input_types":["user identity and role assignments","SSO configuration"],"output_types":["access control enforcement","audit logs with timestamps and user actions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_14","uri":"capability://data.processing.analysis.cost.tracking.and.optimization.for.llm.evaluations","name":"cost tracking and optimization for llm evaluations","description":"Tracks and displays the cost of running evaluations, including LLM-as-judge costs (e.g., $0.0733 per run with GPT-4o and 3 judges) and Luna model costs (claimed 97% cheaper). Enables teams to understand evaluation economics and optimize evaluation strategies by comparing cost vs accuracy tradeoffs.","intents":["I need to understand how much my evaluations are costing","I want to compare the cost of different evaluation approaches (LLM-as-judge vs Luna)","I need to optimize my evaluation strategy to reduce costs while maintaining quality"],"best_for":["teams evaluating high-volume production traffic with cost constraints","organizations trying to optimize evaluation spend","developers comparing evaluation approaches (LLM-as-judge vs Luna)"],"limitations":["Luna model costs not disclosed — only claimed '97% lower cost' without absolute pricing","Cost tracking scope unclear — does it include trace ingestion costs or only evaluation costs?","No documentation on cost optimization recommendations or strategies","Cost data may not be real-time — unclear if costs are calculated immediately or batched"],"requires":["Galileo Observe account with evaluation capability","Evaluation runs using LLM-as-judge or Luna models"],"input_types":["evaluation runs with model and judge configuration"],"output_types":["per-evaluation cost breakdown","total evaluation costs over time","cost comparison between evaluation approaches"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_2","uri":"capability://data.processing.analysis.retrieval.quality.assessment.with.failure.mode.detection","name":"retrieval quality assessment with failure mode detection","description":"Evaluates whether retrieved documents are relevant, complete, and sufficient to answer user queries by analyzing retrieval precision/recall and identifying failure modes like missing documents, ranking errors, or semantic gaps. Surfaces patterns in retrieval failures (e.g., 'queries about Q3 financials consistently retrieve Q2 documents') and recommends fixes like embedding model tuning or chunking strategy changes.","intents":["I need to measure if my retriever is finding the right documents for user queries","I want to identify why certain query types fail to retrieve relevant context","I need to optimize my retrieval pipeline by understanding which failure modes are most common"],"best_for":["RAG engineers tuning retrieval components (embedding models, chunking, ranking)","teams with large document collections struggling with retrieval precision","developers building domain-specific RAG systems needing retrieval diagnostics"],"limitations":["Failure mode detection mechanism not disclosed — unclear if uses heuristics, LLM analysis, or learned classifiers","Recommendations ('Add few-shot examples', 'Adjust chunking') are examples only — no documentation on recommendation engine","Requires ground truth labels or relevance judgments to measure recall — free tier may not support this"],"requires":["Galileo Observe account with retrieval eval capability","Traces including: query, retrieved documents, ground truth relevant documents (for recall measurement)","Optional: relevance labels or annotations for precision measurement"],"input_types":["user query","retrieved document list with ranking scores","ground truth relevant documents (optional)","document metadata (date, source, category)"],"output_types":["precision/recall metrics","failure mode classification (missing docs, ranking errors, semantic gaps)","pattern analysis (e.g., 'date-based queries fail 40% of the time')","actionable recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_3","uri":"capability://automation.workflow.production.traffic.monitoring.with.real.time.alerting","name":"production traffic monitoring with real-time alerting","description":"Ingests 100% of production traces from LLM and RAG applications, analyzes them against evaluation metrics in real-time, and triggers alerts when quality degrades or anomalies are detected. Supports trace-based pricing (5K-unlimited traces/month depending on tier) with configurable alert thresholds for hallucination rates, latency, cost, and custom metrics, enabling teams to catch production issues before users report them.","intents":["I need to monitor my production RAG system for quality regressions in real-time","I want to set up alerts when hallucination rate exceeds 5% or latency spikes","I need to track evaluation metrics across millions of production requests without sampling"],"best_for":["production teams running RAG or agent systems with SLAs","enterprises requiring 24/7 monitoring with real-time alerting","teams using Luna models for cost-effective evaluation at scale"],"limitations":["Definition of 'trace' unclear — pricing is per-trace but whether a trace = request, conversation, or evaluation run is not documented","Free tier limited to 5,000 traces/month (~167/day) — insufficient for high-volume production systems","Real-time alerting latency not specified — 'real-time' is marketing language without SLA","Alert routing and notification channels not documented — unclear if supports PagerDuty, Slack, email, webhooks"],"requires":["Galileo Observe account (minimum free tier for basic monitoring)","Integration with Galileo trace ingestion API or MCP server","Instrumentation of LLM/RAG application to emit traces with model outputs, context, latency, cost","For Enterprise: deployment option (hosted, VPC, or on-premises)"],"input_types":["application traces (model, prompt, context, output, latency, cost)","alert threshold configuration","evaluation metric definitions"],"output_types":["real-time dashboards with metric timeseries","alert notifications (channel/format unknown)","trace-level drill-down for root cause analysis"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_4","uri":"capability://data.processing.analysis.luna.model.based.evaluation.with.cost.optimization","name":"luna model-based evaluation with cost optimization","description":"Runs evaluation using distilled, compact Luna models instead of full-size LLM-as-judge evaluators, achieving claimed 97% cost reduction while maintaining evaluation quality. Luna models are proprietary to Galileo and optimized for specific evaluation tasks (hallucination detection, context adherence, etc.), running on dedicated inference servers with low-latency guarantees for production use.","intents":["I need to evaluate LLM outputs at scale without the cost of running GPT-4 as a judge","I want to run evaluations in production with sub-second latency for real-time feedback","I need to reduce my evaluation costs by 97% compared to LLM-as-judge approaches"],"best_for":["teams evaluating high-volume production traffic (100K+ traces/month)","cost-sensitive organizations needing evaluation at scale","enterprises requiring low-latency evaluation for real-time guardrails"],"limitations":["Luna model accuracy not benchmarked publicly — claims 'research-backed' but no F1 scores, comparison to GPT-4 judge, or validation dataset disclosed","Distillation process not documented — unclear how Luna models are trained, what data they use, or how they generalize to new domains","Luna models are proprietary and locked to Galileo platform — cannot be exported or run independently","Latency guarantees mentioned for Enterprise tier only — free/Pro tier latency SLAs unknown","Unclear which evaluation tasks have Luna models available — documentation lists 20+ evals but doesn't specify which use Luna vs LLM-as-judge"],"requires":["Galileo Observe account (Luna models available on all tiers but Enterprise gets dedicated inference servers)","Trace ingestion with model outputs and context","For Enterprise: dedicated inference servers for low-latency guarantees"],"input_types":["LLM output text","source context/documents","evaluation task specification (hallucination, adherence, etc.)"],"output_types":["evaluation score (0-1 range implied)","evaluation latency (milliseconds)","cost per evaluation (documented example: $0.0733 for GPT-4o with 3 judges, Luna cost unknown)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_5","uri":"capability://data.processing.analysis.custom.evaluation.definition.and.execution","name":"custom evaluation definition and execution","description":"Allows teams to define custom evaluation logic beyond the 20+ built-in evaluators, enabling domain-specific quality checks tailored to application requirements. Supports unlimited custom evaluators on all pricing tiers and integrates with the trace ingestion pipeline to run custom logic against production data, though the mechanism for defining custom evaluators (code, YAML, UI builder) is not documented.","intents":["I need to evaluate my domain-specific LLM outputs against custom criteria not covered by built-in evals","I want to run proprietary evaluation logic on production traces without exporting data","I need to measure application-specific quality metrics like 'response follows company tone guidelines'"],"best_for":["teams with specialized evaluation requirements (domain-specific quality criteria)","enterprises with proprietary evaluation methodologies","developers building custom evaluation frameworks on top of Galileo"],"limitations":["Custom evaluator definition mechanism completely undocumented — unclear if uses Python code, YAML, UI builder, or other approach","No documentation on custom evaluator performance characteristics — latency, cost, resource limits unknown","Unclear if custom evaluators can call external APIs or are limited to local computation","No version control or testing framework mentioned for custom evaluators","Unlimited custom evals claim is marketing language — likely has practical limits on execution time/cost"],"requires":["Galileo Observe account (custom evals available on all tiers)","Understanding of custom evaluator definition syntax/framework (unknown)","Traces with appropriate data fields for custom evaluation logic"],"input_types":["trace data (model output, context, metadata)","custom evaluator definition (format unknown)"],"output_types":["custom evaluation score or result","integration with Galileo dashboards and alerting"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_6","uri":"capability://planning.reasoning.agent.behavior.analysis.and.tool.selection.evaluation","name":"agent behavior analysis and tool selection evaluation","description":"Evaluates agent decision-making by analyzing tool selection accuracy, action sequences, and failure modes in agentic workflows. Tracks whether agents select appropriate tools for tasks, identifies when agents get stuck in loops or make incorrect decisions, and provides visibility into multi-step reasoning patterns across production agent deployments.","intents":["I need to monitor whether my agent is selecting the right tools for user requests","I want to identify when agents fail to complete tasks and understand why","I need to measure agent success rates and identify common failure patterns"],"best_for":["teams building production agent systems (ReAct, tool-use agents)","developers debugging agent decision-making and tool selection","enterprises monitoring agent reliability and safety"],"limitations":["Agent evaluation metrics not detailed — documentation shows example (67% tool selection accuracy) but methodology unclear","Unclear how platform handles multi-step agent reasoning — does it evaluate each step or final outcome?","No documentation on how agent loops/failures are detected or classified","Requires agent traces to include tool calls and outcomes — cannot evaluate agents without explicit tool-use instrumentation"],"requires":["Galileo Observe account with agent eval capability","Agent traces including: tool calls, tool selection rationale, tool outcomes, final action","Ground truth labels for tool selection correctness (optional, for accuracy measurement)"],"input_types":["agent trace with tool calls and outcomes","tool definitions and descriptions","user intent/query","ground truth correct tool selection (optional)"],"output_types":["tool selection accuracy score","agent success/failure classification","failure mode analysis (wrong tool, tool error, loop detection)","multi-step reasoning visualization"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_7","uri":"capability://safety.moderation.safety.and.security.evaluation.with.guardrails","name":"safety and security evaluation with guardrails","description":"Evaluates LLM outputs for safety risks including harmful content, prompt injection vulnerabilities, jailbreak attempts, and policy violations. Provides both evaluation metrics for monitoring safety in production and real-time guardrails (Enterprise tier) that can block unsafe outputs before they reach users, with integration to NVIDIA NeMo Guardrails for additional safety controls.","intents":["I need to detect when my LLM generates harmful, biased, or policy-violating content","I want to block unsafe outputs in real-time before users see them","I need to monitor safety metrics across production to ensure compliance"],"best_for":["enterprises deploying LLMs in regulated industries (healthcare, finance, legal)","teams building customer-facing LLM applications requiring safety guarantees","organizations with strict content moderation requirements"],"limitations":["Safety evaluation metrics not detailed — unclear which specific harms are detected (toxicity, bias, PII, jailbreak, etc.)","Real-time guardrails only available on Enterprise tier — free/Pro tiers limited to evaluation/monitoring","Integration with NVIDIA NeMo Guardrails mentioned but not documented — unclear how it works or what additional safety controls it provides","No documentation on false positive rates for safety evaluations — overly aggressive guardrails could block legitimate use cases"],"requires":["Galileo Observe account (safety evals on all tiers, guardrails on Enterprise only)","For guardrails: Enterprise tier with dedicated inference servers","Optional: NVIDIA NeMo Guardrails integration for additional safety controls"],"input_types":["LLM output text","user input/prompt (for jailbreak detection)","safety policy definitions (for policy violation detection)"],"output_types":["safety score (0-1 range implied)","harm classification (toxicity, bias, PII, jailbreak, policy violation, etc.)","guardrail action (block, flag, allow) for Enterprise tier"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_8","uri":"capability://data.processing.analysis.evaluation.dataset.management.with.synthetic.and.production.data","name":"evaluation dataset management with synthetic and production data","description":"Manages evaluation datasets built from synthetic data, development data, and live production traces, with support for subject matter expert annotations and versioning. Enables teams to build evaluation datasets from production failures, curate them with expert labels, and use them for continuous evaluation and model improvement without manual data collection.","intents":["I need to build evaluation datasets from my production failures to prevent regressions","I want to curate and annotate evaluation data with domain expert labels","I need to version and track evaluation datasets as my system evolves"],"best_for":["teams building evaluation datasets from production data","organizations with domain experts available for annotation","developers iterating on model/prompt improvements with evaluation-driven development"],"limitations":["Dataset management features not detailed — unclear if supports versioning, branching, or collaborative annotation","Annotation workflow not documented — unclear if uses Galileo UI, external tools, or API","No documentation on dataset size limits or storage costs","Unclear how synthetic data is generated — if using LLMs, cost implications unknown"],"requires":["Galileo Observe account with dataset management capability","Production traces or development data to build datasets from","Optional: subject matter experts for annotation"],"input_types":["production traces (for failure-based datasets)","development data","synthetic data generation prompts (if applicable)","annotation labels from experts"],"output_types":["versioned evaluation datasets","dataset statistics (size, label distribution, etc.)","integration with evaluation pipelines"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__cap_9","uri":"capability://tool.use.integration.trace.ingestion.and.context.management.via.mcp.server","name":"trace ingestion and context management via mcp server","description":"Ingests application traces through a Model Context Protocol (MCP) server integration, capturing models, prompts, functions, context, datasets, and traces in a structured format. Enables seamless integration with LLM applications and agents without requiring custom API clients, with automatic context extraction and storage for evaluation and analysis.","intents":["I need to send traces from my LLM application to Galileo without writing custom integration code","I want to automatically capture context, prompts, and outputs in a structured format","I need to integrate Galileo observability into my existing MCP-compatible application"],"best_for":["teams using MCP-compatible LLM frameworks and tools","developers wanting minimal integration overhead for observability","applications already using MCP for tool/context management"],"limitations":["MCP server integration details not documented — unclear what MCP operations are supported or how context is extracted","Trace schema not documented — unclear what fields are required vs optional","No documentation on MCP server latency or throughput limits","Unclear if MCP integration is available on all pricing tiers or Enterprise-only"],"requires":["Galileo Observe account with MCP integration enabled","MCP-compatible application or framework","MCP server running and configured to connect to Galileo"],"input_types":["MCP protocol messages with model, prompt, context, function calls","application traces"],"output_types":["structured traces in Galileo format","automatic context extraction and storage"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"galileo-observe__headline","uri":"capability://observability.ai.observability.and.evaluation.platform.for.rag.and.llm.applications","name":"ai observability and evaluation platform for rag and llm applications","description":"Galileo Observe is an AI observability platform designed to enhance the performance of RAG and LLM applications through automated hallucination detection, context adherence scoring, and real-time monitoring, ensuring reliable AI outputs.","intents":["best AI observability platform","AI evaluation tools for RAG applications","how to monitor LLM performance","automated hallucination detection solutions","real-time AI performance monitoring tools"],"best_for":["AI developers","data scientists","machine learning engineers"],"limitations":[],"requires":[],"input_types":["synthetic data","development data","live production data"],"output_types":["evaluation scores","alerts for failures","performance metrics"],"categories":["observability"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Active Galileo Observe account (free tier: 5,000 traces/month minimum)","Integration with Galileo trace ingestion API or MCP server","Source context/documents available in trace payload for comparison","Galileo Observe account with trace ingestion enabled","RAG pipeline instrumented to include retrieved documents/context in trace payloads","LLM-generated responses paired with source context in same trace","Galileo Observe account with sufficient trace volume (pattern detection likely requires 1000+ traces minimum)","Diverse trace data capturing different failure modes","Optional: ground truth labels for failure classification","Enterprise tier Galileo Observe account"],"failure_modes":["Hallucination detection accuracy not benchmarked in public documentation — claims 'research-backed' but no F1 scores or comparison to baselines provided","Mechanism for detecting hallucinations unclear — likely uses LLM-as-judge or Luna models but specific approach not disclosed","May produce false positives on edge cases like creative writing or speculative reasoning where hallucination is intentional","Scoring mechanism not detailed — unclear if uses embedding similarity, LLM-as-judge, or hybrid approach","No documentation on how context adherence score handles multi-document reasoning or conflicting information in retrieved context","Requires context to be explicitly included in traces — cannot retroactively evaluate systems without context payloads","Pattern detection mechanism not documented — unclear if uses statistical analysis, clustering, or LLM-based analysis","Recommendation generation not detailed — examples given ('Add few-shot examples') but methodology unknown","Unclear how patterns are ranked/prioritized — which failures get recommendations first?","Recommendations are examples only — no documentation on coverage or accuracy of recommendations","builder identity is not verified yet","no observed match outcomes 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