{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"arize-phoenix","slug":"arize-phoenix","name":"Arize Phoenix","type":"repo","url":"https://github.com/Arize-ai/phoenix","page_url":"https://unfragile.ai/arize-phoenix","categories":["observability","rag-knowledge","model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"arize-phoenix__cap_0","uri":"capability://data.processing.analysis.opentelemetry.native.span.ingestion.and.storage","name":"opentelemetry-native span ingestion and storage","description":"Accepts OpenTelemetry Protocol (OTLP) traces via gRPC server on port 4317, parses span hierarchies with parent-child relationships, and persists them to PostgreSQL or SQLite with automatic schema migrations. Implements the full OTLP specification for trace collection without requiring vendor lock-in or custom instrumentation adapters.","intents":["Ingest traces from any OpenTelemetry-instrumented Python or Node.js application without custom adapters","Store multi-span traces with full parent-child relationships for distributed tracing analysis","Run Phoenix locally or in production with flexible database backends (PostgreSQL for scale, SQLite for dev)"],"best_for":["Teams already using OpenTelemetry instrumentation in their LLM applications","Developers building observability into FastAPI, LangChain, or LlamaIndex applications","Organizations requiring on-premise or self-hosted trace storage without cloud dependencies"],"limitations":["gRPC server requires network connectivity; no built-in batching or local buffering for offline scenarios","SQLite backend suitable only for development; PostgreSQL required for production workloads >10K spans/day","No automatic trace sampling — all spans ingested consume storage; requires client-side sampling configuration"],"requires":["OpenTelemetry SDK for Python (opentelemetry-api >= 1.0) or TypeScript (opentelemetry-api >= 1.4)","PostgreSQL 12+ or SQLite 3.30+ for storage backend","Network access to Phoenix gRPC server on port 4317 (default)"],"input_types":["OTLP ExportTraceServiceRequest (protobuf)","Span attributes (key-value pairs, strings, numbers, booleans)","Span events and links with timestamps"],"output_types":["Persisted span records in database","Span hierarchies queryable via GraphQL/REST APIs"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_1","uri":"capability://search.retrieval.span.level.trace.querying.and.filtering.via.graphql","name":"span-level trace querying and filtering via graphql","description":"Exposes a Strawberry GraphQL API (api/schema.py) that enables complex queries over ingested spans with filters on span name, status, duration, attributes, and parent-child relationships. Supports cursor-based pagination and aggregations (count, latency percentiles) without requiring SQL knowledge, allowing developers to programmatically extract trace subsets for analysis.","intents":["Query traces by LLM model name, token count, or retrieval source to debug specific application paths","Filter spans by error status or latency thresholds to identify performance bottlenecks in RAG pipelines","Retrieve span hierarchies with full context (parent spans, child spans, attributes) for root-cause analysis"],"best_for":["Data scientists and ML engineers analyzing LLM application behavior programmatically","Backend developers building custom dashboards or alerting on top of Phoenix traces","Teams integrating trace analysis into CI/CD pipelines for automated quality gates"],"limitations":["GraphQL queries execute against database directly; no query optimization or caching layer for repeated queries","Complex nested queries (>5 levels deep) may timeout on large datasets (>1M spans); requires manual pagination","No built-in time-series aggregations; requires client-side computation for latency trends over time"],"requires":["HTTP access to Phoenix server on port 6006","GraphQL client library (graphql-core for Python, apollo-client for JavaScript) or curl/HTTP client","Understanding of GraphQL query syntax"],"input_types":["GraphQL query strings with filters (span name, status, attribute predicates)","Pagination cursors and limits"],"output_types":["Span objects with attributes, events, links, and parent/child references","Aggregated metrics (count, min/max/p50/p99 latency)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_10","uri":"capability://data.processing.analysis.span.attribute.annotation.and.feedback.collection","name":"span attribute annotation and feedback collection","description":"Provides APIs and UI for adding human feedback and annotations to spans after they are ingested (e.g., marking a retrieval result as 'relevant' or 'irrelevant', or adding a human score to an LLM response). Feedback is stored separately from spans and linked via span ID, enabling human-in-the-loop evaluation and ground-truth dataset creation from production traces.","intents":["Collect human feedback on LLM responses in production to create ground-truth labels for model training","Annotate retrieval results as relevant/irrelevant to build a labeled dataset for retrieval evaluation","Mark spans with quality scores (1-5 stars) to track user satisfaction over time"],"best_for":["Teams building labeled datasets from production traces for model fine-tuning","Organizations collecting human feedback on LLM outputs for continuous improvement","Researchers creating ground-truth datasets for RAG or LLM evaluation benchmarks"],"limitations":["Feedback collection is manual (UI or API); no automatic feedback from user interactions (e.g., thumbs up/down buttons)","Feedback is not versioned; overwriting feedback loses historical annotations","No built-in workflow for feedback review or conflict resolution when multiple annotators disagree","Feedback storage is separate from spans; requires JOIN queries to correlate feedback with span data"],"requires":["Phoenix server with database backend","Span ID to link feedback to spans","Feedback schema definition (e.g., categorical labels, numeric scores)"],"input_types":["Span ID","Feedback value (text, numeric score, categorical label)","Annotator metadata (user ID, timestamp)"],"output_types":["Feedback records linked to spans","Feedback statistics (distribution of labels, average scores)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_11","uri":"capability://data.processing.analysis.batch.span.export.and.dataset.creation.from.traces","name":"batch span export and dataset creation from traces","description":"Provides APIs to export spans matching query criteria (e.g., all spans from the last 7 days, or spans with error status) into structured datasets (CSV, JSON, Parquet) for external analysis. Supports filtering, sampling, and transformation (e.g., extracting input/output pairs for fine-tuning datasets) during export.","intents":["Export production traces to create fine-tuning datasets for custom LLM models","Extract failed spans for root-cause analysis in external tools (Jupyter, Pandas)","Create benchmark datasets from historical traces for evaluating new models or prompts"],"best_for":["ML engineers preparing training data from production LLM traces","Data analysts performing exploratory analysis on trace data in Jupyter notebooks","Teams building evaluation benchmarks from historical production data"],"limitations":["Export is one-time snapshot; no automatic incremental exports or streaming","Large exports (>100K spans) may timeout or consume significant memory; requires pagination or sampling","Transformation logic is limited to simple field extraction; complex transformations require post-processing","Exported data loses Phoenix-specific metadata (span hierarchy, parent-child relationships); requires custom reconstruction"],"requires":["Phoenix server with database backend","Query filters to select spans for export","External storage (S3, local filesystem) for exported files"],"input_types":["Span query filters (time range, status, attributes)","Export format (CSV, JSON, Parquet)","Transformation rules (field selection, renaming)"],"output_types":["Exported dataset files (CSV, JSON, Parquet)","Dataset metadata (row count, schema)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_12","uri":"capability://safety.moderation.multi.user.authentication.and.role.based.access.control","name":"multi-user authentication and role-based access control","description":"Implements authentication and authorization (Authentication & Authorization section in DeepWiki) supporting multiple user types (admin, viewer, editor) with fine-grained permissions on datasets, experiments, and traces. Integrates with OAuth2 or API key authentication for programmatic access, and supports RBAC policies for multi-tenant deployments.","intents":["Restrict trace visibility to specific teams or projects in a multi-tenant Phoenix deployment","Grant read-only access to stakeholders (product managers, executives) without allowing modifications","Enforce audit trails by tracking which user modified which dataset or experiment"],"best_for":["Enterprise teams deploying Phoenix in multi-tenant environments","Organizations with compliance requirements (SOC2, HIPAA) needing access control and audit logs","Teams sharing a single Phoenix instance across multiple projects or departments"],"limitations":["RBAC is coarse-grained (admin/viewer/editor); no fine-grained field-level permissions","OAuth2 integration requires external identity provider (Okta, Auth0); no built-in user management","API key authentication is basic (no key rotation, expiration, or scoping); requires external key management","Audit logs are not queryable via UI; requires database access to review access history"],"requires":["OAuth2 provider (Okta, Auth0, Google) or API key management system","User database or directory service (LDAP, Active Directory)","Phoenix server configured with authentication backend"],"input_types":["User credentials (OAuth2 tokens, API keys)","Role assignments (admin, viewer, editor)","Resource permissions (dataset ID, experiment ID)"],"output_types":["Access tokens (JWT, OAuth2 bearer tokens)","Audit logs (user ID, action, resource, timestamp)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_13","uri":"capability://automation.workflow.kubernetes.native.deployment.with.helm.charts.and.kustomize","name":"kubernetes-native deployment with helm charts and kustomize","description":"Provides production-ready Kubernetes manifests (kustomize/ directory) and Helm charts for deploying Phoenix server, PostgreSQL, and supporting services as a scalable cluster. Includes configuration for resource limits, health checks, persistent volumes, and horizontal pod autoscaling based on trace ingestion rate.","intents":["Deploy Phoenix to a Kubernetes cluster with automatic scaling based on trace volume","Run Phoenix in a multi-replica configuration for high availability and fault tolerance","Integrate Phoenix into existing Kubernetes-based observability stacks (Prometheus, Grafana, ELK)"],"best_for":["DevOps teams managing Kubernetes clusters and wanting to deploy Phoenix at scale","Organizations with existing Kubernetes infrastructure and standardized deployment processes","Teams requiring high availability and automatic failover for observability platform"],"limitations":["Kubernetes deployment adds operational complexity; requires Kubernetes expertise and monitoring","Helm charts are opinionated (e.g., PostgreSQL backend required); customization requires chart modification","Persistent volume provisioning depends on cluster storage classes; may require manual configuration","Horizontal scaling is limited by database bottleneck (PostgreSQL); requires read replicas or sharding for >100K spans/sec"],"requires":["Kubernetes cluster 1.20+ with kubectl access","Helm 3.0+ or Kustomize 4.0+","PostgreSQL 12+ for production (or managed service like RDS)","Persistent volume provisioner (e.g., EBS, NFS)"],"input_types":["Helm values.yaml or Kustomize patches for configuration","Container image registry (Docker Hub, ECR, GCR)","PostgreSQL connection string"],"output_types":["Kubernetes Deployment, Service, StatefulSet, ConfigMap resources","Prometheus metrics for monitoring"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_2","uri":"capability://automation.workflow.automatic.llm.span.instrumentation.via.python.opentelemetry.wrapper","name":"automatic llm span instrumentation via python opentelemetry wrapper","description":"The arize-phoenix-otel package provides auto-instrumentation decorators and context managers that wrap LLM calls (OpenAI, Anthropic, LlamaIndex, LangChain) and automatically emit spans with model name, token counts, latency, and error status. Uses Python's contextvars for automatic parent-child span linking without manual trace ID propagation.","intents":["Instrument LLM applications with zero changes to business logic using decorators or context managers","Automatically capture token counts and model names from LLM API responses for cost and performance tracking","Trace nested LLM calls (e.g., LangChain agent → multiple LLM calls) with automatic parent-child relationships"],"best_for":["Python developers using LangChain, LlamaIndex, or direct OpenAI/Anthropic SDK calls","Teams wanting observability without rewriting application code","Rapid prototyping scenarios where minimal instrumentation overhead is critical"],"limitations":["Python-only; no auto-instrumentation for Node.js LLM libraries (requires manual TypeScript client)","Decorator approach requires application code to import and apply decorators; not fully transparent like Java agents","Token count extraction depends on LLM API response format; custom models or local LLMs may not expose token counts"],"requires":["Python 3.9+","arize-phoenix-otel package installed (pip install arize-phoenix-otel)","OpenTelemetry SDK (opentelemetry-api, opentelemetry-sdk)","OTLP exporter configured to point to Phoenix server"],"input_types":["Python function calls to LLM APIs (OpenAI, Anthropic, etc.)","Decorator or context manager wrapping"],"output_types":["OTLP spans with model name, token counts, latency, error status","Automatic parent-child span relationships via context propagation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_3","uri":"capability://planning.reasoning.evaluation.framework.with.llm.as.judge.and.custom.metrics","name":"evaluation framework with llm-as-judge and custom metrics","description":"The arize-phoenix-evals package provides a pluggable evaluation system that runs LLM-based judges (using OpenAI, Anthropic, or local models) to score span outputs against criteria (relevance, hallucination, toxicity). Supports custom Python evaluation functions, batch evaluation over datasets, and integration with experiment tracking for A/B testing LLM prompts or models.","intents":["Evaluate RAG retrieval quality by scoring relevance of retrieved documents against queries using an LLM judge","Detect hallucinations in LLM responses by comparing generated text against source documents","Run batch evaluations over historical traces to establish baseline quality metrics for regression detection"],"best_for":["ML teams building quality gates for LLM applications (e.g., reject responses below relevance threshold)","Researchers comparing LLM models or prompt variations using automated evaluation","Production systems requiring continuous quality monitoring via periodic batch evaluations"],"limitations":["LLM-as-judge evaluations are non-deterministic and may vary across runs; requires multiple runs for statistical significance","Evaluation latency scales with number of spans; batch evaluation of 10K spans may take 30+ minutes with rate-limited APIs","Custom evaluation functions require Python code; no low-code UI for defining evaluation logic","No built-in cost estimation; LLM judge calls incur API costs (e.g., $0.01 per evaluation with GPT-4)"],"requires":["arize-phoenix-evals package (pip install arize-phoenix-evals)","API key for LLM judge (OpenAI, Anthropic, or local model endpoint)","Spans with populated 'output' and 'metadata' attributes for evaluation context"],"input_types":["Span objects with input, output, and context attributes","Evaluation criteria as natural language prompts or Python functions","Dataset of test cases (query, expected output, context)"],"output_types":["Evaluation scores (0-1 or categorical: pass/fail)","Evaluation explanations from LLM judges","Aggregated metrics (pass rate, average score) per experiment"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_4","uri":"capability://automation.workflow.experiment.tracking.and.dataset.management.for.prompt.model.comparison","name":"experiment tracking and dataset management for prompt/model comparison","description":"Provides a dataset and experiment system (Datasets & Experiments feature) that allows users to create versioned datasets of test cases, run experiments comparing different LLM prompts or models against those datasets, and track evaluation results over time. Integrates with the evaluation framework to automatically score experiment runs and surface performance deltas.","intents":["Create a golden dataset of Q&A pairs and run A/B tests comparing two prompt variations on the same dataset","Track performance metrics (accuracy, latency, cost) across multiple LLM model versions to identify regressions","Version control datasets and experiments for reproducibility and audit trails in regulated environments"],"best_for":["Product teams iterating on LLM prompts and needing structured A/B testing","ML engineers managing multiple LLM model versions in production","Compliance-heavy organizations requiring audit trails of model changes and performance"],"limitations":["Experiment runs are manual (via API or UI); no built-in scheduling for continuous A/B testing","Dataset versioning is basic (snapshots); no branching or merging workflows for collaborative dataset curation","No statistical significance testing; requires external tools (scipy, statsmodels) to determine if deltas are meaningful","Experiment results stored in Phoenix database; no integration with MLflow or Weights & Biases for centralized experiment tracking"],"requires":["Phoenix server with database backend","Dataset defined as list of test cases (input, expected output, context)","Evaluation function to score experiment runs"],"input_types":["Dataset: list of dicts with 'input', 'expected_output', 'context' keys","Experiment config: model name, prompt template, hyperparameters","Evaluation criteria"],"output_types":["Experiment run results: scores per test case, aggregated metrics","Performance comparison: delta in accuracy, latency, cost between runs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_5","uri":"capability://data.processing.analysis.retrieval.evaluation.with.embedding.based.similarity.scoring","name":"retrieval evaluation with embedding-based similarity scoring","description":"Specialized evaluation module for RAG systems that scores retrieval quality by computing embedding similarity between queries and retrieved documents, and between retrieved documents and ground-truth relevant documents. Supports multiple embedding models (OpenAI, Cohere, local) and metrics (cosine similarity, NDCG, MRR) for ranking evaluation.","intents":["Evaluate RAG retrieval quality by measuring how well retrieved documents match the query intent using embeddings","Detect when retrieval degrades (e.g., after vector DB index update) by comparing embedding similarity scores","Rank retrieval strategies (BM25 vs semantic search vs hybrid) using NDCG or MRR metrics on a test dataset"],"best_for":["RAG system builders optimizing retrieval pipelines (vector DB, embedding model, reranking)","Teams monitoring retrieval quality in production and needing automated degradation alerts","Researchers comparing retrieval strategies with standardized ranking metrics"],"limitations":["Embedding-based evaluation requires ground-truth relevance labels; no automatic label generation","Similarity scores are relative (0-1) and model-dependent; scores from different embedding models are not comparable","NDCG and MRR require ranked lists of documents; cannot evaluate single-document retrieval scenarios","Embedding computation adds latency (~100ms per document with API calls); batch evaluation of large corpora is slow"],"requires":["arize-phoenix-evals package with retrieval evaluation module","Embedding model API key (OpenAI, Cohere) or local embedding endpoint","Spans with 'retrieved_documents' and 'ground_truth_documents' attributes"],"input_types":["Query text","Retrieved document texts (list)","Ground-truth relevant document texts (list)","Embedding model selection (OpenAI, Cohere, local)"],"output_types":["Embedding similarity scores (0-1)","Ranking metrics: NDCG, MRR, precision@k","Per-document relevance scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_6","uri":"capability://tool.use.integration.rest.api.with.openapi.schema.for.programmatic.trace.access","name":"rest api with openapi schema for programmatic trace access","description":"Exposes a REST API (api/routes.py) with OpenAPI/Swagger documentation that mirrors GraphQL capabilities, enabling non-GraphQL clients (curl, Postman, REST-only frameworks) to query spans, create datasets, and trigger evaluations. Provides JSON request/response format with standard HTTP status codes and error messages.","intents":["Query traces from shell scripts or CI/CD pipelines using curl without GraphQL client libraries","Integrate Phoenix with monitoring systems (Prometheus, Grafana) that expect REST APIs","Build custom dashboards in tools that don't support GraphQL (e.g., Metabase, Superset)"],"best_for":["DevOps engineers integrating Phoenix into existing monitoring stacks","Teams using REST-only tools (Postman, Insomnia) for API exploration","Shell script automation for trace extraction and analysis"],"limitations":["REST API is less efficient than GraphQL for complex queries (multiple round-trips required for nested data)","No built-in request batching; querying 100 spans requires 100 separate HTTP requests","OpenAPI schema is auto-generated and may not document all filter options; GraphQL schema is more discoverable"],"requires":["HTTP client (curl, requests library, Postman)","HTTP access to Phoenix server on port 6006","OpenAPI schema documentation (available at /openapi.json)"],"input_types":["HTTP GET/POST requests with JSON payloads","Query parameters for filtering (span_name, status, limit, offset)"],"output_types":["JSON response with span objects","HTTP status codes (200, 400, 404, 500)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_7","uri":"capability://text.generation.language.interactive.playground.for.prompt.testing.and.iteration","name":"interactive playground for prompt testing and iteration","description":"Web-based playground interface (Playground System in frontend) that allows users to write and test LLM prompts directly against live data, with support for variable substitution, model selection, and response comparison. Integrates with Phoenix traces to use real historical data as test inputs, enabling prompt iteration without leaving the platform.","intents":["Test prompt variations against real user queries from production traces without writing code","Compare responses from different LLM models on the same prompt to identify best-performing model","Iterate on prompt templates with variable substitution (e.g., {{context}}, {{query}}) and see results in real-time"],"best_for":["Non-technical product managers and content creators optimizing LLM prompts","Prompt engineers rapidly iterating on templates before committing to code","Teams wanting a low-code interface for prompt A/B testing"],"limitations":["Playground is UI-only; no programmatic API for automated prompt testing","Variable substitution is basic (string templating); no support for complex logic or conditionals","Response comparison is manual (side-by-side viewing); no automated scoring or ranking of responses","Playground state is not persisted; closing the browser tab loses unsaved prompt iterations"],"requires":["Web browser with JavaScript enabled","Access to Phoenix web UI on port 6006","LLM API key (OpenAI, Anthropic) configured in Phoenix settings"],"input_types":["Prompt template text with {{variable}} placeholders","Model selection (GPT-4, Claude, etc.)","Test inputs from Phoenix traces or manual text entry"],"output_types":["LLM response text","Token count and latency","Side-by-side comparison of responses from different models"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_8","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.for.ai.agent.integration","name":"mcp (model context protocol) server for ai agent integration","description":"Exposes Phoenix as an MCP server (js/packages/phoenix-mcp) that allows Claude, ChatGPT, and other AI agents to query traces, run evaluations, and manage datasets through natural language. Implements MCP resource and tool protocols, enabling agents to autonomously analyze LLM application performance and suggest optimizations.","intents":["Ask Claude to analyze traces and identify performance bottlenecks using natural language queries","Have an AI agent automatically run evaluations on new traces and report quality regressions","Enable agents to suggest prompt improvements based on historical trace analysis"],"best_for":["Teams using Claude or ChatGPT for autonomous LLM application analysis","Developers building AI agents that need to introspect LLM application behavior","Organizations wanting natural language interfaces to observability data"],"limitations":["MCP server is read-only for most operations; no write support for modifying traces or datasets","Agent responses are non-deterministic and may hallucinate or misinterpret trace data","MCP protocol is still evolving; compatibility with new Claude versions not guaranteed","No built-in rate limiting; agents can overwhelm Phoenix server with rapid queries"],"requires":["Phoenix MCP server package (js/packages/phoenix-mcp)","Node.js 18+ runtime","Claude API key or local Claude instance","MCP client integration in Claude or custom agent framework"],"input_types":["Natural language queries from AI agents","MCP resource requests (trace IDs, dataset names)","MCP tool calls (run_evaluation, query_spans)"],"output_types":["Trace data in JSON format","Evaluation results","Natural language summaries from agent"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__cap_9","uri":"capability://data.processing.analysis.distributed.tracing.with.automatic.parent.child.span.linking","name":"distributed tracing with automatic parent-child span linking","description":"Implements OpenTelemetry context propagation using Python contextvars and JavaScript async context to automatically link parent and child spans across function calls and async boundaries. Traces are reconstructed as hierarchical trees in the database, enabling visualization of full request flows through multi-step LLM applications (e.g., agent → tool call → LLM call → retrieval).","intents":["Trace a user request through an LLM agent that makes multiple tool calls and LLM invocations, seeing the full call tree","Identify which step in a multi-step LLM pipeline is causing latency by examining span durations in the trace tree","Correlate errors in downstream services (retrieval, embedding) with upstream LLM calls that triggered them"],"best_for":["Teams building complex LLM agents with multiple steps and tool calls","Developers debugging multi-service LLM applications (LLM + vector DB + API calls)","Organizations needing end-to-end visibility into request flows"],"limitations":["Context propagation requires explicit instrumentation at async boundaries; automatic propagation is not guaranteed across all async patterns","Trace tree reconstruction assumes correct parent-child span IDs; malformed trace IDs result in orphaned spans","Large trace trees (>1000 spans) may be slow to render in the UI; requires pagination or filtering","Cross-service tracing requires trace ID propagation headers (traceparent, tracestate); not automatic across service boundaries"],"requires":["OpenTelemetry SDK with context propagation support","Instrumentation at each function/service boundary to create child spans","Trace ID propagation headers for cross-service tracing (HTTP headers, message metadata)"],"input_types":["Parent span ID and trace ID from context","Child span creation with parent span ID reference"],"output_types":["Hierarchical span tree with parent-child relationships","Trace visualization showing call flow and timing"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arize-phoenix__headline","uri":"capability://data.processing.analysis.open.source.observability.platform.for.llm.applications","name":"open-source observability platform for llm applications","description":"Arize Phoenix is an open-source observability platform designed specifically for tracing, evaluating, and managing datasets in LLM applications, enabling developers to analyze and visualize performance effectively.","intents":["best observability platform for LLMs","open-source tool for LLM evaluation","how to trace LLM applications","LLM dataset management solutions","best tools for LLM experiment tracking"],"best_for":["developers working with LLMs","data scientists evaluating AI models"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["OpenTelemetry SDK for Python (opentelemetry-api >= 1.0) or TypeScript (opentelemetry-api >= 1.4)","PostgreSQL 12+ or SQLite 3.30+ for storage backend","Network access to Phoenix gRPC server on port 4317 (default)","HTTP access to Phoenix server on port 6006","GraphQL client library (graphql-core for Python, apollo-client for JavaScript) or curl/HTTP client","Understanding of GraphQL query syntax","Phoenix server with database backend","Span ID to link feedback to spans","Feedback schema definition (e.g., categorical labels, numeric scores)","Query filters to select spans for export"],"failure_modes":["gRPC server requires network connectivity; no built-in batching or local buffering for offline scenarios","SQLite backend suitable only for development; PostgreSQL required for production workloads >10K spans/day","No automatic trace sampling — all spans ingested consume storage; requires client-side sampling configuration","GraphQL queries execute against database directly; no query optimization or caching layer for repeated queries","Complex nested queries (>5 levels deep) may timeout on large datasets (>1M spans); requires manual pagination","No built-in time-series aggregations; requires client-side computation for latency trends over time","Feedback collection is manual (UI or API); no automatic feedback from user interactions (e.g., thumbs up/down buttons)","Feedback is not versioned; overwriting feedback loses historical annotations","No built-in workflow for feedback review or conflict resolution when multiple annotators disagree","Feedback storage is separate from spans; requires JOIN queries to correlate feedback with span data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.6,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","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=arize-phoenix","compare_url":"https://unfragile.ai/compare?artifact=arize-phoenix"}},"signature":"ACX6eh2P1akY2mSbyhW+MCNT5t7FxKs5MVvPnUu6rTNAsNAjMu3VGesEf62qj2QaHhQFGqjClJBg/zMi1phZCg==","signedAt":"2026-06-21T03:42:44.807Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/arize-phoenix","artifact":"https://unfragile.ai/arize-phoenix","verify":"https://unfragile.ai/api/v1/verify?slug=arize-phoenix","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"}}