{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-arize-ai--phoenix","slug":"arize-ai--phoenix","name":"phoenix","type":"mcp","url":"https://arize.com/docs/phoenix","page_url":"https://unfragile.ai/arize-ai--phoenix","categories":["observability"],"tags":["agents","ai-monitoring","ai-observability","aiengineering","anthropic","datasets","evals","langchain","llamaindex","llm-eval","llm-evaluation","llmops","llms","openai","prompt-engineering","smolagents"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-arize-ai--phoenix__cap_0","uri":"capability://data.processing.analysis.opentelemetry.trace.ingestion.via.grpc.otlp.protocol","name":"opentelemetry trace ingestion via grpc otlp protocol","description":"Accepts distributed traces from LLM applications through a dedicated gRPC server listening on port 4317, implementing the OpenTelemetry Protocol (OTLP) specification. Traces are parsed from protobuf messages, validated, and persisted to PostgreSQL or SQLite with automatic schema migrations. Supports multi-language instrumentation (Python, TypeScript, Go, etc.) without requiring application code changes when using auto-instrumentation libraries.","intents":["I want to capture execution traces from my LLM application without modifying core business logic","I need to ingest traces from multiple services running different languages into a centralized store","I want standard OTLP compatibility so I can switch observability backends without re-instrumentation"],"best_for":["teams running distributed LLM applications with multiple services","engineers migrating from proprietary tracing to OpenTelemetry standard","organizations needing vendor-agnostic trace ingestion"],"limitations":["gRPC server adds ~50-100ms latency per trace batch ingestion","No built-in trace sampling at ingestion layer — requires client-side sampling configuration","SQLite backend suitable only for single-instance deployments; PostgreSQL required for production multi-instance setups","Trace retention depends on database storage capacity; no automatic TTL purging without custom maintenance jobs"],"requires":["gRPC client library compatible with OTLP (built into OpenTelemetry SDKs)","Network connectivity to Phoenix server on port 4317","PostgreSQL 12+ or SQLite 3.30+ for trace storage","OpenTelemetry SDK for target language (Python 3.8+, Node.js 14+, etc.)"],"input_types":["protobuf OTLP trace messages","span data with attributes, events, and status codes","trace context propagation headers (W3C Trace Context, Jaeger)"],"output_types":["normalized span records in database","trace hierarchy with parent-child relationships","queryable trace metadata via GraphQL/REST APIs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_1","uri":"capability://search.retrieval.trace.querying.and.filtering.via.graphql.api","name":"trace querying and filtering via graphql api","description":"Exposes a Strawberry GraphQL API (on port 6006) that allows complex queries over ingested traces using a schema-driven approach. Queries support filtering by span attributes, trace duration, status codes, and custom dimensions; supports pagination, sorting, and aggregation operations. The GraphQL layer translates queries into optimized SQL against the trace database, enabling efficient retrieval of trace subsets for analysis and debugging without loading entire trace datasets into memory.","intents":["I want to query traces by specific attributes (e.g., all spans with error status, latency > 1s)","I need to retrieve trace data programmatically for custom analysis or integration with other tools","I want to aggregate metrics across traces (e.g., average latency per LLM model, error rates by endpoint)"],"best_for":["developers building custom dashboards or analysis tools on top of trace data","teams integrating Phoenix traces with data warehouses or BI tools","engineers debugging specific LLM application issues by querying historical traces"],"limitations":["GraphQL query complexity can lead to N+1 query problems if not carefully structured; requires understanding of query optimization","No built-in query result caching — repeated queries hit the database each time","Filtering on custom span attributes requires those attributes to be indexed at database level; unindexed queries degrade with large datasets","GraphQL schema is read-only for traces; mutations are limited to annotations and feedback only"],"requires":["HTTP client capable of POST requests with JSON payloads","Understanding of GraphQL query syntax","Network access to Phoenix server port 6006","Traces already ingested and stored in database"],"input_types":["GraphQL query strings with filter predicates","pagination parameters (first, after, limit)","sort specifications (field, direction)"],"output_types":["JSON-serialized trace objects with nested spans","aggregated metrics (count, sum, average, percentiles)","cursor-based pagination tokens"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_10","uri":"capability://data.processing.analysis.database.abstraction.with.postgresql.and.sqlite.support","name":"database abstraction with postgresql and sqlite support","description":"Provides a database abstraction layer supporting both PostgreSQL (production) and SQLite (development/single-instance) backends, with automatic schema migrations managed by Alembic. The abstraction uses SQLAlchemy ORM for database operations, enabling schema changes without manual SQL. Supports connection pooling, transaction management, and query optimization for both backends. Database schema includes tables for spans, traces, evaluations, datasets, and annotations with appropriate indexes for common query patterns.","intents":["I want to run Phoenix locally with SQLite for development without setting up PostgreSQL","I need to deploy Phoenix to production with PostgreSQL for multi-instance scalability","I want to migrate from SQLite to PostgreSQL without data loss or manual schema management"],"best_for":["developers running Phoenix locally for testing and development","teams deploying Phoenix to production with high-volume trace ingestion","organizations needing to migrate from development to production databases"],"limitations":["SQLite is single-writer; concurrent trace ingestion from multiple processes causes lock contention","Schema migrations are sequential; large datasets may require significant downtime for migration","No built-in data replication or backup; users must implement their own backup strategy","Query performance on large datasets (>1M spans) may require manual index tuning for custom queries"],"requires":["PostgreSQL 12+ (for production) or SQLite 3.30+ (for development)","Alembic for schema migration management","SQLAlchemy 2.0+ for ORM","Network connectivity to database server (PostgreSQL only)"],"input_types":["span and trace objects from OTLP ingestion","evaluation results and annotations","dataset and experiment definitions"],"output_types":["persisted database records","query results for API responses","migration scripts for schema updates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_11","uri":"capability://automation.workflow.cli.for.local.server.management.and.data.export","name":"cli for local server management and data export","description":"Provides a command-line interface for starting the Phoenix server locally, managing database connections, and exporting trace data. CLI commands support starting the server with custom configuration (port, database URL, authentication), running database migrations, exporting traces to CSV/JSON, and importing datasets. The CLI uses Click framework for command definition and supports both interactive and scripted usage.","intents":["I want to start Phoenix server locally for development without writing Python code","I need to export trace data for analysis in external tools (Excel, Python, etc.)","I want to import datasets from CSV files for experiment evaluation"],"best_for":["developers setting up Phoenix locally for the first time","data analysts exporting traces for external analysis","teams automating Phoenix deployment and configuration"],"limitations":["CLI is Python-only; requires Python 3.8+ installation and familiarity with command-line tools","Export operations are synchronous and may be slow for large datasets (>100k spans)","No built-in scheduling for periodic exports; requires external cron/scheduler integration","CLI configuration is not persisted; users must specify parameters each time or use environment variables"],"requires":["Python 3.8+","Phoenix package installed (pip install arize-phoenix)","Command-line shell (bash, zsh, PowerShell, etc.)","Optional: PostgreSQL or SQLite database for persistence"],"input_types":["CLI command names and arguments","configuration parameters (port, database URL, etc.)","file paths for import/export operations"],"output_types":["running Phoenix server process","exported trace data in CSV/JSON format","database migration logs","CLI help and usage information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_12","uri":"capability://image.visual.frontend.visualization.of.trace.execution.flows","name":"frontend visualization of trace execution flows","description":"Provides a React-based web UI that visualizes trace execution flows as interactive diagrams showing span hierarchies, timing, and status. The UI displays spans as nodes with parent-child relationships, color-coded by status (success, error, pending), and includes timeline visualization showing span duration and overlap. Users can click spans to view detailed attributes, logs, and events; filter traces by attributes; and navigate between related traces. The frontend communicates with the backend via GraphQL API.","intents":["I want to visualize how my LLM application executed to understand bottlenecks and failures","I need to inspect detailed span attributes and logs to debug specific issues","I want to filter and search traces to find patterns in LLM application behavior"],"best_for":["engineers debugging LLM application failures using visual trace inspection","teams analyzing LLM application performance and identifying bottlenecks","non-technical stakeholders understanding LLM application execution flows"],"limitations":["Large traces (>1000 spans) may render slowly or become difficult to navigate visually","Trace visualization is read-only; users cannot modify spans or traces from the UI","No built-in export of visualizations (screenshots, diagrams); requires browser screenshot tools","Timeline visualization assumes accurate span timing; clock skew between services may cause misleading visualizations"],"requires":["Web browser with JavaScript support (Chrome, Firefox, Safari, Edge)","Phoenix server running with frontend assets served on port 6006","Traces already ingested and queryable via GraphQL API"],"input_types":["trace IDs or span IDs to visualize","filter criteria (attributes, status, duration)","sorting preferences (by duration, by name, etc.)"],"output_types":["interactive trace visualization diagrams","detailed span information panels","timeline charts showing span duration and overlap","filtered trace lists matching search criteria"],"categories":["image-visual","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_13","uri":"capability://safety.moderation.authentication.and.authorization.with.role.based.access.control","name":"authentication and authorization with role-based access control","description":"Implements authentication and authorization mechanisms (details in DeepWiki) supporting role-based access control (RBAC) for multi-tenant deployments. Users can be assigned roles (admin, analyst, viewer) with corresponding permissions for reading/writing traces, evaluations, and datasets. Authentication supports API keys and optional OAuth2/OIDC integration. Authorization is enforced at the API layer (GraphQL and REST) and database layer to prevent unauthorized data access.","intents":["I want to restrict access to sensitive trace data to specific team members","I need to provide read-only access to stakeholders without allowing data modification","I want to audit who accessed or modified trace data for compliance"],"best_for":["organizations with multiple teams sharing a Phoenix instance","enterprises with compliance requirements for data access control","teams needing to separate development and production trace access"],"limitations":["RBAC is coarse-grained; no fine-grained access control at the span or trace level","API key management is manual; no built-in key rotation or expiration","OAuth2/OIDC integration details are not documented in provided materials","Audit logging is not mentioned; no built-in audit trail of data access"],"requires":["Authentication mechanism configured (API keys or OAuth2/OIDC provider)","User and role definitions in database or external identity provider","API clients configured with authentication credentials"],"input_types":["API keys or OAuth2 tokens","user credentials for login","role assignments for users"],"output_types":["authenticated API responses","role-based access decisions","authorization errors for unauthorized requests"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_2","uri":"capability://data.processing.analysis.llm.evaluation.framework.with.pluggable.evaluators","name":"llm evaluation framework with pluggable evaluators","description":"Provides a Python-based evaluation system (arize-phoenix-evals package) that runs structured evaluators against LLM outputs to measure quality, correctness, and safety. Evaluators are composable functions that accept input/output pairs and return structured scores or classifications. The framework supports both built-in evaluators (hallucination detection, relevance scoring, toxicity detection) and custom user-defined evaluators; results are stored as annotations on spans and can be aggregated across datasets for statistical analysis.","intents":["I want to automatically score LLM outputs for quality metrics like hallucination, relevance, or toxicity","I need to run batch evaluations across historical traces to measure application performance over time","I want to define custom evaluation logic specific to my domain and apply it to all LLM interactions"],"best_for":["ML engineers building evaluation pipelines for LLM applications","teams measuring and tracking LLM output quality in production","researchers comparing different prompts or models using standardized evaluation metrics"],"limitations":["Built-in evaluators require API calls to external LLM services (OpenAI, Anthropic) for scoring, adding latency and cost per evaluation","Custom evaluators must be implemented in Python; no support for evaluators in other languages without wrapping","Evaluation results are eventually consistent with traces; no real-time evaluation feedback during trace ingestion","No built-in statistical significance testing or confidence intervals for evaluation metrics across datasets"],"requires":["Python 3.8+","API keys for LLM providers if using built-in evaluators (OpenAI, Anthropic, etc.)","Traces already ingested with input/output data captured in span attributes","arize-phoenix-evals package installed"],"input_types":["LLM input prompts (text)","LLM outputs (text)","reference answers or ground truth (optional, for comparison-based evaluators)","custom context or metadata for domain-specific evaluation"],"output_types":["numeric scores (0-1 range typically)","categorical classifications (e.g., 'hallucination', 'accurate')","structured evaluation explanations (reasoning for score)","annotations attached to spans in database"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_3","uri":"capability://memory.knowledge.prompt.versioning.and.management.with.experiment.tracking","name":"prompt versioning and management with experiment tracking","description":"Provides a prompt management system that stores prompt templates with version history, enabling A/B testing and experimentation. Prompts are stored in the database with metadata (model, parameters, tags) and can be retrieved by version or tag. The system tracks which prompt version was used for each LLM call via span attributes, allowing correlation between prompt changes and output quality metrics. Experiments can be defined to compare multiple prompt versions against the same dataset of inputs.","intents":["I want to version my prompts and track which version was used in each LLM call","I need to run A/B tests comparing different prompt versions using the same input dataset","I want to correlate prompt changes with changes in output quality metrics over time"],"best_for":["prompt engineers iterating on LLM prompts and measuring impact","teams running controlled experiments to optimize prompt performance","organizations needing audit trails of prompt changes for compliance"],"limitations":["Prompt versioning is manual; no automatic diff or change detection between versions","Experiments require pre-defined datasets; no support for online/streaming experiment evaluation","No built-in statistical significance testing for experiment results; requires external analysis","Prompt retrieval by version requires explicit version specification; no automatic 'latest' resolution without additional logic"],"requires":["Python 3.8+ for prompt management client","Traces with prompt version metadata captured in span attributes","Datasets pre-loaded into Phoenix for experiment evaluation","Access to Phoenix GraphQL API for querying experiment results"],"input_types":["prompt template text with placeholders","model name and parameters (temperature, max_tokens, etc.)","version tags and metadata","input datasets for experiment evaluation"],"output_types":["versioned prompt records with metadata","experiment results with metrics per prompt version","correlation data between prompt versions and output quality"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_4","uri":"capability://automation.workflow.automated.span.instrumentation.for.llm.frameworks","name":"automated span instrumentation for llm frameworks","description":"Provides auto-instrumentation libraries (arize-phoenix-otel) that automatically capture spans for popular LLM frameworks (LangChain, LlamaIndex, OpenAI SDK) without requiring manual span creation code. Uses Python decorators and context managers to wrap framework calls, extracting relevant metadata (model name, tokens, latency) and creating spans automatically. Supports both synchronous and asynchronous execution; integrates with OpenTelemetry context propagation for distributed tracing across service boundaries.","intents":["I want to trace my LangChain/LlamaIndex application without adding instrumentation code to every LLM call","I need to automatically capture token usage, latency, and model information for all LLM interactions","I want distributed tracing across multiple services without manually propagating trace context"],"best_for":["developers using popular LLM frameworks (LangChain, LlamaIndex) who want zero-instrumentation tracing","teams building microservices with LLM components requiring distributed tracing","organizations needing automatic cost tracking (tokens) across all LLM calls"],"limitations":["Auto-instrumentation only works with supported frameworks; custom LLM integrations require manual span creation","Instrumentation adds ~5-10ms overhead per LLM call due to decorator/context manager wrapping","Async instrumentation requires Python 3.7+ and may have compatibility issues with certain async frameworks","Automatic metadata extraction is framework-specific; custom attributes require manual span annotation"],"requires":["Python 3.8+","Supported LLM framework installed (LangChain 0.0.200+, LlamaIndex 0.8+, OpenAI SDK 0.27+, etc.)","arize-phoenix-otel package installed","Phoenix server running and accessible for trace ingestion"],"input_types":["LLM framework objects (chains, indexes, clients)","function calls to framework methods","async/await execution contexts"],"output_types":["automatically generated spans with framework-specific attributes","nested span hierarchies reflecting framework call chains","extracted metadata (model, tokens, latency, status codes)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_5","uri":"capability://text.generation.language.interactive.llm.playground.with.prompt.testing","name":"interactive llm playground with prompt testing","description":"Provides a web-based playground interface (React frontend) for testing LLM prompts interactively with real-time execution. Users can write prompts, select models (OpenAI, Anthropic, local), adjust parameters (temperature, max_tokens), and execute calls with immediate feedback. Playground sessions are persisted and linked to traces, enabling correlation between playground experiments and production traces. Supports multi-turn conversations and prompt templating with variable substitution.","intents":["I want to test and iterate on prompts interactively before deploying to production","I need to compare outputs from different models or parameter settings on the same prompt","I want to save playground experiments and correlate them with production trace data"],"best_for":["prompt engineers and product managers iterating on LLM prompts","teams debugging LLM behavior by testing variations in a controlled environment","non-technical stakeholders experimenting with LLM capabilities"],"limitations":["Playground execution is synchronous; long-running LLM calls block the UI without streaming support","No built-in cost estimation or token counting before execution; users discover costs after running","Playground state is not automatically saved; users must manually save experiments to persist them","Limited to single-turn or simple multi-turn conversations; no support for complex agentic workflows"],"requires":["Web browser with JavaScript support","Phoenix server running with frontend assets served on port 6006","API keys for LLM providers (OpenAI, Anthropic, etc.) if using cloud models","Network access to LLM provider APIs"],"input_types":["prompt text with optional template variables","model selection (provider and model name)","parameter adjustments (temperature, max_tokens, top_p, etc.)","conversation history for multi-turn interactions"],"output_types":["LLM-generated text responses","token usage statistics","execution latency metrics","saved playground sessions linked to traces"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_6","uri":"capability://data.processing.analysis.feedback.and.annotation.capture.on.spans","name":"feedback and annotation capture on spans","description":"Enables users to attach feedback, ratings, and custom annotations to spans after execution, supporting both programmatic and UI-based annotation. Feedback can be numeric scores (0-1), categorical labels, or free-form text; annotations are stored in the database and linked to specific spans. Supports batch annotation operations for applying feedback to multiple spans matching a query. Feedback is queryable via GraphQL, enabling analysis of annotated spans and correlation with evaluation results.","intents":["I want to mark specific LLM outputs as correct or incorrect for later analysis","I need to collect human feedback on LLM outputs and correlate it with execution traces","I want to annotate spans with custom metadata (e.g., 'customer complaint', 'edge case') for categorization"],"best_for":["teams collecting human feedback on LLM outputs for model improvement","organizations building feedback loops from production to model training","engineers debugging specific LLM failures by annotating problematic spans"],"limitations":["Feedback is append-only; no built-in versioning or conflict resolution for conflicting annotations","No access control on feedback; any user with API access can annotate any span","Batch annotation operations are not transactional; partial failures may leave inconsistent state","No built-in UI for human feedback collection; requires custom UI or integration with external feedback tools"],"requires":["Spans already ingested and stored in database","API access to Phoenix (GraphQL or REST endpoint)","Feedback schema defined (numeric range, categorical options, etc.)"],"input_types":["span IDs or trace IDs to annotate","feedback values (numeric scores, categorical labels, text)","annotation metadata (annotator ID, timestamp, reason)"],"output_types":["stored annotations linked to spans","queryable feedback data via GraphQL","aggregated feedback statistics (average score, distribution)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_7","uri":"capability://automation.workflow.dataset.management.and.experiment.execution","name":"dataset management and experiment execution","description":"Provides a dataset management system for storing input/output pairs and running experiments that execute LLM applications against datasets to measure performance. Datasets can be created from historical traces, uploaded as CSV/JSON, or defined programmatically. Experiments execute a specified LLM application (chain, agent, etc.) against each dataset row, capture outputs, run evaluations, and aggregate metrics. Results are stored with full traceability to input data and evaluation logic.","intents":["I want to create a benchmark dataset from production traces and use it to test new prompts or models","I need to run batch experiments comparing different LLM configurations against the same inputs","I want to measure how changes to my LLM application affect output quality across a representative dataset"],"best_for":["teams building evaluation pipelines for LLM applications","researchers comparing different models or prompts using standardized datasets","organizations measuring regression in LLM output quality after changes"],"limitations":["Experiment execution is sequential by default; parallel execution requires manual configuration and may hit rate limits","Large datasets (>10k rows) may require significant time and API costs to evaluate; no built-in cost estimation","Dataset versioning is manual; no automatic tracking of dataset changes or lineage","Experiment results are immutable; no support for re-running experiments with different evaluation logic on stored results"],"requires":["Python 3.8+ for dataset and experiment APIs","Input data in structured format (CSV, JSON, or Python objects)","LLM application code that can be executed programmatically","Evaluation logic (built-in or custom evaluators)","API keys for LLM providers if using cloud models"],"input_types":["dataset rows with input fields (prompts, context, etc.)","LLM application callable (function, chain, agent)","evaluation functions or evaluator specifications","experiment configuration (parameters, model selection)"],"output_types":["experiment results with input, output, and evaluation scores per row","aggregated metrics (average score, pass rate, error rate)","detailed traces for each experiment execution","comparison reports across multiple experiments"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_8","uri":"capability://tool.use.integration.rest.api.with.openapi.schema.for.programmatic.access","name":"rest api with openapi schema for programmatic access","description":"Exposes a REST API (alongside GraphQL) with auto-generated OpenAPI/Swagger documentation for programmatic access to traces, evaluations, and datasets. REST endpoints support standard CRUD operations and filtering via query parameters. The API is fully documented with interactive Swagger UI, enabling API discovery and testing without external tools. Supports both JSON request/response format and streaming responses for large result sets.","intents":["I want to integrate Phoenix data with external tools or dashboards via REST API","I need to programmatically retrieve traces and evaluation results for custom analysis","I want to automate feedback submission and annotation via API calls"],"best_for":["developers integrating Phoenix with external systems (data warehouses, BI tools, custom dashboards)","teams building custom analysis tools on top of trace data","organizations with REST-only API requirements (no GraphQL support)"],"limitations":["REST API is less flexible than GraphQL for complex filtering; requires multiple requests for nested data","No built-in request batching; high-volume data retrieval requires many sequential requests","API rate limiting not documented; no guidance on safe request rates for production use","Streaming responses require client-side handling of chunked responses; not all HTTP clients support this well"],"requires":["HTTP client library (curl, requests, fetch, etc.)","Network access to Phoenix server port 6006","Understanding of REST conventions and HTTP status codes","Optional: OpenAPI client generation tools for strongly-typed clients"],"input_types":["HTTP GET/POST/PUT/DELETE requests","query parameters for filtering and pagination","JSON request bodies for mutations"],"output_types":["JSON-serialized trace and span objects","evaluation results and metrics","pagination metadata (total count, next page token)","OpenAPI schema for API documentation"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arize-ai--phoenix__cap_9","uri":"capability://tool.use.integration.mcp.server.integration.for.claude.and.other.ai.assistants","name":"mcp server integration for claude and other ai assistants","description":"Implements a Model Context Protocol (MCP) server that exposes Phoenix capabilities to Claude and other AI assistants, enabling natural language interaction with traces, evaluations, and datasets. The MCP server translates natural language requests into Phoenix API calls, returning results in a format optimized for LLM consumption. Supports querying traces, running evaluations, creating datasets, and executing experiments through conversational interfaces.","intents":["I want to ask Claude about my LLM application traces using natural language","I need to run evaluations and experiments through a conversational interface without writing code","I want Claude to help me analyze trace data and suggest optimizations"],"best_for":["non-technical stakeholders analyzing LLM application performance","developers using Claude for debugging and analysis workflows","teams integrating Phoenix with AI assistant-based workflows"],"limitations":["MCP server adds latency for each request (LLM → MCP → Phoenix → LLM); not suitable for real-time analysis","Natural language queries are ambiguous; MCP server may misinterpret complex filtering requirements","LLM context window limits the amount of trace data that can be returned per query","No support for streaming results through MCP; large result sets may exceed context limits"],"requires":["Claude or other MCP-compatible AI assistant","MCP server running and configured in assistant settings","Network connectivity between assistant and Phoenix server","Phoenix server with MCP server component enabled"],"input_types":["natural language queries about traces and evaluations","conversational requests for analysis and optimization suggestions","dataset and experiment creation requests in natural language"],"output_types":["natural language summaries of trace data","analysis results and recommendations","structured data formatted for LLM consumption"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["gRPC client library compatible with OTLP (built into OpenTelemetry SDKs)","Network connectivity to Phoenix server on port 4317","PostgreSQL 12+ or SQLite 3.30+ for trace storage","OpenTelemetry SDK for target language (Python 3.8+, Node.js 14+, etc.)","HTTP client capable of POST requests with JSON payloads","Understanding of GraphQL query syntax","Network access to Phoenix server port 6006","Traces already ingested and stored in database","PostgreSQL 12+ (for production) or SQLite 3.30+ (for development)","Alembic for schema migration management"],"failure_modes":["gRPC server adds ~50-100ms latency per trace batch ingestion","No built-in trace sampling at ingestion layer — requires client-side sampling configuration","SQLite backend suitable only for single-instance deployments; PostgreSQL required for production multi-instance setups","Trace retention depends on database storage capacity; no automatic TTL purging without custom maintenance jobs","GraphQL query complexity can lead to N+1 query problems if not carefully structured; requires understanding of query optimization","No built-in query result caching — repeated queries hit the database each time","Filtering on custom span attributes requires those attributes to be indexed at database level; unindexed queries degrade with large datasets","GraphQL schema is read-only for traces; mutations are limited to annotations and feedback only","SQLite is single-writer; concurrent trace ingestion from multiple processes causes lock contention","Schema migrations are sequential; large datasets may require significant downtime for migration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6519802174372478,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-03T08:37:09Z"},"community":{"stars":9509,"forks":850,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=arize-ai--phoenix","compare_url":"https://unfragile.ai/compare?artifact=arize-ai--phoenix"}},"signature":"QaBicyOTi774dhHRX07hqme0lZ3aASAUjYXq4unToqJGWZ2Ls8p236RqcYykf2cwLcZfR2lf7y/Ulz8McOoYCw==","signedAt":"2026-06-21T07:52:50.729Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/arize-ai--phoenix","artifact":"https://unfragile.ai/arize-ai--phoenix","verify":"https://unfragile.ai/api/v1/verify?slug=arize-ai--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"}}