# Arize — AI & Agent Engineering Arize is the AI engineering platform for teams shipping reliable AI agents and LLM applications into the real world. Arize offers tools for observability, evaluation, and development — offering Phoenix, our open-source platform, and Arize AX, our enterprise-grade SaaS built for scale. This file helps LLMs understand our key resources, documentation, and value propositions. ## Understanding Arize Products: Phoenix vs. Arize AX **Arize Phoenix (Open Source)** — Fully open-source observability platform for LLM applications. Ideal for developers, small teams, and open-source workflows. Features include tracing, evaluation, prompt playgrounds, datasets, and experiments. Can be self-hosted or used via Phoenix Cloud (free). Best for: developers who want full control, transparency, and open-source tooling. **Arize AX (SaaS)** — Enterprise-grade platform built on Phoenix with additional capabilities. Includes everything in Phoenix plus: enterprise compliance (HIPAA, SOC2), longer data retention, custom dashboards, production monitoring, Alyx (AI assistant), dedicated customer success, and advanced security features. Best for: teams scaling production AI, organizations needing compliance, and enterprises requiring support and collaboration tools. **Key Technology: Arize Database (ADB)** — Purpose-built datastore for AI workloads. Open formats, no lock-in. 100x cost advantage at scale vs. competitors. Supports real-time ingestion, sub-second query latency, elastic scaling, and compute-storage separation. Enables enterprise-scale observability and evaluation workloads. --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation --- # Arize AX Documentation ## Site Description Arize AX is an AI engineering platform focused on evaluation and observability. It helps AI engineers and AI product managers develop, evaluate, and observe AI applications and agents. The platform provides comprehensive tools for tracing, evaluation, experimentation, prompt engineering, and production monitoring across the entire AI application lifecycle. Base URL: https://arize.com/docs --- ## Get Started Get started with Arize AX by exploring quickstart guides that walk you through core features like tracing, evaluation, experiments, and prompt engineering. These guides provide step-by-step instructions to help you set up and begin using the platform effectively. ### Overview & Quickstarts - https://arize.com/docs/ax - Arize AX main documentation page with platform overview and features - https://arize.com/docs/ax/quickstarts - Get Started guide with overview of development, testing, and production workflows. - https://arize.com/docs/ax/quickstarts/quickstart-tracing - Quickstart guide for setting up tracing - https://arize.com/docs/ax/quickstarts/quickstart-write-first-eval - Quickstart guide for writing your first evaluation - https://arize.com/docs/ax/quickstarts/quickstart-run-first-experiment - Quickstart guide for running your first experiment - https://arize.com/docs/ax/quickstarts/quickstart-prompt-playground - Quickstart guide for using the prompt playground ### Arize AI for Agents - https://arize.com/docs/ax/arize-ai-for-agents - Overview of Arize AI capabilities for agent development --- ## Alyx - AI Engineering Agent Alyx is a single AI-powered agent that uses subagents by surface; skills and context vary by where you use Alyx (trace slideover, Prompt Playground, Eval Builder, search bar, traces page, datasets page). ### Overview - https://arize.com/docs/ax/alyx/our-vision-for-alyx - Vision and overview of Alyx - https://arize.com/docs/ax/alyx/arize-copilot - Alyx AI Engineering Agent overview with available skills - https://arize.com/docs/ax/agents/tracing-assistant - Tracing Assistant documentation ### Alyx by surface Alyx adapts by surface; each surface has different context and exposes different skills. - https://arize.com/docs/ax/alyx/arize-copilot/trace-analysis-agent - Trace slideover: trace troubleshooting, span analysis, build evals - https://arize.com/docs/ax/alyx/arize-copilot/playground-agent - Prompt Playground: optimize prompts, build evals, run experiments - https://arize.com/docs/ax/alyx/arize-copilot/eval-agent - Eval Builder / Task Builder: build custom evals, configure tasks - https://arize.com/docs/ax/alyx/arize-copilot/search-bar-agent - Traces search bar: natural language to filter syntax - https://arize.com/docs/ax/alyx/arize-copilot/traces-page-agent - Traces page: multi-trace analysis, pattern discovery - https://arize.com/docs/ax/alyx/arize-copilot/dataset-page-agent - Datasets / Experiments: analyze experiments, manage datasets ### Alyx Skills - https://arize.com/docs/ax/alyx/arize-copilot/arizeql-generator - Arize Query Language (ArizeQL) generator --- ## Develop The Develop section covers tools for building and testing AI applications, including datasets and experiments. Learn how to create test datasets, run experiments, evaluate results, and integrate with CI/CD pipelines for automated testing. ### Datasets - https://arize.com/docs/ax/develop/datasets - Overview of datasets for testing and evaluation - https://arize.com/docs/ax/develop/datasets/how-to-datasets - How to create and manage datasets - https://arize.com/docs/ax/develop/datasets/update-a-dataset - How to update existing datasets - https://arize.com/docs/ax/develop/datasets/export-a-dataset - How to export datasets - https://arize.com/docs/ax/develop/datasets/automated-dataset-curation - Automated dataset curation ### Experiments - https://arize.com/docs/ax/develop/datasets-and-experiments - Overview of experiments and datasets - https://arize.com/docs/ax/develop/datasets-and-experiments/run-experiment - How to run experiments - https://arize.com/docs/ax/develop/datasets-and-experiments/log-experiment - How to log experiment results - https://arize.com/docs/ax/develop/datasets-and-experiments/create-an-experiment-evaluator - How to create evaluators for experiments - https://arize.com/docs/ax/develop/datasets-and-experiments/compare-experiments - How to compare experiment results - https://arize.com/docs/ax/develop/datasets-and-experiments/experiment-classification-metrics - Compute binary classification metrics (F1, Accuracy, Precision, Recall) across experiments using ground truth and predicted column mapping - https://arize.com/docs/ax/develop/datasets-and-experiments/download-experiment-results - How to download experiment results - https://arize.com/docs/ax/develop/datasets-and-experiments/ci-cd-for-automated-experiments - CI/CD integration for automated experiments - https://arize.com/docs/ax/develop/datasets-and-experiments/ci-cd-for-automated-experiments/github-action-basics - GitHub Actions integration - https://arize.com/docs/ax/develop/datasets-and-experiments/ci-cd-for-automated-experiments/gitlab-ci-cd-basics - GitLab CI/CD integration --- ## Prompts The Prompts section provides comprehensive tools for prompt engineering, including the Prompt Hub for version control, the Prompt Playground for interactive testing, and AI-powered optimization features. These tools help you iterate on prompts, compare variations, and systematically improve prompt performance. ### Prompt Hub - https://arize.com/docs/ax/prompts/prompt-hub - Overview of Prompt Hub for managing and versioning prompts - https://arize.com/docs/ax/prompts/prompt-hub/create-a-prompt - How to create prompts in Prompt Hub - https://arize.com/docs/ax/prompts/prompt-hub/version-control - Version control for prompts ### Prompt Playground - https://arize.com/docs/ax/prompts/prompt-playground - Overview of Prompt Playground for interactive prompt testing - https://arize.com/docs/ax/prompts/prompt-playground/save-playground-outputs-as-an-experiment - Save playground outputs as experiments - https://arize.com/docs/ax/prompts/prompt-playground/production-replay - Replay production data in playground - https://arize.com/docs/ax/prompts/prompt-playground/compare-prompts-side-by-side - Compare multiple prompts side by side - https://arize.com/docs/ax/prompts/prompt-playground/using-tools-in-playground - Using tools in the playground - https://arize.com/docs/ax/prompts/prompt-playground/image-inputs-in-playground - Working with image inputs - https://arize.com/docs/ax/prompts/prompt-playground/saving-and-managing-playground-views - Managing playground views ### Prompt Optimization - https://arize.com/docs/ax/prompts/prompt-optimization - Overview of prompt optimization features - https://arize.com/docs/ax/prompts/prompt-optimization/prompt-learning - Prompt learning for optimization - https://arize.com/docs/ax/prompts/prompt-optimization/prompt-learning-sdk - Prompt learning SDK documentation - https://arize.com/docs/ax/prompts/prompt-optimization/ai-powered-prompt-builder - AI-powered prompt builder --- ## Evaluate The Evaluate section covers all aspects of evaluation, from creating custom evaluators to running online and offline evaluations. Learn about LLM-as-a-judge evaluations, code-based evaluations, trace and session-level evaluations, and human annotation workflows. ### Evaluators Overview - https://arize.com/docs/ax/evaluate/evaluators - Overview of evaluators and evaluation framework - https://arize.com/docs/ax/evaluate/evaluators/llm-as-a-judge - LLM as a Judge evaluator - https://arize.com/docs/ax/evaluate/evaluators/code-evaluations - Code-based evaluations - https://arize.com/docs/ax/evaluate/evaluators/trace-and-session-evals/span-level-evals - Span-level evaluations - https://arize.com/docs/ax/evaluate/evaluators/trace-and-session-evals/trace-level-evaluations - Trace-level evaluations - https://arize.com/docs/ax/evaluate/evaluators/trace-and-session-evals/session-level-evaluations - Session-level evaluations - https://arize.com/docs/ax/evaluate/evaluators/retrieval-evaluation - Retrieval evaluation ### Online Evals - https://arize.com/docs/ax/evaluate/online-evals - Overview of online evaluations for production monitoring - https://arize.com/docs/ax/evaluate/online-evals/setting-up-online-evals - Setting up online evaluations and reusable evaluators with EvalHub - https://arize.com/docs/ax/evaluate/online-evals/log-evals-to-traces - Logging evaluations to traces ### Offline Evals - https://arize.com/docs/ax/evaluate/offline-evals - Offline evaluations for testing and development ### Human Annotations - https://arize.com/docs/ax/evaluate/human-annotations - Human annotation workflows - https://arize.com/docs/ax/evaluate/annotation-queues - Annotation queues for human evaluation --- ## Observe The Observe section provides comprehensive observability tools including tracing for complete visibility into application flows, projects for organizing work, dashboards for visualization, and production monitoring with alerts. These tools help you understand system behavior, track performance, and respond to issues in real-time. ### Tracing - https://arize.com/docs/ax/observe/tracing - Complete visibility into how your agents and models work - https://arize.com/docs/ax/observe/tracing/setup - Setting up tracing - https://arize.com/docs/ax/observe/tracing/setup/integrations - Integration-based tracing setup - https://arize.com/docs/ax/observe/tracing/setup/manual-instrumentation - Manual instrumentation for tracing - https://arize.com/docs/ax/observe/tracing/configure - Configuring tracing - https://arize.com/docs/ax/observe/tracing/configure/input - Configuring input tracking - https://arize.com/docs/ax/observe/tracing/configure/evals-on-traces - Running evaluations on traces - https://arize.com/docs/ax/observe/tracing/configure/instrumenting-prompt-templates-and-prompt-variables - Instrumenting prompt templates and variables - https://arize.com/docs/ax/observe/tracing/configure/add-attributes-metadata-and-tags - Adding attributes, metadata, and tags - https://arize.com/docs/ax/observe/tracing/configure/add-events-exceptions-and-status-to-spans - Adding events, exceptions, and status to spans - https://arize.com/docs/ax/observe/tracing/configure/add-cost-tracking - Cost tracking configuration - https://arize.com/docs/ax/observe/tracing/configure/logging-latent-metadata - Logging latent metadata - https://arize.com/docs/ax/observe/tracing/configure/masking-span-attributes - Masking span attributes - https://arize.com/docs/ax/observe/tracing/configure/redact-sensitive-data-from-traces - Redacting sensitive data - https://arize.com/docs/ax/observe/tracing/configure/customize-auto-instrumentation - Customizing auto-instrumentation - https://arize.com/docs/ax/observe/tracing/configure/batch-vs-simple-span-processor - Batch vs simple span processor - https://arize.com/docs/ax/observe/tracing/configure/otel-collector-deployment-patterns - OpenTelemetry collector deployment patterns - https://arize.com/docs/ax/observe/tracing/configure/advanced-tracing-otel-examples - Advanced OpenTelemetry tracing examples - https://arize.com/docs/ax/observe/tracing/view-and-manage-traces - Viewing and managing traces - https://arize.com/docs/ax/observe/tracing/saved-views - Saved views for the Tracing table (filters, columns, sort, time range) - https://arize.com/docs/ax/observe/tracing/spans - Understanding span kinds and span data formats - https://arize.com/docs/ax/observe/tracing/sessions-and-users - Sessions and user tracking - https://arize.com/docs/ax/observe/tracing/agents - Agent visualizations and analysis ### Projects - https://arize.com/docs/ax/observe/projects - Projects overview - https://arize.com/docs/ax/observe/projects/custom-metrics-api - Custom metrics API - https://arize.com/docs/ax/observe/projects/custom-metrics-api/custom-metric-syntax - Custom metric syntax (ArizeQL) - https://arize.com/docs/ax/observe/projects/custom-metrics-api/custom-metric-syntax/conditionals-and-filters - Conditionals and filters in custom metrics - https://arize.com/docs/ax/observe/projects/custom-metrics-api/custom-metric-examples - Custom metric examples ### Dashboards - https://arize.com/docs/ax/observe/dashboards - Dashboards overview - https://arize.com/docs/ax/observe/dashboards/widgets - Dashboard widgets - https://arize.com/docs/ax/observe/dashboards/token-counting - Token counting in dashboards - https://arize.com/docs/ax/observe/dashboards/dashboard-widget-creation - Creating dashboard widgets with AI ### Production Monitoring - https://arize.com/docs/ax/observe/production-monitoring - Production monitoring overview - https://arize.com/docs/ax/observe/production-monitoring/configure-monitors - Configuring monitors (Slack altering, OpsGenie, and PagerDuty) --- ## Integrations Arize AX supports integrations with over 30+ LLM providers, agent frameworks, platforms, and tools for seamless instrumentation. These integrations enable automatic tracing, evaluation capabilities, and observability across your entire AI stack without manual instrumentation. ### Overview - https://arize.com/docs/ax/integrations - Integrations overview and types ### LLM Provider Integrations - https://arize.com/docs/ax/integrations/llm-providers/openai - OpenAI integration (tracing, evals, Agents SDK tracing, Node.js SDK) - https://arize.com/docs/ax/integrations/llm-providers/anthropic - Anthropic integration (tracing, evals) - https://arize.com/docs/ax/integrations/llm-providers/google-gen-ai - Google Generative AI integration (tracing, Gemini evals) - https://arize.com/docs/ax/integrations/llm-providers/amazon-bedrock - Amazon Bedrock integration (tracing, evals, agents tracing) - https://arize.com/docs/ax/integrations/llm-providers/vertexai - Vertex AI integration (tracing, evals) - https://arize.com/docs/ax/integrations/llm-providers/mistralai - Mistral AI integration (tracing, evals) - https://arize.com/docs/ax/integrations/llm-providers/groq - Groq integration (tracing) - https://arize.com/docs/ax/integrations/llm-providers/openrouter - OpenRouter integration (tracing) - https://arize.com/docs/ax/integrations/llm-providers/litellm - LiteLLM integration (tracing, evals) - https://arize.com/docs/ax/integrations/llm-providers/llama - Llama/Ollama integration (tracing) ### Python Agent Framework Integrations - https://arize.com/docs/ax/integrations/python-agent-frameworks/langchain - LangChain integration (tracing, JS support) - https://arize.com/docs/ax/integrations/python-agent-frameworks/langgraph - LangGraph integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/llamaindex - LlamaIndex integration (tracing, workflows tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/autogen - AutoGen integration (tracing, AgentChat tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/crewai - CrewAI integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/dspy - DSPy integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/haystack - Haystack integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/instructor - Instructor integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/guardrails-ai - Guardrails AI integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/pydantic - Pydantic AI integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/semantic-kernel - Semantic Kernel integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/together-ai - Together AI integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/portkey - Portkey integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/google-adk - Google ADK integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/agno - Agno integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/beeai - BeeAI integration (Python tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/hugging-face-smolagents - Hugging Face smolagents integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/model-context-protocol - Model Context Protocol (MCP) integration (tracing) - https://arize.com/docs/ax/integrations/python-agent-frameworks/aws-strands - AWS Strands integration (tracing, Bedrock AgentCore) ### TypeScript/JavaScript Agent Framework Integrations - https://arize.com/docs/ax/integrations/ts-js-agent-frameworks/langchain - LangChain JS/TS integration (tracing) - https://arize.com/docs/ax/integrations/ts-js-agent-frameworks/mastra - Mastra integration (tracing) - https://arize.com/docs/ax/integrations/ts-js-agent-frameworks/vercel - Vercel AI SDK integration (tracing) - https://arize.com/docs/ax/integrations/ts-js-agent-frameworks/beeai - BeeAI JS integration (tracing) ### Java Integrations - https://arize.com/docs/ax/integrations/java/langchain4j - LangChain4j integration (tracing) - https://arize.com/docs/ax/integrations/java/spring-ai - Spring AI integration (tracing) - https://arize.com/docs/ax/integrations/java/arconia - Arconia integration (tracing) ### Coding Agent Integrations - https://arize.com/docs/ax/integrations/platforms/claude-code - Claude Code integration overview - https://arize.com/docs/ax/integrations/platforms/claude-code/claude-code-tracing - Claude Code tracing with Arize AX - https://arize.com/docs/ax/integrations/platforms/codex - Codex integration overview - https://arize.com/docs/ax/integrations/platforms/codex/codex-tracing - Codex CLI tracing with Arize AX - https://arize.com/docs/ax/integrations/platforms/cursor - Cursor integration overview - https://arize.com/docs/ax/integrations/platforms/cursor/cursor-tracing - Cursor tracing with Arize AX using Cursor hooks ### Platform Integrations - https://arize.com/docs/ax/integrations/platforms/langflow - LangFlow integration (tracing) - https://arize.com/docs/ax/integrations/platforms/flowise - Flowise integration (tracing) - https://arize.com/docs/ax/integrations/platforms/dify - Dify integration (tracing) - https://arize.com/docs/ax/integrations/platforms/prompt-flow - Prompt Flow integration (tracing) ### OpenTelemetry Integrations - https://arize.com/docs/ax/integrations/opentelemetry/overview - OpenTelemetry overview - https://arize.com/docs/ax/integrations/opentelemetry/opentelemetry-arize-otel - OpenTelemetry Arize OTel integration - https://arize.com/docs/ax/integrations/opentelemetry/openlit - OpenLIT integration - https://arize.com/docs/ax/integrations/opentelemetry/openllmetry - OpenLLMetry integration - https://arize.com/docs/ax/integrations/opentelemetry/traceloop-sdk - Traceloop SDK integration ### Other Evaluation Integrations - https://arize.com/docs/ax/integrations/evaluation-integrations/ragas - RAGAS integration (NVIDIA RAG metrics) - https://arize.com/docs/ax/integrations/evaluation-integrations/microsoft - Microsoft evaluation integration ### Vector Database Integrations - https://arize.com/docs/ax/integrations/vector-databases - Vector database integrations overview --- ## API Reference The API Reference section provides comprehensive documentation for all Arize AX APIs, including REST and GraphQL endpoints. It also includes SDK documentation for Python and TypeScript, with tutorials and migration guides to help you integrate programmatically. ### REST API - https://arize.com/docs/ax/rest-reference/overview - Arize REST API overview - https://arize.com/docs/ax/rest-reference/download-openapi-spec - Download OpenAPI specification - https://api.arize.com/v2/spec.yaml - OpenAPI specification (YAML) ### GraphQL API - https://arize.com/docs/ax/graphql-reference - GraphQL API reference overview - https://arize.com/docs/ax/graphql-reference/overview/getting-started-with-graphql - Getting started with GraphQL - https://arize.com/docs/ax/graphql-reference/overview/how-to-use-graphql - How to use GraphQL - https://arize.com/docs/ax/graphql-reference/overview/how-to-use-graphql/forming-calls - Forming GraphQL calls - https://arize.com/docs/ax/graphql-reference/overview/how-to-use-graphql/using-global-node-ids - Using global node IDs - https://arize.com/docs/ax/graphql-reference/overview/how-to-use-graphql/querying-nested-data - Querying nested data - https://arize.com/docs/ax/graphql-reference/overview/how-to-use-graphql/notebook-examples - Notebook examples - https://arize.com/docs/ax/graphql-reference/overview/how-to-use-graphql/mutations - GraphQL mutations - https://arize.com/docs/ax/graphql-reference/overview/resource-limitations - Resource limitations - https://arize.com/docs/ax/graphql-reference/apis/admin-api - Admin API - https://arize.com/docs/ax/graphql-reference/apis/annotations-api - Annotations API - https://arize.com/docs/ax/graphql-reference/apis/custom-metrics-api - Custom metrics API - https://arize.com/docs/ax/graphql-reference/apis/dashboards-api - Dashboards API - https://arize.com/docs/ax/graphql-reference/apis/file-importer-api - File importer API - https://arize.com/docs/ax/graphql-reference/apis/metrics-api - Metrics API - https://arize.com/docs/ax/graphql-reference/apis/models-api - Models API - https://arize.com/docs/ax/graphql-reference/apis/monitors-api - Monitors API - https://arize.com/docs/ax/graphql-reference/apis/online-tasks-api - Online tasks API - https://arize.com/docs/ax/graphql-reference/apis/table-importer-api - Table importer API ### Python SDK (API Clients) - https://arize.com/docs/api-clients/python/overview - Python SDK overview - https://arize.com/docs/api-clients/python/version-8/overview - Python SDK v8 overview (pre-release) - https://arize.com/docs/api-clients/python/version-8/tracing - Tracing with Python SDK v8 - https://arize.com/docs/api-clients/python/version-8/embeddings - Embeddings with Python SDK v8 - https://arize.com/docs/api-clients/python/version-8/client-resources/annotation-configs - Annotation configs client [Beta] - https://arize.com/docs/api-clients/python/version-8/client-resources/datasets - Datasets client [Beta] - https://arize.com/docs/api-clients/python/version-8/client-resources/experiments - Experiments client [Beta] - https://arize.com/docs/api-clients/python/version-8/client-resources/projects - Projects client [Beta] - https://arize.com/docs/api-clients/python/version-8/client-resources/spans - Spans client [Alpha] - https://arize.com/docs/api-clients/python/version-8/client-resources/spaces - Spaces client [Alpha] - https://arize.com/docs/api-clients/python/version-8/client-resources/api-keys - API keys client [Alpha] - https://arize.com/docs/api-clients/python/version-8/client-resources/ai-integrations - AI integrations client [Alpha] - https://arize.com/docs/api-clients/python/version-8/client-resources/ml-models - ML models client - https://arize.com/docs/api-clients/python/version-8/migration/index - Migration guide from v7 - https://arize.com/docs/api-clients/python/version-8/migration/datasets-client - Datasets client migration - https://arize.com/docs/api-clients/python/version-8/migration/experiments-client - Experiments client migration - https://arize.com/docs/api-clients/python/version-8/migration/exporter-client - Exporter client migration - https://arize.com/docs/api-clients/python/version-8/migration/pandas-client - Pandas client migration - https://arize.com/docs/api-clients/python/version-8/migration/stream-client - Stream client migration - https://arize.com/docs/api-clients/python/version-8/tutorial/get-started-tracing-with-arize-sdk - Get started with tracing tutorial - https://arize.com/docs/api-clients/python/version-8/tutorial/get-started-datasets-experiments-with-arize-sdk - Get started with datasets and experiments tutorial - https://arize.com/docs/api-clients/python/version-8/tutorial/get-started-evaluations-with-arize-sdk - Get started with evaluations tutorial - https://arize.com/docs/api-clients/python/version-7/overview - Python SDK v7 overview ### TypeScript SDK (API Clients) - https://arize.com/docs/api-clients/typescript/version-1/overview - TypeScript SDK v1 overview [Beta] - https://arize.com/docs/api-clients/typescript/version-1/client-resources/annotation-configs - Annotation configs client [Beta] - https://arize.com/docs/api-clients/typescript/version-1/client-resources/datasets - Datasets client [Beta] - https://arize.com/docs/api-clients/typescript/version-1/client-resources/experiments - Experiments client [Beta] - https://arize.com/docs/api-clients/typescript/version-1/client-resources/projects - Projects client [Beta] --- ## Security & Settings The Security & Settings section covers all security, compliance, and configuration options for your Arize AX instance. This includes API key management, SSO and RBAC setup, compliance features, cost tracking, guardrails, and integration playgrounds for testing LLM connections. ### API Keys & Authentication - https://arize.com/docs/ax/security-and-settings/api-keys - API keys management - https://arize.com/docs/ax/security-and-settings/service-keys - Service keys ### Security Features - https://arize.com/docs/ax/security-and-settings/llm-security/guardrails - Guardrails for LLM security - https://arize.com/docs/ax/security-and-settings/llm-security/llm-red-teaming - LLM red teaming - https://arize.com/docs/ax/security-and-settings/whitelisting - Whitelisting configuration - https://arize.com/docs/ax/security-and-settings/arize-private-connect - Arize Private Connect ### SSO & RBAC - https://arize.com/docs/ax/security-and-settings/sso-and-rbac - SSO and RBAC overview - https://arize.com/docs/ax/security-and-settings/sso-and-rbac/saml-configuration - SAML configuration - https://arize.com/docs/ax/security-and-settings/sso-and-rbac/setting-up-sso-with-okta - Setting up SSO with Okta ### Compliance - https://arize.com/docs/ax/security-and-settings/compliance - Compliance overview - https://arize.com/docs/ax/security-and-settings/compliance/arize-audit-log - Audit log - https://arize.com/docs/ax/security-and-settings/compliance/delete-traces-with-sensitive-data - Deleting traces with sensitive data ### Cost Tracking - https://arize.com/docs/ax/security-and-settings/cost-tracking - Cost tracking configuration - https://arize.com/docs/ax/security-and-settings/space-rate-limiting - Per-space monthly ingestion limits in Organization settings; enterprise, opt-in; admins rebalance caps; contract total via Arize/support team ### Data Fabric - https://arize.com/docs/ax/security-and-settings/data-fabric - Data fabric overview ### Tags - https://arize.com/docs/ax/security-and-settings/tags - Tags management ### Integrations Playground - https://arize.com/docs/ax/security-and-settings/integrations-playground/openai - OpenAI integration playground - https://arize.com/docs/ax/security-and-settings/integrations-playground/azure-openai - Azure OpenAI integration playground - https://arize.com/docs/ax/security-and-settings/integrations-playground/vertexai - Vertex AI integration playground - https://arize.com/docs/ax/security-and-settings/integrations-playground/aws-bedrock - AWS Bedrock integration playground - https://arize.com/docs/ax/security-and-settings/integrations-playground/custom-llm-models - Custom LLM models playground ### Phoenix Migration - https://arize.com/docs/ax/security-and-settings/send-traces-from-phoenix-greater-than-arize - Sending traces from Phoenix to Arize --- ## Self-Hosting The Self-Hosting section provides complete documentation for deploying Arize AX on-premise or in your own cloud infrastructure. It includes installation guides for AWS, Azure, GCP, and OpenShift, along with configuration options for ingress, SAML, and SDK usage in self-hosted environments. ### Overview - https://arize.com/docs/ax/selfhosting - Self-hosting overview - https://arize.com/docs/ax/selfhosting/architecture - Architecture - https://arize.com/docs/ax/selfhosting/getting-started/overview - Getting started (installation flow) - https://arize.com/docs/ax/selfhosting/getting-started/prerequisites - Prerequisites - https://arize.com/docs/ax/selfhosting/installation/installation-on-aws - Installation on AWS - https://arize.com/docs/ax/selfhosting/installation/installation-on-azure - Installation on Azure - https://arize.com/docs/ax/selfhosting/installation/installation-on-gcp - Installation on GCP - https://arize.com/docs/ax/selfhosting/installation/installation-on-openshift - Installation on OpenShift - https://arize.com/docs/ax/selfhosting/installation/installation-on-single-host - Installation on Single Host - https://arize.com/docs/ax/selfhosting/installation/configuring-ingress-endpoints - Configuring ingress endpoints - https://arize.com/docs/ax/selfhosting/installation/configuring-saml - Configuring SAML - https://arize.com/docs/ax/selfhosting/guides/integrations - Integrations - https://arize.com/docs/ax/selfhosting/guides/sdk-usage - On-premise SDK usage - https://arize.com/docs/ax/selfhosting/guides/releases - On-premise releases --- ## Machine Learning The Machine Learning section covers ML observability for traditional machine learning models, including data upload, monitoring, drift detection, explainability, and performance analysis. It also includes computer vision observability, use cases for different model types, integrations with ML platforms, and comprehensive API references. ### Machine Learning Observability - https://arize.com/docs/ax/machine-learning/machine-learning - Machine learning observability overview - https://arize.com/docs/ax/machine-learning/machine-learning/quickstart - ML observability quickstart - https://arize.com/docs/ax/machine-learning/machine-learning/concepts-ml - ML concepts ### How To: ML - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml - How-to guides for ML observability - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/upload-data-to-arize - Uploading data to Arize - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/monitors - Setting up monitors - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/dashboards - Creating dashboards - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/drift-tracing - Drift tracing - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/performance-tracing - Performance tracing - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/explainability - Model explainability - https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/custom-metrics-api - Custom metrics API for ML ### ML Use Cases - https://arize.com/docs/ax/machine-learning/machine-learning/use-cases-ml - ML use cases overview ### ML Integrations - https://arize.com/docs/ax/machine-learning/machine-learning/integrations-ml - ML integrations overview ### ML API Reference - https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml - ML API reference overview - https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml/python-sdk - Python SDK reference - https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml/java-sdk - Java SDK reference - https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml/r-sdk - R SDK reference - https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml/grpc-api - gRPC API reference ### Computer Vision - https://arize.com/docs/ax/machine-learning/computer-vision - Computer vision observability overview --- ## Cookbooks The Cookbooks section contains practical tutorials and examples for building AI applications with Arize AX, covering agents, evaluations, experiments, and prompt optimization. These step-by-step guides demonstrate real-world workflows and best practices for common AI engineering tasks. ### Overview - https://arize.com/docs/ax/cookbooks - Cookbooks and tutorials overview ### Agent Cookbooks - https://arize.com/docs/ax/cookbooks/agent-workflow-patterns - Agent workflow patterns - https://arize.com/docs/ax/cookbooks/agents/tracing-and-evaluating-agents - Tracing and evaluating agents - https://arize.com/docs/ax/cookbooks/agents/arize-+-mosaic-ai-agent-framework - Online evals and monitoring for agents in production - https://arize.com/docs/ax/cookbooks/agents/evaluating-agentic-rag-using-arize-and-couchbase - Evaluating agentic RAG with Couchbase - https://arize.com/docs/ax/cookbooks/agents/evaluating-and-improving-ai-agents-at-scale-with-microsoft-foundry - Evaluating and improving AI agents at scale with Microsoft Foundry - https://arize.com/docs/ax/cookbooks/agents/foundry-red-team - Foundry red team - https://arize.com/docs/ax/cookbooks/agents/openai-agents-cookbook - OpenAI agents cookbook - https://arize.com/docs/ax/cookbooks/agents/ragas-agents-cookbook - RAGAS agents cookbook - https://arize.com/docs/ax/cookbooks/agents/trace-evaluate-browser-use-agent-with-l-lama4 - Trace and evaluate browser use agent with Llama 4 - https://arize.com/docs/ax/cookbooks/agents/tracing-a-langgraph-application-with-agent-engine-in-vertex-ai - Tracing a LangGraph application with Agent Engine in Vertex AI - https://arize.com/docs/ax/cookbooks/agents/tracing-a-routing-agent - Tracing a routing agent - https://arize.com/docs/ax/cookbooks/agents/tracing-a2a-agent - Tracing A2A agent ### Evaluation Cookbooks - https://arize.com/docs/ax/cookbooks/evaluation/evaluation - Evaluation cookbooks overview - https://arize.com/docs/ax/cookbooks/evaluation/evaluating-rag - Evaluating RAG systems - https://arize.com/docs/ax/cookbooks/evaluation/gemini-audio-evals - Gemini audio evaluations - https://arize.com/docs/ax/cookbooks/evaluation/llamaindex-evals - LlamaIndex evaluations - https://arize.com/docs/ax/cookbooks/evaluation/session-level-evaluations-for-an-ai-tutor - Session-level evaluations for an AI tutor - https://arize.com/docs/ax/cookbooks/evaluation/trace-level-evaluations-for-a-recommendation-agent - Trace-level evaluations for a recommendation agent - https://arize.com/docs/ax/cookbooks/evaluation/tracing-and-evaluating-audio - Tracing and evaluating audio ### Experiment Cookbooks - https://arize.com/docs/ax/cookbooks/experiments/model-comparison-for-an-email-text-extraction-service - Model comparison for email text extraction - https://arize.com/docs/ax/cookbooks/experiments/summarization - Summarization experiments - https://arize.com/docs/ax/cookbooks/experiments/text2sql - Text-to-SQL experiments ### Prompt Learning Cookbooks - https://arize.com/docs/ax/cookbooks/prompt-learning/optimizing-your-eval-prompts - Optimizing your eval prompts - https://arize.com/docs/ax/cookbooks/prompt-learning/improving-structured-output-generation-with-prompt-learning - Improving structured output generation - https://arize.com/docs/ax/cookbooks/prompt-learning/optimizing-coding-agent-prompts-for-execution - Optimizing coding agent prompts for execution - https://arize.com/docs/ax/cookbooks/prompt-learning/optimizing-coding-agent-prompts-for-planning - Optimizing coding agent prompts for planning ### AI Engineering Workflows - https://arize.com/docs/ax/cookbooks/ai-engineering-workflows/guardrails - Guardrails for realtime detection ### Human-in-the-Loop Workflows - https://arize.com/docs/ax/cookbooks/human-in-the-loop-workflows-annotations/creating-a-custom-llm-evaluator-with-a-benchmark-dataset - Creating a custom LLM evaluator with a benchmark dataset ### Tracing Integrations - https://arize.com/docs/ax/cookbooks/tracing-integrations - Tracing integrations cookbook --- ## Additional Resources Find community support, blog posts, and related documentation to help you get the most out of Arize AX. Connect with other developers, stay updated on new features, and explore our open-source Phoenix project for additional observability tools. - Community Slack: https://arize-ai.slack.com/ssb/redirect#/shared-invite/email - Blog: https://arize.com/blog/ - Phoenix OSS Documentation: http://docs.arize.com/phoenix --- # Phoenix Documentation Index > Phoenix is an open-source AI observability platform built on OpenTelemetry for tracing, evaluation, prompt engineering, and experimentation in LLM-based systems. ## Overview - [Main page](https://arize.com/docs/phoenix): Install Phoenix and choose a deployment option - [User Guide](https://arize.com/docs/phoenix/user-guide): End-to-end workflows for development, production, and optimization - [Production Guide](https://arize.com/docs/phoenix/production-guide): Deploy Phoenix in production with best practices for scale and reliability - [Environments](https://arize.com/docs/phoenix/environments): Configure Phoenix across development, staging, and production environments ## Quick Start - [Get Started Overview](https://arize.com/docs/phoenix/get-started): Walk through the full Phoenix workflow from tracing to experiments - [Tracing Quickstart](https://arize.com/docs/phoenix/get-started/get-started-tracing): Instrument an app and send your first traces to Phoenix - [Evaluations Quickstart](https://arize.com/docs/phoenix/get-started/get-started-evaluations): Run your first LLM evaluator and view results - [Playground Quickstart](https://arize.com/docs/phoenix/get-started/get-started-prompt-playground): Test prompt variations against real examples in the Playground - [Experiments Quickstart](https://arize.com/docs/phoenix/get-started/get-started-datasets-and-experiments): Create a dataset and run a comparative experiment - [TS: Tracing Quickstart](https://arize.com/docs/phoenix/get-started/ts-get-started-tracing): Instrument a TypeScript app and send traces to Phoenix - [TS: Evaluations Quickstart](https://arize.com/docs/phoenix/get-started/ts-get-started-evaluations): Run evaluators in TypeScript with @arizeai/phoenix-evals - [TS: Playground Quickstart](https://arize.com/docs/phoenix/get-started/ts-get-started-prompt-playground): Test prompt variations in TypeScript - [TS: Experiments Quickstart](https://arize.com/docs/phoenix/get-started/ts-get-started-datasets-and-experiments): Create datasets and run experiments in TypeScript ## Tracing - [Tracing Features](https://arize.com/docs/phoenix/tracing/features-tracing): Discover tracing capabilities — auto-instrumentation, manual spans, annotations, sessions ### Tutorial - [Tracing Tutorial](https://arize.com/docs/phoenix/tracing/tutorial): Build a fully observable AI agent from scratch - [Your First Traces](https://arize.com/docs/phoenix/tracing/tutorial/your-first-traces): Instrument and view your first traces - [Annotations and Evaluations](https://arize.com/docs/phoenix/tracing/tutorial/annotations-and-evaluations): Add quality signals to traces - [Sessions Tutorial](https://arize.com/docs/phoenix/tracing/tutorial/sessions): Track multi-turn conversations ### Core Concepts - [LLM Traces](https://arize.com/docs/phoenix/tracing/llm-traces): Understand how traces and spans model LLM calls, tool execution, and retrieval - [Projects](https://arize.com/docs/phoenix/tracing/llm-traces/projects): Organize traces by environment, application, or team - [Sessions](https://arize.com/docs/phoenix/tracing/llm-traces/sessions): Group related traces into multi-turn conversational sessions - [Annotating Traces](https://arize.com/docs/phoenix/tracing/llm-traces/how-to-annotate-traces): Attach feedback, labels, and scores to traces for quality tracking - [Metrics](https://arize.com/docs/phoenix/tracing/llm-traces/metrics): Monitor latency, token usage, cost, and error rates across traces ### Setup - [Setup Tracing](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing): Configure Phoenix to receive traces via OpenTelemetry - [Instrument Your App](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/instrument): Use OpenInference decorators and manual span creation - [Setup Projects](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-projects): Create projects to organize traces - [Setup Sessions](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-sessions): Configure multi-turn session tracking - [Setup Using Phoenix OTEL](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-using-phoenix-otel): Simplified OpenTelemetry setup with phoenix.otel ### Metadata & Annotations - [Add Metadata](https://arize.com/docs/phoenix/tracing/how-to-tracing/add-metadata): Enrich traces with custom attributes and tags - [Customize Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/add-metadata/customize-spans): Add session IDs, user IDs, metadata via OTel context - [Prompt Templates](https://arize.com/docs/phoenix/tracing/how-to-tracing/add-metadata/instrumenting-prompt-templates-and-prompt-variables): Capture prompt templates and variables in traces - [Annotate Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations): Add scores, labels, human feedback, LLM evaluations - [Annotating Auto-Instrumented Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/annotating-auto-instrumented-spans): Add annotations to spans generated by auto-instrumentation libraries - [Annotating in the UI](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/annotating-in-the-ui): Use the Phoenix web UI to label and score traces manually - [Capture Feedback](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/capture-feedback): Collect end-user thumbs-up/down feedback and attach it to traces via SDK - [Evaluating Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/evaluating-phoenix-traces): Run batch LLM evaluations over existing traces in a project - [LLM Evaluations on Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/llm-evaluations): Set up continuous LLM-as-judge scoring on incoming traces ### Import & Export - [Importing & Exporting](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces): Import, export, and migrate trace data between Phoenix instances - [Exporting Annotated Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/exporting-annotated-spans): Export spans with annotations - [Extract Data from Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/extract-data-from-spans): Query span attributes via Phoenix client - [Importing Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/importing-existing-traces): Import traces from external sources - [Import ATIF Trajectories](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/importing-atif-trajectories): Upload agent trajectories in ATIF format and visualize them as Phoenix traces - [Retrieve Traces via CLI](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/retrieve-traces-via-cli): Query traces from the command line ### Advanced Tracing - [Cost Tracking](https://arize.com/docs/phoenix/tracing/how-to-tracing/cost-tracking): Monitor LLM token usage and cost trends - [Advanced Configuration](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced): Configure batching, gRPC transport, custom instrumentation, and performance tuning - [Masking Span Attributes](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/masking-span-attributes): Hide sensitive data in traces via environment variables - [Modifying Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/modifying-spans): Use SpanProcessors to filter or enrich spans - [Multimodal Tracing](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/multimodal-tracing): Capture image, audio, and multimodal data in traces - [Suppress Tracing](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/suppress-tracing): Disable tracing for specific code blocks - [Construct Shareable URLs](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/constructing-urls): Link to projects, traces, spans, and sessions by human-readable identifiers ## Evaluation - [Python Quickstart](https://arize.com/docs/phoenix/evaluation/python-quickstart): Get started with evaluations in Python - [TypeScript Quickstart](https://arize.com/docs/phoenix/evaluation/typescript-quickstart): Get started with evaluations in TypeScript ### Overview - [LLM Evals](https://arize.com/docs/phoenix/evaluation/llm-evals): LLM-as-judge evaluation with pre-built and custom evaluators - [Use Any LLM](https://arize.com/docs/phoenix/evaluation/llm-evals/use-any-llm): Configure any LLM provider as an evaluation model - [Executors](https://arize.com/docs/phoenix/evaluation/llm-evals/executors): Concurrency, rate limits, batching for evaluations - [Evaluator Traces](https://arize.com/docs/phoenix/evaluation/llm-evals/evaluator-traces): View evaluation execution details and model reasoning ### How-to - [How to Evals](https://arize.com/docs/phoenix/evaluation/how-to-evals): Set up and run evaluations end-to-end - [Custom LLM Evaluators](https://arize.com/docs/phoenix/evaluation/how-to-evals/custom-llm-evaluators): Build evaluators with custom prompts and scoring logic - [Configuring the LLM](https://arize.com/docs/phoenix/evaluation/how-to-evals/configuring-the-llm): Set up LLM providers for evaluations - [Prompt Formats](https://arize.com/docs/phoenix/evaluation/how-to-evals/prompt-formats): Customize evaluator prompt structures - [Code Evaluators](https://arize.com/docs/phoenix/evaluation/how-to-evals/code-evaluators): Deterministic evaluation with Python or TypeScript - [Batch Evaluations](https://arize.com/docs/phoenix/evaluation/how-to-evals/batch-evaluations): Run evaluations at scale with automatic concurrency - [Using Evals with Phoenix](https://arize.com/docs/phoenix/evaluation/how-to-evals/using-evals-with-phoenix): Run evals on traces, datasets, or custom data sources ### Tutorials - [Run Built-in Evals](https://arize.com/docs/phoenix/evaluation/tutorials/run-evals-with-built-in-evals): Step-by-step guide to pre-built evaluators - [Customize Eval Template](https://arize.com/docs/phoenix/evaluation/tutorials/customize-eval-template): Design custom evaluator templates - [Custom LLM Endpoint](https://arize.com/docs/phoenix/evaluation/tutorials/customize-your-llm-endpoint): Configure custom or self-hosted model endpoints ### Pre-Built Metrics - [Pre-Built Metrics](https://arize.com/docs/phoenix/evaluation/pre-built-metrics): All pre-built LLM and code evaluators — faithfulness, hallucination, toxicity, RAG relevance, tool calling, and more ### Server-Side Evaluations - [Server Evals Overview](https://arize.com/docs/phoenix/evaluation/server-evals/overview): Attach evaluators to datasets so they run server-side on every Playground experiment - [LLM Evaluators](https://arize.com/docs/phoenix/evaluation/server-evals/llm-evaluators): Configure LLM-as-judge evaluators server-side - [Built-in Evaluators](https://arize.com/docs/phoenix/evaluation/server-evals/builtin-evaluators): Pre-built server-side evaluators - [Server Eval Input Mapping](https://arize.com/docs/phoenix/evaluation/server-evals/input-mapping): Map span attributes to server-side evaluator input variables - [Server Pre-Built Metrics](https://arize.com/docs/phoenix/evaluation/server-evals/pre-built-metrics): All server-side code metrics — contains, exact match, regex, JSON distance, Levenshtein, tool selection/invocation ## Datasets & Experiments - [Quickstart](https://arize.com/docs/phoenix/datasets-and-experiments/quickstart-datasets): Create datasets and run experiments quickly ### Tutorial - [Defining the Dataset](https://arize.com/docs/phoenix/datasets-and-experiments/tutorial/defining-the-dataset): Create a golden dataset with reference outputs - [Code Evals Experiments](https://arize.com/docs/phoenix/datasets-and-experiments/tutorial/run-experiments-with-code-evals): Evaluate against ground truth with code evaluators - [LLM Judge Experiments](https://arize.com/docs/phoenix/datasets-and-experiments/tutorial/run-experiments-with-llm-judge): Use LLM as a Judge for quality assessment - [Iteration Workflow](https://arize.com/docs/phoenix/datasets-and-experiments/tutorial/iteration-workflow-experiments): Iterate on your agent and compare performance ### Datasets - [Datasets Overview](https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets): Understand datasets as versioned collections of examples with inputs and expected outputs - [Creating Datasets](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-datasets/creating-datasets): Build from traces, code, CSV, or curated examples - [Exporting Datasets](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-datasets/exporting-datasets): Export datasets in JSONL and CSV formats ### Experiments - [Run Experiments](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/run-experiments): Run task functions against datasets with evaluators - [Using Evaluators](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/using-evaluators): Configure code and LLM evaluators for experiments - [Dataset Evaluators](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/how-to-dataset-evaluators): Attach evaluators to datasets for automatic scoring - [Repetitions](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/repetitions): Run multiple iterations for statistical confidence - [Splits](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/splits): Organize datasets into train/test/validation splits ## Prompt Engineering ### Tutorial - [Prompt Engineering Tutorial](https://arize.com/docs/phoenix/prompt-engineering/tutorial): Walk through the full prompt optimization lifecycle - [Identify and Edit Prompts](https://arize.com/docs/phoenix/prompt-engineering/tutorial/identify-and-edit-prompts): Find underperforming prompts in traces and iterate on them - [Test Prompts at Scale](https://arize.com/docs/phoenix/prompt-engineering/tutorial/test-prompts-at-scale): Evaluate prompt variants across a dataset - [Compare Prompt Versions](https://arize.com/docs/phoenix/prompt-engineering/tutorial/compare-prompt-versions): Diff outputs between prompt iterations side-by-side - [Auto-Optimize Prompts](https://arize.com/docs/phoenix/prompt-engineering/tutorial/optimize-prompts-automatically): Use DSPy-style optimizers to improve prompts programmatically ### Overview - [Prompts Overview](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts): Understand prompt templates, invocation parameters, tool definitions, and response formats - [Prompt Management](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/prompt-management): Version, store, deploy, and track prompt changes - [Prompt Playground](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/prompt-playground): Interactive testing with variations, models, and tools - [Span Replay](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/span-replay): Replay LLM calls with different prompts to debug failures - [Prompts in Code](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/prompts-in-code): Sync prompts via SDK across applications ### How-to - [Configure AI Providers](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/configure-ai-providers): Set up LLM providers for playground - [Using the Playground](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/using-the-playground): Test variations, view traces, evaluate prompts - [Create a Prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/create-a-prompt): Save prompts with templates, parameters, tools - [Test a Prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/test-a-prompt): Test with dataset examples or custom inputs - [Tag a Prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/tag-a-prompt): Tag prompts for deployment control across environments - [Using a Prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/using-a-prompt): Load prompts programmatically via SDK ## Integrations - [Integrations Overview](https://arize.com/docs/phoenix/integrations): All Phoenix integrations — LLM providers, frameworks, platforms ### Developer Tools - [Coding Agents](https://arize.com/docs/phoenix/integrations/developer-tools/coding-agents): Claude Code, Cursor, and AI coding assistant integration - [Phoenix MCP Server](https://arize.com/docs/phoenix/integrations/phoenix-mcp-server): Connect AI assistants to Phoenix via Model Context Protocol ### LLM Providers - [OpenAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-tracing): Auto-instrument OpenAI Python SDK with openinference-instrumentation-openai - [OpenAI Agents SDK Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-agents-sdk-tracing): Trace OpenAI Agents SDK workflows including tool calls and handoffs - [OpenAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-evals): Use OpenAI models as evaluation judges - [OpenAI Node.js SDK](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-node-js-sdk): Instrument OpenAI calls in TypeScript/Node.js - [Anthropic Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/anthropic/anthropic-tracing): Auto-instrument Anthropic Python SDK - [Anthropic Evals](https://arize.com/docs/phoenix/integrations/llm-providers/anthropic/anthropic-evals): Use Claude models as evaluation judges - [Anthropic TypeScript SDK](https://arize.com/docs/phoenix/integrations/llm-providers/anthropic/anthropic-sdk-typescript): Instrument Anthropic calls in TypeScript - [Amazon Bedrock Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-tracing): Auto-instrument Bedrock SDK with openinference-instrumentation-bedrock - [Amazon Bedrock Agents Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-agents-tracing): Trace Bedrock agent orchestration workflows - [Amazon Bedrock Evals](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-evals): Use Bedrock models as evaluation judges - [Amazon Bedrock Agent Runtime JS](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-agent-runtime-js): Instrument Bedrock Agent Runtime in TypeScript - [Amazon Bedrock SDK JS](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-sdk-js): Instrument Bedrock SDK in TypeScript - [Google GenAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/google-genai-tracing): Auto-instrument Google GenAI SDK - [Gemini Evals](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/gemini-evals): Use Gemini models as evaluation judges - [Google GenAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/google-gen-ai-evals): Configure Google GenAI as eval provider - [Google ADK Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/google-adk/google-adk-tracing): Trace Google Agent Development Kit workflows - [Groq Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/groq/groq-tracing): Auto-instrument Groq SDK - [LiteLLM Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/litellm/litellm-tracing): Auto-instrument LiteLLM unified API calls - [LiteLLM Evals](https://arize.com/docs/phoenix/integrations/llm-providers/litellm/litellm-evals): Use LiteLLM as eval model proxy - [MistralAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/mistralai/mistralai-tracing): Auto-instrument MistralAI SDK - [MistralAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/mistralai/mistralai-evals): Use MistralAI as evaluation judge - [OpenRouter Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/openrouter/openai-tracing): Trace OpenRouter API calls - [VertexAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/vertexai/vertexai-tracing): Auto-instrument VertexAI SDK - [VertexAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/vertexai/vertexai-evals): Use VertexAI as evaluation judge ### Python Frameworks - [Agno Tracing](https://arize.com/docs/phoenix/integrations/python/agno/agno-tracing): Auto-instrument Agno agent workflows - [AgentSpec Tracing](https://arize.com/docs/phoenix/integrations/python/agentspec/agentspec-tracing): Auto-instrument AgentSpec agents - [AutoGen Tracing](https://arize.com/docs/phoenix/integrations/python/autogen/autogen-tracing): Auto-instrument AutoGen conversations - [AutoGen AgentChat Tracing](https://arize.com/docs/phoenix/integrations/python/autogen/autogen-agentchat-tracing): Trace AutoGen AgentChat multi-agent conversations - [BeeAI Tracing (Python)](https://arize.com/docs/phoenix/integrations/python/beeai/beeai-tracing-python): Auto-instrument BeeAI agent workflows - [Claude Agent SDK (Python)](https://arize.com/docs/phoenix/integrations/python/claude-agent-sdk): Instrument Claude Agent SDK with OpenInference - [CrewAI Tracing](https://arize.com/docs/phoenix/integrations/python/crewai/crewai-tracing): Auto-instrument CrewAI agent crews - [DSPy Tracing](https://arize.com/docs/phoenix/integrations/python/dspy/dspy-tracing): Auto-instrument DSPy modules and optimizers - [Google ADK Tracing (Python)](https://arize.com/docs/phoenix/integrations/python/google-adk/google-adk-tracing): Auto-instrument Google ADK agents - [Graphite Integration](https://arize.com/docs/phoenix/integrations/python/graphite/graphite-integration-guide): Integrate Graphite knowledge graphs with Phoenix - [Guardrails AI Tracing](https://arize.com/docs/phoenix/integrations/python/guardrails-ai/guardrails-ai-tracing): Auto-instrument Guardrails AI validation - [Haystack Tracing](https://arize.com/docs/phoenix/integrations/python/haystack/haystack-tracing): Auto-instrument Haystack pipelines - [smolagents Tracing](https://arize.com/docs/phoenix/integrations/python/hugging-face-smolagents/smolagents-tracing): Auto-instrument Hugging Face smolagents - [Instructor Tracing](https://arize.com/docs/phoenix/integrations/python/instructor/instructor-tracing): Auto-instrument Instructor structured outputs - [LangChain Tracing](https://arize.com/docs/phoenix/integrations/python/langchain/langchain-tracing): Auto-instrument LangChain chains and agents - [LangGraph Tracing](https://arize.com/docs/phoenix/integrations/python/langgraph/langgraph-tracing): Auto-instrument LangGraph stateful workflows - [LlamaIndex Tracing](https://arize.com/docs/phoenix/integrations/python/llamaindex/llamaindex-tracing): Auto-instrument LlamaIndex queries and pipelines - [LlamaIndex Workflows Tracing](https://arize.com/docs/phoenix/integrations/python/llamaindex/llamaindex-workflows-tracing): Trace LlamaIndex workflow orchestration - [MCP Tracing (Python)](https://arize.com/docs/phoenix/integrations/python/mcp-tracing): Trace MCP client and server interactions - [NeMo Agent Tracing](https://arize.com/docs/phoenix/integrations/python/nvidia/nemo-agent-tracing): Trace NVIDIA NeMo agent workflows - [Portkey Tracing](https://arize.com/docs/phoenix/integrations/python/portkey/portkey-tracing): Auto-instrument Portkey gateway calls - [Pydantic AI Tracing](https://arize.com/docs/phoenix/integrations/python/pydantic/pydantic-tracing): Auto-instrument Pydantic AI agents - [Pydantic Evals](https://arize.com/docs/phoenix/integrations/python/pydantic/pydantic-evals): Use Pydantic AI evaluation framework - [Strands Agents Tracing](https://arize.com/docs/phoenix/integrations/python/strands-agents/strands-agents-tracing): Auto-instrument Strands Agents with openinference-instrumentation-strands-agents ### TypeScript Frameworks - [BeeAI Tracing (TypeScript)](https://arize.com/docs/phoenix/integrations/typescript/beeai/beeai-tracing-js): Auto-instrument BeeAI in TypeScript - [Claude Agent SDK (TypeScript)](https://arize.com/docs/phoenix/integrations/typescript/claude-agent-sdk): Instrument Claude Agent SDK in TypeScript - [LangChain.js Tracing](https://arize.com/docs/phoenix/integrations/typescript/langchain/langchain-js): Auto-instrument LangChain.js chains and agents - [Mastra Tracing](https://arize.com/docs/phoenix/integrations/typescript/mastra/mastra-tracing): Auto-instrument Mastra agent workflows - [MCP Tracing (TypeScript)](https://arize.com/docs/phoenix/integrations/typescript/mcp/mcp-tracing-typescript): Trace MCP interactions in TypeScript - [Vercel AI SDK Tracing](https://arize.com/docs/phoenix/integrations/typescript/vercel/vercel-ai-sdk-tracing-js): Auto-instrument Vercel AI SDK ### Java Frameworks - [Arconia Tracing](https://arize.com/docs/phoenix/integrations/java/arconia/arconia-tracing): Auto-instrument Arconia Java applications - [LangChain4j Tracing](https://arize.com/docs/phoenix/integrations/java/langchain4j/langchain4j-tracing): Auto-instrument LangChain4j chains and agents - [Spring AI Tracing](https://arize.com/docs/phoenix/integrations/java/springai/springai-tracing): Auto-instrument Spring AI applications ### Platforms - [Dify Tracing](https://arize.com/docs/phoenix/integrations/platforms/dify/dify-tracing): Send traces from Dify workflows - [Flowise Tracing](https://arize.com/docs/phoenix/integrations/platforms/flowise/flowise-tracing): Send traces from Flowise flows - [LangFlow Tracing](https://arize.com/docs/phoenix/integrations/platforms/langflow/langflow-tracing): Send traces from LangFlow workflows - [Prompt Flow Tracing](https://arize.com/docs/phoenix/integrations/platforms/prompt-flow/prompt-flow-tracing): Send traces from Azure Prompt Flow ## Settings - [Access Control RBAC](https://arize.com/docs/phoenix/settings/access-control-rbac): Role-based permissions and project access - [API Keys](https://arize.com/docs/phoenix/settings/api-keys): Generate and manage API keys - [Data Retention](https://arize.com/docs/phoenix/settings/data-retention): Configure data retention policies - [Custom AI Providers](https://arize.com/docs/phoenix/settings/custom-ai-providers): Server-managed AI provider credentials for Playground and evals ## Concepts ### Tracing - [What Are Traces](https://arize.com/docs/phoenix/tracing/concepts-tracing/what-are-traces): Understand how traces model request paths and spans model units of work - [How Tracing Works](https://arize.com/docs/phoenix/tracing/concepts-tracing/how-tracing-works): Learn how Phoenix collects traces via OpenTelemetry and processes them - [Annotations Concepts](https://arize.com/docs/phoenix/tracing/concepts-tracing/annotations-concepts): Understand how scores, labels, and feedback attach to traces as quality signals - [Translating Conventions](https://arize.com/docs/phoenix/tracing/concepts-tracing/translating-conventions): Map framework-specific conventions to Phoenix's trace structure - [Tracing FAQs](https://arize.com/docs/phoenix/tracing/concepts-tracing/faqs-tracing): Troubleshoot trace collection, local vs remote setup, and common configuration issues ### Prompts - [Prompts Concepts](https://arize.com/docs/phoenix/prompt-engineering/concepts-prompts/prompts-concepts): Understand prompt templates, versioning, tagging, and invocation parameters - [Context Engineering](https://arize.com/docs/phoenix/prompt-engineering/concepts-prompts/context-engineering-basics): Structure and optimize the context window for better LLM outputs ### Datasets - [Datasets Concepts](https://arize.com/docs/phoenix/datasets-and-experiments/concepts-datasets): Understand how datasets organize examples with inputs and expected outputs for testing ### Evaluation - [LLM as a Judge](https://arize.com/docs/phoenix/evaluation/concepts-evals/llm-as-a-judge): LLM-based evaluation best practices - [Evaluators](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluators): LLM and code evaluator types and score properties - [Evaluation Types](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluation-types): Categorical vs score evaluations - [Building Your Own Evals](https://arize.com/docs/phoenix/evaluation/concepts-evals/building-your-own-evals): Design custom evaluator templates - [Evaluating Multi-Agent Systems](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluating-multi-agent-systems): Multi-agent evaluation strategies - [Eval Input Mapping](https://arize.com/docs/phoenix/evaluation/concepts-evals/input-mapping): Understand how evaluators transform complex data structures into input variables ## Resources - [General FAQs](https://arize.com/docs/phoenix/resources/frequently-asked-questions): Common questions about Phoenix features, pricing, and compatibility - [Contribute](https://arize.com/docs/phoenix/resources/contribute-to-phoenix): Open-source contribution guide - [Phoenix to Arize AX Migration](https://arize.com/docs/phoenix/resources/phoenix-to-arize-ax-migration): Migration guide from Phoenix to Arize AX ## Use Cases - [RAG Evaluation](https://arize.com/docs/phoenix/use-cases/rag-evaluation): Build and evaluate RAG pipelines ## SDK & API Reference - [SDK Overview](https://arize.com/docs/phoenix/sdk-api-reference): Navigate all Phoenix SDK and API reference docs ### Python - [Phoenix Client (Python)](https://arize.com/docs/phoenix/sdk-api-reference/python/arize-phoenix-client): Manage traces, datasets, experiments, and prompts via arize-phoenix-client - [Phoenix Evals (Python)](https://arize.com/docs/phoenix/sdk-api-reference/python/arize-phoenix-evals): Run LLM and code evaluators via arize-phoenix-evals - [Phoenix OTEL (Python)](https://arize.com/docs/phoenix/sdk-api-reference/python/arize-phoenix-otel): Configure OpenTelemetry tracing via arize-phoenix-otel ### TypeScript - [TypeScript SDK Overview](https://arize.com/docs/phoenix/sdk-api-reference/typescript/overview): Install and configure the Phoenix TypeScript SDK - [TypeScript API Reference](https://arize-ai.github.io/phoenix/modules.html): Full auto-generated TypeScript API docs - [Phoenix Client (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-client): Manage traces, datasets, and prompts via @arizeai/phoenix-client - [Phoenix Client Overview (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/overview): Typed TypeScript client for Phoenix platform APIs — install, configure, and use module entrypoints - [Phoenix Client Annotations (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/annotations): Attach span, session, and document annotations via @arizeai/phoenix-client - [Phoenix Client Datasets (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/datasets): Create and inspect datasets via @arizeai/phoenix-client - [Phoenix Client Document Annotations (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/document-annotations): Log document-level annotations for RAG evaluation via @arizeai/phoenix-client - [Phoenix Client Experiments (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/experiments): Run experiments via @arizeai/phoenix-client - [Phoenix Client Prompts (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/prompts): Manage prompts via @arizeai/phoenix-client - [Phoenix Client Session Annotations (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/session-annotations): Log session-level annotations for conversation evaluation via @arizeai/phoenix-client - [Phoenix Client Sessions (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/sessions): Work with sessions via @arizeai/phoenix-client - [Phoenix Client Span Annotations (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/span-annotations): Log and retrieve span-level annotations via @arizeai/phoenix-client - [Phoenix Client Spans (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/spans): Search and manage spans via @arizeai/phoenix-client - [Phoenix Client Traces (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-client/traces): Retrieve traces via @arizeai/phoenix-client - [Phoenix Evals (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-evals): Run LLM and code evaluators via @arizeai/phoenix-evals - [Phoenix Evals Overview (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-evals/overview): Install and configure @arizeai/phoenix-evals for TypeScript evaluation workflows - [Phoenix Evals Classification (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-evals/classification): Run classification evaluations with @arizeai/phoenix-evals - [Phoenix Evals Create Evaluator (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-evals/create-evaluator): Build custom evaluators with @arizeai/phoenix-evals - [Phoenix Evals LLM Evaluators (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-evals/llm-evaluators): Use LLM-backed evaluators in @arizeai/phoenix-evals - [Phoenix Evals Phoenix Integration (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-evals/phoenix-integration): Connect @arizeai/phoenix-evals to Phoenix experiments for end-to-end evaluation - [Phoenix Evals Templates (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-evals/templates): Template helpers for structuring evaluator prompts in @arizeai/phoenix-evals - [Phoenix OTEL (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-otel): Register Phoenix tracing and use OpenInference helpers via @arizeai/phoenix-otel - [Phoenix OTEL Overview (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-otel/overview): Configure `register()`, OTLP export, instrumentations, and provider lifecycle in @arizeai/phoenix-otel - [Phoenix OTEL Tracing Helpers (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-otel/tracing-helpers): Wrap functions and methods with `withSpan`, `traceChain`, `traceAgent`, and `traceTool` - [Phoenix OTEL Context Attributes (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-otel/context-attributes): Propagate session, user, metadata, prompt template, and custom attributes in @arizeai/phoenix-otel - [Phoenix OTEL Manual Spans (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/packages/phoenix-otel/manual-spans): Build raw spans, OpenInference attributes, and redaction with `OITracer` - [OpenInference Core (TS)](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-openinference-core): Semantic conventions and span utilities via @arizeai/openinference-core - [MCP Server SDK](https://arize.com/docs/phoenix/sdk-api-reference/typescript/mcp-server): Build MCP servers that expose Phoenix data to AI assistants - [Phoenix CLI](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-cli): Fetch docs, manage traces, and run experiments from the terminal ### REST API - [REST API Overview](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/overview): Authenticate and call the Phoenix REST API - [API Reference](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference): Endpoint docs for annotations, datasets, experiments, traces, spans, prompts, projects - [OpenAPI Spec](https://raw.githubusercontent.com/Arize-ai/phoenix/refs/heads/main/schemas/openapi.json): Machine-readable OpenAPI 3 specification ### OpenInference - [OpenInference Python](https://arize.com/docs/phoenix/sdk-api-reference/openinference-sdk/openinference-python): Build custom Python instrumentors with OpenInference semantic conventions - [OpenInference Java](https://arize.com/docs/phoenix/sdk-api-reference/openinference-sdk/openinference-java): Build custom Java instrumentors with OpenInference semantic conventions - [OpenInference JavaScript](https://arize.com/docs/phoenix/sdk-api-reference/openinference-sdk/openinference-javascript): Build custom JS instrumentors with OpenInference semantic conventions ## Self-Hosting - [Self-Hosting Overview](https://arize.com/docs/phoenix/self-hosting): Choose a self-hosting deployment option and get started - [Architecture](https://arize.com/docs/phoenix/self-hosting/architecture): Understand Phoenix components, data flow, and infrastructure requirements - [License](https://arize.com/docs/phoenix/self-hosting/license): Review licensing terms for self-hosted Phoenix instances - [Configuration](https://arize.com/docs/phoenix/self-hosting/configuration): Set environment variables, database connections, and deployment parameters - [Amazon Aurora](https://arize.com/docs/phoenix/self-hosting/configuration/using-amazon-aurora): Aurora/RDS IAM database authentication - [Azure Database for PostgreSQL](https://arize.com/docs/phoenix/self-hosting/configuration/using-azure-managed-identity): Configure managed-identity authentication for Azure PostgreSQL deployments ### Deployment - [Terminal](https://arize.com/docs/phoenix/self-hosting/deployment-options/terminal): Run from terminal - [Docker](https://arize.com/docs/phoenix/self-hosting/deployment-options/docker): Deploy with Docker - [Kubernetes](https://arize.com/docs/phoenix/self-hosting/deployment-options/kubernetes): Deploy on Kubernetes - [Helm](https://arize.com/docs/phoenix/self-hosting/deployment-options/kubernetes-helm): Deploy with Helm charts - [AWS CloudFormation](https://arize.com/docs/phoenix/self-hosting/deployment-options/aws-with-cloudformation): Deploy on AWS - [Railway](https://arize.com/docs/phoenix/self-hosting/deployment-options/railway): Deploy on Railway ### Features - [Provisioning](https://arize.com/docs/phoenix/self-hosting/features/provisioning): Initial instance setup - [Authentication](https://arize.com/docs/phoenix/self-hosting/features/authentication): User management, SSO, security - [Email](https://arize.com/docs/phoenix/self-hosting/features/email): Notification and alert configuration - [Management](https://arize.com/docs/phoenix/self-hosting/features/management): Monitoring, scaling, administration ### Upgrade & Security - [Migrations](https://arize.com/docs/phoenix/self-hosting/upgrade/migrations): Database migration guide - [Privacy](https://arize.com/docs/phoenix/self-hosting/security/privacy): Privacy and data protection - [Self-Hosting FAQs](https://arize.com/docs/phoenix/self-hosting/misc/frequently-asked-questions): Troubleshoot common self-hosting issues ## Phoenix Cloud - [Phoenix Cloud](https://arize.com/docs/phoenix/phoenix-cloud): Managed service overview ## Cookbooks - [Cookbook Index](https://arize.com/docs/phoenix/cookbook): All examples — agents, evaluation, tracing, prompt engineering, datasets