# Arize Phoenix

## Documentation

- [Arize Phoenix](https://arize.com/docs/phoenix/readme.md): AI Observability and Evaluation
- [Get Started](https://arize.com/docs/phoenix/get-started.md)
- [Get Started: Tracing](https://arize.com/docs/phoenix/get-started/get-started-tracing.md)
- [Get Started: Evaluations](https://arize.com/docs/phoenix/get-started/get-started-evaluations.md)
- [Get Started: Datasets & Experiments](https://arize.com/docs/phoenix/get-started/get-started-datasets-and-experiments.md)
- [Get Started: Prompt Playground](https://arize.com/docs/phoenix/get-started/get-started-prompt-playground.md)
- [User Guide](https://arize.com/docs/phoenix/user-guide.md)
- [Environments](https://arize.com/docs/phoenix/environments.md)
- [Production Guide](https://arize.com/docs/phoenix/production-guide.md): Moving your application to production: steps for reliability and scale
- [Integrations](https://arize.com/docs/phoenix/section-integrations.md)
- [Overview: Tracing](https://arize.com/docs/phoenix/tracing/llm-traces.md): Tracing the execution of LLM applications using Telemetry
- [Projects](https://arize.com/docs/phoenix/tracing/llm-traces/projects.md): Use projects to organize your LLM traces
- [Sessions](https://arize.com/docs/phoenix/tracing/llm-traces/sessions.md): Track and analyze multi-turn conversations
- [Annotations](https://arize.com/docs/phoenix/tracing/llm-traces/how-to-annotate-traces.md)
- [Metrics](https://arize.com/docs/phoenix/tracing/llm-traces/metrics.md): Each project comes with a pre-defined metrics dashboard
- [Features: Tracing](https://arize.com/docs/phoenix/tracing/features-tracing.md): Tracing is a critical part of AI Observability and should be used both in production and development
- [How-to: Tracing](https://arize.com/docs/phoenix/tracing/how-to-tracing.md): Guides on how to use traces
- [Setup Tracing](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing.md)
- [Setup using Phoenix OTEL](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-using-phoenix-otel.md)
- [Setup using base OTEL](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/custom-spans.md): While the spans created via Phoenix and OpenInference create a solid foundation for tracing your application, sometimes you need to create and customize your LLM spans
- [Using Tracing Helpers](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/instrument.md): OpenInference packages provide helpful abstractions to make manual instrumentation of agents simpler.
- [Setup Tracing (TS)](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/javascript.md)
- [Setup Projects](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-projects.md)
- [Setup Sessions](https://arize.com/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-sessions.md): How to track sessions across multiple traces
- [Add Metadata](https://arize.com/docs/phoenix/tracing/how-to-tracing/add-metadata.md)
- [Add Attributes, Metadata, Users](https://arize.com/docs/phoenix/tracing/how-to-tracing/add-metadata/customize-spans.md)
- [Instrument Prompt Templates and Prompt Variables](https://arize.com/docs/phoenix/tracing/how-to-tracing/add-metadata/instrumenting-prompt-templates-and-prompt-variables.md)
- [Annotate Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations.md)
- [Annotating in the UI](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/annotating-in-the-ui.md): How to annotate traces in the UI for analysis and dataset curation
- [Annotating via the Client](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/capture-feedback.md): Use the phoenix client to capture end-user feedback
- [Annotating Auto-Instrumented Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/annotating-auto-instrumented-spans.md): Use the capture\_span\_context context manager to annotate auto-instrumented spans
- [Running Evals on Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/evaluating-phoenix-traces.md): How to use an LLM judge to label and score your application
- [Log Evaluation Results](https://arize.com/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/llm-evaluations.md): This guide shows how LLM evaluation results in dataframes can be sent to Phoenix.
- [Importing & Exporting Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces.md)
- [Import Existing Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/importing-existing-traces.md)
- [Export Data & Query Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/extract-data-from-spans.md): Various options for to help you get data out of Phoenix
- [Exporting Annotated Spans](https://arize.com/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/exporting-annotated-spans.md)
- [Cost Tracking](https://arize.com/docs/phoenix/tracing/how-to-tracing/cost-tracking.md)
- [Advanced](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced.md)
- [Mask Span Attributes](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/masking-span-attributes.md)
- [Suppress Tracing](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/suppress-tracing.md)
- [Filter Spans to Export](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/modifying-spans.md)
- [Capture Multimodal Traces](https://arize.com/docs/phoenix/tracing/how-to-tracing/advanced/multimodal-tracing.md)
- [Concepts: Tracing](https://arize.com/docs/phoenix/tracing/concepts-tracing.md)
- [What are Traces](https://arize.com/docs/phoenix/tracing/concepts-tracing/what-are-traces.md)
- [How Tracing Works](https://arize.com/docs/phoenix/tracing/concepts-tracing/how-tracing-works.md)
- [Annotations Concepts](https://arize.com/docs/phoenix/tracing/concepts-tracing/annotations-concepts.md)
- [FAQs: Tracing](https://arize.com/docs/phoenix/tracing/concepts-tracing/faqs-tracing.md)
- [Tutorial](https://arize.com/docs/phoenix/prompt-engineering/tutorial.md): Phoenix Prompts Tutorial
- [Identify & Edit Prompts](https://arize.com/docs/phoenix/prompt-engineering/tutorial/identify-and-edit-prompts.md): Fix and store bad prompts from your spans
- [Test Prompts at Scale](https://arize.com/docs/phoenix/prompt-engineering/tutorial/test-prompts-at-scale.md): Measure and Edit Prompts at Scale
- [Compare Prompt Versions](https://arize.com/docs/phoenix/prompt-engineering/tutorial/compare-prompt-versions.md): Build New Prompt Versions and Compare
- [Optimize Prompts Automatically](https://arize.com/docs/phoenix/prompt-engineering/tutorial/optimize-prompts-automatically.md): Automatically Optimize Prompts with Prompt Learning
- [Overview: Prompts](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts.md)
- [Prompt Management](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/prompt-management.md): Version and track changes made to prompt templates
- [Prompt Playground](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/prompt-playground.md)
- [Span Replay](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/span-replay.md): Replay LLM spans traced in your application directly in the playground
- [Prompts in Code](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/prompts-in-code.md): Pull and push prompt changes via Phoenix's Python and TypeScript Clients
- [How to: Prompts](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts.md): Guides on how to do prompt engineering with Phoenix
- [Configure AI Providers](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/configure-ai-providers.md)
- [Using the Playground](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/using-the-playground.md): General guidelines on how to use Phoenix's prompt playground
- [Create a prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/create-a-prompt.md): Store and track prompt versions in Phoenix
- [Test a prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/test-a-prompt.md): Testing your prompts before you ship them is vital to deploying reliable AI applications
- [Tag a prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/tag-a-prompt.md): How to deploy prompts to different environments safely
- [Using a prompt](https://arize.com/docs/phoenix/prompt-engineering/how-to-prompts/using-a-prompt.md)
- [Concepts: Prompts](https://arize.com/docs/phoenix/prompt-engineering/concepts-prompts.md)
- [Prompts Concepts](https://arize.com/docs/phoenix/prompt-engineering/concepts-prompts/prompts-concepts.md)
- [Context Engineering Basics](https://arize.com/docs/phoenix/prompt-engineering/concepts-prompts/context-engineering-basics.md)
- [Overview: Datasets & Experiments](https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets.md)
- [How-to: Datasets](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-datasets.md): Datasets are critical assets for building robust prompts, evals, fine-tuning,
- [Creating Datasets](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-datasets/creating-datasets.md)
- [Exporting Datasets](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-datasets/exporting-datasets.md)
- [How-to: Experiments](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments.md)
- [Run Experiments](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/run-experiments.md): The following are the key steps of running an experiment illustrated by simple example.
- [Using Evaluators](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/using-evaluators.md)
- [Repetitions](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/repetitions.md): How to leverage repetitions to get an understanding of indeterminate LLM outputs
- [Splits](https://arize.com/docs/phoenix/datasets-and-experiments/how-to-experiments/splits.md): How to run experiments over select splits of your dataset for targeted experimentation
- [Concepts: Datasets](https://arize.com/docs/phoenix/datasets-and-experiments/concepts-datasets.md)
- [TypeScript Quickstart](https://arize.com/docs/phoenix/evaluation/typescript-quickstart.md)
- [Overview: Evals](https://arize.com/docs/phoenix/evaluation/llm-evals.md)
- [Executors](https://arize.com/docs/phoenix/evaluation/llm-evals/executors.md): Phoenix Evals leverages executors that make the execution of evaluations many times faster.
- [Evaluator Traces](https://arize.com/docs/phoenix/evaluation/llm-evals/evaluator-traces.md)
- [Use Any LLM](https://arize.com/docs/phoenix/evaluation/llm-evals/use-any-llm.md)
- [Concepts: Evals](https://arize.com/docs/phoenix/evaluation/concepts-evals.md)
- [Eval Data Types](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluation-types.md)
- [LLM as a Judge](https://arize.com/docs/phoenix/evaluation/concepts-evals/llm-as-a-judge.md)
- [Custom Task Evaluation](https://arize.com/docs/phoenix/evaluation/concepts-evals/building-your-own-evals.md)
- [Evaluating Multi-Agent Systems](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluating-multi-agent-systems.md)
- [Evaluators](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluators.md): Definition and types of Evaluators. Score abstraction.
- [Input Mapping](https://arize.com/docs/phoenix/evaluation/concepts-evals/input-mapping.md)
- [How to: Evals](https://arize.com/docs/phoenix/evaluation/how-to-evals.md)
- [Custom LLM Evaluators](https://arize.com/docs/phoenix/evaluation/how-to-evals/custom-llm-evaluators.md)
- [Configuring the LLM](https://arize.com/docs/phoenix/evaluation/how-to-evals/configuring-the-llm.md)
- [Prompt Formats](https://arize.com/docs/phoenix/evaluation/how-to-evals/configuring-the-llm/prompt-formats.md)
- [Calling models with LiteLLM](https://arize.com/docs/phoenix/evaluation/how-to-evals/configuring-the-llm/calling-models-with-litellm.md)
- [Code Evaluators](https://arize.com/docs/phoenix/evaluation/how-to-evals/code-evaluators.md)
- [Batch Evaluations](https://arize.com/docs/phoenix/evaluation/how-to-evals/batch-evaluations.md)
- [Using Evals with Phoenix](https://arize.com/docs/phoenix/evaluation/how-to-evals/using-evals-with-phoenix.md)
- [Pre-Built Evals](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals.md)
- [Code Metrics](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/code-metrics.md)
- [Hallucination](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/hallucinations.md)
- [Q\&A on Retrieved Data](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/q-and-a-on-retrieved-data.md)
- [Retrieval (RAG) Relevance](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/retrieval-rag-relevance.md)
- [Summarization](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/summarization-eval.md)
- [Code Generation](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/code-generation-eval.md)
- [Toxicity](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/toxicity.md)
- [AI vs Human (Groundtruth)](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/ai-vs-human-groundtruth.md): This LLM evaluation is used to compare AI answers to Human answers. Its very useful in RAG system benchmarking to compare the human generated groundtruth.
- [Reference (citation) Link](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/reference-link-evals.md)
- [User Frustration](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/user-frustration.md)
- [SQL Generation Eval](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/sql-generation-eval.md)
- [Agent Function Calling Eval](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/tool-calling-eval.md)
- [Agent Path Convergence](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/agent-path-convergence.md)
- [Agent Planning](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/agent-planning.md)
- [Agent Reflection](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/agent-reflection.md)
- [Audio Emotion Detection](https://arize.com/docs/phoenix/evaluation/running-pre-tested-evals/audio-emotion-detection.md)
- [Access Control (RBAC)](https://arize.com/docs/phoenix/settings/access-control-rbac.md)
- [API Keys](https://arize.com/docs/phoenix/settings/api-keys.md)
- [Data Retention](https://arize.com/docs/phoenix/settings/data-retention.md)
- [Phoenix to Arize AX Migration](https://arize.com/docs/phoenix/settings/phoenix-to-arize-ax-migration.md): Seamlessly migrate your data from Phoenix to Arize AX
- [Frequently Asked Questions](https://arize.com/docs/phoenix/resources/frequently-asked-questions.md)
- [What is the difference between Phoenix and Arize?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/what-is-the-difference-between-phoenix-and-arize.md)
- [What is my Phoenix Endpoint?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/what-is-my-phoenix-endpoint.md)
- [What is LlamaTrace vs Phoenix Cloud?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/what-is-llamatrace-vs-phoenix-cloud.md)
- [Phoenix Cloud Migration Guide: Legacy to New Version](https://arize.com/docs/phoenix/resources/frequently-asked-questions/phoenix-cloud-migration-guide-legacy-to-new-version.md)
- [Will Phoenix Cloud be on the latest version of Phoenix?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/will-phoenix-cloud-be-on-the-latest-version-of-phoenix.md)
- [Can I add other users to my Phoenix Instance?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/can-i-add-other-users-to-my-phoenix-instance.md)
- [Can I use Azure OpenAI?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/can-i-use-azure-openai.md)
- [Can I use Phoenix locally from a remote Jupyter instance?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/can-i-use-phoenix-locally-from-a-remote-jupyter-instance.md)
- [How can I configure the backend to send the data to the phoenix UI in another container?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/how-can-i-configure-the-backend-to-send-the-data-to-the-phoenix-ui-in-another-container.md)
- [Can I run Phoenix on Sagemaker?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/can-i-run-phoenix-on-sagemaker.md)
- [Can I persist data in a notebook?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/can-i-persist-data-in-a-notebook.md)
- [What is the difference between GRPC and HTTP?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/what-is-the-difference-between-grpc-and-http.md)
- [Can I use gRPC for trace collection?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/can-i-use-grpc-for-trace-collection.md)
- [How do I resolve Phoenix Evals showing NOT\_PARSABLE?](https://arize.com/docs/phoenix/resources/frequently-asked-questions/how-do-i-resolve-phoenix-evals-showing-not_parsable.md)
- [Braintrust Open Source Alternative? LLM Evaluation Platform Comparison](https://arize.com/docs/phoenix/resources/frequently-asked-questions/braintrust-open-source-alternative-llm-evaluation-platform-comparison.md)
- [Langfuse alternative? Arize Phoenix vs Langfuse: key differences](https://arize.com/docs/phoenix/resources/frequently-asked-questions/langfuse-alternative-arize-phoenix-vs-langfuse-key-differences.md)
- [Open Source LangSmith Alternative: Arize Phoenix vs. LangSmith](https://arize.com/docs/phoenix/resources/frequently-asked-questions/open-source-langsmith-alternative-arize-phoenix-vs.-langsmith.md)
- [Contribute to Phoenix](https://arize.com/docs/phoenix/resources/contribute-to-phoenix.md)

* [Arize Phoenix](https://arize.com/docs/phoenix/jp/readme.md)

- [Arize Phoenix](https://arize.com/docs/phoenix/zh/readme.md)

## Phoenix Cloud

- [Phoenix Cloud](https://arize.com/docs/phoenix/phoenix-cloud/readme.md)

## Cookbooks

- [Cookbooks](https://arize.com/docs/phoenix/cookbook/readme.md): Cookbooks & tutorials to help you build with Phoenix
- [Agent Workflow Patterns](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns.md)
- [AutoGen](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns/autogen.md)
- [CrewAI](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns/crewai.md)
- [Google GenAI SDK (Manual Orchestration)](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns/google-genai-sdk-manual-orchestration.md)
- [OpenAI Agents](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns/openai-agents.md)
- [LangGraph](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns/langgraph.md)
- [Smolagents](https://arize.com/docs/phoenix/cookbook/agent-workflow-patterns/smolagents.md)
- [Iterative Evaluation & Experimentation Workflow (Python)](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/iterative-evaluation-and-experimentation-workflow-python.md): Phoenix Tracing, Evaluating, and Experimentation Walkthrough
- [Iterative Evaluation & Experimentation Workflow (TypeScript)](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/iterative-evaluation-and-experimentation-workflow-typescript.md): Phoenix Tracing, Evaluating, and Experimentation Walkthrough
- [LLM Ops Overview](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/llm-ops-overview.md): This tutorial demonstrates how to build, observe, evaluate, and analyze LLM-powered applications using Phoenix.
- [Write your First Custom Eval](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/creating-a-custom-llm-evaluator-with-a-benchmark-dataset.md): Learn how to build a custom LLM-as-a-Judge evaluator by creating a benchmark dataset tailored to your use case, enabling rigorous evaluation beyond standard templates.
- [Align Evals with Human Feedback](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/aligning-llm-evals-with-human-annotations-typescript.md): In this tutorial, we’ll run a Mastra agent and build a custom evaluator for it. The goal is to understand the workflow for creating evaluators that align with specific use cases.
- [Prompt Optimization Techniques](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/prompt-optimization.md)
- [Run Repetition Experiments](https://arize.com/docs/phoenix/cookbook/ai-engineering-workflows/analyzing-customer-review-evals-with-repetition-experiments.md)
- [Agentic RAG Tracing](https://arize.com/docs/phoenix/cookbook/tracing/agentic-rag-tracing.md): This tutorial demonstrates building an agentic RAG system using LlamaIndex's ReAct agent framework combined with vector and SQL query tools.
- [Generating Synthetic Datasets for LLM Evaluators & Agents](https://arize.com/docs/phoenix/cookbook/tracing/generating-synthetic-datasets-for-llm-evaluators-and-agents.md): Learn different strategies for dataset generation and show how they can be used to run experiments and test evaluators
- [Structured Data Extraction](https://arize.com/docs/phoenix/cookbook/tracing/structured-data-extraction.md)
- [Product Recommendation Agent: Google Agent Engine & LangGraph](https://arize.com/docs/phoenix/cookbook/tracing/product-recommendation-agent-google-agent-engine-and-langgraph.md): This notebook is adapted from Google's "Building and Deploying a LangGraph Application with Agent Engine in Vertex AI"
- [More Cookbooks](https://arize.com/docs/phoenix/cookbook/tracing/cookbooks.md)
- [Using Human Annotations for Eval-Driven Development](https://arize.com/docs/phoenix/cookbook/human-in-the-loop-workflows-annotations/using-human-annotations-for-eval-driven-development.md): How to leverage human annotations to build evaluations and experiments that improve your system
- [Optimizing Coding Agent Prompts - Prompt Learning](https://arize.com/docs/phoenix/cookbook/prompt-engineering/optimizing-coding-agent-prompts-prompt-learning.md): Optimizing coding agent prompts and tracking coding agent improvement
- [Optimizing Prompts for LLM Classification - Prompt Learning](https://arize.com/docs/phoenix/cookbook/prompt-engineering/prompt-learning-optimizing-prompts-for-classification.md): Using Prompt Learning to boost accuracy on a classification dataset
- [Few Shot Prompting](https://arize.com/docs/phoenix/cookbook/prompt-engineering/few-shot-prompting.md)
- [ReAct Prompting](https://arize.com/docs/phoenix/cookbook/prompt-engineering/react-prompting.md)
- [Chain-of-Thought Prompting](https://arize.com/docs/phoenix/cookbook/prompt-engineering/chain-of-thought-prompting.md)
- [LLM as a Judge Prompt Optimization](https://arize.com/docs/phoenix/cookbook/prompt-engineering/llm-as-a-judge-prompt-optimization.md)
- [OpenAI Agents SDK Cookbook](https://arize.com/docs/phoenix/cookbook/evaluation/openai-agents-sdk-cookbook.md)
- [Evaluate a Talk-to-your-Data Agent](https://arize.com/docs/phoenix/cookbook/evaluation/evaluate-an-agent.md)
- [Evaluate RAG](https://arize.com/docs/phoenix/cookbook/evaluation/evaluate-rag.md): Building a RAG pipeline and evaluating it with Phoenix Evals.
- [Code Readability Evaluation](https://arize.com/docs/phoenix/cookbook/evaluation/code-readability-evaluation.md): Evaluate the readability of code generated by LLM applications using Phoenix's evaluation framework.
- [Relevance Classification Evaluation](https://arize.com/docs/phoenix/cookbook/evaluation/relevance-classification-evaluation.md): Evaluate the relevance of documents retrieved by RAG applications using Phoenix's evaluation framework.
- [Using Ragas to Evaluate a Math Problem-Solving Agent](https://arize.com/docs/phoenix/cookbook/evaluation/using-ragas-to-evaluate-a-math-problem-solving-agent.md)
- [More Cookbooks](https://arize.com/docs/phoenix/cookbook/evaluation/cookbooks.md)
- [Experiment with a Customer Support Agent](https://arize.com/docs/phoenix/cookbook/datasets-and-experiments/experiment-with-a-customer-support-agent.md)
- [Model Comparison for an Email Text Extraction Service](https://arize.com/docs/phoenix/cookbook/datasets-and-experiments/model-comparison-for-an-email-text-extraction-service.md): Summarize emails by testing prompts and models with Jaro-Winkler-based evaluation.
- [Comparing LlamaIndex Query Engines with a Pairwise Evaluator](https://arize.com/docs/phoenix/cookbook/datasets-and-experiments/comparing-llamaindex-query-engines-with-a-pairwise-evaluator.md)
- [Prompt Template Iteration for a Summarization Service](https://arize.com/docs/phoenix/cookbook/datasets-and-experiments/summarization.md)
- [Text2SQL Experiments](https://arize.com/docs/phoenix/cookbook/datasets-and-experiments/text2sql.md)
- [More Cookbooks](https://arize.com/docs/phoenix/cookbook/datasets-and-experiments/cookbooks.md)
- [Embeddings Analysis](https://arize.com/docs/phoenix/cookbook/retrieval-and-inferences/embeddings-analysis.md)
- [More Cookbooks](https://arize.com/docs/phoenix/cookbook/retrieval-and-inferences/cookbooks.md)

## Integrations

- [Overview](https://arize.com/docs/phoenix/integrations/readme.md)
- [Phoenix MCP Server](https://arize.com/docs/phoenix/integrations/phoenix-mcp-server.md): Phoenix MCP Server is an implementation of the Model Context Protocol for the Arize Phoenix platform. It provides a unified interface to Phoenix's capabilites.
- [Amazon Bedrock](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock.md): Amazon Bedrock is a managed service that provides access to top AI models for building scalable applications.
- [Amazon Bedrock Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-tracing.md): Instrument LLM calls to AWS Bedrock via the boto3 client using the BedrockInstrumentor
- [Amazon Bedrock Evals](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-evals.md): Configure and run Bedrock for evals
- [Amazon Bedrock Agents Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-agents-tracing.md): Instrument LLM calls to AWS Bedrock via the boto3 client using the BedrockInstrumentor
- [Anthropic](https://arize.com/docs/phoenix/integrations/llm-providers/anthropic.md): Anthropic is an AI research company that develops LLMs, including Claude, with a focus on alignment and reliable behavior.
- [Anthropic Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/anthropic/anthropic-tracing.md)
- [Anthropic Evals](https://arize.com/docs/phoenix/integrations/llm-providers/anthropic/anthropic-evals.md): Configure and run Anthropic for evals
- [Google](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai.md): Google GenAI is a suite of AI tools and models from Google Cloud, designed to help businesses build, deploy, and scale AI applications.
- [Google Gen AI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/google-genai-tracing.md): Instrument LLM calls made using the Google Gen AI Python SDK
- [Gemini Evals](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/gemini-evals.md): Configure and run Gemini for evals
- [Google Gen AI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/google-gen-ai-evals.md)
- [Evaluate CrewAI Agents with Google Gen AI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/google-gen-ai-evals-1.md): Evaluate multi-agent systems using Arize Phoenix, Google Evals, and CrewAI
- [Groq](https://arize.com/docs/phoenix/integrations/llm-providers/groq.md): Groq provides ultra-low latency inference for LLMs through its custom-built LPU™ architecture.
- [Groq Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/groq/groq-tracing.md): Instrument LLM applications built with Groq
- [LiteLLM](https://arize.com/docs/phoenix/integrations/llm-providers/litellm.md): LiteLLM is an open-source platform that provides a unified interface to manage and access over 100 LLMs from various providers.
- [LiteLLM Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/litellm/litellm-tracing.md)
- [LiteLLM Evals](https://arize.com/docs/phoenix/integrations/llm-providers/litellm/litellm-evals.md): Configure and run LiteLLM for evals
- [MistralAI](https://arize.com/docs/phoenix/integrations/llm-providers/mistralai.md): Mistral AI develops open-weight large language models, focusing on efficiency, customization, and cost-effective AI solutions.
- [MistralAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/mistralai/mistralai-tracing.md): Instrument LLM calls made using MistralAI's SDK via the MistralAIInstrumentor
- [MistralAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/mistralai/mistralai-evals.md): Configure and run MistralAI for evals
- [OpenAI](https://arize.com/docs/phoenix/integrations/llm-providers/openai.md): OpenAI provides state-of-the-art LLMs for natural language understanding and generation.
- [OpenAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-tracing.md)
- [OpenAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-evals.md): Configure and run OpenAI for evals
- [OpenAI Agents SDK Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-agents-sdk-tracing.md): Use Phoenix and OpenAI Agents SDK for powerful multi-agent tracing
- [OpenAI Node.js SDK](https://arize.com/docs/phoenix/integrations/llm-providers/openai/openai-node.js-sdk.md): Instrument and observe OpenAI calls
- [OpenRouter](https://arize.com/docs/phoenix/integrations/llm-providers/openrouter.md): OpenRouter is a platform that connects developers to multiple AI models through a unified API, making it easier to compare, switch between, and integrate different models.
- [OpenRouter Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/openrouter/openai-tracing.md)
- [VertexAI](https://arize.com/docs/phoenix/integrations/llm-providers/vertexai.md): Vertex AI is a fully managed platform by Google Cloud for building, deploying, and scaling machine learning models.
- [VertexAI Tracing](https://arize.com/docs/phoenix/integrations/llm-providers/vertexai/vertexai-tracing.md): Instrument LLM calls made using VertexAI's SDK via the VertexAIInstrumentor
- [VertexAI Evals](https://arize.com/docs/phoenix/integrations/llm-providers/vertexai/vertexai-evals.md): Configure and run VertexAI for evals
- [BeeAI](https://arize.com/docs/phoenix/integrations/typescript/beeai.md): BeeAI is an open-source platform that enables developers to discover, run, and compose AI agents from any framework, facilitating the creation of interoperable multi-agent systems
- [BeeAI Tracing (JS)](https://arize.com/docs/phoenix/integrations/typescript/beeai/beeai-tracing-js.md): Auto-instrument and observe BeeAI agents
- [Mastra](https://arize.com/docs/phoenix/integrations/typescript/mastra.md): Mastra is an open-source TypeScript AI agent framework designed for building production-ready AI applications with agents, workflows, RAG, and observability
- [Mastra Tracing](https://arize.com/docs/phoenix/integrations/typescript/mastra/mastra-tracing.md): Instrument agent applications built with Mastra
- [MCP Tracing](https://arize.com/docs/phoenix/integrations/typescript/mcp-tracing.md)
- [LangChain](https://arize.com/docs/phoenix/integrations/typescript/langchain.md): LangChain is an open-source framework for building language model applications with prompt chaining, memory, and external integrations
- [LangChain.js](https://arize.com/docs/phoenix/integrations/typescript/langchain/langchain-js.md)
- [Vercel](https://arize.com/docs/phoenix/integrations/typescript/vercel.md): Vercel is a cloud platform that simplifies building, deploying, and scaling modern web applications with features like serverless functions, edge caching, and seamless Git integration
- [Vercel AI SDK Tracing (JS)](https://arize.com/docs/phoenix/integrations/typescript/vercel/vercel-ai-sdk-tracing-js.md)
- [LangChain4j](https://arize.com/docs/phoenix/integrations/java/langchain4j.md): LangChain4j is a Java library that provides APIs, tools, and patterns to easily build and integrate LLM-powered Java applications.
- [LangChain4j Tracing](https://arize.com/docs/phoenix/integrations/java/langchain4j/langchain4j-tracing.md): How to use OpenInference instrumentation with LangChain4j and export traces to Arize Phoenix.
- [Spring AI](https://arize.com/docs/phoenix/integrations/java/springai.md): Spring AI extends the Spring Framework, making it easier to integrate AI capabilities into Java applications using familiar Spring patterns.
- [Spring AI Tracing](https://arize.com/docs/phoenix/integrations/java/springai/springai-tracing.md): How to use OpenInference instrumentation with Spring AI and export traces to Arize Phoenix.
- [Arconia](https://arize.com/docs/phoenix/integrations/java/arconia.md): Arconia is an open-source framework and set of tools for building modern, cloud-native enterprise applications using Java and the Spring Boot framework
- [Arconia Tracing](https://arize.com/docs/phoenix/integrations/java/arconia/arconia-tracing.md): How to use OpenInference instrumentation with Arconia and export traces to Arize Phoenix.
- [Agno](https://arize.com/docs/phoenix/integrations/python/agno.md): Agno is an open-source Python framework for building lightweight, model-agnostic AI agents with built-in memory, knowledge, tools, and reasoning capabilities
- [Agno Tracing](https://arize.com/docs/phoenix/integrations/python/agno/agno-tracing.md)
- [AutoGen](https://arize.com/docs/phoenix/integrations/python/autogen.md): AutoGen is an open-source Python framework for orchestrating multi-agent LLM interactions with shared memory and tool integrations to build scalable AI workflows
- [AutoGen Tracing](https://arize.com/docs/phoenix/integrations/python/autogen/autogen-tracing.md)
- [AutoGen AgentChat Tracing](https://arize.com/docs/phoenix/integrations/python/autogen/autogen-agentchat-tracing.md): Auto-instrument your AgentChat application for seamless observability
- [BeeAI](https://arize.com/docs/phoenix/integrations/python/beeai.md): BeeAI is an open-source platform that enables developers to discover, run, and compose AI agents from any framework, facilitating the creation of interoperable multi-agent systems
- [BeeAI Tracing (Python)](https://arize.com/docs/phoenix/integrations/python/beeai/beeai-tracing-python.md): Instrument and observe BeeAI agents
- [CrewAI](https://arize.com/docs/phoenix/integrations/python/crewai.md): CrewAI is an open-source Python framework for orchestrating role-playing, autonomous AI agents into collaborative “crews” and “flows,” combining high-level simplicity with fine-grained control.
- [CrewAI Tracing](https://arize.com/docs/phoenix/integrations/python/crewai/crewai-tracing.md): Instrument multi-agent applications using CrewAI
- [DSPy](https://arize.com/docs/phoenix/integrations/python/dspy.md): DSPy is an open-source Python framework for declaratively programming modular LLM pipelines and automatically optimizing prompts and model weights
- [DSPy Tracing](https://arize.com/docs/phoenix/integrations/python/dspy/dspy-tracing.md): Instrument and observe your DSPy application via the DSPyInstrumentor
- [Google ADK](https://arize.com/docs/phoenix/integrations/python/google-adk.md): Google ADK is a Python SDK for building AI applications with Google's Gemini models and agent framework capabilities
- [Google ADK Tracing](https://arize.com/docs/phoenix/integrations/python/google-adk/google-adk-tracing.md): Instrument LLM calls made using the Google ADK Python SDK
- [Graphite](https://arize.com/docs/phoenix/integrations/python/graphite.md): Graphite is an open-source Python framework for designing and orchestrating multi-agent LLM workflows with a visual builder and node-based composition.
- [Graphite Integration Guide](https://arize.com/docs/phoenix/integrations/python/graphite/arize-integration.md)
- [Guardrails AI](https://arize.com/docs/phoenix/integrations/python/guardrails-ai.md): Guardrails is an open-source Python framework for adding programmable input/output validators to LLM applications, ensuring safe, structured, and compliant model interactions
- [Guardrails AI Tracing](https://arize.com/docs/phoenix/integrations/python/guardrails-ai/guardrails-ai-tracing.md): Instrument LLM applications that use the Guardrails AI framework
- [Haystack](https://arize.com/docs/phoenix/integrations/python/haystack.md): Haystack is an open-source framework for building scalable semantic search and QA pipelines with document indexing, retrieval, and reader components
- [Haystack Tracing](https://arize.com/docs/phoenix/integrations/python/haystack/haystack-tracing.md): Instrument LLM applications built with Haystack
- [Hugging Face smolagents](https://arize.com/docs/phoenix/integrations/python/hugging-face-smolagents.md): Hugging Face smolagents is a minimalist Python library for building powerful AI agents with simple abstractions, tool integrations, and flexible LLM support
- [smolagents Tracing](https://arize.com/docs/phoenix/integrations/python/hugging-face-smolagents/smolagents-tracing.md): How to use the SmolagentsInstrumentor to trace smolagents by Hugging Face
- [Instructor](https://arize.com/docs/phoenix/integrations/python/instructor.md): Instructor is a library that helps you define structured output formats for LLMs.
- [Instructor Tracing](https://arize.com/docs/phoenix/integrations/python/instructor/instructor-tracing.md)
- [LlamaIndex](https://arize.com/docs/phoenix/integrations/python/llamaindex.md): LlamaIndex is an open-source framework that streamlines connecting, ingesting, indexing, and retrieving structured or unstructured data to power efficient, data-aware language model applications.
- [LlamaIndex Tracing](https://arize.com/docs/phoenix/integrations/python/llamaindex/llamaindex-tracing.md): How to use the python LlamaIndexInstrumentor to trace LlamaIndex
- [LlamaIndex Workflows Tracing](https://arize.com/docs/phoenix/integrations/python/llamaindex/llamaindex-workflows-tracing.md): How to use the python LlamaIndexInstrumentor to trace LlamaIndex Workflows
- [LangChain](https://arize.com/docs/phoenix/integrations/python/langchain.md): LangChain is an open-source framework for building language model applications with prompt chaining, memory, and external integrations
- [LangChain Tracing](https://arize.com/docs/phoenix/integrations/python/langchain/langchain-tracing.md): How to use the python LangChainInstrumentor to trace LangChain
- [LangGraph](https://arize.com/docs/phoenix/integrations/python/langgraph.md): LangGraph is an open-source framework for building graph-based LLM pipelines with modular nodes and seamless data integrations
- [LangGraph Tracing](https://arize.com/docs/phoenix/integrations/python/langgraph/langgraph-tracing.md)
- [MCP Tracing](https://arize.com/docs/phoenix/integrations/python/mcp-tracing.md): Phoenix provides tracing for MCP clients and servers through OpenInference. This includes the unique capability to trace client to server interactions under a single trace in the correct hierarchy.
- [Portkey](https://arize.com/docs/phoenix/integrations/python/portkey.md): Portkey is an AI Gateway and observability platform that provides routing,  guardrails, caching, and monitoring for 200+ LLMs with enterprise-grade  security and reliability features.
- [Portkey Tracing](https://arize.com/docs/phoenix/integrations/python/portkey/portkey-tracing.md): How to trace Portkey AI Gateway requests with Phoenix for comprehensive LLM observability
- [Pydantic AI](https://arize.com/docs/phoenix/integrations/python/pydantic.md): PydanticAI is a Python agent framework designed to make it less painful to  build production-grade applications with Generative AI, built by the team  behind Pydantic with type-safe structured outputs
- [Pydantic AI Tracing](https://arize.com/docs/phoenix/integrations/python/pydantic/pydantic-tracing.md): How to use the python PydanticAIInstrumentor to trace PydanticAI agents
- [Pydantic AI Evals](https://arize.com/docs/phoenix/integrations/python/pydantic/pydantic-evals.md): How to use Pydantic Evals with Phoenix to evaluate AI applications using structured evaluation frameworks
- [Dify](https://arize.com/docs/phoenix/integrations/platforms/dify.md): Dify lets you visually build, orchestrate, and deploy AI-native apps using LLMs, with low-code workflows and agent frameworks for fast deployment.
- [Dify Tracing](https://arize.com/docs/phoenix/integrations/platforms/dify/dify-tracing.md): Configure your Dify application to view traces in Phoenix
- [Flowise](https://arize.com/docs/phoenix/integrations/platforms/flowise.md): Flowise is a low-code platform for building customized chatflows and agentflows.
- [Flowise Tracing](https://arize.com/docs/phoenix/integrations/platforms/flowise/flowise-tracing.md)
- [LangFlow](https://arize.com/docs/phoenix/integrations/platforms/langflow.md): Langflow is an open-source visual framework that enables developers to rapidly design, prototype, and deploy custom applications powered by large language models (LLMs)
- [LangFlow Tracing](https://arize.com/docs/phoenix/integrations/platforms/langflow/langflow-tracing.md)
- [Prompt Flow](https://arize.com/docs/phoenix/integrations/platforms/prompt-flow.md): PromptFlow is a framework for designing, orchestrating, testing, and monitoring end-to-end LLM prompt workflows with built-in versioning and analytics
- [Prompt Flow Tracing](https://arize.com/docs/phoenix/integrations/platforms/prompt-flow/prompt-flow-tracing.md): Create flows using Microsoft PromptFlow and send their traces to Phoenix
- [Cleanlab](https://arize.com/docs/phoenix/integrations/evaluation-integrations/cleanlab.md)
- [Ragas](https://arize.com/docs/phoenix/integrations/evaluation-integrations/ragas.md)
- [MongoDB](https://arize.com/docs/phoenix/integrations/vector-databases/mongodb.md): MongoDB is a database platform. Their Atlas product is built for GenAI applications.
- [Pinecone](https://arize.com/docs/phoenix/integrations/vector-databases/pinecone.md): Pinecone is a vector database that can be used to power RAG in various applications.
- [Qdrant](https://arize.com/docs/phoenix/integrations/vector-databases/qdrant.md): Qdrant is an open-source vector search engine built for high-dimensional vectors and large scale workflows
- [Weaviate](https://arize.com/docs/phoenix/integrations/vector-databases/weaviate.md): Weaviate is an open source, AI-native vector database.
- [Zilliz / Milvus](https://arize.com/docs/phoenix/integrations/vector-databases/zilliz-milvus.md): Milvus is an open-source vector database built for GenAI applications.
- [Couchbase](https://arize.com/docs/phoenix/integrations/vector-databases/couchbase.md): Couchbase is an enterprise-grade NoSQL database platform with integrated vector search capabilities for building scalable AI applications.

## SDK and API Reference

- [Overview](https://arize.com/docs/phoenix/sdk-api-reference/python/readme.md)
- [arize-phoenix-client](https://arize.com/docs/phoenix/sdk-api-reference/python/arize-phoenix-client.md): Phoenix Client is a comprehensive SDK for interacting with the Phoenix
- [arize-phoenix-evals](https://arize.com/docs/phoenix/sdk-api-reference/python/arize-phoenix-evals.md): Tooling to evaluate LLM applications including RAG relevance, answer relevance, and more.
- [arize-phoenix-otel](https://arize.com/docs/phoenix/sdk-api-reference/python/arize-phoenix-otel.md)
- [Overview](https://arize.com/docs/phoenix/sdk-api-reference/typescript/overview.md): Overview of TypeScript packages for the Arize Phoenix API.
- [@arizeai/phoenix-client](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-client.md)
- [@arizeai/phoenix-evals](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-evals.md)
- [@arizeai/phoenix-otel](https://arize.com/docs/phoenix/sdk-api-reference/typescript/arizeai-phoenix-otel.md)
- [@arizeai/phoenix-mcp](https://arize.com/docs/phoenix/sdk-api-reference/typescript/mcp-server.md): MCP server implementation for Arize Phoenix providing unified interface to Phoenix's capabilities.
- [REST API Overview](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/overview.md)
- [API Reference](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference.md)
- [Annotation Config](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/annotationconfig.md)
- [Annotations](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/annotations.md)
- [Datasets](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/datasets.md): REST API methods for interacting with Phoenix datasets
- [Experiments](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/experiments.md): REST API methods for interacting with Phoenix experiments
- [Spans](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/spans.md): REST API methods for interacting with Phoenix spans
- [Traces](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/traces.md): REST API methods for interacting with Phoenix traces
- [Prompts](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/prompts.md): REST API methods for interacting with Phoenix prompts
- [Projects](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/projects.md): REST API methods for interacting with Phoenix projects
- [Sessions](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/sessions.md): REST API methods for interacting with sessions in Phoenix
- [Users](https://arize.com/docs/phoenix/sdk-api-reference/rest-api/api-reference/users.md)
- [OpenInference Java](https://arize.com/docs/phoenix/sdk-api-reference/openinference-sdk/openinference-java.md)

## Self-Hosting

- [Self-Hosting](https://arize.com/docs/phoenix/self-hosting/readme.md): How to self-host a Phoenix instance
- [License](https://arize.com/docs/phoenix/self-hosting/license.md): Arize Phoenix is fully open-source free to self-host
- [Configuration](https://arize.com/docs/phoenix/self-hosting/configuration.md): How to customize your self-hosted deployment of Phoenix
- [Using Amazon Aurora](https://arize.com/docs/phoenix/self-hosting/configuration/using-amazon-aurora.md)
- [Docker](https://arize.com/docs/phoenix/self-hosting/deployment-options/docker.md): Deploy using docker compose for a local or cloud deployment
- [Kubernetes (kustomize)](https://arize.com/docs/phoenix/self-hosting/deployment-options/kubernetes.md): Phoenix can be deployed on Kubernetes with PostgreSQL using kustomize.
- [Kubernetes (helm)](https://arize.com/docs/phoenix/self-hosting/deployment-options/kubernetes-helm.md): Deploy Phoenix via Helm
- [AWS with CloudFormation](https://arize.com/docs/phoenix/self-hosting/deployment-options/aws-with-cloudformation.md): Phoenix can be deployed on AWS Fargate using CloudFormation
- [Railway](https://arize.com/docs/phoenix/self-hosting/deployment-options/railway.md): Use this guide to deploy Arize Phoenix on Railway via the prebuilt template.
- [Provisioning](https://arize.com/docs/phoenix/self-hosting/features/provisioning.md): How to provision Phoenix at deploy time
- [Authentication](https://arize.com/docs/phoenix/self-hosting/features/authentication.md)
- [Email](https://arize.com/docs/phoenix/self-hosting/features/email.md)
- [Management](https://arize.com/docs/phoenix/self-hosting/features/management.md): How to manage your Phoenix instance
- [Migrations](https://arize.com/docs/phoenix/self-hosting/upgrade/migrations.md): Migrations are run at boot
- [FAQs](https://arize.com/docs/phoenix/self-hosting/misc/frequently-asked-questions.md)

## Release Notes

- [Release Notes](https://arize.com/docs/phoenix/release-notes/readme.md): The latest from the Phoenix team.
- [11.2025](https://arize.com/docs/phoenix/release-notes/11.2025.md)
- [11.29.2025: Add support for Claude Opus 4-5 🤖](https://arize.com/docs/phoenix/release-notes/11.2025/11.29.2025-add-support-for-claude-opus-4-5.md): Available in Phoenix 12.18+
- [11.27.2025: Show Server Credential Setup in Playground API Keys 🔐](https://arize.com/docs/phoenix/release-notes/11.2025/11.27.2025-show-server-credential-setup-in-playground-api-keys.md): Available in Phoenix 12.18+
- [11.25.2025: Split Assignments When Uploading a Dataset 🗂️](https://arize.com/docs/phoenix/release-notes/11.2025/11.25.2025-split-assignments-when-uploading-a-dataset.md): Available in Phoenix 12.18+
- [11.23.2025: Repetitions for Manual Playground Invocations 🛝](https://arize.com/docs/phoenix/release-notes/11.2025/11.23.2025-repetitions-for-manual-playground-invocations.md): Available in Phoenix 12.17+
- [11.19.2025: Expanded Provider Support with OpenAI 5.1 + Gemini 3🔧](https://arize.com/docs/phoenix/release-notes/11.2025/11.19.2025-expanded-provider-support-with-openai-5.1-+-gemini-3.md): Available in Phoenix 12.16+
- [11.12.2025: Updated Anthropic Model List 🧠](https://arize.com/docs/phoenix/release-notes/11.2025/11.12.2025-updated-anthropic-model-list.md): Available in Phoenix 12.15+
- [11.09.2025 OpenInference TypeScript 2.0 💻](https://arize.com/docs/phoenix/release-notes/11.2025/11.09.2025-openinference-typescript-2.0.md)
- [11.07.2025: Timezone Preference 🌍](https://arize.com/docs/phoenix/release-notes/11.2025/11.07.2025-timezone-preference.md): Available in Phoenix 12.11+
- [11.05.2025: Metadata for Prompts 🗂️](https://arize.com/docs/phoenix/release-notes/11.2025/11.05.2025-metadata-for-prompts.md): Available in Phoenix 12.10+
- [11.03.2025: Playground Dataset Label Display 🏷️](https://arize.com/docs/phoenix/release-notes/11.2025/11.03.2025-playground-dataset-label-display.md): Available in Phoenix 12.10+
- [11.01.2025:  Resume Experiments and Evaluations 🔄](https://arize.com/docs/phoenix/release-notes/11.2025/11.01.2025-resume-experiments-and-evaluations.md): Available in Phoenix 12.10+
- [10.2025](https://arize.com/docs/phoenix/release-notes/10.2025.md)
- [10.30.2025: Metadata Support for Experiment Run Annotations 🧩](https://arize.com/docs/phoenix/release-notes/10.2025/10.30.2025-metadata-support-for-experiment-run-annotations.md): Available in Phoenix 12.9+
- [10.28.2025: Enable AWS IAM Auth for DB Configuration 🔐](https://arize.com/docs/phoenix/release-notes/10.2025/10.28.2025-enable-aws-iam-auth-for-db-configuration.md): Available in Phoenix 12.9+
- [10.26.2025: Add Split Edit Menu to Examples ䷖](https://arize.com/docs/phoenix/release-notes/10.2025/10.26.2025-add-split-edit-menu-to-examples.md): Available in Phoenix 12.8+
- [10.24.2025: Filter Prompts Page by Label 🏷️](https://arize.com/docs/phoenix/release-notes/10.2025/10.24.2025-filter-prompts-page-by-label.md): Available in Phoenix 12.7+
- [10.20.2025: Splits ䷖](https://arize.com/docs/phoenix/release-notes/10.2025/10.20.2025-splits.md): Available in Phoenix 12.7+
- [10.18.2025: Filter Annotations in Compare Experiments Slideover ✍️](https://arize.com/docs/phoenix/release-notes/10.2025/10.18.2025-filter-annotations-in-compare-experiments-slideover.md): Available in Phoenix 12.7+
- [10.15.2025: Enhanced Filtering for Examples Table 🔍](https://arize.com/docs/phoenix/release-notes/10.2025/10.15.2025-enhanced-filtering-for-examples-table.md): Available in Phoenix 12.5+
- [10.13.2025: View Traces in Compare Experiments 🧪](https://arize.com/docs/phoenix/release-notes/10.2025/10.13.2025-view-traces-in-compare-experiments.md): Available in Phoenix 12.5+
- [10.10.2025: Viewer Role 👀](https://arize.com/docs/phoenix/release-notes/10.2025/10.10.2025-viewer-role.md): Available in Phoenix 12.5+
- [10.08.2025: Dataset Labels 🏷️](https://arize.com/docs/phoenix/release-notes/10.2025/10.08.2025-dataset-labels.md): Available in Phoenix 12.3+
- [10.06.2025: Paginate Compare Experiments 📃](https://arize.com/docs/phoenix/release-notes/10.2025/10.06.2025-paginate-compare-experiments.md): Available in Phoenix 12.3+
- [10.05.2025: Load Prompt by Tag into Playground 🛝](https://arize.com/docs/phoenix/release-notes/10.2025/10.05.2025-load-prompt-by-tag-into-playground.md): Available in Phoenix 12.2+
- [10.03.2025: Prompt Version Editing in Playground 🛝](https://arize.com/docs/phoenix/release-notes/10.2025/10.03.2025-prompt-version-editing-in-playground.md): Available in Phoenix 12.2+
- [09.2025](https://arize.com/docs/phoenix/release-notes/09.2025.md)
- [09.29.2025: Day 0 support for Claude Sonnet 4.5 ⚡](https://arize.com/docs/phoenix/release-notes/09.2025/09.29.2025-day-0-support-for-claude-sonnet-4.5.md): Available in Phoenix 12.1+
- [09.27.2025: Dataset Splits 📊](https://arize.com/docs/phoenix/release-notes/09.2025/09.27.2025-dataset-splits.md): Available in Phoenix 12.0+
- [09.26.2025: Session Annotations🗂️](https://arize.com/docs/phoenix/release-notes/09.2025/09.26.2025-session-annotations.md): Available in Phoenix 12.0+
- [09.25.2025: Repetitions 🔁](https://arize.com/docs/phoenix/release-notes/09.2025/09.25.2025-repetitions.md): Available in Phoenix 11.38+
- [09.24.2025: Custom HTTP headers for requests in Playground 🛠️](https://arize.com/docs/phoenix/release-notes/09.2025/09.24.2025-custom-http-headers-for-requests-in-playground.md): Available in Phoenix 11.36+
- [09.23.2025: Repetitions in experiment compare slideover 🔄](https://arize.com/docs/phoenix/release-notes/09.2025/09.23.2025-repetitions-in-experiment-compare-slideover.md): Available in Phoenix 11.35+
- [09.22.2025: Helm configurable image registry & IPv6 support 🌐](https://arize.com/docs/phoenix/release-notes/09.2025/09.22.2025-helm-configurable-image-registry-and-ipv6-support.md): Available in Phoenix 11.35+
- [09.17.2025: Experiment compare details slideover in list view 🔍](https://arize.com/docs/phoenix/release-notes/09.2025/09.17.2025-experiment-compare-details-slideover-in-list-view.md): Available in Phoenix 11.34+
- [09.15.2025: Prompt Labels 🏷️](https://arize.com/docs/phoenix/release-notes/09.2025/09.15.2025-prompt-labels.md): Available in Phoenix 11.33+
- [09.12.2025: Enable Paging in Experiment Compare Details 📄](https://arize.com/docs/phoenix/release-notes/09.2025/09.12.2025-enable-paging-in-experiment-compare-details.md): Available in Phoenix 11.33+
- [09.08.2025: Experiment Annotation Popover in Detail View 🔍](https://arize.com/docs/phoenix/release-notes/09.2025/09.08.2025-experiment-annotation-popover-in-detail-view.md): Available in Phoenix 11.33+
- [09.04.2025: Experiment Lists Page Frontend Enhancements 💻](https://arize.com/docs/phoenix/release-notes/09.2025/09.04.2025-experiment-lists-page-frontend-enhancements.md): Available in Phoenix 11.32+
- [09.03.2025: Add Methods to Log Document Annotations 📜](https://arize.com/docs/phoenix/release-notes/09.2025/09.03.2025-add-methods-to-log-document-annotations.md): Available in Phoenix 11.31+
- [08.2025](https://arize.com/docs/phoenix/release-notes/08.2025.md)
- [08.28.2025: New arize-phoenix-client Package 📦](https://arize.com/docs/phoenix/release-notes/08.2025/08.28.2025-new-arize-phoenix-client-package.md)
- [08.22.2025: New Trace Timeline View 🔭](https://arize.com/docs/phoenix/release-notes/08.2025/08.22.2025-new-trace-timeline-view.md): Available in Phoenix 11.26+
- [08.20.2025: New Experiment and Annotation Quick Filters 🏎️](https://arize.com/docs/phoenix/release-notes/08.2025/08.20.2025-new-experiment-and-annotation-quick-filters.md): Available in Phoenix 11.25+
- [08.15.2025: Enhance Experiment Comparison Views 🧪](https://arize.com/docs/phoenix/release-notes/08.2025/08.15.2025-enhance-experiment-comparison-views.md): Available in Phoenix 11.24+
- [08.14.2025: Trace Transfer for Long-Term Storage 📦](https://arize.com/docs/phoenix/release-notes/08.2025/08.14.2025-trace-transfer-for-long-term-storage.md): Available in Phoenix 11.23+
- [08.12.2025: UI Design Overhauls 🎨](https://arize.com/docs/phoenix/release-notes/08.2025/08.12.2025-ui-design-overhauls.md): Available in Phoenix 11.22+
- [08.09.2025: Playground Support for GPT-5 🚀](https://arize.com/docs/phoenix/release-notes/08.2025/08.09.2025-playground-support-for-gpt-5.md): Available in Phoenix 11.21+
- [08.07.2025: Improved Error Handling in Prompt Playground ⚠️](https://arize.com/docs/phoenix/release-notes/08.2025/08.07.2025-improved-error-handling-in-prompt-playground.md): Available in Phoenix 11.20+
- [08.06.2025: Expanded Search Capabilities 🔍](https://arize.com/docs/phoenix/release-notes/08.2025/08.06.2025-expanded-search-capabilities.md): Available in Phoenix 11.19+
- [08.05.2025: Claude Opus 4-1 Support 🤖](https://arize.com/docs/phoenix/release-notes/08.2025/08.05.2025-claude-opus-4-1-support.md): Available in Phoenix 11.19+
- [08.04.2025: Manual Project Creation & Trace Duplication 📂](https://arize.com/docs/phoenix/release-notes/08.2025/08.04.2025-manual-project-creation-and-trace-duplication.md): Available in Phoenix 11.19+
- [08.03.2025: Delete Spans via REST API 🧹](https://arize.com/docs/phoenix/release-notes/08.2025/08.03.2025-delete-spans-via-rest-api.md): Available in Phoenix 11.19+
- [07.2025](https://arize.com/docs/phoenix/release-notes/07.2025.md)
- [07.29.2025: Google GenAI Evals 🌐](https://arize.com/docs/phoenix/release-notes/07.2025/07.29.2025-google-genai-evals.md)
- [07.25.2025: Project Dashboards 📈](https://arize.com/docs/phoenix/release-notes/07.2025/07.25.2025-project-dashboards.md): Available in Phoenix 11.12+
- [07.25.2025: Average Metrics in Experiment Comparison Table 📊](https://arize.com/docs/phoenix/release-notes/07.2025/07.25.2025-average-metrics-in-experiment-comparison-table.md): Available in Phoenix 11.12+
- [07.21.2025: Project and Trace Management via GraphQL 📤](https://arize.com/docs/phoenix/release-notes/07.2025/07.21.2025-project-and-trace-management-via-graphql.md): Available in Phoenix 11.9+
- [07.18.2025: OpenInference Java ✨](https://arize.com/docs/phoenix/release-notes/07.2025/07.18.2025-openinference-java.md)
- [07.13.2025: Experiments Module in phoenix-client 🧪](https://arize.com/docs/phoenix/release-notes/07.2025/07.13.2025-experiments-module-in-phoenix-client.md): Available in Phoenix 11.7+
- [07.09.2025: Baseline for Experiment Comparisons 🔁](https://arize.com/docs/phoenix/release-notes/07.2025/07.09.2025-baseline-for-experiment-comparisons.md): Available in Phoenix 11.4+
- [07.07.2025: Databse Disk Usage Monitor 🛑](https://arize.com/docs/phoenix/release-notes/07.2025/07.07.2025-databse-disk-usage-monitor.md): Available in Phoenix 11.5+
- [07.03.2025: Cost Summaries in Trace Headers 💸](https://arize.com/docs/phoenix/release-notes/07.2025/07.03.2025-cost-summaries-in-trace-headers.md): Available in Phoenix 11.4+
- [07.02.2025: Cursor MCP Button ⚡️](https://arize.com/docs/phoenix/release-notes/07.2025/07.02.2025-cursor-mcp-button.md): Available in Phoenix 11.3+
- [06.2025](https://arize.com/docs/phoenix/release-notes/06.2025.md)
- [06.25.2025: Cost Tracking 💰](https://arize.com/docs/phoenix/release-notes/06.2025/06.25.2025-cost-tracking.md): Available in Phoenix 11.0+
- [06.25.2025: New Phoenix Cloud ☁️](https://arize.com/docs/phoenix/release-notes/06.2025/06.25.2025-new-phoenix-cloud.md)
- [06.25.2025: Amazon Bedrock Support in Playground 🛝](https://arize.com/docs/phoenix/release-notes/06.2025/06.25.2025-amazon-bedrock-support-in-playground.md): Available in Phoenix 10.15+
- [06.13.2025: Session Filtering 🪄](https://arize.com/docs/phoenix/release-notes/06.2025/06.13.2025-session-filtering.md): Available in Phoenix 10.12+
- [06.13.2025: Enhanced Span Creation and Logging 🪐](https://arize.com/docs/phoenix/release-notes/06.2025/06.13.2025-enhanced-span-creation-and-logging.md): Available in Phoenix 10.12+
- [06.12.2025: Dataset Filtering 🔍](https://arize.com/docs/phoenix/release-notes/06.2025/06.12.2025-dataset-filtering.md): Available in Phoenix 10.11+
- [06.06.2025: Experiment Progress Graph 📊](https://arize.com/docs/phoenix/release-notes/06.2025/06.06.2025-experiment-progress-graph.md): Available in Phoenix 10.9+
- [06.04.2025: Ollama Support in Playground 🛝](https://arize.com/docs/phoenix/release-notes/06.2025/06.04.2025-ollama-support-in-playground.md): Available in Phoenix 10.7+
- [06.03.2025: Deploy via Helm ☸️](https://arize.com/docs/phoenix/release-notes/06.2025/06.03.2025-deploy-via-helm.md): Available in Phoenix 10.6+
- [05.2025](https://arize.com/docs/phoenix/release-notes/05.2025.md)
- [05.30.2025: xAI and Deepseek Support in Playground 🛝](https://arize.com/docs/phoenix/release-notes/05.2025/05.30.2025-xai-and-deepseek-support-in-playground.md): Available in Phoenix 10.5+
- [05.20.2025: Datasets and Experiment Evaluations in the JS Client 🧪](https://arize.com/docs/phoenix/release-notes/05.2025/05.20.2025-datasets-and-experiment-evaluations-in-the-js-client.md)
- [05.14.2025: Experiments in the JS Client 🔬](https://arize.com/docs/phoenix/release-notes/05.2025/05.14.2025-experiments-in-the-js-client.md)
- [05.09.2025: Annotations, Data Retention Policies, Hotkeys 📓](https://arize.com/docs/phoenix/release-notes/05.2025/05.09.2025-annotations-data-retention-policies-hotkeys.md): Available in Phoenix 9.0.0+
- [05.05.2025: OpenInference Google GenAI Instrumentation 🧩](https://arize.com/docs/phoenix/release-notes/05.2025/05.05.2025-openinference-google-genai-instrumentation.md)
- [04.2025](https://arize.com/docs/phoenix/release-notes/04.2025.md)
- [04.30.2025: Span Querying & Data Extraction for Phoenix Client 📊](https://arize.com/docs/phoenix/release-notes/04.2025/04.30.2025-span-querying-and-data-extraction-for-phoenix-client.md): Available in Phoenix 8.30+
- [04.28.2025: TLS Support for Phoenix Server 🔐](https://arize.com/docs/phoenix/release-notes/04.2025/04.28.2025-tls-support-for-phoenix-server.md): Available in Phoenix 8.29+
- [04.28.2025: Improved Shutdown Handling 🛑](https://arize.com/docs/phoenix/release-notes/04.2025/04.28.2025-improved-shutdown-handling.md): Available in Phoenix 8.28+
- [04.25.2025: Scroll Selected Span Into View 🖱️](https://arize.com/docs/phoenix/release-notes/04.2025/04.25.2025-scroll-selected-span-into-view.md): Available in Phoenix 8.27+
- [04.18.2025: Tracing for MCP Client-Server Applications 🔌](https://arize.com/docs/phoenix/release-notes/04.2025/04.18.2025-tracing-for-mcp-client-server-applications.md): Available in Phoenix 8.26+
- [04.16.2025: API Key Generation via API 🔐](https://arize.com/docs/phoenix/release-notes/04.2025/04.16.2025-api-key-generation-via-api.md): Available in Phoenix 8.26+
- [04.15.2025: Display Tool Call and Result IDs in Span Details 🫆](https://arize.com/docs/phoenix/release-notes/04.2025/04.15.2025-display-tool-call-and-result-ids-in-span-details.md): Available in Phoenix 8.25+
- [04.09.2025: Project Management API Enhancements ✨](https://arize.com/docs/phoenix/release-notes/04.2025/04.09.2025-project-management-api-enhancements.md): Available in Phoenix 8.24+
- [04.09.2025: New REST API for Projects with RBAC 📽️](https://arize.com/docs/phoenix/release-notes/04.2025/04.09.2025-new-rest-api-for-projects-with-rbac.md): Available in Phoenix 8.23+
- [04.03.2025: Phoenix Client Prompt Tagging 🏷️](https://arize.com/docs/phoenix/release-notes/04.2025/04.03.2025-phoenix-client-prompt-tagging.md): Available in Phoenix 8.22+
- [04.02.2025 Improved Span Annotation Editor ✍️](https://arize.com/docs/phoenix/release-notes/04.2025/04.02.2025-improved-span-annotation-editor.md): Available in Phoenix 8.21+
- [04.01.2025: Support for MCP Span Tool Info in OpenAI Agents SDK 🔨](https://arize.com/docs/phoenix/release-notes/04.2025/04.01.2025-support-for-mcp-span-tool-info-in-openai-agents-sdk.md): Available in Phoenix 8.20+
- [03.2025](https://arize.com/docs/phoenix/release-notes/03.2025.md)
- [03.27.2025 Span View Improvements 👀](https://arize.com/docs/phoenix/release-notes/03.2025/03.27.2025-span-view-improvements.md): Available in Phoenix 8.20+
- [03.24.2025: Tracing Configuration Tab 🖌️](https://arize.com/docs/phoenix/release-notes/03.2025/03.24.2025-tracing-configuration-tab.md): Available in Phoenix 8.19+
- [03.21.2025: Environment Variable Based Admin User Configuration 🗝️](https://arize.com/docs/phoenix/release-notes/03.2025/03.21.2025-environment-variable-based-admin-user-configuration.md): Available in Phoenix 8.17+
- [03.20.2025: Delete Experiment from Action Menu 🗑️](https://arize.com/docs/phoenix/release-notes/03.2025/03.20.2025-delete-experiment-from-action-menu.md): Available in Phoenix 8.19+
- [03.19.2025: Access to New Integrations in Projects 🔌](https://arize.com/docs/phoenix/release-notes/03.2025/03.19.2025-access-to-new-integrations-in-projects.md): Available in Phoenix 8.15+
- [03.18.2025: Resize Span, Trace, and Session Tables 🔀](https://arize.com/docs/phoenix/release-notes/03.2025/03.18.2025-resize-span-trace-and-session-tables.md): Available in Phoenix 8.14+
- [03.14.2025: OpenAI Agents Instrumentation 📡](https://arize.com/docs/phoenix/release-notes/03.2025/03.14.2025-openai-agents-instrumentation.md): Available in Phoenix 8.13+
- [03.07.2025: Model Config Enhancements for Prompts 💡](https://arize.com/docs/phoenix/release-notes/03.2025/03.07.2025-model-config-enhancements-for-prompts.md): Available in Phoenix 8.11+
- [03.07.2025: New Prompt Playground, Evals, and Integration Support 🦾](https://arize.com/docs/phoenix/release-notes/03.2025/03.07.2025-new-prompt-playground-evals-and-integration-support.md): Available in Phoenix 8.9+
- [03.06.2025: Project Improvements 📽️](https://arize.com/docs/phoenix/release-notes/03.2025/03.06.2025-project-improvements.md): Available in Phoenix 8.5+
- [02.2025](https://arize.com/docs/phoenix/release-notes/02.2025.md)
- [02.19.2025: Prompts 📃](https://arize.com/docs/phoenix/release-notes/02.2025/02.19.2025-prompts.md): Available in Phoenix 8.0+
- [02.18.2025: One-Line Instrumentation⚡️](https://arize.com/docs/phoenix/release-notes/02.2025/02.18.2025-one-line-instrumentation.md): Available in Phoenix 8.0+
- [01.2025](https://arize.com/docs/phoenix/release-notes/01.2025.md)
- [01.18.2025: Automatic & Manual Span Tracing ⚙️](https://arize.com/docs/phoenix/release-notes/01.2025/01.18.2025-automatic-and-manual-span-tracing.md): Available in Phoenix 7.9+
- [2024](https://arize.com/docs/phoenix/release-notes/2024.md)
- [12.09.2024: Sessions 💬](https://arize.com/docs/phoenix/release-notes/2024/12.09.2024-sessions.md): Available in Phoenix 7.0+
- [11.18.2024: Prompt Playground 🛝](https://arize.com/docs/phoenix/release-notes/2024/11.18.2024-prompt-playground.md): Available in Phoenix 6.0+
- [09.26.2024: Authentication & RBAC 🔐](https://arize.com/docs/phoenix/release-notes/2024/09.26.2024-authentication-and-rbac.md): Available in Phoenix 5.0+
- [07.18.2024: Guardrails AI Integrations💂](https://arize.com/docs/phoenix/release-notes/2024/07.18.2024-guardrails-ai-integrations.md): Available in Phoenix 4.11+
- [07.11.2024: Hosted Phoenix and LlamaTrace 💻](https://arize.com/docs/phoenix/release-notes/2024/07.11.2024-hosted-phoenix-and-llamatrace.md): Phoenix is now available for deployment as a fully hosted service.
- [07.03.2024: Datasets & Experiments 🧪](https://arize.com/docs/phoenix/release-notes/2024/07.03.2024-datasets-and-experiments.md): Available in Phoenix 4.6+
- [07.02.2024: Function Call Evaluations ⚒️](https://arize.com/docs/phoenix/release-notes/2024/07.02.2024-function-call-evaluations.md): Available in Phoenix 4.6+


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on a page URL with the `ask` query parameter:
```
GET https://arize.com/docs/phoenix/readme.md?ask=<question>
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.