# Phoenix

## Documentation

- [Arize Phoenix](/docs/phoenix/readme.md): AI Observability and Evaluation
- [Quickstarts](/docs/phoenix/quickstart.md): Not sure where to start? Try a quickstart:
- [User Guide](/docs/phoenix/user-guide.md)
- [Environments](/docs/phoenix/environments.md)
- [Overview: Tracing](/docs/phoenix/tracing/llm-traces.md): Tracing the execution of LLM applications using Telemetry
- [Quickstart: Tracing](/docs/phoenix/tracing/llm-traces-1.md)
- [Quickstart: Tracing (Python)](/docs/phoenix/tracing/llm-traces-1/quickstart-tracing-python.md)
- [Quickstart: Tracing (TS)](/docs/phoenix/tracing/llm-traces-1/quickstart-tracing-ts.md)
- [Features: Tracing](/docs/phoenix/tracing/features-tracing.md): Tracing is a critical part of AI Observability and should be used both in production and development
- [Projects](/docs/phoenix/tracing/features-tracing/projects.md): Use projects to organize your LLM traces
- [Annotations](/docs/phoenix/tracing/features-tracing/how-to-annotate-traces.md)
- [Sessions](/docs/phoenix/tracing/features-tracing/sessions.md): Track and analyze multi-turn conversations
- [How-to: Tracing](/docs/phoenix/tracing/how-to-tracing.md): Guides on how to use traces
- [Setup Tracing](/docs/phoenix/tracing/how-to-tracing/setup-tracing.md)
- [Setup using Phoenix OTEL](/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-using-phoenix-otel.md)
- [Setup using base OTEL](/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 Phoenix Decorators](/docs/phoenix/tracing/how-to-tracing/setup-tracing/instrument-python.md): As part of the OpenInference library, Phoenix provides helpful abstractions to make manual instrumentation easier.
- [Setup Tracing (TS)](/docs/phoenix/tracing/how-to-tracing/setup-tracing/javascript.md)
- [Setup Projects](/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-projects.md)
- [Setup Sessions](/docs/phoenix/tracing/how-to-tracing/setup-tracing/setup-sessions.md): How to track sessions across multiple traces
- [Add Metadata](/docs/phoenix/tracing/how-to-tracing/add-metadata.md)
- [Add Attributes, Metadata, Users](/docs/phoenix/tracing/how-to-tracing/add-metadata/customize-spans.md)
- [Instrument Prompt Templates and Prompt Variables](/docs/phoenix/tracing/how-to-tracing/add-metadata/instrumenting-prompt-templates-and-prompt-variables.md)
- [Annotate Traces](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations.md)
- [Annotating in the UI](/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](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/capture-feedback.md): Use the phoenix client to capture end-user feedback
- [Running Evals on Traces](/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](/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](/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces.md)
- [Import Existing Traces](/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/importing-existing-traces.md)
- [Export Data & Query Spans](/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](/docs/phoenix/tracing/how-to-tracing/importing-and-exporting-traces/exporting-annotated-spans.md)
- [Advanced](/docs/phoenix/tracing/how-to-tracing/advanced.md)
- [Mask Span Attributes](/docs/phoenix/tracing/how-to-tracing/advanced/masking-span-attributes.md)
- [Suppress Tracing](/docs/phoenix/tracing/how-to-tracing/advanced/suppress-tracing.md)
- [Filter Spans to Export](/docs/phoenix/tracing/how-to-tracing/advanced/modifying-spans.md)
- [Capture Multimodal Traces](/docs/phoenix/tracing/how-to-tracing/advanced/multimodal-tracing.md)
- [Overview: Prompts](/docs/phoenix/prompt-engineering/overview-prompts.md)
- [Prompt Management](/docs/phoenix/prompt-engineering/overview-prompts/prompt-management.md): Version and track changes made to prompt templates
- [Prompt Playground](/docs/phoenix/prompt-engineering/overview-prompts/prompt-playground.md)
- [Span Replay](/docs/phoenix/prompt-engineering/overview-prompts/span-replay.md): Replay LLM spans traced in your application directly in the playground
- [Prompts in Code](/docs/phoenix/prompt-engineering/overview-prompts/prompts-in-code.md): Pull and push prompt changes via Phoenix's Python and TypeScript Clients
- [Quickstart: Prompts](/docs/phoenix/prompt-engineering/quickstart-prompts.md)
- [Quickstart: Prompts (UI)](/docs/phoenix/prompt-engineering/quickstart-prompts/quickstart-prompts-ui.md)
- [Quickstart: Prompts (Python)](/docs/phoenix/prompt-engineering/quickstart-prompts/quickstart-prompts-python.md): This guide will show you how to setup and use Prompts through Phoenix's Python SDK
- [Quickstart: Prompts (TS)](/docs/phoenix/prompt-engineering/quickstart-prompts/quickstart-prompts-ts.md): This guide will walk you through setting up and using Phoenix Prompts with TypeScript.
- [How to: Prompts](/docs/phoenix/prompt-engineering/how-to-prompts.md): Guides on how to do prompt engineering with Phoenix
- [Configure AI Providers](/docs/phoenix/prompt-engineering/how-to-prompts/configure-ai-providers.md)
- [Using the Playground](/docs/phoenix/prompt-engineering/how-to-prompts/using-the-playground.md): General guidelines on how to use Phoenix's prompt playground
- [Create a prompt](/docs/phoenix/prompt-engineering/how-to-prompts/create-a-prompt.md): Store and track prompt versions in Phoenix
- [Test a prompt](/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](/docs/phoenix/prompt-engineering/how-to-prompts/tag-a-prompt.md): How to deploy prompts to different environments safely
- [Using a prompt](/docs/phoenix/prompt-engineering/how-to-prompts/using-a-prompt.md)
- [Overview: Datasets & Experiments](/docs/phoenix/datasets-and-experiments/overview-datasets.md)
- [Quickstart: Datasets & Experiments](/docs/phoenix/datasets-and-experiments/quickstart-datasets.md)
- [How-to: Datasets](/docs/phoenix/datasets-and-experiments/how-to-datasets.md): Datasets are critical assets for building robust prompts, evals, fine-tuning,
- [Creating Datasets](/docs/phoenix/datasets-and-experiments/how-to-datasets/creating-datasets.md)
- [Exporting Datasets](/docs/phoenix/datasets-and-experiments/how-to-datasets/exporting-datasets.md)
- [How-to: Experiments](/docs/phoenix/datasets-and-experiments/how-to-experiments.md)
- [Run Experiments](/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](/docs/phoenix/datasets-and-experiments/how-to-experiments/using-evaluators.md)
- [Overview: Evals](/docs/phoenix/evaluation/llm-evals.md)
- [Agent Evaluation](/docs/phoenix/evaluation/llm-evals/agent-evaluation.md)
- [Quickstart: Evals](/docs/phoenix/evaluation/evals.md)
- [How to: Evals](/docs/phoenix/evaluation/how-to-evals.md)
- [Pre-Built Evals](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals.md)
- [Hallucinations](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/hallucinations.md)
- [Q\&A on Retrieved Data](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/q-and-a-on-retrieved-data.md)
- [Retrieval (RAG) Relevance](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/retrieval-rag-relevance.md)
- [Summarization](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/summarization-eval.md)
- [Code Generation](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval.md)
- [Toxicity](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/toxicity.md)
- [AI vs Human (Groundtruth)](/docs/phoenix/evaluation/how-to-evals/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](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/reference-link-evals.md)
- [User Frustration](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/user-frustration.md)
- [SQL Generation Eval](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/sql-generation-eval.md)
- [Agent Function Calling Eval](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/tool-calling-eval.md)
- [Agent Path Convergence](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/agent-path-convergence.md)
- [Agent Planning](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/agent-planning.md)
- [Agent Reflection](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/agent-reflection.md)
- [Audio Emotion Detection](/docs/phoenix/evaluation/how-to-evals/running-pre-tested-evals/audio-emotion-detection.md)
- [Eval Models](/docs/phoenix/evaluation/how-to-evals/evaluation-models.md): Evaluation model classes powering your LLM Evals
- [Build an Eval](/docs/phoenix/evaluation/how-to-evals/bring-your-own-evaluator.md): This guide shows you how to build and improve an LLM as a Judge Eval from scratch.
- [Build a Multimodal Eval](/docs/phoenix/evaluation/how-to-evals/multimodal-evals.md)
- [Online Evals](/docs/phoenix/evaluation/how-to-evals/online-evals.md)
- [Evals API Reference](/docs/phoenix/evaluation/how-to-evals/evals-reference.md): Evals are LLM-powered functions that you can use to evaluate the output of your LLM or generative application
- [Overview: Retrieval](/docs/phoenix/retrieval/overview-retrieval.md)
- [Quickstart: Retrieval](/docs/phoenix/retrieval/quickstart-retrieval.md): Debug your Search and Retrieval LLM workflows
- [Quickstart: Inferences](/docs/phoenix/inferences/phoenix-inferences.md): Observability for all model types (LLM, NLP, CV, Tabular)
- [How-to: Inferences](/docs/phoenix/inferences/how-to-inferences.md)
- [Import Your Data](/docs/phoenix/inferences/how-to-inferences/define-your-schema.md): How to create Phoenix inferences and schemas for common data formats
- [Prompt and Response (LLM)](/docs/phoenix/inferences/how-to-inferences/define-your-schema/prompt-and-response-llm.md): How to import prompt and response from Large Large Model (LLM)
- [Retrieval (RAG)](/docs/phoenix/inferences/how-to-inferences/define-your-schema/retrieval-rag.md): How to import data for the Retrieval-Augmented Generation (RAG) use case
- [Corpus Data](/docs/phoenix/inferences/how-to-inferences/define-your-schema/corpus-data.md): How to create Phoenix inferences and schemas for the corpus data
- [Export Data](/docs/phoenix/inferences/how-to-inferences/export-your-data.md): How to export your data for labeling, evaluation, or fine-tuning
- [Generate Embeddings](/docs/phoenix/inferences/how-to-inferences/generating-embeddings.md)
- [Manage the App](/docs/phoenix/inferences/how-to-inferences/manage-the-app.md): How to define your inference set(s), launch a session, open the UI in your notebook or browser, and close your session when you're done
- [Use Example Inferences](/docs/phoenix/inferences/how-to-inferences/use-example-inferences.md): Quickly explore Phoenix with concrete examples
- [API: Inferences](/docs/phoenix/inferences/inference-and-schema.md): Detailed descriptions of classes and methods related to Phoenix inferences and schemas
- [Access Control (RBAC)](/docs/phoenix/settings/access-control-rbac.md)
- [API Keys](/docs/phoenix/settings/api-keys.md)
- [Data Retention](/docs/phoenix/settings/data-retention.md)

* [Arize Phoenix](/docs/phoenix/documentation/jp/readme.md)

- [Arize Phoenix](/docs/phoenix/documentation/zh/readme.md)

## Self-Hosting

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

## SDK and API Reference

- [Overview](/docs/phoenix/sdk-api-reference/readme.md)
- [Overview](/docs/phoenix/sdk-api-reference/python/overview.md)
- [arize-phoenix-evals](/docs/phoenix/sdk-api-reference/python-pacakges/arize-phoenix-evals.md): Tooling to evaluate LLM applications including RAG relevance, answer relevance, and more.
- [arize-phoenix-client](/docs/phoenix/sdk-api-reference/python-pacakges/arize-phoenix-client.md): Phoenix Client is a lightweight package for interacting with the Phoenix server.
- [arize-phoenix-otel](/docs/phoenix/sdk-api-reference/python-pacakges/arize-phoenix-otel.md)
- [Overview](/docs/phoenix/sdk-api-reference/typescript/overview.md): This package provides a TypeSript client for the Arize Phoenix API.
- [MCP Server](/docs/phoenix/sdk-api-reference/typescript-packages/mcp-server.md): MCP server implementation for Arize Phoenix providing unified interface to Phoenix's capabilities.
- [Overview](/docs/phoenix/sdk-api-reference/rest-api/overview.md)
- [Datasets](/docs/phoenix/sdk-api-reference/datasets.md): REST API methods for interacting with Phoenix datasets
- [Experiments](/docs/phoenix/sdk-api-reference/experiments.md): REST API methods for interacting with Phoenix experiments
- [Spans](/docs/phoenix/sdk-api-reference/spans.md): REST API methods for interacting with Phoenix spans
- [Traces](/docs/phoenix/sdk-api-reference/traces.md): REST API methods for interacting with Phoenix traces
- [Prompts](/docs/phoenix/sdk-api-reference/prompts.md): REST API methods for interacting with Phoenix prompts
- [Projects](/docs/phoenix/sdk-api-reference/projects.md): REST API methods for interacting with Phoenix projects
- [Users](/docs/phoenix/sdk-api-reference/users.md)

## Integrations

- [Overview](/docs/phoenix/integrations/readme.md)
- [Amazon Bedrock](/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](/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](/docs/phoenix/integrations/llm-providers/amazon-bedrock/amazon-bedrock-evals.md): Configure and run Bedrock for evals
- [Amazon Bedrock Agents Tracing](/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](/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](/docs/phoenix/integrations/llm-providers/anthropic/anthropic-tracing.md)
- [Anthropic Evals](/docs/phoenix/integrations/llm-providers/anthropic/anthropic-evals.md): Configure and run Anthropic for evals
- [Google Gen AI](/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 GenAI Tracing](/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](/docs/phoenix/integrations/llm-providers/google-gen-ai/gemini-evals.md): Configure and run Gemini for evals
- [LiteLLM](/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](/docs/phoenix/integrations/llm-providers/litellm/litellm-tracing.md)
- [LiteLLM Evals](/docs/phoenix/integrations/llm-providers/litellm/litellm-evals.md): Configure and run LiteLLM for evals
- [MistralAI](/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](/docs/phoenix/integrations/llm-providers/mistralai/mistralai-tracing.md): Instrument LLM calls made using MistralAI's SDK via the MistralAIInstrumentor
- [MistralAI Evals](/docs/phoenix/integrations/llm-providers/mistralai/mistralai-evals.md): Configure and run MistralAI for evals
- [Groq](/docs/phoenix/integrations/llm-providers/groq.md): Groq provides ultra-low latency inference for LLMs through its custom-built LPU™ architecture.
- [Groq Tracing](/docs/phoenix/integrations/llm-providers/groq/groq-tracing.md): Instrument LLM applications built with Groq
- [OpenAI](/docs/phoenix/integrations/llm-providers/openai.md): OpenAI provides state-of-the-art LLMs for natural language understanding and generation.
- [OpenAI Tracing](/docs/phoenix/integrations/llm-providers/openai/openai-tracing.md)
- [OpenAI Evals](/docs/phoenix/integrations/llm-providers/openai/openai-evals.md): Configure and run OpenAI for evals
- [OpenAI Agents SDK Tracing](/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](/docs/phoenix/integrations/llm-providers/openai/openai-node.js-sdk.md)
- [VertexAI](/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](/docs/phoenix/integrations/llm-providers/vertexai/vertexai-tracing.md): Instrument LLM calls made using VertexAI's SDK via the VertexAIInstrumentor
- [VertexAI Evals](/docs/phoenix/integrations/llm-providers/vertexai/vertexai-evals.md): Configure and run VertexAI for evals
- [Agno](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/agno/agno-tracing.md)
- [AutoGen](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/autogen/autogen-tracing.md)
- [BeeAI](/docs/phoenix/integrations/frameworks/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)](/docs/phoenix/integrations/frameworks/beeai/beeai-tracing-js.md)
- [CrewAI](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/crewai/crewai-tracing.md): Instrument multi agent applications using CrewAI
- [DSPy](/docs/phoenix/integrations/frameworks/dspy.md): DSPy is an open-source Python framework for declaratively programming modular LLM pipelines and automatically optimizing prompts and model weights
- [DSPy Tracing](/docs/phoenix/integrations/frameworks/dspy/dspy-tracing.md): Instrument and observe your DSPy application via the DSPyInstrumentor
- [Flowise](/docs/phoenix/integrations/frameworks/flowise.md): Flowise is a low-code platform for building customized chatflows and agentflows.
- [Flowise Tracing](/docs/phoenix/integrations/frameworks/flowise/flowise-tracing.md)
- [Guardrails AI](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/guardrails-ai/guardrails-ai-tracing.md): Instrument LLM applications that use the Guardrails AI framework
- [Haystack](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/haystack/haystack-tracing.md): Instrument LLM applications built with Haystack
- [Hugging Face smolagents](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/hugging-face-smolagents/smolagents-tracing.md): How to use the SmolagentsInstrumentor to trace smolagents by Hugging Face
- [Instructor](/docs/phoenix/integrations/frameworks/instructor.md): Instructor is a library that helps you define structured output formats for LLMs.
- [Instructor Tracing](/docs/phoenix/integrations/frameworks/instructor/instructor-tracing.md)
- [LlamaIndex](/docs/phoenix/integrations/frameworks/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](/docs/phoenix/integrations/frameworks/llamaindex/llamaindex-tracing.md): How to use the python LlamaIndexInstrumentor to trace LlamaIndex
- [LlamaIndex Workflows Tracing](/docs/phoenix/integrations/frameworks/llamaindex/llamaindex-workflows-tracing.md): How to use the python LlamaIndexInstrumentor to trace LlamaIndex Workflows
- [LangChain](/docs/phoenix/integrations/frameworks/langchain.md): LangChain is an open-source framework for building language model applications with prompt chaining, memory, and external integrations
- [LangChain Tracing](/docs/phoenix/integrations/frameworks/langchain/langchain-tracing.md): How to use the python LangChainInstrumentor to trace LangChain
- [LangChain.js](/docs/phoenix/integrations/frameworks/langchain/langchain.js.md)
- [LangGraph](/docs/phoenix/integrations/frameworks/langgraph.md): LangGraph is an open-source framework for building graph-based LLM pipelines with modular nodes and seamless data integrations
- [LangGraph Tracing](/docs/phoenix/integrations/frameworks/langgraph/langgraph-tracing.md)
- [LangFlow](/docs/phoenix/integrations/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](/docs/phoenix/integrations/langflow/langflow-tracing.md)
- [Mastra](/docs/phoenix/integrations/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](/docs/phoenix/integrations/mastra/mastra-tracing.md)
- [Model Context Protocol](/docs/phoenix/integrations/model-context-protocol.md): Anthropic's Model Context Protocol is a standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments.
- [Phoenix MCP Server](/docs/phoenix/integrations/model-context-protocol/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.
- [MCP Tracing](/docs/phoenix/integrations/model-context-protocol/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](/docs/phoenix/integrations/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](/docs/phoenix/integrations/portkey/portkey-tracing.md): How to trace Portkey AI Gateway requests with Phoenix for comprehensive LLM observability
- [Prompt Flow](/docs/phoenix/integrations/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](/docs/phoenix/integrations/prompt-flow/prompt-flow-tracing.md): Create flows using Microsoft PromptFlow and send their traces to Phoenix
- [Pydantic AI](/docs/phoenix/integrations/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](/docs/phoenix/integrations/pydantic/pydantic-tracing.md): How to use the python PydanticAIInstrumentor to trace PydanticAI agents
- [Pydantic AI Evals](/docs/phoenix/integrations/pydantic/pydantic-evals.md): How to use Pydantic Evals with Phoenix to evaluate AI applications using structured evaluation frameworks
- [Vercel](/docs/phoenix/integrations/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)](/docs/phoenix/integrations/vercel/vercel-ai-sdk-tracing-js.md)
- [Cleanlab](/docs/phoenix/integrations/evaluation-libraries/cleanlab.md)
- [Ragas](/docs/phoenix/integrations/evaluation-libraries/ragas.md)
- [MongoDB](/docs/phoenix/integrations/vector-databases/mongodb.md): MongoDB is a database platform. Their Atlas product is built for GenAI applications.
- [Pinecone](/docs/phoenix/integrations/vector-databases/pinecone.md): Pinecone is a vector database that can be used to power RAG in various applications.
- [Qdrant](/docs/phoenix/integrations/vector-databases/qdrant.md): Qdrant is an open-source vector database built for high-dimensional vectors and large scale workflows
- [Weaviate](/docs/phoenix/integrations/vector-databases/weaviate.md): Weaviate is an open source, AI-native vector database.
- [Zilliz / Milvus](/docs/phoenix/integrations/vector-databases/zilliz-milvus.md): Milvus is an open-source vector database built for GenAI applications.

## Cookbooks

- [Featured Tutorials](/docs/phoenix/cookbook/readme.md)
- [Agent Cookbooks](/docs/phoenix/cookbook/agent-cookbooks.md)
- [Agent Demos](/docs/phoenix/cookbook/agent-demos.md): Example agents are fully instrumented with OpenInference and utilize end-to-end tracing with Phoenix for comprehensive performance analysis. Enter your Phoenix and OpenAI keys to view traces.
- [Cookbooks](/docs/phoenix/cookbook/tracing/cookbooks.md)
- [Structured Data Extraction](/docs/phoenix/cookbook/tracing/structured-data-extraction.md)
- [Few Shot Prompting](/docs/phoenix/cookbook/prompt-engineering/few-shot-prompting.md)
- [ReAct Prompting](/docs/phoenix/cookbook/prompt-engineering/react-prompting.md)
- [Chain-of-Thought Prompting](/docs/phoenix/cookbook/prompt-engineering/chain-of-thought-prompting.md)
- [Prompt Optimization](/docs/phoenix/cookbook/prompt-engineering/prompt-optimization.md)
- [LLM as a Judge Prompt Optimization](/docs/phoenix/cookbook/prompt-engineering/llm-as-a-judge-prompt-optimization.md)
- [Cookbooks](/docs/phoenix/cookbook/datasets-and-experiments/cookbooks.md)
- [Summarization](/docs/phoenix/cookbook/datasets-and-experiments/summarization.md)
- [Text2SQL](/docs/phoenix/cookbook/datasets-and-experiments/text2sql.md)
- [Cookbooks](/docs/phoenix/cookbook/evaluation/cookbooks.md)
- [Evaluate RAG](/docs/phoenix/cookbook/evaluation/evaluate-rag.md): Building a RAG pipeline and evaluating it with Phoenix Evals.
- [Evaluate an Agent](/docs/phoenix/cookbook/evaluation/evaluate-an-agent.md)
- [OpenAI Agents SDK Cookbook](/docs/phoenix/cookbook/evaluation/openai-agents-sdk-cookbook.md)
- [Cookbooks](/docs/phoenix/cookbook/retrieval-and-inferences/cookbooks.md)
- [Embeddings Analysis](/docs/phoenix/cookbook/retrieval-and-inferences/embeddings-analysis.md)

## Learn

- [Agent Workflow Patterns](/docs/phoenix/learn/agents/readme.md)
- [AutoGen](/docs/phoenix/learn/agents/readme/autogen.md): Use Phoenix to trace and evaluate AutoGen agents
- [CrewAI](/docs/phoenix/learn/agents/readme/crewai.md): Use Phoenix to trace and evaluate different CrewAI agent patterns
- [Google GenAI SDK (Manual Orchestration)](/docs/phoenix/learn/agents/readme/google-genai-sdk.md): Everything you need to know about Google's GenAI framework
- [OpenAI Agents](/docs/phoenix/learn/agents/readme/openai-agents.md): Build multi-agent workflows with OpenAI Agents
- [LangGraph](/docs/phoenix/learn/agents/readme/langgraph.md): Use Phoenix to trace and evaluate agent frameworks built using Langgraph
- [Smolagents](/docs/phoenix/learn/agents/readme/smolagents.md)
- [What are Traces](/docs/phoenix/learn/tracing/what-are-traces.md): A deep dive into the details of a trace
- [How Tracing Works](/docs/phoenix/learn/tracing/how-tracing-works.md): The components behind tracing
- [FAQs: Tracing](/docs/phoenix/learn/tracing/faqs-tracing.md)
- [Prompts Concepts](/docs/phoenix/learn/prompt-engineering/prompts-concepts.md)
- [Datasets Concepts](/docs/phoenix/learn/datasets-and-experiments/datasets-concepts.md)
- [Evaluators](/docs/phoenix/learn/evaluation/evaluators.md)
- [Eval Data Types](/docs/phoenix/learn/evaluation/eval-data-types.md)
- [Evals With Explanations](/docs/phoenix/learn/evaluation/evals-with-explanations.md)
- [LLM as a Judge](/docs/phoenix/learn/evaluation/llm-as-a-judge.md)
- [Custom Task Evaluation](/docs/phoenix/learn/evaluation/custom-task-evaluation.md)
- [Retrieval with Embeddings](/docs/phoenix/learn/retrieval-and-inferences/retrieval-with-embeddings.md)
- [Benchmarking Retrieval](/docs/phoenix/learn/retrieval-and-inferences/benchmarking-retrieval.md): Benchmarking Chunk Size, K and Retrieval Approach
- [Retrieval Evals on Document Chunks](/docs/phoenix/learn/retrieval-evals-on-document-chunks.md)
- [Inferences Concepts](/docs/phoenix/learn/inferences-concepts.md)
- [Frequently Asked Questions](/docs/phoenix/learn/resources/faqs.md)
- [What is the difference between Phoenix and Arize?](/docs/phoenix/learn/resources/faqs/what-is-the-difference-between-phoenix-and-arize.md)
- [What is my Phoenix Endpoint?](/docs/phoenix/learn/resources/faqs/what-is-my-phoenix-endpoint.md)
- [What is LlamaTrace vs Phoenix Cloud?](/docs/phoenix/learn/resources/faqs/what-is-llamatrace-vs-phoenix-cloud.md)
- [Will Phoenix Cloud be on the latest version of Phoenix?](/docs/phoenix/learn/resources/faqs/will-phoenix-cloud-be-on-the-latest-version-of-phoenix.md)
- [Can I add other users to my Phoenix Instance?](/docs/phoenix/learn/resources/faqs/can-i-add-other-users-to-my-phoenix-instance.md)
- [Can I use Azure OpenAI?](/docs/phoenix/learn/resources/faqs/can-i-use-azure-openai.md)
- [Can I use Phoenix locally from a remote Jupyter instance?](/docs/phoenix/learn/resources/faqs/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?](/docs/phoenix/learn/resources/faqs/how-can-i-configure-the-backend-to-send-the-data-to-the-phoenix-ui-in-another-container.md)
- [Can I run Phoenix on Sagemaker?](/docs/phoenix/learn/resources/faqs/can-i-run-phoenix-on-sagemaker.md)
- [Can I persist data in a notebook?](/docs/phoenix/learn/resources/faqs/can-i-persist-data-in-a-notebook.md)
- [What is the difference between GRPC and HTTP?](/docs/phoenix/learn/resources/faqs/what-is-the-difference-between-grpc-and-http.md)
- [Can I use gRPC for trace collection?](/docs/phoenix/learn/resources/faqs/can-i-use-grpc-for-trace-collection.md)
- [How do I resolve Phoenix Evals showing NOT\_PARSABLE?](/docs/phoenix/learn/resources/faqs/how-do-i-resolve-phoenix-evals-showing-not_parsable.md)
- [Langfuse alternative? Arize Phoenix vs Langfuse: key differences](/docs/phoenix/learn/resources/faqs/langfuse-alternatives.md)
- [Langsmith alternatives? Arize Phoenix vs LangSmith: key differences](/docs/phoenix/learn/resources/faqs/langsmith-alternatives.md)
- [Contribute to Phoenix](/docs/phoenix/learn/resources/contribute-to-phoenix.md)

## Release Notes

- [Release Notes](/docs/phoenix/release-notes/readme.md): The latest from the Phoenix team.
- [05.30.2025: xAI and Deepseek Support in Playground](/docs/phoenix/release-notes/05.30.2025-xai-and-deepseek-support-in-playground.md)
- [05.20.2025: Datasets and Experiment Evaluations in the JS Client](/docs/phoenix/release-notes/05.20.2025-datasets-and-experiment-evaluations-in-the-js-client.md)
- [05.14.2025: Experiments in the JS Client](/docs/phoenix/release-notes/05.14.2025-experiments-in-the-js-client.md)
- [05.09.2025: Annotations, Data Retention Policies, Hotkeys 📓](/docs/phoenix/release-notes/05.09.2025-annotations-data-retention-policies-hotkeys.md)
- [05.05.2025: OpenInference Google GenAI Instrumentation](/docs/phoenix/release-notes/05.05.2025-openinference-google-genai-instrumentation.md)
- [04.30.2025: Span Querying & Data Extraction for Phoenix Client 📊](/docs/phoenix/release-notes/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 🔐](/docs/phoenix/release-notes/04.28.2025-tls-support-for-phoenix-server.md): Available in Phoenix 8.29+
- [04.28.2025: Improved Shutdown Handling 🛑](/docs/phoenix/release-notes/04.28.2025-improved-shutdown-handling.md): Available in Phoenix 8.28+
- [04.25.2025: Scroll Selected Span Into View 🖱️](/docs/phoenix/release-notes/04.25.2025-scroll-selected-span-into-view.md): Available in Phoenix 8.27+
- [04.18.2025: Tracing for MCP Client-Server Applications 🔌](/docs/phoenix/release-notes/04.18.2025-tracing-for-mcp-client-server-applications.md): Available in Phoenix 8.26+
- [04.16.2025: API Key Generation via API 🔐](/docs/phoenix/release-notes/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 🫆](/docs/phoenix/release-notes/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 ✨](/docs/phoenix/release-notes/04.09.2025-project-management-api-enhancements.md): Available in Phoenix 8.24+
- [04.09.2025: New REST API for Projects with RBAC 📽️](/docs/phoenix/release-notes/04.09.2025-new-rest-api-for-projects-with-rbac.md): Available in Phoenix 8.23+
- [04.03.2025: Phoenix Client Prompt Tagging 🏷️](/docs/phoenix/release-notes/04.03.2025-phoenix-client-prompt-tagging.md): Available in Phoenix 8.22+
- [04.02.2025 Improved Span Annotation Editor ✍️](/docs/phoenix/release-notes/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 🔨](/docs/phoenix/release-notes/04.01.2025-support-for-mcp-span-tool-info-in-openai-agents-sdk.md): Available in Phoenix 8.20+
- [03.27.2025 Span View Improvements 👀](/docs/phoenix/release-notes/03.27.2025-span-view-improvements.md): Available in Phoenix 8.20+
- [03.24.2025: Tracing Configuration Tab 🖌️](/docs/phoenix/release-notes/03.24.2025-tracing-configuration-tab.md): Available in Phoenix 8.19+
- [03.21.2025: Environment Variable Based Admin User Configuration 🗝️](/docs/phoenix/release-notes/03.21.2025-environment-variable-based-admin-user-configuration.md): Available in Phoenix 8.17+
- [03.20.2025: Delete Experiment from Action Menu 🗑️](/docs/phoenix/release-notes/03.20.2025-delete-experiment-from-action-menu.md): Available in Phoenix 8.19+
- [03.19.2025: Access to New Integrations in Projects 🔌](/docs/phoenix/release-notes/03.19.2025-access-to-new-integrations-in-projects.md): Available in Phoenix 8.15+
- [03.18.2025: Resize Span, Trace, and Session Tables 🔀](/docs/phoenix/release-notes/03.18.2025-resize-span-trace-and-session-tables.md): Available in Phoenix 8.14+
- [03.14.2025: OpenAI Agents Instrumentation 📡](/docs/phoenix/release-notes/03.14.2025-openai-agents-instrumentation.md): Available in Phoenix 8.13+
- [03.07.2025: Model Config Enhancements for Prompts 💡](/docs/phoenix/release-notes/03.07.2025-model-config-enhancements-for-prompts.md): Available in Phoenix 8.11+
- [03.07.2025: New Prompt Playground, Evals, and Integration Support 🦾](/docs/phoenix/release-notes/03.07.2025-new-prompt-playground-evals-and-integration-support.md): Available in Phoenix 8.9+
- [03.06.2025: Project Improvements 📽️](/docs/phoenix/release-notes/03.06.2025-project-improvements.md): Available in Phoenix 8.5+
- [02.19.2025: Prompts 📃](/docs/phoenix/release-notes/02.19.2025-prompts.md): Available in Phoenix 8.0+
- [02.18.2025: One-Line Instrumentation⚡️](/docs/phoenix/release-notes/02.18.2025-one-line-instrumentation.md): Available in Phoenix 8.0+
- [01.18.2025: Automatic & Manual Span Tracing ⚙️](/docs/phoenix/release-notes/01.18.2025-automatic-and-manual-span-tracing.md): Available in Phoenix 7.9+
- [12.09.2024: Sessions 💬](/docs/phoenix/release-notes/12.09.2024-sessions.md): Available in Phoenix 7.0+
- [11.18.2024: Prompt Playground 🛝](/docs/phoenix/release-notes/11.18.2024-prompt-playground.md): Available in Phoenix 6.0+
- [09.26.2024: Authentication & RBAC 🔐](/docs/phoenix/release-notes/09.26.2024-authentication-and-rbac.md): Available in Phoenix 5.0+
- [07.18.2024: Guardrails AI Integrations💂](/docs/phoenix/release-notes/07.18.2024-guardrails-ai-integrations.md): Available in Phoenix 4.11+
- [07.11.2024: Hosted Phoenix and LlamaTrace 💻](/docs/phoenix/release-notes/07.11.2024-hosted-phoenix-and-llamatrace.md): Phoenix is now available for deployment as a fully hosted service.
- [07.03.2024: Datasets & Experiments 🧪](/docs/phoenix/release-notes/07.03.2024-datasets-and-experiments.md): Available in Phoenix 4.6+
- [07.02.2024: Function Call Evaluations ⚒️](/docs/phoenix/release-notes/07.02.2024-function-call-evaluations.md): Available in Phoenix 4.6+