Release Notes - Phoenix

Releases · Arize-ai/phoenix

03.11.2026

03.11.2026: Session Turns API

Available in arize-phoenix-client 2.0.0+ (Python) and @arizeai/phoenix-client 6.4.0+ (TypeScript)Phoenix now provides a dedicated session turns API that reconstructs the ordered input/output pairs across all traces in a session. The new get_session_turns() method (Python) and getSessionTurns() function (TypeScript) extract root span input.value / output.value attributes and return chronologically ordered SessionTurn objects.

Chronological turn ordering from session traces sorted by start time
SessionTurnIO with MIME type — supports text/plain, application/json, and image types
Batched root span fetching with pagination to handle large sessions
Async variants available in both Python and TypeScript clients

03.10.2026

03.10.2026: Session Management REST APIs

Available in arize-phoenix 13.13.0+ (server), arize-phoenix-client 1.31.0+ (Python), @arizeai/phoenix-client 6.1.0+ (TypeScript)Phoenix now exposes comprehensive session management through REST API endpoints on the server. Retrieve individual sessions, list sessions with pagination and project filtering, and delete sessions with cascading cleanup of associated traces, spans, and annotations.

Single session retrieval by ID or GlobalID with optional project filtering
Bulk session listing with pagination, project filtering, and sorting
Session deletion with automatic cascade through traces and spans
DataFrame export in Python for sessions data analysis
Configurable timeouts for all session operations

03.09.2026

03.09.2026: Filter Spans by Trace ID and Parent Relationships

Available in arize-phoenix 13.12.0+ (server), arize-phoenix-client 2.0.0+ (Python), @arizeai/phoenix-client 6.3.0+ (TypeScript)Span queries now support filtering by trace ID and parent relationships, enabling precise navigation of trace hierarchies. Query for root spans using parent_id=null or retrieve all children of a specific parent span to reconstruct execution trees programmatically.

Trace ID filtering to retrieve spans from specific traces
Parent ID filtering to query root spans or span children
Multi-trace queries via repeated trace ID parameters
Composable with existing filters like time ranges and limits

03.04.2026

03.04.2026: Incremental Evaluation Metrics in Playground

Available in arize-phoenix 13.8.0+The Playground now displays evaluation metrics, cost, and latency aggregates in real-time as dataset experiments run. Metrics update incrementally every ~2 seconds, providing immediate feedback on experiment performance without waiting for completion.

03.04.2026

Available in arize-phoenix 13.8.0+Phoenix now automatically protects login endpoints against brute force attacks. After 5 consecutive failed attempts, the account is temporarily locked for 5 minutes. Enabled by default with configurable thresholds via PHOENIX_BRUTE_FORCE_LOGIN_PROTECTION_MAX_ATTEMPTS.

03.07.2026

03.07.2026: Unified Dataset Upload with Drag-and-Drop

Available in arize-phoenix 13.9.0+Dataset creation from files is now streamlined with automatic file type detection and a unified upload experience. Drag-and-drop CSV or JSONL files anywhere in the upload form, and Phoenix automatically parses headers and previews data without loading entire files into memory.

Automatic format detection for CSV and JSONL files
Drag-and-drop file selection with visual feedback
Streaming parser that handles large files efficiently
RFC 4180 CSV support including quoted fields, escaped quotes, and BOM handling
Detailed error messages for parsing issues with line-by-line feedback

03.10.2026

03.10.2026: Drag-and-Drop Column Assignment for Datasets

Available in arize-phoenix 13.13.0+Dataset creation now features an intuitive drag-and-drop column assignment interface. Assign columns to input, output, or metadata buckets with automatic suggestions based on common naming conventions, and preview exactly how your data will appear in the final dataset.

Visual column assignment with draggable chips and drop targets
Smart auto-assignment based on column names like “input”, “output”, “reference”
Live dataset preview showing the final structure as you make changes
Keyboard navigation support for accessibility
Raw data preview in tabular format alongside final dataset view

03.08.2026

03.08.2026: Extended Model Provider Support

Available in arize-phoenix 13.10.0+ (Cerebras, Fireworks, Groq, Moonshot) and arize-phoenix 13.11.0+ (Perplexity, Together AI)Phoenix Playground now supports six additional OpenAI-compatible model providers: Perplexity AI, Together AI, Cerebras, Fireworks AI, Groq, and Moonshot (Kimi). Access hundreds of new models including specialized reasoning models and fine-tuned variants through familiar OpenAI-compatible APIs.

Perplexity AI for research and web-grounded responses
Together AI with models from Moonshot, DeepSeek, Qwen, and GLM
Cerebras for ultra-fast inference with Llama models
Fireworks AI with Llama 4 Scout and Maverick variants
Groq for low-latency Llama and Qwen deployments
Moonshot (Kimi) with extended 128k and 32k context models
Cost tracking enabled for Cerebras, Fireworks, Groq, and Moonshot

03.10.2026

03.10.2026: Provider Visibility Controls

Available in arize-phoenix 13.13.0+Control which model providers appear in the Phoenix UI using the PHOENIX_ALLOWED_PROVIDERS environment variable. Set it to a comma-separated list of provider names to show only those providers, keeping your interface focused on the tools you actually use.

Allow-list mode to show only specified providers
Case-insensitive configuration with typo detection warnings
Set to NONE to hide all providers from the UI

03.08.2026

03.08.2026: Latest OpenAI GPT Models

Available in arize-phoenix 13.10.0+Phoenix Playground now includes the latest OpenAI models: GPT-5.4 family, GPT-5.3-chat-latest, GPT-5.2-pro variants, and o3-pro-2025-06-10. All models include cost tracking and are ready to use in experiments and prompt testing.

03.08.2026

03.08.2026: Project Editing from Settings

Available in arize-phoenix 13.10.0+Edit project descriptions and customize gradient colors directly from the Project Settings page. Click the edit button to update project metadata inline, with changes persisting immediately across the Phoenix UI.

03.11.2026

03.11.2026: Breaking Change: Removed Deprecated Annotations API

Breaking change in arize-phoenix-client 2.0.0The deprecated client.annotations module has been removed. All annotation methods remain available on client.spans. Update your code to use client.spans.add_span_annotation() and client.spans.log_span_annotations() instead of the client.annotations variants.

03.05.2026

03.05.2026: SDK Session Retrieval

Session retrieval is now available in the Python client (client.sessions.get(), client.sessions.list(), get_sessions_dataframe()) and the TypeScript client (getSession(), listSessions()). Both SDKs support automatic pagination and async usage for working with session turns data.

02.27.2026

02.27.2026: Sessions API and CLI Support

REST API endpoints for listing and getting sessions (GET /v1/sessions, GET /v1/sessions/{id}) and CLI commands (px sessions, px session <id>) for exploring multi-turn conversations from the terminal.

02.24.2026

02.24.2026: Claude Agent SDK Integration

Phoenix now supports tracing for Anthropic’s Claude Agent SDK via a new OpenInference instrumentation package. The integration automatically captures AGENT and TOOL spans, giving you full visibility into your Claude Agent SDK applications.

Install @arizeai/openinference-instrumentation-claude-agent-sdk alongside @arizeai/phoenix-otel
See agent execution flows and tool invocations in Phoenix’s trace UI
Get started →

02.14.2026

02.14.2026: Phoenix 13.0

Phoenix 13.0 is a major release centered on Dataset Evaluators, with support for custom model providers, OpenAI Responses API type selection, and extensive Playground and dataset/experiment UX improvements.Highlights include:

Attach evaluator suites directly to datasets and run them server-side on every Playground experiment.
Reuse server-managed custom providers (OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Google GenAI) across Playground, prompts, and dataset evaluators.
Choose OpenAI API type per configuration (chat.completions.create or responses.create) with automatic parameter compatibility handling.
Use new Playground workflows such as cancellation, template variable autocomplete, appended messages, improved prompt selection, and URL state for prompt IDs/versions/tags.
Get expanded dataset and experiment ergonomics, model/provider updates (including Claude Opus 4.6 and Azure OpenAI v1 migration), and infrastructure improvements like a session ID index for spans.

02.12.2026

02.12.2026: OpenAI Responses API Type Support

Phoenix now supports selecting the OpenAI API type for OpenAI and Azure OpenAI calls in the Playground and custom providers. Choose Chat Completions (chat.completions.create) or Responses (responses.create) depending on the model and features you want to use.Key capabilities:

API type selection: Choose Chat Completions or Responses per model configuration.
Custom provider support: OpenAI and Azure OpenAI custom providers can be configured with an API type for consistent routing.
Parameter compatibility: Phoenix maps shared invocation parameters to the chosen API type and filters unsupported fields automatically.

To get started, open the Playground model configuration panel and select an OpenAI API type, or set it in Settings → AI Providers → Custom Providers for server-managed routing.

02.12.2026

02.12.2026: Dataset Evaluators

Requires Phoenix 13.x. Dataset evaluators let you attach evaluators directly to a dataset so they automatically run server-side whenever you execute experiments from the Phoenix UI (for example, from the Playground). This turns your dataset into a reusable evaluation suite and removes the need to reconfigure evaluators for every experiment.Key capabilities:

Attach once, evaluate everywhere: Add LLM or built-in code evaluators to a dataset and reuse them across Playground experiments.
Flexible input mapping: Map evaluator inputs to dataset fields so each example is evaluated consistently.
Built-in visibility: Each evaluator captures traces for debugging and refinement, with details available from the evaluator view.

To get started, open a dataset, navigate to the Evaluators tab, click Add evaluator, configure your input mapping, and run an experiment from the Playground to see server-side scores and traces.

02.11.2026

02.11.2026: Custom Providers for Playground and Prompts

Phoenix now supports custom providers for OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, and Google GenAI. Custom providers let you store provider credentials and routing configuration on the server and reuse them across the playground and saved prompt versions. Custom provider configuration in Phoenix

Custom provider configuration in Phoenix

Key capabilities:

Centralized configuration: Manage provider credentials and routing in Settings and reuse them across the playground and prompt versions.
SDK-specific authentication: Support API keys, Azure AD token providers, or default credentials (Azure/AWS) depending on the SDK.
Model selection integration: Custom providers show up in model menus as their own provider group and inherit model listings from the underlying SDK.
Request-level overrides: Continue to supply custom request headers per prompt while using custom provider configuration for routing and authentication.

To get started, open Settings → AI Providers → Custom Providers, create a provider configuration, and select it from the model menu in the playground.

02.09.2026

02.09.2026: Claude Opus 4.6 Model Support

Phoenix playground now supports Claude Opus 4.6, Anthropic’s latest flagship model. Select claude-opus-4-6 in the Anthropic provider or anthropic.claude-opus-4-6-v1 in AWS Bedrock to start using the model with full extended thinking parameter support and accurate cost tracking.Key capabilities:

Anthropic provider integration: Access Claude Opus 4.6 directly through the playground with the thinking invocation parameter enabled for extended reasoning workflows
AWS Bedrock support: Deploy Opus 4.6 through Bedrock with the region-specific model identifier
Automatic cost tracking: Token costs are calculated using the latest pricing ( $5 per million input tokens,$ 25 per million output tokens, plus cache read/write rates)

The model appears in playground dropdowns alongside other Claude models and inherits the same reasoning capabilities as other Claude 4.x models.

01.31.2026

01.31.2026: Tool Selection and Tool Invocation Evaluators

Available in arize-phoenix-evals 0.16.0+ (Python) and @arizeai/phoenix-evals 1.3.0+ (TypeScript)Phoenix now provides two specialized evaluators for assessing AI agent tool usage. The Tool Selection Evaluator judges whether an agent correctly chose the most appropriate tool from its available toolkit to answer a user’s question, without evaluating the parameters passed. The Tool Invocation Evaluator assesses whether the agent correctly invoked a tool with proper parameters, JSON formatting, and safe values.These evaluators help developers:

Identify tool selection errors where agents choose suboptimal or incorrect tools
Debug parameter issues including hallucinated fields, malformed JSON, and incorrect values
Improve tool descriptions and agent prompts based on systematic evaluation
Validate multi-tool and multi-turn interactions across complex agent workflows

Both evaluators are available as ToolSelectionEvaluator and ToolInvocationEvaluator in Python’s phoenix.evals.metrics module, and as createToolSelectionEvaluator and createToolInvocationEvaluator in TypeScript.

01.28.2026

01.28.2026: Configurable Email Extraction for OAuth2 Providers

Available in Phoenix 12.33.1+Phoenix now supports custom email extraction from OAuth2 identity providers through the PHOENIX_OAUTH2_{IDP}_EMAIL_ATTRIBUTE_PATH environment variable. This solves authentication issues with providers like Azure AD/Entra ID where the standard email claim may be null but alternative claims like preferred_username contain the user’s identity.Configure email extraction using JMESPath expressions:

</Update>

<Update label="02.01.2026">
## [02.01.2026: Extract from Azure AD preferred_username claim](/docs/phoenix/release-notes/02-2026/02-01-2026-tool-selection-and-tool-invocation-evaluators)
PHOENIX_OAUTH2_AZURE_AD_EMAIL_ATTRIBUTE_PATH=preferred_username
</Update>

<Update label="02.01.2026">
## [02.01.2026: Extract from nested claims](/docs/phoenix/release-notes/02-2026/02-01-2026-tool-selection-and-tool-invocation-evaluators)
PHOENIX_OAUTH2_CUSTOM_IDP_EMAIL_ATTRIBUTE_PATH=user.contact.email

The default behavior remains unchanged, using the standard OIDC email claim when no custom path is specified. JMESPath expressions are validated at startup for immediate feedback on configuration errors.

01.22.2026

01.22.2026: CLI Commands for Prompts, Datasets, and Experiments

Available in @arizeai/phoenix-cli 0.4.0+The Phoenix CLI now provides comprehensive commands for managing prompts, datasets, and experiments directly from your terminal. Access version-controlled prompts, create evaluation datasets, and run experiments—all without leaving your development environment.Prompt Management:

List and view prompts with px prompts and px prompt <name>
Pipe prompts to AI assistants for optimization and analysis
Text format output with XML-style role tags for LLM consumption

Dataset Operations:

Create and manage datasets with px datasets and px dataset <name>
Add examples and query dataset contents
Export datasets for offline analysis

Experiment Workflows:

Run experiments and compare results across configurations
View experiment details and performance metrics
Track changes across prompt and model variations

These commands integrate seamlessly with AI coding assistants and enable systematic testing of LLM applications through terminal-based workflows.

01.23.2026

01.23.2026: CLI Authentication Configuration

Available in @arizeai/phoenix-cli 0.4.0+The Phoenix CLI now includes enhanced authentication configuration commands, resolving database race conditions and improving connection reliability. Users can configure authentication settings directly through the CLI for more predictable and stable connections to Phoenix servers.

01.21.2026

01.21.2026: Create Datasets from Traces with Span Associations

Available in arize-phoenix-client 1.28.0+ (Python) and @arizeai/phoenix-client 2.0.0+ (TypeScript)Phoenix now enables converting production traces into curated datasets while preserving bidirectional links back to source spans. Use the new span_id_key parameter to maintain traceability from evaluation examples to their original production executions.Python Example:

from phoenix.client import Client

client = Client()
dataset = client.datasets.create_dataset(
    name="production-queries",
    dataframe=spans_df,
    input_keys=["input"],
    output_keys=["output"],
    span_id_key="context.span_id"  # Links examples to spans
)

TypeScript Example:

import { createClient } from '@arizeai/phoenix-client';

const client = createClient();
await client.createDataset({
    name: "production-queries",
    examples: examples.map(ex => ({
        input: ex.input,
        output: ex.output,
        spanId: ex.spanId  // Preserves trace links
    }))
});

Key capabilities:

Batch resolution of span IDs for optimal performance
Graceful fallback when span IDs are missing or invalid
Backwards compatible with existing dataset creation workflows
Bidirectional navigation between evaluation results and production traces

01.19.2026

01.19.2026: Export Annotations with Traces

Available in @arizeai/phoenix-cli 0.3.0+The Phoenix CLI now supports exporting annotations alongside traces using the --include-annotations flag. Annotations—including manual labels, LLM evaluation scores, and programmatic feedback—are now preserved when exporting traces for offline analysis, backup, or migration workflows.

px traces export --include-annotations > traces_with_feedback.jsonl

This enables teams to maintain complete evaluation history when moving data between environments or conducting retrospective analysis of model performance.

01.22.2026

01.22.2026: CLI Prompt Commands: Pipe Prompts to AI Assistants 📝

Available in @arizeai/phoenix-cli 0.4.0+Phoenix CLI now supports prompt introspection with px prompts and px prompt. List prompts, view their content, and pipe them directly to AI assistants like Claude Code for optimization suggestions. The --format text option outputs prompts with XML-style role tags, ideal for analysis workflows.

01.21.2026

01.21.2026: Create Datasets from Traces with Span Associations 🔗

Available in arize-phoenix-client 1.28.0+ (Python) and @arizeai/phoenix-client 2.0.0+ (TypeScript)The Phoenix client now enables converting production traces into curated datasets while preserving associations back to source spans. Query spans using client methods, then create datasets with span associations to maintain bidirectional links. Use this to build golden datasets from validated interactions, curate edge cases from failed traces, or create regression test suites from critical user flows.

01.21.2026

01.21.2026: Phoenix CLI: Datasets, Experiments & Annotations 🧪

Available in @arizeai/phoenix-cli 0.2.0+The Phoenix CLI now supports datasets, experiments, and annotations. Pull evaluation data, export experiment results, and access human feedback directly from the terminal. Works well with AI coding assistants for analyzing test cases and reviewing results.

01.17.2026

01.17.2026: Phoenix CLI: Terminal Access for AI Coding Assistants 🖥️

Available in @arizeai/phoenix-cli 0.1.0+AI coding assistants operate through terminals and files—they run shell commands, read output, and process data. The new Phoenix CLI makes trace data accessible through these interfaces, enabling tools like Claude Code, Cursor, and Windsurf to query your Phoenix instance directly. Export traces to JSON, pipe to jq, or save to disk for analysis.

01.05.2026

01.05.2026: Appended Messages for Playground Experiments 💬

Available in Phoenix 13.0+The Prompt Playground now supports appending conversation history from dataset examples to your prompts. This enables powerful A/B testing workflows for comparing models and system prompts against the same conversation threads. Specify a dot-notation path to messages in your dataset (e.g., messages or input.messages) and run experiments across all prompt variants.

12.20.2025

12.20.2025: Improved User Preferences ⚙️

Available in Phoenix 12.27+Phoenix now offers enhanced user preference settings, giving you more control over your experience. This update includes theme selection in viewer preferences and programming language preference.

12.12.2025

12.12.2025: Support for Gemini Tool Calls 🤖

Available in Phoenix 12.25+Phoenix now supports Gemini tool calls, enabling enhanced integration capabilities with Google’s Gemini models. This update allows for more robust and feature-complete interactions with Gemini, including improved request/response translation and advanced conversation handling with tool calls.

12.09.2025

12.09.2025: Span Notes API 📝

Available in Phoenix 12.21+New dedicated endpoints for span notes enable open coding and seamless annotation integrations. Add notes to spans programmatically using the Phoenix client in both Python and TypeScript—perfect for debugging sessions, human feedback, and building custom annotation pipelines.

12.06.2025

12.06.2025: LDAP Authentication Support 🔐

Available in Phoenix 12.20+Phoenix now supports authentication against LDAP directories, enabling integration with enterprise identity infrastructure including Microsoft Active Directory, OpenLDAP, and any LDAP v3 compliant directory. Key features include group-based role mapping, multi-server failover, TLS encryption, and automatic user provisioning.

12.04.2025

12.04.2025: Evaluator Message Formats 💬

Available in phoenix-evals 0.22+ (Python) and @arizeai/phoenix-evals 2.0+ (TypeScript)Phoenix evaluators now support flexible prompt formats including simple string templates and OpenAI-style message arrays for multi-turn prompts. Python supports both f-string and mustache syntax, while TypeScript uses mustache syntax. Adapters handle provider-specific transformations automatically.

12.03.2025

12.03.2025: TypeScript createEvaluator 🧪

Available in @arizeai/phoenix-evals 2.0+The createEvaluator utility provides a type-safe way to build custom code evaluators for experiments in TypeScript. Define evaluators with full type inference, access input, output, expected, and metadata parameters, and integrate seamlessly with runExperiment.

12.01.2025

12.01.2025: Splits on Experiments Table 📊

Available in Phoenix 12.20+You can now view and filter experiment results by data splits directly in the experiments table. This enhancement makes it easier to analyze performance across different data subsets (such as train, validation, and test) and compare how your models perform on each split.

11.29.2025

11.29.2025: Add support for Claude Opus 4-5 🤖

Available in Phoenix 12.18+

Phoenix now supports Claude Opus 4 and 4-5 as models you can invoke from the Playground.

11.27.2025

11.27.2025: Show Server Credential Setup in Playground API Keys 🔐

Available in Phoenix 12.18+

The Playground now clearly indicates when server credentials are configured.

11.25.2025

11.25.2025: Split Assignments When Uploading a Dataset 🗂️

Available in Phoenix 12.18+

You can now assign data splits (ex: train/test/validation) directly when uploading a dataset into Arize Phoenix.

11.23.2025

11.23.2025: Repetitions for Manual Playground Invocations 🛝

Available in Phoenix 12.17+

This update adds an easy way to run several repetitions of the same prompt directly from the Playground.

11.14.2025

11.14.2025: Expanded Provider Support with OpenAI 5.1 + Gemini 3 🔧

Available in Phoenix 12.15+This update enhances LLM provider support by adding OpenAI v5.1 compatibility (including reasoning capabilities), expanding support for Google DeepMind/Gemini models, and introducing the gemini-3 model variant.

11.12.2025

11.12.2025: Updated Anthropic Model List 🧠

Available in Phoenix 12.15+This update enhances the Anthropic model registrations in Arize Phoenix by adding support for the 4.5 Sonnet/Haiku variants and removing several legacy 3.x Sonnet/Opus entries.

11.09.2025

11.09.2025: OpenInference TypeScript 2.0 💻

Added easy manual instrumentation with the same decorators, wrappers, and attribute helpers found in the Python openinference-instrumentation package.
Introduced function tracing utilities that automatically create spans for sync/async function execution, including specialized wrappers for chains, agents, and tools.
Added decorator-based method tracing, enabling automatic span creation on class methods via the @observe decorator.
Expanded attribute helper utilities for standardized OpenTelemetry metadata creation, including helpers for inputs/outputs, LLM operations, embeddings, retrievers, and tool definitions.
Overall, tracing workflows, agent behavior, and external tool calls is now significantly simpler and more consistent across languages.

11.07.2025

11.07.2025: Timezone Preference 🌍

Available in Phoenix 12.11+

This update adds a new display timezone preference feature for users: you can now specify how timestamps are shown across the UI, making time-based data more intuitive and aligned with your locale.

11.05.2025

11.05.2025: Metadata for Prompts 🗂️

Available in Phoenix 12.10+

Added full prompt-level metadata support across API, UI, and clients: you can now create, clone, patch, and display a JSON metadata field for prompts.

11.03.2025

11.03.2025: Playground Dataset Label Display 🏷️

Available in Phoenix 12.10+

You can now view dataset labels as you load datasets into the Playground. This enhancement makes it easier to identify and select your desired dataset.

11.01.2025

11.01.2025: Resume Experiments and Evaluations 🔄

Available in Phoenix 12.10+This release allows you to resume your experiments and evaluations at your convenience. If certain examples fail, there is no need to repeat an entire task you already completed. This feature provides you with new management capabilities across servers and clients. It’s designed to save effort, making your experimentation workflow more flexible.

10.30.2025

10.30.2025: Metadata Support for Experiment Run Annotations 🧩

Available in Phoenix 12.9+

Added metadata support for experiment run annotations, with GraphQL updates to fetch and expose this information. The annotation details view now displays formatted JSON metadata across both compare and example views for easier inspection and debugging.

10.28.2025

10.28.2025: Enable AWS IAM Auth for DB Configuration 🔐

Available in Phoenix 12.9+Added support for AWS IAM–based authentication for PostgreSQL connections to AWS Aurora and RDS. This enhancement enables the use of short-lived IAM tokens instead of static passwords, improving security and compliance for database access.

10.26.2025

Available in Phoenix 12.8+

Added a new “Split” dropdown to single-example view on the dataset pages, allowing users to update the data split classification (e.g., train/validation/test) directly from the example level. This improvement makes it easier to correct or adjust split assignments dynamically.

10.24.2025

10.24.2025: Filter Prompts Page by Label 🏷️

Available in Phoenix 12.7+

Added filtering by label on the Prompts page—users can now pick one or more labels to narrow the prompts list.

10.20.2025

10.20.2025: Splits ䷖

Available in Phoenix 12.7+In Arize Phoenix, splits let you categorize your dataset into distinct subsets—such as train, validation, or test—enabling structured workflows for experiments and evaluations. This capability offers more flexibility in how you organize, filter, and compare your data across different stages or experimental conditions.

10.18.2025

10.18.2025: Filter Annotations in Compare Experiments Slideover ✍️

Available in Phoenix 12.7+

Added filtering of annotations in the experiment compare slideover so that only annotations present on the selected experiment runs are displayed. This ensures a cleaner UI and avoids filters for annotations that don’t appear in the comparison set.

10.15.2025

10.15.2025: Enhanced Filtering for Examples Table 🔍

Available in Phoenix 12.5+

Added filtering capabilities to the Dataset Examples table, allowing users to search examples by text or split ID. Additionally, the split-management filter menu has been reorganized to separate filtering by splits from split management actions.

10.13.2025

10.13.2025: View Traces in Compare Experiments 🧪

Available in Phoenix 12.5+

We’ve added trace-links to the experiment compare slideover for runs and annotations. Clicking the new trace icons opens the Trace View.

10.10.2025

10.10.2025: Viewer Role 👀

Available in Phoenix 12.5+Introduced a new VIEWER role with enforced read-only permissions across both GraphQL and REST APIs, improving access control and security.

10.08.2025

10.08.2025: Dataset Labels 🏷️

Available in Phoenix 12.3+

Added support for dataset labels — you can now label datasets and view these labels in a dedicated column on the dataset list page, making it easier to filter and group datasets. All dataset labels can also be managed and viewed in the “Datasets” tab on the Settings page.

10.06.2025

10.06.2025: Paginate Compare Experiments 📃

Available in Phoenix 12.3+

We added pagination to the experiment comparison slideover on the list page for smoother navigation through results. We also introduced a new repetition number column, visible only when the base experiment includes multiple repetitions.

10.05.2025

10.05.2025: Load Prompt by Tag into Playground 🛝

Available in Phoenix 12.2+

We have added support for selecting and loading prompts by tag in the Playground. Users can now open specific prompts tagged for easier comparison and reproducibility.

10.03.2025

10.03.2025: Prompt Version Editing in Playground 🛝

Available in Phoenix 12.2+

We added support for prompt versioning in the Playground — users can now select, edit, and experiment with specific prompt versions directly. This update improves traceability and reproducibility for prompt iterations, making it easier to manage and compare different versions.

09.29.2025

09.29.2025: Day 0 support for Claude Sonnet 4.5 ⚡

Available in Phoenix 12.1+

Day-0 support for Claude Sonnet 4.5.

09.27.2025

09.27.2025: Dataset Splits 📊

Available in Phoenix 12.0+Add support for custom dataset splits to organize examples by category.

09.26.2025

09.26.2025: Session Annotations 🗂️

Available in Phoenix 12.0+

You can now annotate sessions with conversational evaluations like coherency and tone.

09.25.2025

09.25.2025: Repetitions 🔁

Available in Phoenix 11.38+

Support for repetitions is now enabled in Playground and SDK workflows.

09.24.2025

09.24.2025: Custom HTTP headers for requests in Playground 🛠️

Available in Phoenix 11.36+

Enable configuring custom HTTP headers for playground requests.

09.23.2025

09.23.2025: Repetitions in experiment compare slideover 🔄

Available in Phoenix 11.36+

Show experiment repetitions as separate cards in the compare slideover 🔄

09.22.2025

09.22.2025: Helm configurable image registry & IPv6 support 🌐

Available in Phoenix 11.35+

09.17.2025

09.17.2025: Experiment compare details slideover in list view 🔍

Available in Phoenix 11.34+

Added a slideover in the experiments list view to show compare details inline.

09.15.2025

09.15.2025: Prompt Labels 🏷️

Available in Phoenix 11.33+

We’ve added support for labeling prompts so you can categorize them by use-case, provider, or any custom tag.

09.12.2025

09.12.2025: Enable Paging in Experiment Compare Details 📄

Available in Phoenix 11.33+

We’ve added paging functionality to the Experiment Compare details slide-over view, allowing users to navigate between individual examples using arrow buttons or keyboard shortcuts (J / K). Pagination

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2026

2025

Releases · Arize-ai/phoenix

​02.24.2026: Claude Agent SDK Integration

​01.22.2026: CLI Prompt Commands: Pipe Prompts to AI Assistants 📝

​01.21.2026: Create Datasets from Traces with Span Associations 🔗

​01.21.2026: Phoenix CLI: Datasets, Experiments & Annotations 🧪

​01.17.2026: Phoenix CLI: Terminal Access for AI Coding Assistants 🖥️

​01.05.2026: Appended Messages for Playground Experiments 💬

​12.20.2025: Improved User Preferences ⚙️

​12.12.2025: Support for Gemini Tool Calls 🤖

​12.09.2025: Span Notes API 📝

​12.06.2025: LDAP Authentication Support 🔐

​12.04.2025: Evaluator Message Formats 💬

​12.03.2025: TypeScript createEvaluator 🧪

​12.01.2025: Splits on Experiments Table 📊

​11.29.2025: Add support for Claude Opus 4-5 🤖

​11.27.2025: Show Server Credential Setup in Playground API Keys 🔐

​11.25.2025: Split Assignments When Uploading a Dataset 🗂️

​11.23.2025: Repetitions for Manual Playground Invocations 🛝

​11.14.2025: Expanded Provider Support with OpenAI 5.1 + Gemini 3 🔧

​11.12.2025: Updated Anthropic Model List 🧠

​11.09.2025: OpenInference TypeScript 2.0 💻

​11.07.2025: Timezone Preference 🌍

​11.05.2025: Metadata for Prompts 🗂️

​11.03.2025: Playground Dataset Label Display 🏷️

​11.01.2025: Resume Experiments and Evaluations 🔄

​10.30.2025: Metadata Support for Experiment Run Annotations 🧩

​10.28.2025: Enable AWS IAM Auth for DB Configuration 🔐

​10.26.2025: Add Split Edit Menu to Examples ䷖

​10.24.2025: Filter Prompts Page by Label 🏷️

​10.20.2025: Splits ䷖

​10.18.2025: Filter Annotations in Compare Experiments Slideover ✍️

​10.15.2025: Enhanced Filtering for Examples Table 🔍

​10.13.2025: View Traces in Compare Experiments 🧪

​10.10.2025: Viewer Role 👀

​10.08.2025: Dataset Labels 🏷️

​10.06.2025: Paginate Compare Experiments 📃

​10.05.2025: Load Prompt by Tag into Playground 🛝

​10.03.2025: Prompt Version Editing in Playground 🛝

​09.29.2025: Day 0 support for Claude Sonnet 4.5 ⚡

​09.27.2025: Dataset Splits 📊

​09.25.2025: Repetitions 🔁

​09.24.2025: Custom HTTP headers for requests in Playground 🛠️

​09.23.2025: Repetitions in experiment compare slideover 🔄

​09.22.2025: Helm configurable image registry & IPv6 support 🌐

​09.17.2025: Experiment compare details slideover in list view 🔍

​See more

02.24.2026: Claude Agent SDK Integration

01.22.2026: CLI Prompt Commands: Pipe Prompts to AI Assistants 📝

01.21.2026: Create Datasets from Traces with Span Associations 🔗

01.21.2026: Phoenix CLI: Datasets, Experiments & Annotations 🧪

01.17.2026: Phoenix CLI: Terminal Access for AI Coding Assistants 🖥️

01.05.2026: Appended Messages for Playground Experiments 💬

12.20.2025: Improved User Preferences ⚙️

12.12.2025: Support for Gemini Tool Calls 🤖

12.09.2025: Span Notes API 📝

12.06.2025: LDAP Authentication Support 🔐

12.04.2025: Evaluator Message Formats 💬

12.03.2025: TypeScript createEvaluator 🧪

12.01.2025: Splits on Experiments Table 📊

11.29.2025: Add support for Claude Opus 4-5 🤖

11.27.2025: Show Server Credential Setup in Playground API Keys 🔐

11.25.2025: Split Assignments When Uploading a Dataset 🗂️

11.23.2025: Repetitions for Manual Playground Invocations 🛝

11.14.2025: Expanded Provider Support with OpenAI 5.1 + Gemini 3 🔧

11.12.2025: Updated Anthropic Model List 🧠

11.09.2025: OpenInference TypeScript 2.0 💻

11.07.2025: Timezone Preference 🌍

11.05.2025: Metadata for Prompts 🗂️

11.03.2025: Playground Dataset Label Display 🏷️

11.01.2025: Resume Experiments and Evaluations 🔄

10.30.2025: Metadata Support for Experiment Run Annotations 🧩

10.28.2025: Enable AWS IAM Auth for DB Configuration 🔐

10.26.2025: Add Split Edit Menu to Examples ䷖

10.24.2025: Filter Prompts Page by Label 🏷️

10.20.2025: Splits ䷖

10.18.2025: Filter Annotations in Compare Experiments Slideover ✍️

10.15.2025: Enhanced Filtering for Examples Table 🔍

10.13.2025: View Traces in Compare Experiments 🧪

10.10.2025: Viewer Role 👀

10.08.2025: Dataset Labels 🏷️

10.06.2025: Paginate Compare Experiments 📃

10.05.2025: Load Prompt by Tag into Playground 🛝

10.03.2025: Prompt Version Editing in Playground 🛝

09.29.2025: Day 0 support for Claude Sonnet 4.5 ⚡

09.27.2025: Dataset Splits 📊

09.25.2025: Repetitions 🔁

09.24.2025: Custom HTTP headers for requests in Playground 🛠️

09.23.2025: Repetitions in experiment compare slideover 🔄

09.22.2025: Helm configurable image registry & IPv6 support 🌐

09.17.2025: Experiment compare details slideover in list view 🔍

See more