The latest from the Phoenix team.
New in phoenix-evals: Added support for Google's Gemini models via the Google GenAI SDK — multimodal, async, and ready to scale. Huge shoutout to Siddharth Sahu for this contribution!
Available in Phoenix 11.12+
Phoenix now has comprehensive project dashboards for detailed performance, cost, and error insights.
Available in Phoenix 11.12+
View average run metrics directly in the headers of the experiment comparison table for quick insights.
Available in Phoenix 11.9+
Create new projects and transfer traces between them via GraphQL, with full preservation of annotations and cost data.
OpenInference Java now offers full OpenTelemetry-compatible tracing for AI apps, including auto-instrumentation for LangChain4j and semantic conventions.
Available in Phoenix 11.7+
New experiments feature set in phoenix-client, enabling sync and async execution with task runs, evaluations, rate limiting, and progress reporting.
Available in Phoenix 11.6+
Compare experiments relative to a baseline run to easily spot regressions and improvements across metrics.
Available in Phoenix 11.5+
Monitor database disk usage, notify admins when nearing capacity, and automatically block writes when critical thresholds are reached.
Available in Phoenix 11.4+
Added cost summaries to trace headers, showing total and segmented (prompt & completion) costs at a glance while debugging.
Available in Phoenix 11.3+
Phoenix README now has a “Add to Cursor” button for seamless IDE integration with Cursor. @arizeai/phoenix-mcp@2.2.0 also includes a new tool called phoenix-support, letting agents like Cursor auto-instrument your apps using Phoenix and OpenInference best practices.
Available in Phoenix 11.0+
Phoenix now automatically tracks token-based LLM costs using model pricing and token counts, rolling them up to trace and project levels for clear, actionable cost insights.
Phoenix now supports multiple customizable spaces with individual user access and collaboration, enabling teams to work together seamlessly.
Available in Phoenix 10.15+
Phoenix’s Playground now supports Amazon Bedrock, letting you run, compare, and track Bedrock models alongside others—all in one place.
Available in Phoenix 10.12+
Now you can filter sessions by their unique session_id across the API and UI, making it easier to pinpoint and inspect specific sessions.
Available in Phoenix 10.12+
Now you can create spans directly via a new POST API and client methods, with helpers to safely regenerate IDs and prevent conflicts on insertion.
Available in Phoenix 10.11+
Dataset name filtering with live search support across the API and UI.
Available in Phoenix 10.9+
Phoenix now has experiment graphs to track how your evaluation scores and latency evolve over time.
Ollama is now supported in the Playground, letting you experiment with its models and customize parameters for tailored prompting.
Available in Phoenix 10.6+
Added Helm chart support for Phoenix, making Kubernetes deployment fast, consistent, and easy to upgrade.
Available in Phoenix 10.7+
Deepseek and xAI models are now available in Prompt Playground!
We've added a host of new methods to the JS client:
getExperiment - allows you to retrieve an Experiment to view its results, and run evaluations on it
evaluateExperiment - allows you to evaluate previously run Experiments using LLM as a Judge or Code-based evaluators
createDataset - allows you to create Datasets in Phoenix using the client
appendDatasetExamples - allows you to append additional examples to a Dataset
You can now run Experiments using the Phoenix JS client! Use Experiments to test different iterations of your applications over a set of test cases, then evaluate the results. This release includes:
Native tracing of tasks and evaluators
Async concurrency queues
Support for any evaluator (including bring your own evals)
Available in Phoenix 9.0+
Major Release: Phoenix v9.0.0
Phoenix's v9.0.0 release brings with it:
A host of improvements to Annotations, including one-to-many support, API access, annotation configs, and custom metadata
Customizable data retention policies
Hotkeys! 🔥
We’ve added a Python auto-instrumentation library for the Google GenAI SDK. This enables seamless tracing of GenAI workflows with full OpenTelemetry compatibility. Additionally, the Google GenAI instrumentor is now supported and works seamlessly with Span Replay in Phoenix.
Available in Phoenix 8.30+
The Phoenix client now includes the SpanQuery DSL for more advanced span querying. Additionally, a get_spans_dataframe method has been added to facilitate easier data extraction for span-related information.
Available in Phoenix 8.29+
Phoenix now supports Transport Layer Security (TLS) for both HTTP and gRPC connections, enabling encrypted communication and optional mutual TLS (mTLS) authentication. This enhancement provides a more secure foundation for production deployments.
Available in Phoenix 8.28+
When stopping the Phoenix server via Ctrl+C, the shutdown process now exits cleanly with code 0 to reflect intentional termination. Previously, this would trigger a traceback with KeyboardInterrupt, misleadingly indicating a failure.
Available in Phoenix 8.27+
Improved trace navigation by automatically scrolling the selected span into view when a user navigates to a specific trace. This enhances usability by making it easier to locate and focus on the relevant span without manual scrolling.
Available in Phoenix 8.26+
We’ve released openinference-instrumentation-mcp, a new package in the OpenInference OSS library that enables seamless OpenTelemetry context propagation across MCP clients and servers. It automatically creates spans, injects and extracts context, and connects the full trace across services to give you complete visibility into your MCP-based AI systems.
Big thanks to Adrian Cole and Anuraag Agrawal for their contributions to this feature.
Available in Phoenix 8.26+
Phoenix now supports programmatic API key creation through a new endpoint, making it easier to automate project setup and trace logging. To enable this, set the PHOENIX_ADMIN_SECRET environment variable in your deployment.
Available in Phoenix 8.25+
Tool call and result IDs are now shown in the span details view. Each ID is placed within a collapsible header and can be easily copied. This update also supports spans with multiple tool calls. Get started with tracing your tool calls here.
Available in Phoenix 8.24+
This update enhances the Project Management API with more flexible project identification We've added support for identifying projects by both ID and hex-encoded name and introduced a new _get_project_by_identifier helper function.
Available in Phoenix 8.23+
This release introduces a REST API for managing projects, complete with full CRUD functionality and access control. Key features include CRUD Operations and Role-Based Access Control. Check out our new documentation to test these features.
Available in Phoenix 8.22+
We’ve added support for Prompt Tagging in the Phoenix client. This new feature gives you more control and visibility over your prompts throughout the development lifecycle. Tag prompts directly in code, label prompt versions, and add tag descriptions. Check out documentation on prompt tags.
Available in Phoenix 8.21+
The new span aside moves the Span Annotation editor into a dedicated panel, providing a clearer view for adding annotations and enhancing customization of your setup. Read this documentation to learn how annotations can be used.
Available in Phoenix 8.20+
Newly added to the OpenAI Agent SDK is support for MCP Span Info, allowing for the tracing and extraction of useful information about MCP tool listings. Use the Phoenix OpenAI Agents SDK for powerful agent tracing.
Available in Phoenix 8.20+
You can now toggle the option to treat orphan spans as root when viewing your spans. Additionally, we've enhanced the UI with an icon view in span details for better visibility in smaller displays. Learn more .
Available in Phoenix 8.19+
Within each project, there is now a Config tab to enhance customization. The default tab can now be set per project, ensuring the preferred view is displayed. Learn more in .
Available in Phoenix 8.17+
You can now preconfigure admin users at startup using an environment variable, making it easier to manage access during deployment. Admins defined this way are automatically seeded into the database and ready to log in.
Available in Phoenix 8.16+
You can now delete experiments directly from the action menu, making it quicker to manage and clean up your workspace.
Available in Phoenix 8.15+
In the New Project tab, we've added quick setup to instrument your application for BeeAI, SmolAgents, and the OpenAI Agents SDK. Easily configure these integrations with streamlined instructions. Check out all Phoenix tracing integrations here.
Available in Phoenix 8.14+
We've added the ability to resize Span, Trace, and Session tables. Resizing preferences are now persisted in the tracing store, ensuring settings are maintained per-project and per-table.
Available in Phoenix 8.13+
We've introduced the OpenAI Agents SDK for Python which provides enhanced visibility into agent behavior and performance. For more details on a quick setup, check out our docs.
pip install openinference-instrumentation-openai-agents openai-agentsAvailable in Phoenix 8.11+
You can now save and load configurations directly from prompts or default model settings. Additionally, you can adjust the budget token value and enable/disable the "thinking" feature, giving you more control over model behavior and resource allocation.
Available in Phoenix 8.9+
Prompt Playground now supports new GPT and Anthropic models with enhanced configuration options. Instrumentation options have been improved for better traceability, and evaluation capabilities have expanded to cover Audio & Multi-Modal Evaluations. Phoenix also introduces new integration support for LiteLLM Proxy & Cleanlabs evals.
Available in Phoenix 8.8+
We’ve rolled out several enhancements to Projects, offering more flexibility and control over your data. Key updates include persistent column selection, advanced filtering options for metadata and spans, custom time ranges, and improved performance for tracing views. These changes streamline workflows, making data navigation and debugging more efficient.
Check out docs for more.
Available in Phoenix 8.0+
Phoenix prompt management will now let you create, modify, tag, and version control prompts for your applications. Some key highlights from this release:
Versioning & Iteration: Seamlessly manage prompt versions in both Phoenix and your codebase.
New TypeScript Client: Sync prompts with your JavaScript runtime, now with native support for OpenAI, Anthropic, and the Vercel AI SDK.
New Python Client: Sync templates and apply them to AI SDKs like OpenAI, Anthropic, and more.
Standardized Prompt Handling: Native normalization for OpenAI, Anthropic, Azure OpenAI, and Google AI Studio.
Enhanced Metadata Propagation: Track prompt metadata on Playground spans and experiment metadata in dataset runs.
Check out the docs and this walkthrough for more on prompts!📝
Available in Phoenix 8.0+
Phoenix has made it even simpler to get started with tracing by introducing one-line auto-instrumentation. By using register(auto_instrument=True), you can enable automatic instrumentation in your application, which will set up instrumentors based on your installed packages.
from phoenix.otel import register
register(auto_instrument=True)Available in Phoenix 7.9+
In addition to using our automatic instrumentors and tracing directly using OTEL, we've now added our own layer to let you have the granularity of manual instrumentation without as much boilerplate code.
You can now access a tracer object with streamlined options to trace functions and code blocks. The main two options are using the decorator @tracer.chain and using the tracer in a with clause.
Check out the docs for more on how to use tracer objects.
Available in Phoenix 7.0+
Sessions allow you to group multiple responses into a single thread. Each response is still captured as a single trace, but each trace is linked together and presented in a combined view.
Sessions make it easier to visualize multi-turn exchanges with your chatbot or agent. Sessions launches with Python and TS/JS support. For more on sessions, check out a walkthrough video and the docs.
Available in Phoenix 6.0+
Prompt Playground is now available in the Phoenix platform! This new release allows you to test the effects of different prompts, tools, and structured output formats to see which performs best.
Replay individual spans with modified prompts, or run full Datasets through your variations.
Easily test different models, prompts, tools, and output formats side-by-side, directly in the platform.
Automatically capture traces as Experiment runs for later debugging. See here for more information on Prompt Playground, or jump into the platform to try it out for yourself.
Available in Phoenix 5.0+
We've added Authentication and Rules-based Access Controls to Phoenix. This was a long-requested feature set, and we're excited for the new uses of Phoenix this will unlock!
The auth feature set includes secure access, RBAC, API keys, and OAuth2 Support. For all the details on authentication, view our docs.
Available in Phoenix 4.11.0+
Our integration with Guardrails AI allows you to capture traces on guard usage and create datasets based on these traces. This integration is designed to enhance the safety and reliability of your LLM applications, ensuring they adhere to predefined rules and guidelines.
Check out the Cookbook here.
Phoenix is now available for deployment as a fully hosted service.
In addition to our existing notebook, CLI, and self-hosted deployment options, we’re excited to announce that Phoenix is now available as a fully hosted service. With hosted instances, your data is stored between sessions, and you can easily share your work with team members.
We are partnering with LlamaIndex to power a new observability platform in LlamaCloud: LlamaTrace. LlamaTrace will automatically capture traces emitted from your LlamaIndex application.
Hosted Phoenix is 100% free-to-use, check it out today!
Available in Phoenix 4.6+
Datasets: Datasets are a new core feature in Phoenix that live alongside your projects. They can be imported, exported, created, curated, manipulated, and viewed within the platform, and make fine-tuning and experimentation easier.
For more details on using datasets see our documentation or example notebook.
Experiments: Our new Datasets and Experiments feature enables you to create and manage datasets for rigorous testing and evaluation of your models. Check out our full walkthrough.
Available in Phoenix 4.6+
We are introducing a new built-in function call evaluator that scores the function/tool-calling capabilities of your LLMs. This off-the-shelf evaluator will help you ensure that your models are not just generating text but also effectively interacting with tools and functions as intended. Check out a full walkthrough of the evaluator.