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.

This evaluator checks for issues arising from function routing, parameter extraction, and function generation.

Check out a full walkthrough of the evaluator.

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 visual multi-turn exchanges with your chatbot or agent Sessions launches with Python and TS/JS support. For more on sessions, check out and the .

Bug Fixes and Improvements 🐛

• Added support for FastAPI and GraphQL extensions

• Fixed a bug where Anthropic LLM as a Judge responses would be labeled as unparseable

• Fixed a bug causing 500 errors on client.get_traces_dataset() and client.get_spans_dataframe()

• Added the ability for authentication to work from behind a proxy

• Added an environment variable to set default admin passwords in auth

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.

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: All of Phoenix’s UI & APIs (REST, GraphQL, gRPC) now require access tokens or API keys. Keep your data safe!

• RBAC (Role-Based Access Control): Admins can manage users; members can update their profiles—simple & secure.

• API Keys: Now available for seamless, secure data ingestion & querying.

• OAuth2 Support: Easily integrate with Google, AWS Cognito, or Auth0. ✉ Password Resets via SMTP to make security a breeze.

For all the details on authentication, view our .

Bug Fixes and Improvements 🐛

• Numerous stability improvements to our hosted Phoenix instances accessed on app.phoenix.arize.com

• Added a new command to easily launch a Phoenix client from the cli: phoenix serve

• Implemented simple email sender to simplify dependencies

• Improved error handling for imported spans

• Replaced hdbscan with fast-hdbscan Added PHOENIX_CSRF_TRUSTED_ORIGINS environment variable to set trusted origins

• Added support for Mistral 1.0

• Fixed an issue that caused px.Client().get_spans_dataframe() requests to time out

In addition to our existing notebook, CLI, and self-hosted deployment options, we’re excited to announce that Phoenix is now available as a .

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 applications, and store them in a persistent, cloud- accessible Phoenix instance.

Hosted Phoenix is 100% free-to-use, .

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 visual 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.

Bug Fixes and Improvements 🐛

• Prompt Playground: Added support for arbitrary string model names Added support for Gemini 2.0 Flash Improved template editor ergonomics

• Evals: Added multimodal message template support

• Tracing: Added JSON pretty printing for structured data outputs (thank you sraibagiwith100x!) Added a breakdown of token types in project summary

• Bug Fixes: Changed trace latency to be computed every time, rather than relying on root span latency, Added additional type checking to handle non-string values when manually instrumenting (thank you Manuel del Verme!)

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 should make a few flows much easier:

• Fine-tuning: You can now create a dataset based on conditions in the UI, or by manually choosing examples, then export these into csv or jsonl formats readymade for fine-tuning APIs.

• Experimentation: External datasets can be uploaded into Phoenix to serve as the test cases for experiments run in the platform.

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. You can now run comprehensive experiments to measure and analyze the performance of your LLMs in various scenarios.

For more details, check out our full walkthrough.