Changelog

Schedule for monitors to run hourly, daily, weekly, or monthly.

Improved traces export

Specify which columns of data you'd like to export when exporting data via the ArizeExportClient by specifying columns .

primary_df = client.export_model_to_df(
    columns=['context.span_id', 'attributes.llm.input'] # <---- HERE
    space_id='',
    model_id='',
    environment=Environments.TRACING,
    start_time=datetime(2025, 3, 25),
    end_time=datetime(2025, 4, 25),
)

Create dataset from CSVs

You can now create datasets through many methods, from traces, code, manually in the UI, or CSV upload. Read more

OTEL tracing Via HTTP

Support for HTTP when sending traces to Arize! See GitHub for more info.

tracer_provider = register(
    endpoint="https://otlp.arize.com/v1/traces",     # NEW
    transport=Transport.HTTP,                        # NEW
    space_id=SPACE_ID,
    api_key=API_KEY
    project_name="test-project-http",
)

Voice application tracing and evaluation

January 21, 2025

Audio tracing: Capture, process, and send audio data to Arize and observe your application behavior.

Evaluation: Assess how well your models identify emotional tones like frustration, joy, or neutrality.

Dashboard colors

January 21, 2025

We’ve added new ways to plot your charts, with custom colors and better UX!

Prompt hub

Manage, iterate, and deploy your prompts in one place. Version control your templates and use them across playground, tasks, and APIs. Read more

Managed code evaluators

Use our pre-built, off-the-shelf evaluators to evaluate spans without requiring requests to an LLM-as-a-Judge. These include Regex matching, JSON validation, Contains keyword, and more!

Create experiments from playground

Quickly experiment with your prompts across your datasets. All you have to do is click "Save as experiment" Read more

Monitor alert status

See exactly how and when your monitors are triggered

LangChain Instrumentation

Support for sessions via LangChain native thread tracking in TypeScript is now available. Easily track multi-turn conversations / threads using LangChain.js.

Analyze your spans with Copilot

Extract key insights quickly from your spans instead of trying to decipher meaning in hundreds of spans. Ask questions and run evals right in the trace view.

Generate dashboards with Copilot

Building dashboard plots just got way easier. Create time series plots and even translate code into ready to go visualizations.

The Custom Metric skill now supports a conversational flow, making it easier for users to iterate and refine metrics dynamically

View your experiment traces

Experiment traces for a dataset are now consolidated accessed under "Experiment Projects".

Multi-class calibration chart

For your multi-class ML models, you can see how your model is calibrated in one visualization

Log experiments in Python SDK

You can now log experiment data manually using a dataframe, instead of running an experiment. This is useful if you already have the data you need, and re-running the query would be expensive. SDK Reference

arize_client.log_experiment(
    space_id=SPACE_ID,
    experiment_name="my_experiment",
    experiment_df=experiment_run_df,
    task_columns=task_columns,
    evaluator_columns={"correctness": evaluator_columns},
    dataset_name=dataset_name,
)

Create custom metrics with Copilot

Users can generate their desired metric by having copilot translate natural language descriptions or existing code (e.g., SQL, Python) into AQL. Learn more →

Summarize embeddings with Copilot

Copilot now works for embeddings! Users can select embedding data point and Copilot will analyze for patterns and insights. Learn more →

Local explainability support for ML models

Local Explainability is now live, providing both a table view and waterfall style plot for detailed, per-feature SHAP values on individual predictions. Learn more →

See experiment results over time

Visualize specific evaluations over time in dashboards. Learn more →

Function calling replay in prompt playground

Now users can follow the full function calling tutorial from OpenAI and iterate on different functions in different messages from within the Prompt Playground.

Vercel AI auto-instrumentation

User can now ingest traces created by the Vercel AI SDK into Arize. Learn more →

Track sessions and context attributes in instrumentation

You can add metadata and context that will be picked up by all of our auto instrumentations and added to spans. Learn more →

Easily test your online tasks and evals

October 24, 2024

Users now have the option to to test a task, such as online eval, by running it once on existing data, or apply evaluation labels to older traces. Learn more →

Experiment filters

October 24, 2024

Users can now filter experiments based on dataset attributes or experiment results, making it easy to identify areas for improvement and track their experiment progress with more precision. Learn more →

Embedding traces

With Embeddings Tracing, you can effortlessly select embedding spans and dive straight into the UMAP visualizer, simplifying troubleshooting for your genAI applications. Learn more →

Experiments Details Visualization

Users can now view a detailed breakdown of labels for their experiments on the Experiments Details page.

Support for o1-mini and o1-preview in playground

We've added full support for all available OpenAI models in the playground including the o1-mini and o1-preview.

Improved auto-complete in playground

We've added better input variable behavior, autocompletion enhancements, support for mustache/f-string input variables, and more.

Filter history

We now store the last three filters used by a user! Users can easily access their filter history in the query filters dropdown, making it simpler to reuse filters for future queries.

Tracing quick filters

Apply filters directly from the table by hovering over the text to reveal the filter icon.

New arize-otel package

We made it way simpler to add automatic tracing to your applications! It's now just a few lines of code to use OpenTelemetry to trace your LLM application. Check out our new quickstart guide which uses our arize-otel package.

Easily add spans to datasets