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Arize Introduces Open Source LLM Evals Library and Support for Traces and Spans

Arize Phoenix rolled out several capabilities in its latest release.

Phoenix's new support for LLM traces and spans means that AI engineers and developers can get visibility at a span-level and see exactly where an app breaks, with tools to analyze each step rather than just the end-result.

This capability is particularly useful for early app developers because it doesn't require them to send data to a SaaS platform to perform LLM evaluation and troubleshooting -- instead, the open-source solution provides a mechanism for pre-deployment LLM observability directly from their local machine. Phoenix supports all common spans and has a native integration into LlamaIndex and LangChain.

The new Phoenix LLM evals library is also designed for fast and accurate LLM-assisted evaluations, ultimately making the use of the evaluation LLM easy to implement. Applying data science rigor to the testing of model and template combinations, Phoenix offers proven LLM evals for common use cases and needs around retrieval (RAG) relevance, reducing hallucinations, question-and-answer on retrieved data, toxicity, code generation, summarization, and classification. The Phoenix LLM evals library is optimized to run evaluations quickly with support for the notebook, Python pipeline, and app frameworks such as LangChain and LlamaIndex.

"Large language models are poised to transform industries and society, but when it comes to robust performance going from toy to production remains a challenge," said Jason Lopatecki, CEO and Co-Founder of Arize AI. "These industry-first updates from Phoenix promise to provide better LLM evals and deeper troubleshooting to make complex LLM-powered systems ready and reliable in the real world."

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Arize Introduces Open Source LLM Evals Library and Support for Traces and Spans

Arize Phoenix rolled out several capabilities in its latest release.

Phoenix's new support for LLM traces and spans means that AI engineers and developers can get visibility at a span-level and see exactly where an app breaks, with tools to analyze each step rather than just the end-result.

This capability is particularly useful for early app developers because it doesn't require them to send data to a SaaS platform to perform LLM evaluation and troubleshooting -- instead, the open-source solution provides a mechanism for pre-deployment LLM observability directly from their local machine. Phoenix supports all common spans and has a native integration into LlamaIndex and LangChain.

The new Phoenix LLM evals library is also designed for fast and accurate LLM-assisted evaluations, ultimately making the use of the evaluation LLM easy to implement. Applying data science rigor to the testing of model and template combinations, Phoenix offers proven LLM evals for common use cases and needs around retrieval (RAG) relevance, reducing hallucinations, question-and-answer on retrieved data, toxicity, code generation, summarization, and classification. The Phoenix LLM evals library is optimized to run evaluations quickly with support for the notebook, Python pipeline, and app frameworks such as LangChain and LlamaIndex.

"Large language models are poised to transform industries and society, but when it comes to robust performance going from toy to production remains a challenge," said Jason Lopatecki, CEO and Co-Founder of Arize AI. "These industry-first updates from Phoenix promise to provide better LLM evals and deeper troubleshooting to make complex LLM-powered systems ready and reliable in the real world."

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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