Phoenix OSS – Applying LLM Spans, Traces, and Evals for AI Insights
We are the developers of Phoenix, which we released in April of this year with a goal of bringing LLM observability to the notebook. In the time since, the growth of LLM frameworks and complex agent workflows led us to add support for LLM spans and traces and introduce a simple Eval harness for testing the data from those spans. The latest Traces & Spans release of Phoenix offers: -Out of the box tracing for LlamaIndex and LangChain -Fully local execution, no data sent anywhere, outside of your own LLM calls -Ability to get a common dataframe format across frameworks back to a notebook for…
Phoenix OSS – Applying LLM Spans, Traces, and Evals for AI Insights launched on October 4, 2023 with 23 votes, #1,332 of 4,393 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
Phoenix OSS is an observability tool for large language model development that runs locally in notebooks. It provides out-of-the-box tracing for LlamaIndex and LangChain frameworks, capturing spans and traces without sending data outside the user's environment except for LLM API calls. The tool converts trace data into a common dataframe format for analysis and includes a lightweight evaluation harness for testing LLM outputs. It also offers benchmarking scripts for optimizing retrieval configurations like chunk size and ranking parameters. Phoenix is designed for developers building LLM applications who need visibility into their AI workflows.
written from the facts on this page · September 2026
From the sources
AI Observability & Evaluation. Contribute to Arize-ai/phoenix development by creating an account on GitHub.
In the maker’s words, at launch
We are the developers of Phoenix, which we released in April of this year with a goal of bringing LLM observability to the notebook. In the time since, the growth of LLM frameworks and complex agent workflows led us to add support for LLM spans and traces and introduce a simple Eval harness for testing the data from…
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