Selecting The Right AI Evals Tool

2025-10-01 · Source: Hamel Husain's Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Hamel Husain's article, published October 1, 2025, provides a framework for selecting AI evaluation tools, asserting that the "best" tool depends on a team's skillset, technical stack, and maturity, rather than offering a single recommendation. To illustrate, a panel of data scientists from an AI Evals course assessed three prominent vendors—Langsmith, Braintrust, and Arize Phoenix—by having them complete an identical homework assignment. This evaluation highlighted critical criteria such as Workflow and Developer Experience, Human-in-the-Loop Support, Transparency and Control versus "Magic" features, and Ecosystem Integration. The analysis revealed specific strengths like Langsmith's seamless trace-to-playground workflow, Braintrust's structured process, and Arize Phoenix's notebook-centric, open-source design, alongside areas for improvement for each. Ultimately, the content serves as a guide for teams to make informed decisions, with the author personally favoring a backend data store approach combined with Jupyter notebooks and custom annotation interfaces.

Key takeaway

A panel of AI Evals experts evaluated Langsmith, Braintrust, and Arize Phoenix by having each vendor complete the same LLM evaluation assignment. This revealed critical selection criteria: prioritizing workflow, human-in-the-loop support, transparency over "magic" automation, and ecosystem integration. The analysis highlights that the "best" tool depends on team workflow and stack, offering a framework to avoid common pitfalls like over-automation or proprietary lock-in.

Topics

• AI Evals
• Langsmith
• Braintrust
• Arize Phoenix
• Developer Experience

Code references

• ai-evals-course/recipe-chatbot

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

Related on AIssential

• The maturity phases of running evals — Phil Hetzel, Braintrust — Evals mature from human judgment to automated, production-integrated systems, crucial for agent quality and risk management.
• The AI Industry Is Resetting | Google, OpenAI, Anthropic, Meta & Huawei — The AI industry's focus is shifting from model superiority to infrastructure, ecosystems, and enterprise revenue.
• Agent Observability with LangSmith, Langfuse, and Arize: A Hands-On Comparison — LangSmith, Langfuse, and Arize offer distinct approaches to AI agent observability, crucial for debugging and performance.
• stop trying to build an ai employee right now — Focus on small, reviewable "packets" of work for initial AI automation wins, not giant autonomous agents.
• Artificial Analysis: Independent LLM Evals as a Service — with George Cameron and Micah-Hill Smith — Independent benchmarking is crucial for navigating the complex, rapidly evolving AI model landscape and ensuring objective performance assessment.
• Maybe AI agents can be lawyers after all — AI agent performance on professional tasks is rapidly improving, challenging previous assumptions about human job security.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Hamel Husain's Blog.