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NiceEval is an agent eval tool that helps teams measure, evaluate, and improve AI in production. With NiceEval, teams can compare models, iterate on agents, catch regressions, and keep improving their AI applications using real user data. NiceEval is local-first at its core: your evals run in your own environment. When your team needs to share eval results or track regressions, you can push a Report to platforms like BrainTrust, or export a custom report. It can evaluate Plugins, Hooks, and Skills for Claude Code / Codex, and it can also evaluate your own AI agent application directly. Whether your agent is built on AI SDK, LangGraph, Pi, or a custom agent SDK, you can connect it through an Adapter into the same eval framework.

Build an eval

Why NiceEval when DeepEval, LangFuse, and BrainTrust already exist

NiceEval is an Agent-Native eval tool. The Dataset / golden pattern of building an Input and an Expected Output doesn’t fit real agent evaluation — today’s agents need to be checked at a finer grain: multi-turn conversations, multi-agent collaboration, tool calls, Skill loading. NiceEval is designed for exactly this kind of scenario: assertions land directly on tool calls, message content, structured output, and usage, instead of requiring you to hand-build a golden dataset first. Tools like LangFuse and BrainTrust lean more toward tracing and monitoring, which is too heavy for the path of “write evals, run evals, inspect results.” NiceEval is built from the start with a developer experience friendly to that path. The two aren’t mutually exclusive either: NiceEval coexists with LangFuse and BrainTrust — use them for tracing, or upload eval results to either.

What you can evaluate

Coding agents

Put Claude Code, Codex, or bub into a Sandbox, give them a task, then verify the result with real tests and file assertions.

Your own AI agent application

Connect directly to your app’s interface and assert on replies, tool calls, and structured outputs. Switch between different prompt versions with a Flag.

Two integration modes

Fits coding agents like Codex and Claude Code that must edit code and run commands on a real file system.

Core concepts at a glance

See the full glossary in the architecture overview.

Start from your scenario

Connect your agent

Connect your agent by writing an Adapter that translates messages, tool calls, and structured outputs into the standard event stream.

Evaluate coding-agent extensions

Measure the effect of Skills, prompts, and Plugin benchmarks with a real workspace and comparison experiments.

Learn to write evals and experiments

Learn how to write evals, assertions, experiment configs, and reports.
Quickstart walks you through installing NiceEval, writing an eval, and getting an eval report.