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
- Sandbox mode
- Direct mode
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.