Skip to main content
Every example here has real, runnable source. Choose by subject under test — you don’t need to read them in order. If you haven’t run your first eval yet, read Quickstart first. If you already know your task, go straight to the matching Tutorials page.

​
Connect an Existing Agent Framework

Each of the five examples below keeps both the plain application before integration and the full project after integrating NiceEval. The code diffs on each page are generated from those two sources. Every example’s source lives under examples/zh/origin/ and examples/zh/tier1/. When you need OTel or Experiment Flags, follow the same project into tier2/ and tier3/.

​
Full Eval Projects

Eval an AI Agent Application with a Hand-Written Adapter

An AI SDK v6 web agent covering tool calls, image understanding, multi-turn sessions, model comparison, and dual observability.

Eval Coding Agent Extensions

Use a real workspace and controlled experiments to measure how Skills, prompts, and plugin benchmarks affect task outcomes.

​
Real Projects

​
Coding Agent Memory Evals

coding-agent-memory-evals uses the same batch of development tasks and the same model to compare task success rate, cost, latency, and behavior differences between a coding agent with persistent memory and one without.

​
Terminal-Bench

terminal-bench runs the audited Harbor Terminal-Bench task set on NiceEval with the original run-tests.sh verifiers, and compares coding agents and models side by side.

​
Scope of Examples

  • Single-snippet API usage belongs in a task tutorial or the Reference, not here.
  • Toy projects without runnable source code are not included.
  • Roadmap items and integrations that aren’t implemented yet are not included.
  • The same project doesn’t get duplicated across multiple case pages just because it’s also called a Skill, a Plugin, or a Hook.