From 84cd6a91a9ea6402b1bad76074d7031dbbc738f8 Mon Sep 17 00:00:00 2001
From: Yinghan Ma <yinghan.ma@fireworks.ai>
Date: Sat, 1 Nov 2025 18:37:35 -0700
Subject: [PATCH 1/3] add

---
 development/FINAL_SUMMARY.md                  | 111 +++
 development/auto_test_discovery.md            | 127 ++++
 development/pytest_discovery_improvements.md  | 184 +++++
 .../pytest_discovery_guide.mdx                | 320 ++++++++
 eval_protocol/pytest/dual_mode_wrapper.py     |   7 +-
 eval_protocol/pytest/evaluation_test.py       |  14 +
 eval_protocol/pytest/evaluation_test.py.bak   | 717 ++++++++++++++++++
 eval_protocol/pytest/parameterize.py          |   6 +
 examples/auto_discovery_example.py            |  90 +++
 tests/test_auto_discovery_simple.py           |  41 +
 10 files changed, 1616 insertions(+), 1 deletion(-)
 create mode 100644 development/FINAL_SUMMARY.md
 create mode 100644 development/auto_test_discovery.md
 create mode 100644 development/pytest_discovery_improvements.md
 create mode 100644 docs/developer_guide/pytest_discovery_guide.mdx
 create mode 100644 eval_protocol/pytest/evaluation_test.py.bak
 create mode 100644 examples/auto_discovery_example.py
 create mode 100644 tests/test_auto_discovery_simple.py

diff --git a/development/FINAL_SUMMARY.md b/development/FINAL_SUMMARY.md
new file mode 100644
index 00000000..6b1d1f14
--- /dev/null
+++ b/development/FINAL_SUMMARY.md
@@ -0,0 +1,111 @@
+# ✅ 完成：自动测试发现功能
+
+## 目标
+
+确保所有使用 `@evaluation_test` 装饰的函数都能被 pytest 自动发现，无论函数名是否符合 pytest 命名规范。
+
+## 实现方案
+
+### 核心机制：自动注册
+
+当函数名不以 `test_` 开头时，decorator 会：
+1. 自动在调用者的全局命名空间中注册一个以 `test_` 开头的别名
+2. Pytest 扫描模块时会发现这个别名
+3. 用户无需修改任何代码或命名
+
+### 代码修改
+
+#### 1. `eval_protocol/pytest/evaluation_test.py`
+- ✅ 移除了警告功能
+- ✅ 添加了自动注册逻辑（使用 `sys._getframe` 访问调用者的全局命名空间）
+
+#### 2. `eval_protocol/pytest/parameterize.py`
+- ✅ 确保 wrapper 的 `__name__` 属性以 `test_` 开头
+
+#### 3. `eval_protocol/pytest/dual_mode_wrapper.py`
+- ✅ 确保 dual_mode_wrapper 的 `__name__` 属性以 `test_` 开头
+
+## 使用示例
+
+```python
+from eval_protocol.pytest import evaluation_test
+from eval_protocol.models import EvaluationRow, EvaluateResult
+
+# ✅ 不需要以 test_ 开头 - 会自动注册为 test_my_evaluation
+@evaluation_test(
+    input_rows=[[EvaluationRow(messages=[{"role": "user", "content": "Hello"}])]]
+)
+async def my_evaluation(row: EvaluationRow) -> EvaluationRow:
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+
+# ✅ 已经符合命名规范 - 正常工作
+@evaluation_test(
+    input_rows=[[EvaluationRow(messages=[{"role": "user", "content": "Hello"}])]]
+)
+async def test_my_evaluation(row: EvaluationRow) -> EvaluationRow:
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+## 验证结果
+
+```bash
+$ pytest --collect-only tests/test_auto_discovery_simple.py -v
+collected 2 items
+
+<Module test_auto_discovery_simple.py>
+  <Coroutine test_my_custom_eval[rows(len=1)]>  # 自动注册！
+  <Coroutine test_proper_eval[rows(len=1)]>
+
+$ pytest tests/test_auto_discovery_simple.py -v
+============================== 2 passed in 0.15s ==============================
+```
+
+## 特点
+
+### ✅ 优点
+1. **零配置**：无需任何额外配置
+2. **无需警告**：静默自动处理，不打扰用户
+3. **完全兼容**：不影响已有代码
+4. **简单直接**：用户只需使用 `@evaluation_test`，其他都自动处理
+5. **可靠**：经过测试验证
+
+### 🎯 工作原理
+- Pytest 通过扫描模块的全局命名空间来发现测试
+- 我们在装饰时自动在命名空间中注册正确命名的别名
+- 用户原始函数名保持不变，可以继续使用
+
+## 测试覆盖
+
+- ✅ `tests/test_auto_discovery_simple.py` - 验证自动发现功能
+  - 测试不以 `test_` 开头的函数能被发现
+  - 测试以 `test_` 开头的函数正常工作
+  - 所有测试通过
+
+## 文档
+
+- `development/auto_test_discovery.md` - 详细技术文档
+- `development/FINAL_SUMMARY.md` - 本文档
+
+## 总结
+
+现在，用户只需要：
+
+```python
+@evaluation_test(...)
+async def any_function_name(row: EvaluationRow) -> EvaluationRow:
+    # 无论函数名是什么，都能被 pytest 发现！
+    ...
+```
+
+**就这么简单！** 🎉
+
+不需要：
+- ❌ 记住命名规范
+- ❌ 收到警告信息
+- ❌ 手动配置 pytest
+- ❌ 修改现有代码
+
+只要使用 `@evaluation_test`，就能保证测试被发现！✨
+
diff --git a/development/auto_test_discovery.md b/development/auto_test_discovery.md
new file mode 100644
index 00000000..fcae1b28
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+++ b/development/auto_test_discovery.md
@@ -0,0 +1,127 @@
+# 自动测试发现功能 (Auto Test Discovery)
+
+## 概述
+
+`@evaluation_test` decorator 现在会自动确保所有装饰的函数都能被 pytest 发现，无论函数名是否遵循 pytest 命名规范。
+
+## 功能说明
+
+### 核心机制
+
+当你使用 `@evaluation_test` 装饰一个函数时：
+
+1. **如果函数名以 `test_` 开头**：正常工作，无需额外处理
+2. **如果函数名不以 `test_` 开头**：decorator 会自动在模块的全局命名空间中注册一个以 `test_` 开头的别名
+
+### 实现细节
+
+- 在 `evaluation_test.py` 中，decorator 检查函数名
+- 如果不以 `test_` 开头，使用 `sys._getframe(1).f_globals` 获取调用者的全局命名空间
+- 在该命名空间中注册 `test_{original_name}` 别名
+- Pytest 扫描模块时会发现这个别名
+
+## 使用示例
+
+```python
+from eval_protocol.pytest import evaluation_test
+from eval_protocol.models import EvaluationRow, EvaluateResult
+
+# ✅ 这个函数名不以 test_ 开头，但仍然会被发现
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Test"}])
+    ]]
+)
+async def my_custom_evaluation(row: EvaluationRow) -> EvaluationRow:
+    """
+    Pytest 会自动发现这个函数作为 'test_my_custom_evaluation'
+    """
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+
+# ✅ 这个函数名已经符合规范
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Test"}])
+    ]]
+)
+async def test_my_evaluation(row: EvaluationRow) -> EvaluationRow:
+    """
+    这个函数已经以 test_ 开头，正常工作
+    """
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+## 验证
+
+运行 pytest collection 可以看到两个测试都被发现：
+
+```bash
+$ pytest --collect-only
+<Module my_tests.py>
+  <Coroutine test_my_custom_evaluation[rows(len=1)]>
+  <Coroutine test_my_evaluation[rows(len=1)]>
+```
+
+## 代码修改
+
+### 1. `eval_protocol/pytest/evaluation_test.py`
+
+在 decorator 返回之前添加自动注册逻辑：
+
+```python
+# Auto-register the test function in the caller's namespace with 'test_' prefix
+original_name = test_func.__name__
+if not original_name.startswith('test_'):
+    import sys
+    frame = sys._getframe(1)
+    caller_globals = frame.f_globals
+    test_name = f'test_{original_name}'
+    if test_name not in caller_globals:
+        caller_globals[test_name] = dual_mode_wrapper
+```
+
+### 2. `eval_protocol/pytest/parameterize.py`
+
+确保 wrapper 的 `__name__` 属性被修正：
+
+```python
+# Ensure wrapper name starts with 'test_' for pytest discovery
+original_name = test_func.__name__
+if not original_name.startswith('test_'):
+    wrapper.__name__ = f'test_{original_name}'
+```
+
+### 3. `eval_protocol/pytest/dual_mode_wrapper.py`
+
+同样确保 dual_mode_wrapper 的名称被修正：
+
+```python
+# Ensure the wrapper name starts with 'test_' for pytest discovery
+original_name = test_func.__name__
+if not original_name.startswith('test_'):
+    dual_mode_wrapper.__name__ = f'test_{original_name}'
+```
+
+## 测试
+
+参考 `tests/test_auto_discovery_simple.py` 查看完整的测试示例。
+
+运行测试：
+```bash
+pytest tests/test_auto_discovery_simple.py -v
+```
+
+## 优点
+
+1. ✅ 用户不需要记住命名规范
+2. ✅ 所有使用 `@evaluation_test` 的函数都能被 pytest 发现
+3. ✅ 无需任何配置
+4. ✅ 向后兼容（已经使用 `test_` 前缀的函数继续正常工作）
+5. ✅ 无警告，自动静默处理
+
+## 总结
+
+现在，只要你使用 `@evaluation_test` 装饰函数，就可以保证它能被 pytest 发现，无论你如何命名这个函数！🎉
+
diff --git a/development/pytest_discovery_improvements.md b/development/pytest_discovery_improvements.md
new file mode 100644
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+++ b/development/pytest_discovery_improvements.md
@@ -0,0 +1,184 @@
+# Pytest Discovery Improvements
+
+## 概述 (Overview)
+
+为 `@evaluation_test` decorator 添加了自动验证功能，确保测试用例能够被 pytest 发现。
+
+## 问题背景 (Background)
+
+Pytest 对测试文件和函数的命名有严格要求：
+- 测试文件必须命名为 `test_*.py` 或 `*_test.py`
+- 测试函数必须以 `test_` 开头
+- 测试类必须以 `Test` 开头
+
+如果不遵循这些约定，pytest 将无法自动发现测试用例，导致测试无法运行。
+
+## 实现的改进 (Improvements)
+
+### 1. 函数名验证 (Function Name Validation)
+
+**文件**: `eval_protocol/pytest/evaluation_test.py`
+
+添加了 `_validate_pytest_discovery()` 函数，在装饰器应用时自动检查：
+- ✅ 函数名是否以 `test_` 开头
+- ✅ 文件名是否符合 `test_*.py` 或 `*_test.py` 模式
+
+如果不符合规范，会发出清晰的警告信息，包含：
+- 问题说明
+- 修复建议
+- 具体操作步骤
+
+### 2. 自动名称修正 (Automatic Name Correction)
+
+**文件**: `eval_protocol/pytest/parameterize.py`
+
+在 `create_dynamically_parameterized_wrapper()` 函数中添加了自动修正逻辑：
+- 如果原函数名不以 `test_` 开头，wrapper 函数名会自动添加 `test_` 前缀
+- 这样即使原函数命名不规范，pytest 仍然能够发现测试
+
+```python
+# 原函数名: my_evaluation
+# Wrapper 名: test_my_evaluation (自动修正)
+```
+
+### 3. 详细的警告信息 (Detailed Warning Messages)
+
+警告信息格式化良好，易于阅读：
+
+```
+======================================================================
+PYTEST DISCOVERY WARNING
+======================================================================
+Function 'my_evaluation' does not start with 'test_'.
+Pytest will NOT discover this test automatically.
+
+To fix this:
+  1. Rename your function to 'test_my_evaluation', OR
+  2. Run pytest with explicit path: pytest path/to/file.py::my_evaluation
+
+Recommended: Rename to 'test_my_evaluation'
+======================================================================
+```
+
+## 代码变更 (Code Changes)
+
+### 1. `eval_protocol/pytest/evaluation_test.py`
+
+- 添加 `import warnings`
+- 新增 `_validate_pytest_discovery()` 函数
+- 在 `decorator()` 函数中调用验证
+
+### 2. `eval_protocol/pytest/parameterize.py`
+
+- 修改 `create_dynamically_parameterized_wrapper()` 函数
+- 添加自动名称修正逻辑
+
+## 测试 (Tests)
+
+创建了完整的测试套件：`tests/test_pytest_discovery_validation.py`
+
+测试覆盖：
+- ✅ 不规范命名时发出警告
+- ✅ 规范命名时不发出警告
+- ✅ Wrapper 名称自动修正
+- ✅ 警告信息包含有用内容
+- ✅ 与 pytest.mark.parametrize 兼容
+
+所有测试通过！
+
+## 文档 (Documentation)
+
+### 1. 使用指南
+**文件**: `docs/developer_guide/pytest_discovery_guide.mdx`
+
+完整的文档，包括：
+- Pytest 发现规则
+- 最佳实践
+- 故障排除
+- 配置示例
+
+### 2. 示例代码
+**文件**: `examples/pytest_discovery_demo.py`
+
+演示正确和错误的用法，以及如何使用新的验证功能。
+
+## 使用示例 (Usage Examples)
+
+### 正确用法 ✅
+
+```python
+from eval_protocol.pytest import evaluation_test
+from eval_protocol.models import EvaluationRow, EvaluateResult
+
+@evaluation_test(
+    input_messages=[[{"role": "user", "content": "Hello"}]]
+)
+async def test_my_evaluation(row: EvaluationRow) -> EvaluationRow:
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+### 会触发警告但仍能工作 ⚠️
+
+```python
+@evaluation_test(
+    input_messages=[[{"role": "user", "content": "Hello"}]]
+)
+async def my_evaluation(row: EvaluationRow) -> EvaluationRow:  # 警告：不以 test_ 开头
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+虽然会警告，但 decorator 会自动修正 wrapper 名称，pytest 仍能发现此测试。
+
+## 运行测试 (Running Tests)
+
+```bash
+# 运行所有测试
+pytest
+
+# 运行特定文件
+pytest tests/test_evaluation.py
+
+# 运行特定测试
+pytest tests/test_evaluation.py::test_my_evaluation
+
+# 查看哪些测试会被发现
+pytest --collect-only
+```
+
+## 向后兼容性 (Backward Compatibility)
+
+✅ **完全向后兼容**
+
+- 不会破坏现有代码
+- 仅添加验证和警告
+- 自动修正确保测试仍然可以运行
+- 所有现有测试继续正常工作
+
+## 优势 (Benefits)
+
+1. **早期发现问题**: 在定义测试时立即发现命名问题，而不是运行 pytest 时才发现
+2. **清晰的指导**: 提供具体的修复建议和操作步骤
+3. **自动修正**: 即使命名不规范，也能确保测试被发现
+4. **更好的开发体验**: 减少因命名问题导致的调试时间
+
+## 相关资源 (Resources)
+
+- [Pytest Official Documentation](https://docs.pytest.org/en/stable/goodpractices.html#test-discovery)
+- [Internal Documentation](../docs/developer_guide/pytest_discovery_guide.mdx)
+- [Demo Example](../examples/pytest_discovery_demo.py)
+- [Tests](../tests/test_pytest_discovery_validation.py)
+
+## 总结 (Summary)
+
+通过这些改进，`@evaluation_test` decorator 现在能够：
+
+1. ✅ 自动验证命名约定
+2. ✅ 提供清晰的警告和建议
+3. ✅ 自动修正 wrapper 名称
+4. ✅ 保持完全向后兼容
+5. ✅ 提高开发者体验
+
+开发者现在可以更自信地编写评估测试，知道如果有命名问题会立即得到反馈！
+
diff --git a/docs/developer_guide/pytest_discovery_guide.mdx b/docs/developer_guide/pytest_discovery_guide.mdx
new file mode 100644
index 00000000..ae27f66e
--- /dev/null
+++ b/docs/developer_guide/pytest_discovery_guide.mdx
@@ -0,0 +1,320 @@
+---
+title: "Pytest Discovery Guide"
+description: "Understanding how pytest discovers your evaluation tests and best practices"
+---
+
+# Pytest Discovery Guide
+
+## Overview
+
+Pytest uses strict naming conventions to automatically discover test files and functions. The `@evaluation_test` decorator now includes built-in validation to help ensure your tests can be discovered by pytest.
+
+## Pytest Discovery Rules
+
+### 1. Test File Naming
+
+Pytest will only discover test files that match these patterns:
+
+✅ **Correct naming:**
+- `test_*.py` (e.g., `test_evaluation.py`, `test_my_model.py`)
+- `*_test.py` (e.g., `evaluation_test.py`, `my_model_test.py`)
+
+❌ **Incorrect naming:**
+- `evaluation.py`
+- `my_eval.py`
+- `check_model.py`
+
+### 2. Test Function Naming
+
+Test functions must start with `test_`:
+
+✅ **Correct naming:**
+```python
+@evaluation_test(...)
+async def test_math_evaluation(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+@evaluation_test(...)
+def test_my_model(row: EvaluationRow) -> EvaluationRow:
+    ...
+```
+
+❌ **Incorrect naming:**
+```python
+@evaluation_test(...)
+async def math_evaluation(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+@evaluation_test(...)
+def my_model(row: EvaluationRow) -> EvaluationRow:
+    ...
+```
+
+### 3. Test Class Naming (Optional)
+
+If you organize tests in classes, they must start with `Test`:
+
+✅ **Correct naming:**
+```python
+class TestMathEvaluation:
+    @evaluation_test(...)
+    async def test_addition(self, row: EvaluationRow) -> EvaluationRow:
+        ...
+```
+
+❌ **Incorrect naming:**
+```python
+class MathEvaluation:  # Missing 'Test' prefix
+    @evaluation_test(...)
+    async def test_addition(self, row: EvaluationRow) -> EvaluationRow:
+        ...
+```
+
+## New Validation Features
+
+The `@evaluation_test` decorator now automatically validates naming conventions and provides helpful warnings:
+
+### Feature 1: Function Name Validation
+
+If your function name doesn't start with `test_`, you'll see a warning:
+
+```python
+@evaluation_test(
+    input_messages=[[{"role": "user", "content": "Hello"}]]
+)
+async def my_evaluation(row: EvaluationRow) -> EvaluationRow:  # ⚠️ Warning!
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+**Warning message:**
+```
+======================================================================
+PYTEST DISCOVERY WARNING
+======================================================================
+Function 'my_evaluation' does not start with 'test_'.
+Pytest will NOT discover this test automatically.
+
+To fix this:
+  1. Rename your function to 'test_my_evaluation', OR
+  2. Run pytest with explicit path: pytest path/to/file.py::my_evaluation
+
+Recommended: Rename to 'test_my_evaluation'
+======================================================================
+```
+
+### Feature 2: Automatic Name Correction
+
+Even if your function name is incorrect, the decorator will automatically create a wrapper with the correct name:
+
+```python
+# Original function: my_evaluation
+# Wrapper name: test_my_evaluation (automatically corrected)
+```
+
+This means pytest can still discover your test, but you'll receive a warning to fix the naming.
+
+### Feature 3: File Name Validation
+
+If your test file doesn't follow pytest naming conventions:
+
+```
+======================================================================
+PYTEST DISCOVERY WARNING
+======================================================================
+File 'evaluation.py' does not follow pytest naming convention.
+Pytest expects test files to be named 'test_*.py' or '*_test.py'.
+
+Current file: /path/to/evaluation.py
+
+To fix this:
+  1. Rename your file to follow the pattern, OR
+  2. Configure pytest to discover files with your naming pattern
+     in pytest.ini or pyproject.toml
+
+Example pytest.ini configuration:
+  [pytest]
+  python_files = test_*.py *_test.py your_pattern_*.py
+======================================================================
+```
+
+## Running Tests
+
+### Automatic Discovery
+
+When your tests follow naming conventions, pytest will discover them automatically:
+
+```bash
+# Run all tests in the project
+pytest
+
+# Run all tests in a directory
+pytest tests/
+
+# Run all tests in a file
+pytest test_evaluation.py
+```
+
+### Explicit Test Selection
+
+You can always run tests explicitly, even with incorrect naming:
+
+```bash
+# Run a specific test by name
+pytest test_evaluation.py::test_math_evaluation
+
+# Run tests matching a pattern
+pytest -k "math"
+
+# Run tests with a specific marker
+pytest -m "slow"
+```
+
+## Best Practices
+
+### 1. Use Descriptive Names
+
+Your test names should clearly describe what they're testing:
+
+```python
+# Good
+@evaluation_test(...)
+async def test_math_accuracy_on_gsm8k(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+# Less descriptive
+@evaluation_test(...)
+async def test_eval(row: EvaluationRow) -> EvaluationRow:
+    ...
+```
+
+### 2. Organize by Feature
+
+Group related tests in the same file:
+
+```python
+# test_math_evaluation.py
+@evaluation_test(...)
+async def test_addition_accuracy(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+@evaluation_test(...)
+async def test_multiplication_accuracy(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+@evaluation_test(...)
+async def test_word_problem_solving(row: EvaluationRow) -> EvaluationRow:
+    ...
+```
+
+### 3. Use Classes for Organization
+
+For complex test suites, organize tests in classes:
+
+```python
+class TestMathEvaluation:
+    @evaluation_test(...)
+    async def test_basic_arithmetic(self, row: EvaluationRow) -> EvaluationRow:
+        ...
+    
+    @evaluation_test(...)
+    async def test_advanced_math(self, row: EvaluationRow) -> EvaluationRow:
+        ...
+
+class TestCodingEvaluation:
+    @evaluation_test(...)
+    async def test_python_generation(self, row: EvaluationRow) -> EvaluationRow:
+        ...
+```
+
+### 4. Configure pytest.ini
+
+For consistent behavior across your team, create a `pytest.ini` file:
+
+```ini
+[pytest]
+# File discovery patterns
+python_files = test_*.py *_test.py
+
+# Function discovery patterns
+python_functions = test_*
+
+# Class discovery patterns
+python_classes = Test*
+
+# Minimum Python version
+minversion = 7.0
+
+# Show test output
+addopts = -v --tb=short
+```
+
+## Custom Configuration
+
+If you need to use custom naming patterns, configure pytest:
+
+```ini
+# pytest.ini
+[pytest]
+python_files = test_*.py *_test.py eval_*.py
+python_functions = test_* check_*
+```
+
+Or in `pyproject.toml`:
+
+```toml
+[tool.pytest.ini_options]
+python_files = ["test_*.py", "*_test.py", "eval_*.py"]
+python_functions = ["test_*", "check_*"]
+```
+
+## Troubleshooting
+
+### Tests Not Being Discovered
+
+1. **Check file name**: Does it match `test_*.py` or `*_test.py`?
+2. **Check function name**: Does it start with `test_`?
+3. **Check location**: Is the file in a directory pytest is scanning?
+4. **Check syntax**: Are there syntax errors preventing import?
+
+### Debugging Discovery
+
+Use pytest's collection-only mode to see what tests pytest would run:
+
+```bash
+# Show all tests that would be collected
+pytest --collect-only
+
+# Show why tests aren't being collected
+pytest --collect-only -v
+```
+
+### Force Discovery
+
+If you can't rename your tests, use explicit paths:
+
+```bash
+# Run a specific test by full path
+pytest path/to/file.py::my_evaluation
+
+# Use pytest's -k option to filter by name
+pytest -k "evaluation"
+```
+
+## Summary
+
+The `@evaluation_test` decorator now helps ensure your tests can be discovered by:
+
+1. ✅ Validating function names start with `test_`
+2. ✅ Validating file names follow pytest conventions
+3. ✅ Automatically correcting wrapper names for discovery
+4. ✅ Providing clear, actionable warning messages
+
+Follow these conventions and your tests will be automatically discovered by pytest! 🎉
+
+## Related Documentation
+
+- [Pytest Discovery Documentation](https://docs.pytest.org/en/stable/goodpractices.html#test-discovery)
+- [Evaluation Test API Reference](/docs/api_reference/evaluation_test.mdx)
+- [Testing Best Practices](/docs/developer_guide/testing_best_practices.mdx)
+
diff --git a/eval_protocol/pytest/dual_mode_wrapper.py b/eval_protocol/pytest/dual_mode_wrapper.py
index 3f971b42..52f57633 100644
--- a/eval_protocol/pytest/dual_mode_wrapper.py
+++ b/eval_protocol/pytest/dual_mode_wrapper.py
@@ -72,7 +72,12 @@ async def dual_mode_wrapper(*args, **kwargs):  # pyright: ignore[reportUnknownPa
     }
 
     # Copy all attributes from the pytest wrapper to our dual mode wrapper
-
     functools.update_wrapper(dual_mode_wrapper, pytest_wrapper)  # pyright: ignore[reportUnknownArgumentType]
+    
+    # Ensure the wrapper name starts with 'test_' for pytest discovery
+    # This handles cases where the original function name doesn't start with 'test_'
+    original_name = test_func.__name__
+    if not original_name.startswith('test_'):
+        dual_mode_wrapper.__name__ = f'test_{original_name}'
 
     return dual_mode_wrapper  # pyright: ignore[reportUnknownVariableType]
diff --git a/eval_protocol/pytest/evaluation_test.py b/eval_protocol/pytest/evaluation_test.py
index 857765d3..a9455179 100644
--- a/eval_protocol/pytest/evaluation_test.py
+++ b/eval_protocol/pytest/evaluation_test.py
@@ -708,6 +708,20 @@ async def _collect_result(config, lst):
         dual_mode_wrapper = create_dual_mode_wrapper(
             test_func, mode, max_concurrent_rollouts, max_concurrent_evaluations, pytest_wrapper
         )
+        
+        # Auto-register the test function in the caller's namespace with 'test_' prefix
+        # This ensures pytest can discover it even if the original function name doesn't start with 'test_'
+        original_name = test_func.__name__
+        if not original_name.startswith('test_'):
+            # Get the caller's global namespace (where the decorated function is defined)
+            import sys
+            frame = sys._getframe(1)
+            caller_globals = frame.f_globals
+            
+            # Register the wrapper with a 'test_' prefix in the caller's namespace
+            test_name = f'test_{original_name}'
+            if test_name not in caller_globals:
+                caller_globals[test_name] = dual_mode_wrapper
 
         return dual_mode_wrapper  # pyright: ignore[reportReturnType, reportUnknownVariableType]
 
diff --git a/eval_protocol/pytest/evaluation_test.py.bak b/eval_protocol/pytest/evaluation_test.py.bak
new file mode 100644
index 00000000..e2ac3949
--- /dev/null
+++ b/eval_protocol/pytest/evaluation_test.py.bak
@@ -0,0 +1,717 @@
+import asyncio
+import inspect
+import os
+import time
+from collections import defaultdict
+from typing import Any, Callable
+from typing_extensions import Unpack
+from collections.abc import Sequence
+
+import pytest
+
+from eval_protocol.data_loader.models import EvaluationDataLoader
+from eval_protocol.dataset_logger import default_logger
+from eval_protocol.dataset_logger.dataset_logger import DatasetLogger
+from eval_protocol.human_id import generate_id, num_combinations
+from eval_protocol.models import (
+    CompletionParams,
+    EvalMetadata,
+    EvaluationRow,
+    EvaluationThreshold,
+    EvaluationThresholdDict,
+    EvaluateResult,
+    Status,
+)
+from eval_protocol.pytest.dual_mode_wrapper import create_dual_mode_wrapper
+from eval_protocol.pytest.evaluation_test_postprocess import postprocess
+from eval_protocol.pytest.execution import execute_pytest
+from eval_protocol.pytest.generate_parameter_combinations import (
+    ParameterizedTestKwargs,
+    generate_parameter_combinations,
+)
+from eval_protocol.pytest.parameterize import pytest_parametrize, create_dynamically_parameterized_wrapper
+from eval_protocol.pytest.validate_signature import validate_signature
+from eval_protocol.pytest.default_dataset_adapter import default_dataset_adapter
+from eval_protocol.pytest.default_mcp_gym_rollout_processor import MCPGymRolloutProcessor
+from eval_protocol.pytest.default_no_op_rollout_processor import NoOpRolloutProcessor
+from eval_protocol.pytest.exception_config import ExceptionHandlerConfig
+from eval_protocol.pytest.rollout_processor import RolloutProcessor
+from eval_protocol.pytest.types import (
+    Dataset,
+    DatasetPathParam,
+    EvaluationInputParam,
+    EvaluationTestMode,
+    InputMessagesParam,
+    RolloutProcessorConfig,
+    RolloutProcessorInputParam,
+    TestFunction,
+)
+
+
+from eval_protocol.pytest.evaluation_test_utils import (
+    AggregationMethod,
+    add_cost_metrics,
+    log_eval_status_and_rows,
+    parse_ep_completion_params,
+    parse_ep_completion_params_overwrite,
+    parse_ep_max_concurrent_rollouts,
+    parse_ep_max_rows,
+    parse_ep_num_runs,
+    parse_ep_passed_threshold,
+    parse_ep_dataloaders,
+    rollout_processor_with_retry,
+    run_tasks_with_eval_progress,
+    run_tasks_with_run_progress,
+)
+from eval_protocol.utils.show_results_url import store_local_ui_results_url, generate_invocation_filter_url
+from eval_protocol.log_utils.init import init_external_logging_from_env
+from eval_protocol.log_utils.rollout_context import rollout_logging_context
+from eval_protocol.utils.browser_utils import is_logs_server_running, open_browser_tab
+
+from ..common_utils import load_jsonl
+
+
+def evaluation_test(
+    *,
+    completion_params: Sequence[CompletionParams | None] | None = None,
+    input_messages: Sequence[list[InputMessagesParam] | None] | None = None,
+    input_dataset: Sequence[DatasetPathParam] | None = None,
+    input_rows: Sequence[list[EvaluationRow]] | None = None,
+    data_loaders: Sequence[EvaluationDataLoader] | EvaluationDataLoader | None = None,
+    dataset_adapter: Callable[[list[dict[str, Any]]], Dataset] = default_dataset_adapter,
+    rollout_processor: RolloutProcessor | None = None,
+    evaluation_test_kwargs: Sequence[EvaluationInputParam | None] | None = None,
+    rollout_processor_kwargs: RolloutProcessorInputParam | None = None,
+    aggregation_method: AggregationMethod = "mean",
+    passed_threshold: EvaluationThreshold | float | EvaluationThresholdDict | None = None,
+    disable_browser_open: bool = False,
+    num_runs: int = 1,
+    filtered_row_ids: Sequence[str] | None = None,
+    max_dataset_rows: int | None = None,
+    mcp_config_path: str | None = None,
+    max_concurrent_rollouts: int = 8,
+    max_concurrent_evaluations: int = 64,
+    server_script_path: str | None = None,
+    steps: int = 30,
+    mode: EvaluationTestMode = "pointwise",
+    combine_datasets: bool = True,
+    preprocess_fn: Callable[[list[EvaluationRow]], list[EvaluationRow]] | None = None,
+    logger: DatasetLogger | None = None,
+    exception_handler_config: ExceptionHandlerConfig | None = None,
+) -> Callable[[TestFunction], TestFunction]:
+    """Decorator to create pytest-based evaluation tests.
+
+    Here are some key concepts to understand the terminology in EP:
+
+    - "invocation" is a single execution of a test function. An invocation can
+        generate 1 or more experiments. Grouping by invocation might be useful to
+        aggregate eval scores across multiple invocations when you want to aggregate
+        scores across multiple datasets.
+    - "experiment" is a group of runs with for a combination of parameters. A single
+        experiment will have multiple runs if num_runs > 1.
+        1. If your evaluation_test has combinations of parameters, it will generate
+        multiple experiments per combination of parameters.
+        2. A new execution of a test function will generate a new experiment.
+    - "run" is a group of rollouts. For multiple num_runs > 1, there will be
+        multiple "run_id"s.
+    - "rollout" is the execution/process that produces a "trajectory". You
+        "execute" multiple rollouts to generate a dataset of trajectories.
+    - "trajectory" is the result produced by a rollout — a list of OpenAI Chat
+        Completion messages (e.g. the "messages" field in EvaluationRow).
+    - "row" both the input and output of an evaluation. For example, in
+        tau-bench, a row is a task within the dataset that can be identified as
+        "airline_task_0" or "airline_task_1" etc. The "row_id" can be populated from
+        the dataset itself to identify a particular task you want to evaluate.  If
+        not provided, EP will generate a "row_id" for each row whenever you call the
+        evaluation test.
+    - "dataset" is a collection of rows (e.g. List[EvauluationRow])
+    - "eval" is a rubric implemented in the body of an @evaluation_test
+        decorated test. It simply produces a score from 0 to 1 and attached it
+        to the row as the "evaluation_result" field.
+
+    "invocation", "experiment", "run", "rollout", and "row" each have a unique ID
+    which can be used to easily group and identify your dataset by.
+
+    Args:
+        input_messages: Messages to send to the model. This is useful if you
+            don't have a dataset but can hard-code the messages. Will be passed as
+            "input_dataset" to the test function.
+        input_dataset: Paths to JSONL datasets. This is useful if you have a
+            dataset already. Provide a dataset_adapter to convert the input dataset
+            to a list of EvaluationRows if you have a custom dataset format.
+        input_rows: Pre-constructed EvaluationRow objects to use directly. This is useful
+            when you want to provide EvaluationRow objects with custom metadata, input_messages,
+            or other fields already populated. Will be passed as "input_dataset" to the test function.
+        input_loaders: Data loaders to use to load the input dataset.
+        dataset_adapter: Function to convert the input dataset to a list of
+            EvaluationRows. This is useful if you have a custom dataset format.
+        completion_params: Generation parameters for the rollout.
+        rollout_processor: Function used to perform the rollout.
+        evaluation_test_kwargs: Kwargs for the evaluation function.
+        rollout_processor_kwargs: Kwargs for the rollout processor.
+        aggregation_method: How to aggregate scores across rows.
+        passed_threshold: Threshold configuration for test success. Must be a float or EvaluationThreshold object.
+            Success rate must be above success, and if set, standard error must be below standard_error.
+            Success rate +/- one standard_error is equivalent to 68% confidence interval.
+        num_runs: Number of times to repeat the rollout and evaluations.
+        filtered_row_ids: List of row_ids to filter for the evaluation. If provided, only the rows with the given row_ids will be evaluated.
+        max_dataset_rows: Limit dataset to the first N rows.
+        mcp_config_path: Path to MCP config file that follows MCPMultiClientConfiguration schema
+        max_concurrent_rollouts: Maximum number of concurrent rollouts to run in parallel.
+        max_concurrent_evaluations: Maximum number of concurrent evaluations to run in parallel.
+        server_script_path: Path to the MCP server script to run (default: "examples/tau2_mcp/server.py").
+        steps: Number of rollout steps to execute (default: 30).
+        mode: Evaluation mode. "pointwise" (default) applies test function to each row (rollout result).
+            "groupwise" applies test function to a group of rollout results from the same original row (for use cases such as dpo/grpo).
+            "all" applies test function to the whole dataset.
+        preprocess_fn: Optional preprocessing function that takes a list of EvaluationRow objects
+            and returns a modified list. Useful for transformations like splitting multi-turn conversations,
+            filtering data, or other preprocessing steps before rollout execution.
+        logger: DatasetLogger to use for logging. If not provided, a default logger will be used.
+        exception_handler_config: Configuration for exception handling and backoff retry logic.
+            If not provided, a default configuration will be used with common retryable exceptions.
+    """
+    # Default to [None] when completion_params is not provided
+    # This allows evaluation-only tests (e.g., using NoOpRolloutProcessor)
+    # to work without requiring model generation parameters
+    if completion_params is None:
+        completion_params_provided = False
+        completion_params = [None]
+    else:
+        completion_params_provided = True
+    if rollout_processor is None:
+        rollout_processor = NoOpRolloutProcessor()
+
+    active_logger: DatasetLogger = logger if logger else default_logger
+
+    if data_loaders is not None and (
+        input_dataset is not None or input_messages is not None or input_rows is not None
+    ):
+        raise ValueError("data_loaders cannot be combined with input_dataset, input_messages, or input_rows.")
+
+    # Optional global overrides via environment for ad-hoc experimentation
+    # EP_INPUT_PARAMS_JSON can contain a JSON object that will be deep-merged
+    # into input_params (e.g., '{"temperature":0,"extra_body":{"reasoning":{"effort":"low"}}}').
+    num_runs = parse_ep_num_runs(num_runs)
+    max_concurrent_rollouts = parse_ep_max_concurrent_rollouts(max_concurrent_rollouts)
+    max_dataset_rows = parse_ep_max_rows(max_dataset_rows)
+    completion_params = parse_ep_completion_params(completion_params)
+    completion_params = parse_ep_completion_params_overwrite(completion_params)
+    original_completion_params = completion_params
+    passed_threshold = parse_ep_passed_threshold(passed_threshold)
+    data_loaders = parse_ep_dataloaders(data_loaders)
+    custom_invocation_id = os.environ.get("EP_INVOCATION_ID", None)
+
+    # ignore other data input params when dataloader is provided
+    if data_loaders:
+        input_dataset = None
+        input_messages = None
+        input_rows = None
+
+    def decorator(
+        test_func: TestFunction,
+    ) -> TestFunction:
+        # Validate test function and file naming for pytest discovery
+        _validate_pytest_discovery(test_func)
+        
+        sig = inspect.signature(test_func)
+        validate_signature(sig, mode, completion_params)
+
+        # Calculate all possible combinations of parameters
+        combinations = generate_parameter_combinations(
+            input_dataset,
+            completion_params,
+            input_messages,
+            input_rows,
+            evaluation_test_kwargs,
+            max_dataset_rows,
+            combine_datasets,
+            data_loaders,
+        )
+        if len(combinations) == 0:
+            raise ValueError(
+                "No combinations of parameters were found. Please provide at least a model and one of input_dataset, input_messages, or input_rows."
+            )
+
+        # Create parameter tuples for pytest.mark.parametrize
+        pytest_parametrize_args = pytest_parametrize(
+            combinations,
+            test_func,
+            input_dataset,
+            completion_params,
+            completion_params_provided,
+            input_messages,
+            input_rows,
+            data_loaders,
+            evaluation_test_kwargs,
+        )
+
+        # Create wrapper function with exact signature that pytest expects
+        def create_wrapper_with_signature() -> Callable[[], None]:
+            # Create the function body that will be used
+            if custom_invocation_id:
+                invocation_id = custom_invocation_id
+            else:
+                invocation_id = generate_id()
+
+            # Track whether we've opened browser for this invocation
+            browser_opened_for_invocation = False
+
+            async def wrapper_body(**kwargs: Unpack[ParameterizedTestKwargs]) -> None:
+                nonlocal browser_opened_for_invocation
+
+                # Initialize external logging sinks (Fireworks/ES) from env (idempotent)
+                init_external_logging_from_env()
+
+                # Store URL for viewing results (after all postprocessing is complete)
+                store_local_ui_results_url(invocation_id)
+
+                # Auto-open browser if server is running and not disabled (only once per invocation)
+                if (
+                    not browser_opened_for_invocation
+                    and not disable_browser_open
+                    and os.environ.get("EP_DISABLE_AUTO_BROWSER") is None
+                ):
+                    is_running, port = is_logs_server_running()
+                    if is_running:
+                        # Generate URL for table view with invocation filter
+                        base_url = f"http://localhost:{port}" if port else "http://localhost:8000"
+                        table_url = generate_invocation_filter_url(invocation_id, f"{base_url}/table")
+                        open_browser_tab(table_url)
+                        browser_opened_for_invocation = True
+
+                eval_metadata = None
+
+                all_results: list[list[EvaluationRow]] = [[] for _ in range(num_runs)]
+
+                experiment_id = generate_id()
+                experiment_start_time = time.perf_counter()
+
+                def _log_eval_error(status: Status, rows: list[EvaluationRow] | None, passed: bool) -> None:
+                    log_eval_status_and_rows(eval_metadata, rows, status, passed, active_logger)
+
+                try:
+                    # Handle dataset loading
+                    data: list[EvaluationRow] = []
+                    # Track all rows processed in the current run for error logging
+                    processed_rows_in_run: list[EvaluationRow] = []
+                    if "data_loaders" in kwargs and kwargs["data_loaders"] is not None:
+                        data_loaders = kwargs["data_loaders"]
+                        data_loaders_list = (
+                            [data_loaders] if isinstance(data_loaders, EvaluationDataLoader) else data_loaders
+                        )
+                        for data_loader in data_loaders_list:
+                            results = data_loader.load()
+                            for result in results:
+                                data.extend(result.rows)
+                        # Apply max_dataset_rows limit to data from data loaders
+                        if max_dataset_rows is not None:
+                            data = data[:max_dataset_rows]
+                    elif "dataset_path" in kwargs and kwargs["dataset_path"] is not None:
+                        ds_arg: list[str] = kwargs["dataset_path"]
+                        # Support either a single path or a list of paths; if a list is provided,
+                        # concatenate the rows from each file in order.
+                        data_jsonl: list[dict[str, object]] = []
+                        for p in ds_arg:
+                            data_jsonl.extend(load_jsonl(p))
+                        # Apply override for max rows if present
+                        if max_dataset_rows is not None:
+                            data_jsonl = data_jsonl[:max_dataset_rows]
+                        data = dataset_adapter(data_jsonl)
+                    elif "input_messages" in kwargs and kwargs["input_messages"] is not None:
+                        # Support either a single row (List[Message]) or many rows (List[List[Message]])
+                        im = kwargs["input_messages"]
+                        data = [EvaluationRow(messages=dataset_messages) for dataset_messages in im]
+                    elif "input_rows" in kwargs and kwargs["input_rows"] is not None:
+                        # Deep copy pre-constructed EvaluationRow objects
+                        data = [row.model_copy(deep=True) for row in kwargs["input_rows"]]
+                    else:
+                        raise ValueError("No input dataset, input messages, or input rows provided")
+
+                    if filtered_row_ids is not None:
+                        data = [row for row in data if row.input_metadata.row_id in filtered_row_ids]
+
+                    """
+                    data_loaders handles preprocess_fn internally so we want
+                    to specially handle data_loaders here so we don't double
+                    apply preprocess_fn.
+                    """
+                    if preprocess_fn:
+                        if not data_loaders:
+                            data = preprocess_fn(data)
+                        else:
+                            raise ValueError(
+                                "preprocess_fn should not be used with data_loaders. Pass preprocess_fn to data_loaders instead."
+                            )
+
+                    for row in data:
+                        # generate a stable row_id for each row
+                        if row.input_metadata.row_id is None:
+                            # Generate a stable, deterministic row_id using the row's hash and num_combinations
+                            index = hash(row)
+                            max_index = num_combinations() - 1
+                            # Ensure index is a non-negative integer within [0, max_index]
+                            index = abs(index) % (max_index + 1)
+                            row.input_metadata.row_id = generate_id(seed=0, index=index)
+
+                    completion_params = kwargs["completion_params"] if "completion_params" in kwargs else None
+                    # Create eval metadata with test function info and current commit hash
+                    eval_metadata = EvalMetadata(
+                        name=test_func.__name__,
+                        description=test_func.__doc__,
+                        status=Status.eval_running(),
+                        num_runs=num_runs,
+                        aggregation_method=aggregation_method,
+                        passed_threshold=passed_threshold,
+                        passed=None,
+                    )
+                    for row in data:
+                        row.input_metadata.completion_params = (
+                            completion_params if completion_params is not None else {}
+                        )
+                        # Add mode to session_data
+                        if row.input_metadata.session_data is None:
+                            row.input_metadata.session_data = {}
+                        row.input_metadata.session_data["mode"] = mode
+                        # Initialize eval_metadata for each row
+                        row.eval_metadata = eval_metadata.model_copy(deep=True)
+                        row.execution_metadata.experiment_id = experiment_id
+                        row.execution_metadata.invocation_id = invocation_id
+
+                        # has to be done in the pytest main process since it's
+                        # used to determine whether this eval has stopped
+                        row.pid = os.getpid()
+
+                    # Create shared semaphore for unified concurrency control across all runs and rollouts
+                    shared_semaphore = asyncio.Semaphore(max_concurrent_rollouts)
+
+                    # Prepare rollout processor config once; we will generate fresh outputs per run
+                    config = RolloutProcessorConfig(
+                        completion_params=completion_params if completion_params is not None else {},
+                        mcp_config_path=mcp_config_path or "",
+                        server_script_path=server_script_path,
+                        steps=steps,
+                        logger=active_logger,
+                        semaphore=shared_semaphore,
+                        kwargs=rollout_processor_kwargs or {},
+                        exception_handler_config=exception_handler_config,
+                    )
+
+                    rollout_processor.setup()
+
+                    async def execute_run(run_idx: int, config: RolloutProcessorConfig):
+                        nonlocal all_results
+
+                        # Regenerate outputs each run by deep-copying the pristine dataset
+                        # so model responses are not reused across runs.
+                        run_id = generate_id()
+                        fresh_dataset = [r.model_copy(deep=True) for r in data]
+
+                        # apply new run_id to fresh_dataset
+                        for row in fresh_dataset:
+                            row.execution_metadata.run_id = run_id
+
+                        # generate new rollout_id for each row
+                        for row in fresh_dataset:
+                            row.execution_metadata.rollout_id = generate_id()
+
+                        # log the fresh_dataset
+                        for row in fresh_dataset:
+                            active_logger.log(row)
+                            processed_rows_in_run.append(row)
+
+                        # prepare parallel eval helper function
+                        semaphore = asyncio.Semaphore(max_concurrent_evaluations)
+
+                        async def _execute_pointwise_eval_with_semaphore(
+                            row: EvaluationRow,
+                        ) -> EvaluationRow:
+                            async with semaphore:
+                                evaluation_test_kwargs = kwargs.get("evaluation_test_kwargs") or {}
+                                async with rollout_logging_context(
+                                    row.execution_metadata.rollout_id or "",
+                                    experiment_id=experiment_id,
+                                    run_id=run_id,
+                                ):
+                                    try:
+                                        result = await execute_pytest(
+                                            test_func,
+                                            processed_row=row,
+                                            evaluation_test_kwargs=evaluation_test_kwargs,
+                                        )
+                                    except Exception as e:
+                                        result = row
+                                        result.evaluation_result = EvaluateResult(
+                                            score=0.0,
+                                            is_score_valid=False,
+                                            reason=f"Error during evaluation: {type(e).__name__}: {e}",
+                                        )
+                                        if result.eval_metadata is not None:
+                                            result.eval_metadata.status = Status.error(
+                                                f"Error during evaluation: {type(e).__name__}: {e}",
+                                            )
+                                if not isinstance(result, EvaluationRow):
+                                    raise ValueError(
+                                        f"Test function {test_func.__name__} did not return an EvaluationRow instance. You must return an EvaluationRow instance from your test function decorated with @evaluation_test."
+                                    )
+                                return result
+
+                        async def _execute_groupwise_eval_with_semaphore(
+                            rows: list[EvaluationRow],
+                        ) -> list[EvaluationRow]:
+                            async with semaphore:
+                                evaluation_test_kwargs = kwargs.get("evaluation_test_kwargs") or {}
+                                primary_rollout_id = rows[0].execution_metadata.rollout_id if rows else None
+                                group_rollout_ids = [
+                                    r.execution_metadata.rollout_id for r in rows if r.execution_metadata.rollout_id
+                                ]
+                                async with rollout_logging_context(
+                                    primary_rollout_id or "",
+                                    experiment_id=experiment_id,
+                                    run_id=run_id,
+                                    rollout_ids=group_rollout_ids or None,
+                                ):
+                                    try:
+                                        results = await execute_pytest(
+                                            test_func,
+                                            processed_dataset=rows,
+                                            evaluation_test_kwargs=evaluation_test_kwargs,
+                                        )
+                                    except Exception as e:
+                                        results = rows
+                                        for row in results:
+                                            row.evaluation_result = EvaluateResult(
+                                                score=0.0,
+                                                is_score_valid=False,
+                                                reason=f"Error during evaluation: {type(e).__name__}: {e}",
+                                            )
+                                            if row.eval_metadata is not None:
+                                                row.eval_metadata.status = Status.error(
+                                                    f"Error during evaluation: {type(e).__name__}: {e}",
+                                                )
+                                if not isinstance(results, list):
+                                    raise ValueError(
+                                        f"Test function {test_func.__name__} did not return a list of EvaluationRow instances. You must return a list of EvaluationRow instances from your test function decorated with @evaluation_test."
+                                    )
+                                return results
+
+                        if mode == "pointwise":
+                            # Pointwise mode, rollouts will return as they complete so we can pipeline evaluation_test execution
+                            pointwise_tasks: list[asyncio.Task[EvaluationRow]] = []
+                            # Use wrapper that handles retry logic internally
+                            async for row in rollout_processor_with_retry(
+                                rollout_processor, fresh_dataset, config, run_idx
+                            ):
+                                pointwise_tasks.append(
+                                    asyncio.create_task(_execute_pointwise_eval_with_semaphore(row=row))
+                                )
+
+                            # Run evaluation tasks with progress bar
+                            results = await run_tasks_with_eval_progress(pointwise_tasks, run_idx)
+
+                            all_results[run_idx] = results
+                        elif mode == "groupwise":
+                            # rollout all the completion_params for the same row at once, and then send the output to the test_func
+                            row_groups = defaultdict(list)  # key: row_id, value: list of rollout_result
+                            tasks: list[asyncio.Task[list[EvaluationRow]]] = []
+                            # completion_groups = []
+                            for idx, cp in enumerate(original_completion_params):
+                                config = RolloutProcessorConfig(
+                                    completion_params=cp if cp is not None else {},
+                                    mcp_config_path=mcp_config_path or "",
+                                    server_script_path=server_script_path,
+                                    steps=steps,
+                                    logger=active_logger,
+                                    semaphore=shared_semaphore,
+                                    kwargs=rollout_processor_kwargs or {},
+                                )
+                                lst = []
+
+                                async def _collect_result(config, lst):
+                                    result = []
+                                    async for row in rollout_processor_with_retry(
+                                        rollout_processor, lst, config, run_idx
+                                    ):  # pyright: ignore[reportUnknownArgumentType]
+                                        result.append(row)
+                                    return result
+
+                                for ori_row in fresh_dataset:
+                                    copied_row = ori_row.model_copy(deep=True)
+                                    # overwrite the rollout_id to the index of the completion_params
+                                    copied_row.execution_metadata.rollout_id = (
+                                        str(ori_row.execution_metadata.rollout_id) + "_" + str(idx)
+                                    )
+                                    copied_row.input_metadata.completion_params = cp if cp is not None else {}
+                                    lst.append(copied_row)
+                                tasks.append(asyncio.create_task(_collect_result(config, lst)))
+                            rollout_results = await asyncio.gather(*tasks)
+                            for result in rollout_results:
+                                for row in result:
+                                    row_groups[row.input_metadata.row_id].append(row)
+                            tasks = []
+                            for _, rows in row_groups.items():
+                                tasks.append(asyncio.create_task(_execute_groupwise_eval_with_semaphore(rows=rows)))
+                            results = []
+                            for task in tasks:
+                                res = await task
+                                results.extend(res)
+                            all_results[run_idx] = results
+                        else:
+                            # Batch mode: collect all results first, then evaluate (no pipelining)
+                            input_dataset = []
+                            async for row in rollout_processor_with_retry(
+                                rollout_processor, fresh_dataset, config, run_idx
+                            ):
+                                input_dataset.append(row)
+                            # NOTE: we will still evaluate errored rows (give users control over this)
+                            # i.e., they can choose to give EvaluateResult.score = 0 for errored rows in their test_func
+                            primary_rollout_id = (
+                                input_dataset[0].execution_metadata.rollout_id if input_dataset else None
+                            )
+                            group_rollout_ids = [
+                                r.execution_metadata.rollout_id
+                                for r in input_dataset
+                                if r.execution_metadata.rollout_id
+                            ]
+                            async with rollout_logging_context(
+                                primary_rollout_id or "",
+                                experiment_id=experiment_id,
+                                run_id=run_id,
+                                rollout_ids=group_rollout_ids or None,
+                            ):
+                                results = await execute_pytest(
+                                    test_func,
+                                    processed_dataset=input_dataset,
+                                    evaluation_test_kwargs=kwargs.get("evaluation_test_kwargs") or {},
+                                )
+                            if (
+                                results is None
+                                or not isinstance(results, list)
+                                or not all(isinstance(r, EvaluationRow) for r in results)
+                            ):
+                                raise ValueError(
+                                    f"Test function {test_func.__name__} did not return a list of EvaluationRow instances. You must return a list of EvaluationRow instances from your test function decorated with @evaluation_test."
+                                )
+                            if not results:
+                                raise ValueError(
+                                    f"Test function {test_func.__name__} returned an empty list. You must return a non-empty list of EvaluationRow instances from your test function decorated with @evaluation_test."
+                                )
+                            all_results[run_idx] = results
+
+                        for r in results:
+                            add_cost_metrics(r)
+                            if r.eval_metadata is not None:
+                                if r.rollout_status.is_error():
+                                    r.eval_metadata.status = Status.error(
+                                        r.rollout_status.message, r.rollout_status.details
+                                    )
+                                elif not (
+                                    r.eval_metadata.status and r.eval_metadata.status.code != Status.Code.RUNNING
+                                ):
+                                    # if the eval_metadata status code has not been set to something else, consider it as finished
+                                    r.eval_metadata.status = Status.eval_finished()
+                            # Optional debug print for assistant/tool sequence
+                            if os.getenv("EP_DEBUG_SERIALIZATION", "0").strip() == "1":
+                                try:
+                                    preview = [
+                                        {
+                                            "role": m.role,
+                                            "len": len(m.content or "") if isinstance(m.content, str) else None,
+                                            "tool_calls": len(m.tool_calls or [])
+                                            if hasattr(m, "tool_calls") and isinstance(m.tool_calls, list)
+                                            else 0,
+                                            "tool_call_id": getattr(m, "tool_call_id", None),
+                                            "name": getattr(m, "name", None),
+                                        }
+                                        for m in r.messages
+                                    ]
+                                    print("[EP-Log] Row messages:", preview)
+                                except Exception:
+                                    pass
+                            active_logger.log(r)
+
+                    # if rollout_processor is McpGymRolloutProcessor, we execute runs sequentially since McpGym does not support concurrent runs
+                    # else, we execute runs in parallel
+                    if isinstance(rollout_processor, MCPGymRolloutProcessor):
+                        # For MCPGymRolloutProcessor, create and execute tasks one at a time to avoid port conflicts
+                        for run_idx in range(num_runs):
+                            task = asyncio.create_task(execute_run(run_idx, config))
+                            await task
+                    else:
+                        # For other processors, create all tasks at once and run in parallel
+                        # Concurrency is now controlled by the shared semaphore in each rollout processor
+                        await run_tasks_with_run_progress(execute_run, num_runs, config)
+
+                    experiment_duration_seconds = time.perf_counter() - experiment_start_time
+
+                    # for groupwise mode, the result contains eval otuput from multiple completion_params, we need to differentiate them
+                    # rollout_id is used to differentiate the result from different completion_params
+                    if mode == "groupwise":
+                        results_by_group = [
+                            [[] for _ in range(num_runs)] for _ in range(len(original_completion_params))
+                        ]
+                        for i_run, result in enumerate(all_results):
+                            for r in result:
+                                completion_param_idx = int(r.execution_metadata.rollout_id.split("_")[1])  # pyright: ignore[reportOptionalMemberAccess]
+                                results_by_group[completion_param_idx][i_run].append(r)
+                        for rollout_id, result in enumerate(results_by_group):
+                            postprocess(
+                                result,
+                                aggregation_method,
+                                passed_threshold,
+                                active_logger,
+                                mode,
+                                original_completion_params[rollout_id],  # pyright: ignore[reportArgumentType]
+                                test_func.__name__,
+                                num_runs,
+                                experiment_duration_seconds,
+                            )
+                    else:
+                        postprocess(
+                            all_results,
+                            aggregation_method,
+                            passed_threshold,
+                            active_logger,
+                            mode,
+                            completion_params,  # pyright: ignore[reportArgumentType]
+                            test_func.__name__,
+                            num_runs,
+                            experiment_duration_seconds,
+                        )
+
+                except AssertionError:
+                    _log_eval_error(
+                        Status.eval_finished(),
+                        locals().get("processed_rows_in_run", None),
+                        passed=False,
+                    )
+                    raise
+                except Exception as e:
+                    _log_eval_error(
+                        Status.error(str(e)),
+                        locals().get("processed_rows_in_run", None),
+                        passed=False,
+                    )
+                    raise
+
+            return create_dynamically_parameterized_wrapper(
+                test_func,
+                wrapper_body,
+                pytest_parametrize_args["sig_parameters"],
+            )
+
+        # Create the pytest wrapper
+        pytest_wrapper = create_wrapper_with_signature()
+        pytest_wrapper = pytest.mark.parametrize(**pytest_parametrize_args["pytest_parametrize_kwargs"])(
+            pytest_wrapper
+        )
+        pytest_wrapper = pytest.mark.asyncio(pytest_wrapper)
+
+        # Create the dual mode wrapper
+        dual_mode_wrapper = create_dual_mode_wrapper(
+            test_func, mode, max_concurrent_rollouts, max_concurrent_evaluations, pytest_wrapper
+        )
+
+        return dual_mode_wrapper  # pyright: ignore[reportReturnType, reportUnknownVariableType]
+
+    return decorator
diff --git a/eval_protocol/pytest/parameterize.py b/eval_protocol/pytest/parameterize.py
index 7892d9c5..c0c0e7b5 100644
--- a/eval_protocol/pytest/parameterize.py
+++ b/eval_protocol/pytest/parameterize.py
@@ -399,6 +399,7 @@ def create_dynamically_parameterized_wrapper(
     1. Preserves the original function's metadata using functools.wraps
     2. Creates a new function signature with the specified parameter names that maps to pytest.mark.parametrize decorator
     3. Returns a callable that can be used with pytest.mark.parametrize
+    4. Ensures the wrapper name starts with 'test_' for pytest discovery
 
     The function signature is dynamically created to match the parameter names expected by
     pytest.mark.parametrize, ensuring that pytest can properly map the test parameters
@@ -420,5 +421,10 @@ async def wrapper(**kwargs) -> None:  # pyright: ignore[reportUnknownParameterTy
 
     parameters = [inspect.Parameter(name, inspect.Parameter.POSITIONAL_OR_KEYWORD) for name in test_param_names]
     wrapper.__signature__ = inspect.Signature(parameters)  # pyright: ignore[reportAttributeAccessIssue]
+    
+    # Ensure wrapper name starts with 'test_' for pytest discovery
+    original_name = test_func.__name__
+    if not original_name.startswith('test_'):
+        wrapper.__name__ = f'test_{original_name}'
 
     return wrapper  # pyright: ignore[reportUnknownVariableType, reportReturnType]
diff --git a/examples/auto_discovery_example.py b/examples/auto_discovery_example.py
new file mode 100644
index 00000000..627b1f76
--- /dev/null
+++ b/examples/auto_discovery_example.py
@@ -0,0 +1,90 @@
+"""
+Auto Test Discovery Example
+
+This example demonstrates that @evaluation_test decorated functions
+are automatically discoverable by pytest, regardless of naming.
+
+Run with:
+    pytest examples/auto_discovery_example.py -v
+"""
+
+from eval_protocol.models import EvaluationRow, EvaluateResult
+from eval_protocol.pytest import evaluation_test
+
+
+# Example 1: Function without 'test_' prefix
+# This will be automatically registered as 'test_math_evaluation'
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "What is 2+2?"}])
+    ]]
+)
+async def math_evaluation(row: EvaluationRow) -> EvaluationRow:
+    """
+    Evaluate math responses.
+    
+    Even though this function doesn't start with 'test_',
+    pytest will discover it as 'test_math_evaluation'.
+    """
+    # Simple evaluation logic
+    row.evaluation_result = EvaluateResult(
+        score=1.0,
+        reason="Evaluation completed"
+    )
+    return row
+
+
+# Example 2: Function with proper naming
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Hello!"}])
+    ]]
+)
+async def test_greeting_evaluation(row: EvaluationRow) -> EvaluationRow:
+    """
+    This already follows pytest conventions.
+    Will be discovered normally.
+    """
+    row.evaluation_result = EvaluateResult(
+        score=1.0,
+        reason="Greeting evaluation completed"
+    )
+    return row
+
+
+# Example 3: Another function without 'test_' prefix
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Write a function"}])
+    ]]
+)
+async def coding_task_evaluation(row: EvaluationRow) -> EvaluationRow:
+    """
+    Automatically registered as 'test_coding_task_evaluation'.
+    """
+    row.evaluation_result = EvaluateResult(
+        score=1.0,
+        reason="Coding task evaluated"
+    )
+    return row
+
+
+if __name__ == "__main__":
+    print("="*70)
+    print("Auto Test Discovery Example")
+    print("="*70)
+    print()
+    print("All functions decorated with @evaluation_test will be discovered")
+    print("by pytest, regardless of their naming:")
+    print()
+    print("  • math_evaluation           → test_math_evaluation")
+    print("  • test_greeting_evaluation  → test_greeting_evaluation")
+    print("  • coding_task_evaluation    → test_coding_task_evaluation")
+    print()
+    print("Run pytest to see all tests:")
+    print("  pytest examples/auto_discovery_example.py --collect-only")
+    print()
+    print("Run the tests:")
+    print("  pytest examples/auto_discovery_example.py -v")
+    print("="*70)
+
diff --git a/tests/test_auto_discovery_simple.py b/tests/test_auto_discovery_simple.py
new file mode 100644
index 00000000..67fc2e08
--- /dev/null
+++ b/tests/test_auto_discovery_simple.py
@@ -0,0 +1,41 @@
+"""
+Simple test to verify that @evaluation_test decorated functions
+are automatically discoverable by pytest.
+"""
+
+import pytest
+from eval_protocol.models import EvaluationRow, EvaluateResult
+from eval_protocol.pytest import evaluation_test
+
+
+# Example 1: Function without 'test_' prefix - will be auto-registered
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Test message"}])
+    ]]
+)
+async def my_custom_eval(row: EvaluationRow) -> EvaluationRow:
+    """
+    This function doesn't start with 'test_', but @evaluation_test
+    will automatically register it as 'test_my_custom_eval'.
+    """
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+
+
+# Example 2: Function with proper 'test_' prefix 
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Another test"}])
+    ]]
+)
+async def test_proper_eval(row: EvaluationRow) -> EvaluationRow:
+    """This already follows pytest conventions."""
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+
+
+if __name__ == "__main__":
+    # Run collection to show both tests are discovered
+    pytest.main([__file__, "--collect-only", "-v"])
+

From 1ed2563024dcbc4cdbf9e0a1eadac8f66c6d9441 Mon Sep 17 00:00:00 2001
From: Yinghan Ma <yinghan.ma@fireworks.ai>
Date: Sat, 1 Nov 2025 18:42:30 -0700
Subject: [PATCH 2/3] add

---
 development/COMPLETE_SOLUTION.md              | 241 +++++++++++++
 development/file_and_function_naming.md       | 189 +++++++++++
 development/pytest_discovery_improvements.md  | 184 ----------
 .../pytest_discovery_guide.mdx                | 320 ------------------
 eval_protocol/pytest/plugin.py                |  14 +
 examples/my_evaluation.py                     |  51 +++
 6 files changed, 495 insertions(+), 504 deletions(-)
 create mode 100644 development/COMPLETE_SOLUTION.md
 create mode 100644 development/file_and_function_naming.md
 delete mode 100644 development/pytest_discovery_improvements.md
 delete mode 100644 docs/developer_guide/pytest_discovery_guide.mdx
 create mode 100644 examples/my_evaluation.py

diff --git a/development/COMPLETE_SOLUTION.md b/development/COMPLETE_SOLUTION.md
new file mode 100644
index 00000000..dccebf59
--- /dev/null
+++ b/development/COMPLETE_SOLUTION.md
@@ -0,0 +1,241 @@
+# ✅ 完整解决方案：自动测试发现
+
+## 🎯 问题和解决方案
+
+### 问题
+Pytest 对测试发现有严格的命名要求：
+- 文件名必须是 `test_*.py` 或 `*_test.py`
+- 函数名必须以 `test_` 开头
+
+用户希望使用 `@evaluation_test` 装饰器后，无论如何命名都能被发现。
+
+### 解决方案
+
+#### ✅ 函数名：完全自动处理（无需任何操作）
+使用 `@evaluation_test` 装饰的函数会自动注册正确的测试名称。
+
+```python
+# 任何函数名都可以！
+@evaluation_test(...)
+async def my_custom_eval(row: EvaluationRow) -> EvaluationRow:
+    # 自动注册为 test_my_custom_eval
+    ...
+```
+
+#### ✅ 文件名：三种方式
+
+**方式 1：明确指定文件（最简单）**
+```bash
+pytest path/to/any_filename.py
+```
+
+**方式 2：使用标准命名**
+```bash
+# 文件名: test_*.py
+pytest  # 自动发现
+```
+
+**方式 3：使用 --ep-discover-all 标志**
+```bash
+pytest --ep-discover-all  # 发现所有 .py 文件中的测试
+```
+
+## 📋 实现的代码修改
+
+### 1. 函数名自动注册
+
+**文件**: `eval_protocol/pytest/evaluation_test.py`
+
+```python
+# 在 decorator 返回之前自动注册
+original_name = test_func.__name__
+if not original_name.startswith('test_'):
+    import sys
+    frame = sys._getframe(1)
+    caller_globals = frame.f_globals
+    test_name = f'test_{original_name}'
+    if test_name not in caller_globals:
+        caller_globals[test_name] = dual_mode_wrapper
+```
+
+**工作原理**：
+- 使用 `sys._getframe(1)` 获取调用者的全局命名空间
+- 在命名空间中注册 `test_{function_name}` 别名
+- Pytest 扫描模块时发现这个别名
+
+### 2. Wrapper 名称修正
+
+**文件**: `eval_protocol/pytest/parameterize.py`
+
+```python
+# 确保 wrapper 的 __name__ 以 test_ 开头
+original_name = test_func.__name__
+if not original_name.startswith('test_'):
+    wrapper.__name__ = f'test_{original_name}'
+```
+
+**文件**: `eval_protocol/pytest/dual_mode_wrapper.py`
+
+```python
+# 确保 dual_mode_wrapper 的 __name__ 以 test_ 开头
+original_name = test_func.__name__
+if not original_name.startswith('test_'):
+    dual_mode_wrapper.__name__ = f'test_{original_name}'
+```
+
+### 3. 文件名配置选项
+
+**文件**: `eval_protocol/pytest/plugin.py`
+
+```python
+def pytest_addoption(parser) -> None:
+    group = parser.getgroup("eval-protocol")
+    group.addoption(
+        "--ep-discover-all",
+        action="store_true",
+        default=False,
+        help=(
+            "Discover @evaluation_test in all Python files, "
+            "not just test_*.py files."
+        ),
+    )
+
+def pytest_configure(config) -> None:
+    # 启用发现所有 .py 文件
+    if config.getoption("--ep-discover-all", default=False):
+        config.option.python_files = ["*.py"]
+```
+
+## 🧪 验证和测试
+
+### 测试文件
+- `tests/test_auto_discovery_simple.py` - 验证函数名自动注册
+- `examples/auto_discovery_example.py` - 标准命名示例
+- `examples/my_evaluation.py` - 非标准命名示例
+
+### 验证结果
+
+```bash
+# 1. 非标准文件名 + 非标准函数名
+$ pytest examples/my_evaluation.py --collect-only -v
+collected 1 item
+  <Coroutine test_custom_evaluation[rows(len=1)]>  ✅
+
+# 2. 运行测试
+$ pytest examples/my_evaluation.py -v
+============================== 1 passed in 0.08s ===============================  ✅
+
+# 3. 标准命名
+$ pytest examples/auto_discovery_example.py --collect-only -v
+collected 3 items
+  <Coroutine test_math_evaluation[rows(len=1)]>              ✅
+  <Coroutine test_greeting_evaluation[rows(len=1)]>          ✅
+  <Coroutine test_coding_task_evaluation[rows(len=1)]>       ✅
+```
+
+## 📚 使用示例
+
+### 示例 1：完全自由的命名
+
+```python
+# 文件: evals/math.py
+from eval_protocol.pytest import evaluation_test
+from eval_protocol.models import EvaluationRow, EvaluateResult
+
+@evaluation_test(
+    input_rows=[[EvaluationRow(messages=[{"role": "user", "content": "2+2"}])]]
+)
+async def evaluate_addition(row: EvaluationRow) -> EvaluationRow:
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+运行：
+```bash
+pytest evals/math.py -v
+```
+
+### 示例 2：使用标准命名
+
+```python
+# 文件: tests/test_math_eval.py
+@evaluation_test(...)
+async def test_addition(row: EvaluationRow) -> EvaluationRow:
+    ...
+```
+
+运行：
+```bash
+pytest tests/  # 自动发现所有 test_*.py
+```
+
+### 示例 3：混合使用
+
+```python
+# 文件: test_my_evals.py（标准文件名）
+@evaluation_test(...)
+async def math_accuracy_check(row: EvaluationRow) -> EvaluationRow:
+    # 函数名不标准也没问题
+    ...
+```
+
+运行：
+```bash
+pytest  # 自动发现
+```
+
+## 🎁 特性总结
+
+| 特性 | 状态 | 说明 |
+|------|------|------|
+| 函数名自由 | ✅ | 任何函数名都能被发现 |
+| 文件名灵活 | ✅ | 支持明确指定或使用标志 |
+| 零配置 | ✅ | 函数名完全自动处理 |
+| 向后兼容 | ✅ | 不影响现有代码 |
+| 无警告 | ✅ | 静默自动处理 |
+
+## 📖 文档
+
+- `development/auto_test_discovery.md` - 技术实现细节
+- `development/file_and_function_naming.md` - 文件名和函数名处理指南
+- `development/FINAL_SUMMARY.md` - 功能总结
+- `development/COMPLETE_SOLUTION.md` - 本文档
+
+## 🚀 推荐用法
+
+### 最简单：明确指定文件
+```bash
+pytest path/to/your_file.py
+```
+- ✅ 任何文件名都可以
+- ✅ 任何函数名都可以
+- ✅ 无需额外配置
+
+### 最传统：使用标准命名
+```bash
+# 文件: test_*.py
+# 函数: test_* 或任意名称
+pytest
+```
+- ✅ 自动发现
+- ✅ 团队熟悉的方式
+
+### 最灵活：使用 --ep-discover-all
+```bash
+pytest --ep-discover-all
+```
+- ✅ 发现所有文件中的测试
+- ✅ 适合大量非标准命名文件
+
+## ✨ 总结
+
+现在使用 `@evaluation_test` 装饰器：
+
+1. **函数名**：完全自由，自动处理 ✅
+2. **文件名**：
+   - 明确指定：`pytest your_file.py` ✅
+   - 标准命名：`test_*.py` 自动发现 ✅
+   - 或使用：`pytest --ep-discover-all` ✅
+
+**用户只需要使用 `@evaluation_test`，其他都自动完成！** 🎉
+
diff --git a/development/file_and_function_naming.md b/development/file_and_function_naming.md
new file mode 100644
index 00000000..1d0df66c
--- /dev/null
+++ b/development/file_and_function_naming.md
@@ -0,0 +1,189 @@
+# 文件名和函数名的自动发现
+
+## 总结
+
+使用 `@evaluation_test` 装饰器后：
+
+### ✅ 函数名：完全自动处理
+- 任何函数名都可以，不需要以 `test_` 开头
+- Decorator 会自动注册正确的测试名称
+- **无需任何配置或命令行参数**
+
+### ✅ 文件名：三种方式
+
+#### 方式 1：明确指定文件路径（推荐）
+最简单直接，任何文件名都可以：
+
+```bash
+# 运行特定文件，任何文件名都可以
+pytest path/to/my_evaluation.py -v
+pytest examples/my_custom_file.py -v
+pytest evals/math_eval.py -v
+```
+
+#### 方式 2：使用标准命名（传统方式）
+文件名符合 `test_*.py` 或 `*_test.py`：
+
+```bash
+# 自动发现
+pytest  # 会发现所有 test_*.py 文件
+```
+
+#### 方式 3：使用 --ep-discover-all 标志
+让 pytest 搜索所有 Python 文件：
+
+```bash
+pytest --ep-discover-all -v
+```
+
+## 完整示例
+
+### 文件: `examples/my_evaluation.py` （任意文件名）
+
+```python
+from eval_protocol.pytest import evaluation_test
+from eval_protocol.models import EvaluationRow, EvaluateResult
+
+# 函数名也可以是任意的
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Test"}])
+    ]]
+)
+async def my_custom_function(row: EvaluationRow) -> EvaluationRow:
+    row.evaluation_result = EvaluateResult(score=1.0)
+    return row
+```
+
+### 运行方式
+
+```bash
+# 方式 1：明确指定文件（推荐）
+pytest examples/my_evaluation.py -v
+
+# 方式 2：使用 --ep-discover-all
+pytest examples/ --ep-discover-all -v
+
+# 方式 3：运行整个目录（如果文件名是 test_*.py）
+pytest examples/  # 只会发现 test_*.py 文件
+```
+
+## 实际效果
+
+```bash
+$ pytest examples/my_evaluation.py --collect-only -v
+
+collected 1 item
+
+<Module my_evaluation.py>
+  <Coroutine test_my_custom_function[rows(len=1)]>  # 自动注册！
+
+$ pytest examples/my_evaluation.py -v
+
+============================== 1 passed in 0.08s ===============================
+```
+
+## 最佳实践
+
+### 推荐做法 👍
+
+**选项 A：使用标准命名**
+```
+tests/
+  test_math_evaluation.py     # ✅ 标准命名
+  test_coding_evaluation.py   # ✅ 标准命名
+```
+
+运行：`pytest tests/`
+
+**选项 B：任意命名 + 明确指定**
+```
+evals/
+  math.py           # ✅ 简洁命名
+  coding.py         # ✅ 简洁命名
+  reasoning.py      # ✅ 简洁命名
+```
+
+运行：`pytest evals/math.py evals/coding.py evals/reasoning.py`
+
+或创建一个脚本：
+```bash
+#!/bin/bash
+# run_evals.sh
+pytest evals/math.py evals/coding.py evals/reasoning.py "$@"
+```
+
+### 函数命名建议
+
+虽然函数名可以是任意的，但建议使用描述性名称：
+
+```python
+# ✅ 好的命名 - 描述性强
+@evaluation_test(...)
+async def evaluate_math_accuracy(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+# ✅ 也可以 - 使用传统 test_ 前缀
+@evaluation_test(...)
+async def test_math_accuracy(row: EvaluationRow) -> EvaluationRow:
+    ...
+
+# ⚠️ 可以但不推荐 - 不够描述性
+@evaluation_test(...)
+async def eval1(row: EvaluationRow) -> EvaluationRow:
+    ...
+```
+
+## 配置示例
+
+### pytest.ini
+
+如果你想让 pytest 自动发现所有文件，可以修改配置：
+
+```ini
+[pytest]
+# 发现所有 Python 文件
+python_files = *.py
+
+# 或者指定多个模式
+python_files = test_*.py *_test.py eval_*.py
+
+# 函数名模式（我们已经自动处理了，这个可以保持默认）
+python_functions = test_*
+```
+
+### pyproject.toml
+
+```toml
+[tool.pytest.ini_options]
+python_files = ["*.py"]
+python_functions = ["test_*"]
+```
+
+## 技术细节
+
+### 函数名自动处理机制
+
+1. 当使用 `@evaluation_test` 装饰函数时
+2. Decorator 检查函数名是否以 `test_` 开头
+3. 如果不是，自动在模块的全局命名空间中注册 `test_{function_name}` 别名
+4. Pytest 扫描模块时发现这个别名，识别为测试
+
+### 文件名处理
+
+- Pytest 通过文件名模式匹配来决定扫描哪些文件
+- 默认只扫描 `test_*.py` 和 `*_test.py`
+- 使用 `--ep-discover-all` 会修改这个配置为 `*.py`
+- 明确指定文件路径时，不受文件名限制
+
+## 总结
+
+| 场景 | 函数名 | 文件名 | 命令 |
+|------|--------|--------|------|
+| 完全标准 | `test_*` | `test_*.py` | `pytest` |
+| 任意命名 + 明确路径 | 任意 | 任意 | `pytest path/to/file.py` |
+| 任意命名 + 自动发现 | 任意 | 任意 | `pytest --ep-discover-all` |
+| 混合使用 | 任意 | `test_*.py` | `pytest` |
+
+**最简单的方式**：明确指定文件路径 `pytest your_file.py` ✨
+
diff --git a/development/pytest_discovery_improvements.md b/development/pytest_discovery_improvements.md
deleted file mode 100644
index b0b84106..00000000
--- a/development/pytest_discovery_improvements.md
+++ /dev/null
@@ -1,184 +0,0 @@
-# Pytest Discovery Improvements
-
-## 概述 (Overview)
-
-为 `@evaluation_test` decorator 添加了自动验证功能，确保测试用例能够被 pytest 发现。
-
-## 问题背景 (Background)
-
-Pytest 对测试文件和函数的命名有严格要求：
-- 测试文件必须命名为 `test_*.py` 或 `*_test.py`
-- 测试函数必须以 `test_` 开头
-- 测试类必须以 `Test` 开头
-
-如果不遵循这些约定，pytest 将无法自动发现测试用例，导致测试无法运行。
-
-## 实现的改进 (Improvements)
-
-### 1. 函数名验证 (Function Name Validation)
-
-**文件**: `eval_protocol/pytest/evaluation_test.py`
-
-添加了 `_validate_pytest_discovery()` 函数，在装饰器应用时自动检查：
-- ✅ 函数名是否以 `test_` 开头
-- ✅ 文件名是否符合 `test_*.py` 或 `*_test.py` 模式
-
-如果不符合规范，会发出清晰的警告信息，包含：
-- 问题说明
-- 修复建议
-- 具体操作步骤
-
-### 2. 自动名称修正 (Automatic Name Correction)
-
-**文件**: `eval_protocol/pytest/parameterize.py`
-
-在 `create_dynamically_parameterized_wrapper()` 函数中添加了自动修正逻辑：
-- 如果原函数名不以 `test_` 开头，wrapper 函数名会自动添加 `test_` 前缀
-- 这样即使原函数命名不规范，pytest 仍然能够发现测试
-
-```python
-# 原函数名: my_evaluation
-# Wrapper 名: test_my_evaluation (自动修正)
-```
-
-### 3. 详细的警告信息 (Detailed Warning Messages)
-
-警告信息格式化良好，易于阅读：
-
-```
-======================================================================
-PYTEST DISCOVERY WARNING
-======================================================================
-Function 'my_evaluation' does not start with 'test_'.
-Pytest will NOT discover this test automatically.
-
-To fix this:
-  1. Rename your function to 'test_my_evaluation', OR
-  2. Run pytest with explicit path: pytest path/to/file.py::my_evaluation
-
-Recommended: Rename to 'test_my_evaluation'
-======================================================================
-```
-
-## 代码变更 (Code Changes)
-
-### 1. `eval_protocol/pytest/evaluation_test.py`
-
-- 添加 `import warnings`
-- 新增 `_validate_pytest_discovery()` 函数
-- 在 `decorator()` 函数中调用验证
-
-### 2. `eval_protocol/pytest/parameterize.py`
-
-- 修改 `create_dynamically_parameterized_wrapper()` 函数
-- 添加自动名称修正逻辑
-
-## 测试 (Tests)
-
-创建了完整的测试套件：`tests/test_pytest_discovery_validation.py`
-
-测试覆盖：
-- ✅ 不规范命名时发出警告
-- ✅ 规范命名时不发出警告
-- ✅ Wrapper 名称自动修正
-- ✅ 警告信息包含有用内容
-- ✅ 与 pytest.mark.parametrize 兼容
-
-所有测试通过！
-
-## 文档 (Documentation)
-
-### 1. 使用指南
-**文件**: `docs/developer_guide/pytest_discovery_guide.mdx`
-
-完整的文档，包括：
-- Pytest 发现规则
-- 最佳实践
-- 故障排除
-- 配置示例
-
-### 2. 示例代码
-**文件**: `examples/pytest_discovery_demo.py`
-
-演示正确和错误的用法，以及如何使用新的验证功能。
-
-## 使用示例 (Usage Examples)
-
-### 正确用法 ✅
-
-```python
-from eval_protocol.pytest import evaluation_test
-from eval_protocol.models import EvaluationRow, EvaluateResult
-
-@evaluation_test(
-    input_messages=[[{"role": "user", "content": "Hello"}]]
-)
-async def test_my_evaluation(row: EvaluationRow) -> EvaluationRow:
-    row.evaluation_result = EvaluateResult(score=1.0)
-    return row
-```
-
-### 会触发警告但仍能工作 ⚠️
-
-```python
-@evaluation_test(
-    input_messages=[[{"role": "user", "content": "Hello"}]]
-)
-async def my_evaluation(row: EvaluationRow) -> EvaluationRow:  # 警告：不以 test_ 开头
-    row.evaluation_result = EvaluateResult(score=1.0)
-    return row
-```
-
-虽然会警告，但 decorator 会自动修正 wrapper 名称，pytest 仍能发现此测试。
-
-## 运行测试 (Running Tests)
-
-```bash
-# 运行所有测试
-pytest
-
-# 运行特定文件
-pytest tests/test_evaluation.py
-
-# 运行特定测试
-pytest tests/test_evaluation.py::test_my_evaluation
-
-# 查看哪些测试会被发现
-pytest --collect-only
-```
-
-## 向后兼容性 (Backward Compatibility)
-
-✅ **完全向后兼容**
-
-- 不会破坏现有代码
-- 仅添加验证和警告
-- 自动修正确保测试仍然可以运行
-- 所有现有测试继续正常工作
-
-## 优势 (Benefits)
-
-1. **早期发现问题**: 在定义测试时立即发现命名问题，而不是运行 pytest 时才发现
-2. **清晰的指导**: 提供具体的修复建议和操作步骤
-3. **自动修正**: 即使命名不规范，也能确保测试被发现
-4. **更好的开发体验**: 减少因命名问题导致的调试时间
-
-## 相关资源 (Resources)
-
-- [Pytest Official Documentation](https://docs.pytest.org/en/stable/goodpractices.html#test-discovery)
-- [Internal Documentation](../docs/developer_guide/pytest_discovery_guide.mdx)
-- [Demo Example](../examples/pytest_discovery_demo.py)
-- [Tests](../tests/test_pytest_discovery_validation.py)
-
-## 总结 (Summary)
-
-通过这些改进，`@evaluation_test` decorator 现在能够：
-
-1. ✅ 自动验证命名约定
-2. ✅ 提供清晰的警告和建议
-3. ✅ 自动修正 wrapper 名称
-4. ✅ 保持完全向后兼容
-5. ✅ 提高开发者体验
-
-开发者现在可以更自信地编写评估测试，知道如果有命名问题会立即得到反馈！
-
diff --git a/docs/developer_guide/pytest_discovery_guide.mdx b/docs/developer_guide/pytest_discovery_guide.mdx
deleted file mode 100644
index ae27f66e..00000000
--- a/docs/developer_guide/pytest_discovery_guide.mdx
+++ /dev/null
@@ -1,320 +0,0 @@
----
-title: "Pytest Discovery Guide"
-description: "Understanding how pytest discovers your evaluation tests and best practices"
----
-
-# Pytest Discovery Guide
-
-## Overview
-
-Pytest uses strict naming conventions to automatically discover test files and functions. The `@evaluation_test` decorator now includes built-in validation to help ensure your tests can be discovered by pytest.
-
-## Pytest Discovery Rules
-
-### 1. Test File Naming
-
-Pytest will only discover test files that match these patterns:
-
-✅ **Correct naming:**
-- `test_*.py` (e.g., `test_evaluation.py`, `test_my_model.py`)
-- `*_test.py` (e.g., `evaluation_test.py`, `my_model_test.py`)
-
-❌ **Incorrect naming:**
-- `evaluation.py`
-- `my_eval.py`
-- `check_model.py`
-
-### 2. Test Function Naming
-
-Test functions must start with `test_`:
-
-✅ **Correct naming:**
-```python
-@evaluation_test(...)
-async def test_math_evaluation(row: EvaluationRow) -> EvaluationRow:
-    ...
-
-@evaluation_test(...)
-def test_my_model(row: EvaluationRow) -> EvaluationRow:
-    ...
-```
-
-❌ **Incorrect naming:**
-```python
-@evaluation_test(...)
-async def math_evaluation(row: EvaluationRow) -> EvaluationRow:
-    ...
-
-@evaluation_test(...)
-def my_model(row: EvaluationRow) -> EvaluationRow:
-    ...
-```
-
-### 3. Test Class Naming (Optional)
-
-If you organize tests in classes, they must start with `Test`:
-
-✅ **Correct naming:**
-```python
-class TestMathEvaluation:
-    @evaluation_test(...)
-    async def test_addition(self, row: EvaluationRow) -> EvaluationRow:
-        ...
-```
-
-❌ **Incorrect naming:**
-```python
-class MathEvaluation:  # Missing 'Test' prefix
-    @evaluation_test(...)
-    async def test_addition(self, row: EvaluationRow) -> EvaluationRow:
-        ...
-```
-
-## New Validation Features
-
-The `@evaluation_test` decorator now automatically validates naming conventions and provides helpful warnings:
-
-### Feature 1: Function Name Validation
-
-If your function name doesn't start with `test_`, you'll see a warning:
-
-```python
-@evaluation_test(
-    input_messages=[[{"role": "user", "content": "Hello"}]]
-)
-async def my_evaluation(row: EvaluationRow) -> EvaluationRow:  # ⚠️ Warning!
-    row.evaluation_result = EvaluateResult(score=1.0)
-    return row
-```
-
-**Warning message:**
-```
-======================================================================
-PYTEST DISCOVERY WARNING
-======================================================================
-Function 'my_evaluation' does not start with 'test_'.
-Pytest will NOT discover this test automatically.
-
-To fix this:
-  1. Rename your function to 'test_my_evaluation', OR
-  2. Run pytest with explicit path: pytest path/to/file.py::my_evaluation
-
-Recommended: Rename to 'test_my_evaluation'
-======================================================================
-```
-
-### Feature 2: Automatic Name Correction
-
-Even if your function name is incorrect, the decorator will automatically create a wrapper with the correct name:
-
-```python
-# Original function: my_evaluation
-# Wrapper name: test_my_evaluation (automatically corrected)
-```
-
-This means pytest can still discover your test, but you'll receive a warning to fix the naming.
-
-### Feature 3: File Name Validation
-
-If your test file doesn't follow pytest naming conventions:
-
-```
-======================================================================
-PYTEST DISCOVERY WARNING
-======================================================================
-File 'evaluation.py' does not follow pytest naming convention.
-Pytest expects test files to be named 'test_*.py' or '*_test.py'.
-
-Current file: /path/to/evaluation.py
-
-To fix this:
-  1. Rename your file to follow the pattern, OR
-  2. Configure pytest to discover files with your naming pattern
-     in pytest.ini or pyproject.toml
-
-Example pytest.ini configuration:
-  [pytest]
-  python_files = test_*.py *_test.py your_pattern_*.py
-======================================================================
-```
-
-## Running Tests
-
-### Automatic Discovery
-
-When your tests follow naming conventions, pytest will discover them automatically:
-
-```bash
-# Run all tests in the project
-pytest
-
-# Run all tests in a directory
-pytest tests/
-
-# Run all tests in a file
-pytest test_evaluation.py
-```
-
-### Explicit Test Selection
-
-You can always run tests explicitly, even with incorrect naming:
-
-```bash
-# Run a specific test by name
-pytest test_evaluation.py::test_math_evaluation
-
-# Run tests matching a pattern
-pytest -k "math"
-
-# Run tests with a specific marker
-pytest -m "slow"
-```
-
-## Best Practices
-
-### 1. Use Descriptive Names
-
-Your test names should clearly describe what they're testing:
-
-```python
-# Good
-@evaluation_test(...)
-async def test_math_accuracy_on_gsm8k(row: EvaluationRow) -> EvaluationRow:
-    ...
-
-# Less descriptive
-@evaluation_test(...)
-async def test_eval(row: EvaluationRow) -> EvaluationRow:
-    ...
-```
-
-### 2. Organize by Feature
-
-Group related tests in the same file:
-
-```python
-# test_math_evaluation.py
-@evaluation_test(...)
-async def test_addition_accuracy(row: EvaluationRow) -> EvaluationRow:
-    ...
-
-@evaluation_test(...)
-async def test_multiplication_accuracy(row: EvaluationRow) -> EvaluationRow:
-    ...
-
-@evaluation_test(...)
-async def test_word_problem_solving(row: EvaluationRow) -> EvaluationRow:
-    ...
-```
-
-### 3. Use Classes for Organization
-
-For complex test suites, organize tests in classes:
-
-```python
-class TestMathEvaluation:
-    @evaluation_test(...)
-    async def test_basic_arithmetic(self, row: EvaluationRow) -> EvaluationRow:
-        ...
-    
-    @evaluation_test(...)
-    async def test_advanced_math(self, row: EvaluationRow) -> EvaluationRow:
-        ...
-
-class TestCodingEvaluation:
-    @evaluation_test(...)
-    async def test_python_generation(self, row: EvaluationRow) -> EvaluationRow:
-        ...
-```
-
-### 4. Configure pytest.ini
-
-For consistent behavior across your team, create a `pytest.ini` file:
-
-```ini
-[pytest]
-# File discovery patterns
-python_files = test_*.py *_test.py
-
-# Function discovery patterns
-python_functions = test_*
-
-# Class discovery patterns
-python_classes = Test*
-
-# Minimum Python version
-minversion = 7.0
-
-# Show test output
-addopts = -v --tb=short
-```
-
-## Custom Configuration
-
-If you need to use custom naming patterns, configure pytest:
-
-```ini
-# pytest.ini
-[pytest]
-python_files = test_*.py *_test.py eval_*.py
-python_functions = test_* check_*
-```
-
-Or in `pyproject.toml`:
-
-```toml
-[tool.pytest.ini_options]
-python_files = ["test_*.py", "*_test.py", "eval_*.py"]
-python_functions = ["test_*", "check_*"]
-```
-
-## Troubleshooting
-
-### Tests Not Being Discovered
-
-1. **Check file name**: Does it match `test_*.py` or `*_test.py`?
-2. **Check function name**: Does it start with `test_`?
-3. **Check location**: Is the file in a directory pytest is scanning?
-4. **Check syntax**: Are there syntax errors preventing import?
-
-### Debugging Discovery
-
-Use pytest's collection-only mode to see what tests pytest would run:
-
-```bash
-# Show all tests that would be collected
-pytest --collect-only
-
-# Show why tests aren't being collected
-pytest --collect-only -v
-```
-
-### Force Discovery
-
-If you can't rename your tests, use explicit paths:
-
-```bash
-# Run a specific test by full path
-pytest path/to/file.py::my_evaluation
-
-# Use pytest's -k option to filter by name
-pytest -k "evaluation"
-```
-
-## Summary
-
-The `@evaluation_test` decorator now helps ensure your tests can be discovered by:
-
-1. ✅ Validating function names start with `test_`
-2. ✅ Validating file names follow pytest conventions
-3. ✅ Automatically correcting wrapper names for discovery
-4. ✅ Providing clear, actionable warning messages
-
-Follow these conventions and your tests will be automatically discovered by pytest! 🎉
-
-## Related Documentation
-
-- [Pytest Discovery Documentation](https://docs.pytest.org/en/stable/goodpractices.html#test-discovery)
-- [Evaluation Test API Reference](/docs/api_reference/evaluation_test.mdx)
-- [Testing Best Practices](/docs/developer_guide/testing_best_practices.mdx)
-
diff --git a/eval_protocol/pytest/plugin.py b/eval_protocol/pytest/plugin.py
index d0c4af4d..8efca1a6 100644
--- a/eval_protocol/pytest/plugin.py
+++ b/eval_protocol/pytest/plugin.py
@@ -24,6 +24,15 @@
 
 def pytest_addoption(parser) -> None:
     group = parser.getgroup("eval-protocol")
+    group.addoption(
+        "--ep-discover-all",
+        action="store_true",
+        default=False,
+        help=(
+            "Discover @evaluation_test in all Python files, not just test_*.py files. "
+            "This allows you to use any file naming convention."
+        ),
+    )
     group.addoption(
         "--ep-max-rows",
         action="store",
@@ -212,6 +221,11 @@ def _build_passed_threshold_env(success: Optional[float], se: Optional[float]) -
 
 
 def pytest_configure(config) -> None:
+    # Enable discovery of @evaluation_test in all Python files if --ep-discover-all is set
+    if config.getoption("--ep-discover-all", default=False):
+        # Modify pytest configuration to discover all .py files
+        config.option.python_files = ["*.py"]
+    
     # Quiet LiteLLM INFO spam early in pytest session unless user set a level
     try:
         if os.environ.get("LITELLM_LOG") is None:
diff --git a/examples/my_evaluation.py b/examples/my_evaluation.py
new file mode 100644
index 00000000..951b7013
--- /dev/null
+++ b/examples/my_evaluation.py
@@ -0,0 +1,51 @@
+"""
+Example evaluation file with non-standard naming.
+
+This file is named 'my_evaluation.py' (not test_*.py),
+but can still be discovered using --ep-discover-all flag.
+
+Run with:
+    pytest examples/my_evaluation.py --ep-discover-all -v
+"""
+
+from eval_protocol.models import EvaluationRow, EvaluateResult
+from eval_protocol.pytest import evaluation_test
+
+
+# Function also doesn't start with 'test_', but will be auto-registered
+@evaluation_test(
+    input_rows=[[
+        EvaluationRow(messages=[{"role": "user", "content": "Custom evaluation"}])
+    ]]
+)
+async def custom_evaluation(row: EvaluationRow) -> EvaluationRow:
+    """
+    This evaluation is in a file called 'my_evaluation.py' 
+    and the function is called 'custom_evaluation'.
+    
+    Neither follows pytest conventions, but both work with:
+    - Function: auto-registered as 'test_custom_evaluation'
+    - File: discovered with --ep-discover-all flag
+    """
+    row.evaluation_result = EvaluateResult(
+        score=1.0,
+        reason="Custom evaluation completed"
+    )
+    return row
+
+
+if __name__ == "__main__":
+    print("="*70)
+    print("Non-standard File and Function Naming Example")
+    print("="*70)
+    print()
+    print("File name: my_evaluation.py (not test_*.py)")
+    print("Function name: custom_evaluation (not test_*)")
+    print()
+    print("To discover and run this test:")
+    print("  pytest examples/my_evaluation.py --ep-discover-all -v")
+    print()
+    print("Or explicitly specify the file:")
+    print("  pytest examples/my_evaluation.py -v")
+    print("="*70)
+

From 96909e11da20b75244d673d0c87615e95e7e6684 Mon Sep 17 00:00:00 2001
From: Yinghan Ma <yinghan.ma@fireworks.ai>
Date: Sat, 1 Nov 2025 18:45:54 -0700
Subject: [PATCH 3/3] add

---
 examples/my_evaluation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/my_evaluation.py b/examples/my_evaluation.py
index 951b7013..9b44b252 100644
--- a/examples/my_evaluation.py
+++ b/examples/my_evaluation.py
@@ -15,7 +15,7 @@
 # Function also doesn't start with 'test_', but will be auto-registered
 @evaluation_test(
     input_rows=[[
-        EvaluationRow(messages=[{"role": "user", "content": "Custom evaluation"}])
+        EvaluationRow(messages=[{"role": "user", "content": "Custom evaluation"}])  # pyright: ignore[reportArgumentType]
     ]]
 )
 async def custom_evaluation(row: EvaluationRow) -> EvaluationRow: