Trae Agent Selector 选择逻辑分析
概述
Selector（选择器）是 Trae Agent 评估模块中的核心组件，用于从多个候选 Patch 中选择最佳解决方案。本文档详细分析 Selector 的核心逻辑和实现。
一、整体架构
yaml
┌─────────────────────────────────────────────────────────────────────────────┐
│ Patch Selection 流程 │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────┐
│ Phase 1: 准备阶段 │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ - 读取候选 Patch 列表 (candidates.jsonl) │ │
│ │ - 回归测试过滤 │ │
│ │ - Patch 去重 │ │
│ │ - 分组处理 │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ Phase 2: 选择阶段 │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ SelectorAgent 运行 │ │
│ │ - 分析每个候选 Patch │ │
│ │ - 在沙箱中验证（可选） │ │
│ │ - 选择最佳 Patch │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ Phase 3: 投票阶段（可选） │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ - 多次运行 SelectorAgent │ │
│ │ - 统计选择频率 │ │
│ │ - 多数投票得出最终结果 │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘二、核心组件
2.1 入口：selector.py
文件 : evaluation/patch_selection/selector.py
python
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--instances_path", required=True) # 实例列表
parser.add_argument("--candidate_path", required=True) # 候选 Patch 文件
parser.add_argument("--num_candidate", type=int, default=10)
parser.add_argument("--group_size", type=int, default=10) # 分组大小
parser.add_argument("--majority_voting", action="store_true") # 多数投票
args = parser.parse_args()
# 加载候选 Patch
candidate_dic = {}
with open(args.candidate_path, "r") as file:
for line in file.readlines():
candidate = json.loads(line.strip())
candidate_dic[candidate["instance_id"]] = candidate
# 创建评估器
evaluation = SelectorEvaluation(
llm_config,
args.num_candidate,
args.max_retry,
args.max_turn,
args.log_path,
args.output_path,
args.patches_path,
instance_list,
candidate_dic,
tools_path,
args.statistics_path,
args.group_size,
majority_voting=args.majority_voting,
)
# 运行所有实例
evaluation.run_all(max_workers=args.max_workers)2.2 SelectorEvaluation 类
文件 : evaluation/patch_selection/trae_selector/selector_evaluation.py
2.2.1 分组处理
python
def run_instance(
instance,
candidate_log, # 候选 Patch 日志
num_candidate,
group_size,
...
):
"""将候选 Patch 分组处理"""
# 将 N 个候选分为 M 组
groups = []
for i in range(0, num_candidate, group_size):
this_group = {
"instance_id": candidate_log["instance_id"],
"issue": candidate_log["issue"],
"patches": candidate_log["patches"][i:i + group_size],
"regressions": candidate_log["regressions"][i:i + group_size],
"success_id": candidate_log["success_id"][i:i + group_size],
}
groups.append(this_group)
# 每组独立选择
for group_id, group in enumerate(groups):
run_instance_by_group(instance=instance, candidate_log=group, ...)分组策略示例：
sql
50 个候选 Patch，group_size=10
↓
Group 0: Patch 0-9
Group 1: Patch 10-19
Group 2: Patch 20-29
Group 3: Patch 30-39
Group 4: Patch 40-49
↓
每组选择 1 个 → 共 5 个候选
↓
最终选择 1 个2.2.2 单组处理流程
python
def run_instance_by_group(instance, candidate_log, ...):
"""处理单个组的候选 Patch"""
# 1. 检查是否已处理
file_path = statistics_path + f"/group_{group_id}/{instance['instance_id']}.json"
if os.path.exists(file_path) and os.path.getsize(file_path) > 0:
print("Already processed. Skipping...")
return
# 2. 检查是否全部失败/成功（边界情况）
all_failed = all(success_id == 0 for success_id in candidate_log["success_id"])
all_success = all(success_id == 1 for success_id in candidate_log["success_id"])
if all_failed or all_success:
# 直接保存结果，跳过选择
save_patches(...)
save_selection_success(...)
return
# 3. 构建候选列表
candidate_list = []
for idx in range(len(candidate_log["patches"])):
candidate_list.append(CandidatePatch(
id=idx,
patch=candidate_log["patches"][idx],
cleaned_patch=clean_patch(candidate_log["patches"][idx]),
is_success_regression=len(candidate_log["regressions"][idx]) == 0,
is_success_patch=candidate_log["success_id"][idx],
))
# 4. 回归测试过滤
candidate_list_regression = [
c for c in candidate_list if c.is_success_regression
]
if len(candidate_list_regression) > 0:
candidate_list = candidate_list_regression
# 5. Patch 去重
candidate_list_deduplication = []
cleaned_candidate_set = set()
for candidate in candidate_list:
if candidate.cleaned_patch not in cleaned_candidate_set:
cleaned_candidate_set.add(candidate.cleaned_patch)
candidate_list_deduplication.append(candidate)
candidate_list = candidate_list_deduplication
# 6. 创建沙箱
sandbox = Sandbox(namespace, image_name, tag, instance, tools_path)
sandbox.start_container()
project_path = sandbox.get_project_path()
# 7. 运行选择
if majority_voting:
# 多数投票模式
final_id_list, final_patch_list = [], []
for idx in range(num_candidate):
select_agent = SelectorAgent(...)
final_id, final_patch = select_agent.run()
final_id_list.append(final_id)
final_patch_list.append(final_patch)
# 提前终止
if max(Counter(final_id_list).values()) > num_candidate / 2:
break
# 统计投票
counter = Counter(final_id_list)
max_count = max(counter.values())
most_common_ids = [elem for elem, count in counter.items() if count == max_count]
final_id = most_common_ids[0]
final_patch = final_patch_list[final_id_list.index(final_id)]
else:
# 单次选择模式
select_agent = SelectorAgent(...)
final_id, final_patch = select_agent.run()
# 8. 保存结果
save_patches(instance_id=instance["instance_id"], patches_path=patches_path,
patches=final_patch, group_id=group_id)
save_selection_success(instance_id=instance["instance_id"], ...)
sandbox.stop_container()2.3 SelectorAgent 类
文件 : evaluation/patch_selection/trae_selector/selector_agent.py
python
class SelectorAgent:
def __init__(
self,
llm_config: ModelConfig,
sandbox: Sandbox,
project_path: str,
issue_description: str,
trajectory_file_name: str,
candidate_list: list[CandidatePatch],
max_turn: int = 50,
):
self.llm_config = llm_config
self.max_turn = max_turn
self.sandbox = sandbox
self.sandbox_session = self.sandbox.get_session()
# 重置代码到基线
self.sandbox_session.execute("git reset --hard HEAD")
# 初始化工具（仅 bash 和 str_replace_based_edit_tool）
self.tools = [
tools_registry[tool_name](model_provider=llm_config.model_provider.provider)
for tool_name in ["bash", "str_replace_based_edit_tool"]
]
# 初始化 LLM 客户端
self.llm_client = LLMClient(llm_config)
# 构建初始消息
self.initial_messages = [
LLMMessage(role="system", content=build_system_prompt(len(candidate_list)))
]
# 添加用户提示
user_prompt = f"""
[Codebase path]: {project_path}
[Github issue description]:{issue_description}
css
[Candidate Patches]:
"""
for idx, candidate in enumerate(candidate_list):
user_prompt += f"\nPatch-{idx + 1}:\n```\n{candidate.patch}\n```"
self.initial_messages.append(LLMMessage(role="user", content=user_prompt))2.4 系统提示词设计
python
def build_system_prompt(candidate_length: int) -> str:
return f"""\
# ROLE: Act as an expert code evaluator.
Given a codebase, an github issue and **{candidate_length} candidate patches**
proposed by your colleagues, your responsibility is to **select the correct one**
to solve the issue.
# WORK PROCESS:
1. Understand the Issue and Codebase
2. Analyze the Candidate Patches
3. Validate Functionality (Optional but Recommended)
4. Select the Best Patch
# FINAL REPORT:
### Status: succeed
### Result: Patch-x
### Analysis: [Explain why Patch-x is correct.]
# IMPORTANT TIPS:
1. Never avoid making a selection.
2. Do not propose new patches.
3. There must be at least one correct patch.
"""三、执行流程
3.1 SelectorAgent.run() 方法
python
def run(self):
"""Selector Agent 主循环"""
turn = 0
final_id, final_patch = self.candidate_list[0].id, self.candidate_list[0].patch
messages = self.initial_messages
while turn < self.max_turn:
turn += 1
# 1. 调用 LLM
llm_response = self.llm_client.chat(messages, self.llm_config, self.tools)
# 2. 记录轨迹
self.trajectory_recorder.record_llm_interaction(...)
# 3. 检查是否完成选择
match = re.search(
r"Status:\s*(success|succeed).*\n.*Result:\s*Patch-(\d+)",
llm_response.content,
)
if match:
# 提取选择的 Patch
selected_idx = int(match.group(2)) - 1
if selected_idx < len(self.candidate_list):
final_id = self.candidate_list[selected_idx].id
final_patch = self.candidate_list[selected_idx].patch
break
# 4. 执行工具调用
messages += parse_tool_response(
llm_response, self.sandbox_session
)
# 清理
self.trajectory_recorder.finalize_recording(True, final_patch)
self.sandbox_session.execute("git reset --hard HEAD")
self.sandbox_session.close()
return final_id, final_patch3.2 工具响应解析
python
def parse_tool_response(answer: LLMResponse, sandbox_session):
"""解析 LLM 工具调用并在沙箱中执行"""
result = []
for tool_call in answer.tool_calls:
tool_call_id = tool_call.call_id
tool_name = tool_call.name
# 1. 构建执行命令
if tool_name == "str_replace_based_edit_tool":
cmd = "cd /home/swe-bench/tools/ && /home/swe-bench/py312/bin/python3 execute_str_replace_editor.py"
elif tool_name == "bash":
cmd = "cd /home/swe-bench/tools/ && /home/swe-bench/py312/bin/python3 execute_bash.py"
else:
# 未知工具
result.append(LLMMessage(
role="user",
content="Tool not available...",
tool_result=ToolResult(success=False, ...)
))
continue
# 2. 添加参数
for key, value in tool_call.arguments.items():
cmd += f" --{key} {shlex.quote(str(value))}"
# 3. 在沙箱中执行
cmd += " > /home/swe-bench/tools/log.out 2>&1"
sandbox_session.execute(cmd)
sandbox_res = sandbox_session.execute("cat /home/swe-bench/tools/log.out")
# 4. 解析执行结果
status = "Tool Call Status: 0" # 假设成功
if "Tool Call Status: -1" in sandbox_res:
status = "Tool Call Status: -1"
result.append(LLMMessage(
role="user",
content=sandbox_res,
tool_result=ToolResult(
success=status == "Tool Call Status: 0",
...
)
))
return result四、关键数据结构
4.1 CandidatePatch
python
class CandidatePatch:
def __init__(
self,
id, # Patch ID
patch, # 原始 Patch
cleaned_patch, # 清理后的 Patch（用于去重）
is_success_regression, # 是否通过回归测试
is_success_patch, # 是否正确（Ground Truth）
):
self.id = id
self.patch = patch
self.cleaned_patch = cleaned_patch
self.is_success_regression = is_success_regression
self.is_success_patch = is_success_patch4.2 输入数据格式
json
{
"instance_id": "astropy__astropy-14369",
"issue": "Issue description...",
"patches": [
"patch diff 1",
"patch diff 2",
"patch diff N"
],
"success_id": [1, 0, 1],
"regressions": [
[],
["test_module.py::test_func"],
[]
]
}| 字段 | 说明 |
|---|---|
instance_id |
实例 ID |
issue |
问题描述 |
patches |
候选 Patch 列表 |
success_id |
是否正确 (1=正确, 0=错误) |
regressions |
回归测试失败的测试列表 |
五、输出结构
bash
results/
├── log/
│ └── group_0/
│ └── instance_id_voting_0_trail_1.json # LLM 交互日志
├── output/
│ └── group_0/
│ └── instance_id.log # 标准输出
├── patch/
│ └── group_0/
│ └── instance_id_1.patch # 选中的 Patch
└── statistics/
└── group_0/
└── instance_id.json # 统计结果六、设计特点
| 特点 | 说明 |
|---|---|
| 分组处理 | 避免上下文过长，提高选择准确性 |
| 回归过滤 | 优先选择通过回归测试的 Patch |
| 去重机制 | 避免重复分析相同的 Patch |
| 多数投票 | 提高选择稳定性 |
| 沙箱验证 | 在隔离环境中验证 Patch |
| 轨迹记录 | 完整记录选择过程用于分析 |
七、关键文件
| 文件 | 功能 |
|---|---|
evaluation/patch_selection/selector.py |
主入口 |
evaluation/patch_selection/trae_selector/selector_evaluation.py |
评估协调 |
evaluation/patch_selection/trae_selector/selector_agent.py |
Agent 实现 |
evaluation/patch_selection/trae_selector/sandbox.py |
沙箱环境 |
最后更新: 2026-03-16