Trae Agent

# 自动生成轨迹文件
trae-cli run "Debug the authentication module"
# 保存到: trajectory_20250612_220546.json

# 自定义轨迹文件名
trae-cli run "Optimize the database queries" --trajectory-file optimization_debug.json

# 轨迹文件包含：
# - LLM交互：所有消息、响应和工具调用
# - 代理步骤：状态转换和决策点
# - 工具使用：调用的工具及其结果
# - 元数据：时间戳、token使用量、执行指标

{
  "default_provider": "anthropic",
  "max_steps": 20,
  "enable_lakeview": true,
  "model_providers": {
    "openai": {
      "api_key": "your_openai_api_key",
      "model": "gpt-4o",
      "max_tokens": 128000,
      "temperature": 0.5
    },
    "anthropic": {
      "api_key": "your_anthropic_api_key",
      "model": "claude-sonnet-4-20250514",
      "max_tokens": 4096,
      "temperature": 0.5
    },
    "openrouter": {
      "api_key": "your_openrouter_api_key",
      "model": "openai/gpt-4o",
      "max_tokens": 4096
    }
  }
}

# 代理状态定义
class AgentState(Enum):
    THINKING = "thinking"
    CALLING_TOOL = "calling_tool"
    REFLECTING = "reflecting"
    COMPLETED = "completed"
    ERROR = "error"

# 状态转换记录
if self.trajectory_recorder:
    self.trajectory_recorder.record_agent_step(
        step_number=step.step_number,
        state=step.state.value,
        llm_messages=messages,
        llm_response=step.llm_response,
        tool_calls=step.tool_calls,
        tool_results=step.tool_results,
        reflection=step.reflection,
        error=step.error
    )

# 透明的模块化设计，便于研究和扩展
from trae_agent.agent.trae_agent import TraeAgent
from trae_agent.utils.llm_client import LLMProvider
from trae_agent.utils.config import ModelParameters

# 创建可定制的代理实例
agent = TraeAgent(
    provider=LLMProvider.ANTHROPIC,
    model_parameters=model_parameters,
    max_steps=20,
    enable_lakeview=True
)

# 设置轨迹记录用于研究分析
trajectory_path = agent.setup_trajectory_recording("research_session.json")

# 执行任务并收集详细数据
execution = await agent.execute_task()
print(f"研究数据保存至: {trajectory_path}")

# 创建自定义工具
from trae_agent.tools.base import BaseTool

class CustomDatabaseTool(BaseTool):
    """自定义数据库操作工具"""

    def __init__(self):
        super().__init__(
            name="database_query",
            description="执行数据库查询操作"
        )

    async def execute(self, query: str, database: str = "default"):
        """执行数据库查询"""
        # 实现数据库查询逻辑
        result = await self.run_query(query, database)
        return {
            "success": True,
            "result": result,
            "rows_affected": len(result)
        }

# 注册自定义工具
agent.register_tool(CustomDatabaseTool())

{
  "default_provider": "anthropic",
  "max_steps": 50,
  "enable_lakeview": true,
  "custom_tools": ["database_tool", "api_tool"],
  "model_providers": {
    "anthropic": {
      "model": "claude-sonnet-4-20250514",
      "max_tokens": 8192,
      "temperature": 0.3,
      "top_p": 0.9,
      "max_retries": 5
    },
    "openrouter": {
      "model": "anthropic/claude-3-5-sonnet",
      "max_tokens": 4096,
      "temperature": 0.5
    }
  },
  "lakeview_config": {
    "model_provider": "anthropic",
    "model_name": "claude-sonnet-4-20250514",
    "summary_length": "concise"
  },
  "trajectory_config": {
    "auto_save": true,
    "include_tool_internals": true,
    "compression": "gzip"
  }
}

# 1. Fork项目并设置开发环境
git clone https://github.com/your-username/trae-agent.git
cd trae-agent
uv sync --all-extras && pre-commit install

# 2. 创建功能分支
git checkout -b feature/amazing-feature

# 3. 开发新功能
# - 遵循PEP 8代码规范
# - 添加相应的测试用例
# - 更新文档说明

# 4. 提交更改
git commit -m 'Add amazing feature'
git push origin feature/amazing-feature

# 5. 创建Pull Request
# 在GitHub上创建PR，描述更改内容和测试情况

# 设置SWE-bench评估环境
cd evaluation
bash swebench_setup.sh

# 运行评估脚本
python swebench.py \
    --provider anthropic \
    --model claude-sonnet-4-20250514 \
    --max-steps 30 \
    --trajectory-dir ./trajectories \
    --results-dir ./results

# 分析评估结果
python analyze_results.py --results-dir ./results