作为国内表现上比较agentic的开发工具之一，trae项目开源了自家trae-agent开发工具，整体也有一段时间了。借着日常闲暇的机会，笔者计划研究下trae-agent的代码实现，看下这个本地AI开发辅助工具是如何运作的。
从开源项目的角度，trae-agent给的文档信息是比较充足的，但代码实现上只能说是MVP版本，只有一些基础功能。当然这个也可以理解，相信实际生产用的trae-agent会远比这个实现更加复杂，以及不可能所有人力都投入到开源项目的建设中。所以今天就简单看看这个项目的主流程。
主流程的话在agent.py当中，可以参考最深处BaseAgent中execute_task和_run_llm_step的实现，此处展示下基础流程部分：
python
class BaseAgent(ABC):
async def execute_task(self) -> AgentExecution:
try:
messages = self._initial_messages
step_number = 1
execution.agent_state = AgentState.RUNNING
while step_number <= self._max_steps:
step = AgentStep(step_number=step_number, state=AgentStepState.THINKING)
try: # 调用_run_llm_step和llm做chat
messages = await self._run_llm_step(step, messages, execution)
await self._finalize_step( # 记录trace
step, messages, execution
) # record trajectory for this step and update the CLI console
if execution.agent_state == AgentState.COMPLETED:
break
step_number += 1
except Exception as error:
execution.agent_state = AgentState.ERROR
step.state = AgentStepState.ERROR
step.error = str(error)
await self._finalize_step(step, messages, execution)
break
if step_number > self._max_steps and not execution.success:
execution.final_result = "Task execution exceeded maximum steps without completion."
execution.agent_state = AgentState.ERROR
return execution
async def _run_llm_step(
self, step: "AgentStep", messages: list["LLMMessage"], execution: "AgentExecution"
) -> list["LLMMessage"]:
# Display thinking state
step.state = AgentStepState.THINKING
self._update_cli_console(step, execution)
# Get LLM response
llm_response = self._llm_client.chat(messages, self._model_config, self._tools)
step.llm_response = llm_response
# Display step with LLM response
self._update_cli_console(step, execution)
# Update token usage
self._update_llm_usage(llm_response, execution)
if self.llm_indicates_task_completed(llm_response):
if self._is_task_completed(llm_response):
execution.agent_state = AgentState.COMPLETED
execution.final_result = llm_response.content
execution.success = True
return messages
else:
execution.agent_state = AgentState.RUNNING
return [LLMMessage(role="user", content=self.task_incomplete_message())]
else:
tool_calls = llm_response.tool_calls
return await self._tool_call_handler(tool_calls, step)
# 支默认持的tools
tools_registry: dict[str, type[Tool]] = {
"bash": BashTool,
"str_replace_based_edit_tool": TextEditorTool,
"json_edit_tool": JSONEditTool,
"sequentialthinking": SequentialThinkingTool,
"task_done": TaskDoneTool,
"ckg": CKGTool,
}在设置里头我们可以设置每个问题的最大step次数，execute_task中就通过loop去要求LLM不断迭代问题解决方案。_run_llm_step中则传递历史消息和tools给到LLM，_llm_client支持了多个provider，给回结果后，通过匹配硬编码的关键字判断是否完成task，否则继续迭代，当然如果识别出需要tool-call的话，就由代码逻辑主动调用对应tool。
针对messages，在execute_task的caller的new_task过程中，已经注入了硬编码的system-prompt，以及带模板的user-prompt，可以在agent_prompt.py中看到。
python
TRAE_AGENT_SYSTEM_PROMPT = """You are an expert AI software engineering agent.
File Path Rule: All tools that take a `file_path` as an argument require an **absolute path**. You MUST construct the full, absolute path by combining the `[Project root path]` provided in the user's message with the file's path inside the project.
For example, if the project root is `/home/user/my_project` and you need to edit `src/main.py`, the correct `file_path` argument is `/home/user/my_project/src/main.py`. Do NOT use relative paths like `src/main.py`.
Your primary goal is to resolve a given GitHub issue by navigating the provided codebase, identifying the root cause of the bug, implementing a robust fix, and ensuring your changes are safe and well-tested.
Follow these steps methodically:"""
# 下面忽略
class TraeAgent(BaseAgent):
@override
def new_task(
self,
task: str,
extra_args: dict[str, str] | None = None,
tool_names: list[str] | None = None,
):
# fixed system prompt
self._initial_messages: list[LLMMessage] = []
self._initial_messages.append(LLMMessage(role="system", content=self.get_system_prompt()))
# user message with issue to solve
user_message = ""
if not extra_args:
raise AgentError("Project path and issue information are required.")
if "project_path" not in extra_args:
raise AgentError("Project path is required")
self.project_path = extra_args.get("project_path", "")
if self.docker_config:
user_message += r"[Project root path]:\workspace\n\n"
else:
user_message += f"[Project root path]:\n{self.project_path}\n\n"
if "issue" in extra_args:
user_message += f"[Problem statement]: We're currently solving the following issue within our repository. Here's the issue text:\n{extra_args['issue']}\n"
optional_attrs_to_set = ["base_commit", "must_patch", "patch_path"]
for attr in optional_attrs_to_set:
if attr in extra_args:
setattr(self, attr, extra_args[attr])
self._initial_messages.append(LLMMessage(role="user", content=user_message))由于每次message只传递当次问答的message，所以针对单个问题，每个LLMClient需要实现对历史问答的记录。比如OllamaClient的实现：
python
class OllamaClient(BaseLLMClient):
@override
def chat(
self,
messages: list[LLMMessage],
model_config: ModelConfig,
tools: list[Tool] | None = None,
reuse_history: bool = True,
) -> LLMResponse:
msgs: ResponseInputParam = self.parse_messages(messages)
if reuse_history:
self.message_history = self.message_history + msgs
else:
self.message_history = msgs主流程大概是这些，代码架构上来看实现也不算太复杂，用专有Agent框架复刻也是可以的，然后硬编码注入的内容比较多，所以整体上来看trae-agent开源版离实际投产的标准还有一些距离。如果自己的项目要复用这套框架开发内容，最好保证以下事情，一是有一个足够可靠确定性高的模型，二是需要单独一套prompt管理模块把硬编码注入的prompt等文本做统一管理，三是有一套执行稳定性跟评测机制让整个agent过程能够针对自己项目的问题给出解决方案，至少得有面子上过得去的output才行。