最近Deep Research, Harness Engineering这些概念挺火的。而在开发LangGraph时发现其团队新开发Agent：Deep Agents，专用于深度研究和规范工程。
https://www.langchain.com/deep-agents
Deep Agents 是一个基于 LangChain 代理核心构建模块构建的独立库。它使用 LangGraph 运行时来实现持久化执行、流式处理、人机协同等特性。
它是一个独立的库，用于构建能够处理复杂、多步骤任务的Agent，具备规划能力、用于上下文管理的文件系统以及派生子Agent的能力。
规划和任务分解
深度Agent包含一个内置的 write_todos 工具，使Agent能够将复杂任务分解为离散的步骤，跟踪进度，并在新信息出现时调整计划。
上下文管理
文件系统工具（ls、read_file、write_file、edit_file）允许Agent将大量上下文卸载到内存中，防止上下文窗口溢出，并能够处理可变长度的工具结果。
子Agent派生
内置的 task 工具使Agent能够为上下文隔离派生专门的子Agent。这可以保持主Agent的上下文清洁，同时仍能深入处理特定的子任务。
长期记忆
使用 LangGraph 的 Store 扩展Agent，使其具有跨线程的持久内存。Agent可以从以前的对话中保存和检索信息。
virtualenv deep_env
source deep_env/Scripts/activate //windows
pip install deepagents -i https://pypi.tuna.tsinghua.edu.cn/simple //0.5.2
pip install langchain-openai python-dotenv -i https://pypi.tuna.tsinghua.edu.cn/simple
from deepagents import create_deep_agent
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
agent = create_deep_agent(
tools=[get_weather],
system_prompt="You are a helpful assistant",
)
# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
from dotenv import dotenv_values
from langchain_openai import ChatOpenAI
from typing import Literal
from deepagents import create_deep_agent
import requests, json
env_vars = dotenv_values('.env')
OPENAI_KEY = env_vars['OPENAI_API_KEY']
OPENAI_BASE_URL = env_vars['OPENAI_API_BASE']
SERPER_KEY = env_vars['SERPER_KEY']
llm = ChatOpenAI(model="gpt-4o-mini", api_key=OPENAI_KEY,base_url=OPENAI_BASE_URL)
def internet_search(query):
"""Get real-time Internet information"""
url = "https://google.serper.dev/search"
payload = json.dumps({
"q": query,
"num": 1,
})
headers = {
'X-API-KEY': SERPER_KEY,
'Content-Type': 'application/json'
}
response = requests.post(url, headers=headers, data=payload)
data = json.loads(response.text)
print(668,"fetch_real_time_info", data)
if 'organic' in data:
return json.dumps(data['organic'], ensure_ascii=False)
else:
return json.dumps({"error": "No organic results found"}, ensure_ascii=False)
# System prompt to steer the agent to be an expert researcher
research_instructions = """You are an expert researcher. Your job is to conduct thorough research and then write a polished report.
You have access to an internet search tool as your primary means of gathering information.
## `internet_search`
Use this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.
"""
agent = create_deep_agent(model=llm, tools=[internet_search], system_prompt=research_instructions)
result = agent.invoke({"messages": [{"role": "user", "content": "what is langgraph?"}]})
# Print the agent's response
print(111, result["messages"][-1].content)
Deep Agents比LangGraph有了更强的封装性，代码也更显简洁。它有不少更新的特性和优点，有待个人发掘！
