简介

在当今快节奏的数字世界中，电子邮件管理已成为一项关键的生产力挑战。本文探讨了一款由人工智能驱动的电子邮件助手，它结合了三种类型的记忆系统来智能地处理通信：

• 语义记忆：存储事实知识（联系人、公司信息）
• 情景记忆：记住电子邮件对话历史
• 程序记忆：了解用户处理电子邮件的偏好

LangMem 是什么？

长期记忆使代理能够记住对话中的重要信息。LangMem 提供了从聊天中提取有意义的细节、存储它们并使用它们来改善未来互动的方法。从本质上讲，LangMem 中的每个记忆操作都遵循相同的模式：

1. 接受对话和当前记忆状态
2. 提示 LLM 确定如何扩展或巩固记忆状态
3. 使用更新后的内存状态进行响应

LangMem 帮助代理从一段时间内的互动中学习和适应。

它提供工具来从对话中提取重要信息，通过迅速改进来优化代理行为，并维持长期记忆。

它提供了可与任何存储系统一起使用的功能原语以及与 LangGraph 存储层的本机集成。

这使你的代理能够不断改进、个性化他们的响应并在会话之间保持一致的行为。

主要特色

• 可与任何存储系统配合使用的核心内存 API
• 代理可以使用内存管理工具在“热路径”中的活动对话期间记录和搜索信息
• 后台内存管理器，可自动提取、合并和更新代理知识
• 与 LangGraph 的长期记忆存储原生集成，默认在所有 LangGraph 平台部署中可用

记忆类型

技术栈

• LangGraph用于工作流程编排
• Groq 的 llama-3.3–70b-versatile用于 AI 处理?
• Streamlit用于用户界面
• LangSmith用于监控和调试 ?

架构：三内存系统

语义记忆

存储和检索概念信息：

from langmem import create_manage_memory_tool, create_search_memory_tool
manage_memory_tool = create_manage_memory_tool(
namespace=("email_assistant", "{user_id}", "collection")"email_assistant", "{user_id}", "collection")
)
search_memory_tool = create_search_memory_tool(
namespace=("email_assistant", "{user_id}", "collection")
)

情景记忆

维护对话历史和过去的互动记录：

class CodeReviewState(TypedDict):
email_input: dict
messages: Annotated[list, add_messages]
profile: dict

程序性记忆 

处理电子邮件处理规则和工作流程：

prompt_instructions = {
"triage_rules": {
"ignore": "Marketing newsletters, spam emails…",
"notify": "Team member out sick, build notifications…",
"respond": "Direct questions, meeting requests…"
}
}

核心组件

1. 电子邮件分类系统

```python

def triage_router（状态：状态，配置，存储）->命令：

#分析收到的电子邮件并将其路由至：

 #— 忽略?

 #— 通知?

# — 回应✍️

```

2. 内存存储

store = InMemoryStore(index={"embed": embeddings})

3. LangGraph 工作流引擎

def create_workflow():
workflow = StateGraph(State)
workflow.add_node(triage_router)
workflow.add_node("response_agent", response_agent)
workflow.add_edge(START, "triage_router")

智能功能

1. 多提示优化器

创建多提示优化器以提高提示有效性。此功能创建一个优化器，可以使用相同的优化策略同时分析和改进多个提示。每个提示都使用所选策略进行优化

from langmem import create_multi_prompt_optimizer
optimizer = create_multi_prompt_optimizer(
    "groq:llama-3.3-70b-versatile","groq:llama-3.3-70b-versatile",
    kind="prompt_memory",
)
# Example conversation with feedback
conversation = [
    {"role": "user", "content": "Tell me about the solar system"},
    {"role": "assistant", "content": "The solar system consists of..."},
]
feedback = {"clarity": "needs more structure"}
# Use conversation history to improve the prompts
trajectories = [(conversation, feedback)]
prompts = [
    {"name": "research", "prompt": "Research the given topic thoroughly"},
    {"name": "summarize", "prompt": "Summarize the research findings"},
]
better_prompts = await optimizer.ainvoke(
    {"trajectories": trajectories, "prompts": prompts}
)
print(better_prompts)

内存优化提示器

从对话历史中学习：

1. 从过去的互动中提取成功模式
2. 从反馈中识别需要改进的领域
3. 将学到的模式应用于新的提示

背景感知的回应

@traceable(name="create_summary")
def create_summary(state: CodeReviewState):
summary_prompt = ChatPromptTemplate.from_messages([
SystemMessage(content="""Create a concise summary…"""),
HumanMessage(content="Review: {review}\nSeverity: {severity}")
])

语义搜索

store.get(("userid,), "namespace").value['prompt']"userid,), "namespace").value['prompt']

高级实施

邮件管理 

• 自动邮件分类
• 基于优先级的处理
• 智能回复生成

会议安排

@tool
def schedule_meeting(
attendees: list[str],
subject: str,
duration_minutes: int,
preferred_day: str
) -> str:
return f"Meeting scheduled for {preferred_day}"

任务追踪

- 监控行动项目

- 跟进未完成的任务

- 更新任务状态

性能监控

LangSmith 集成

from uuid import uuid4
import os
from langsmith import Client
unique_id = uuid4().hex[0:8]
#
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_PROJECT"] = f"Code_Review - {unique_id}"
os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGSMITH_API_KEY"] = os.getenv("LANGSMITH_API_KEY")

代码实现

1.安装

pip install langchain langchain-groq langsmith langgraph

2.环境配置

from langsmith import Client
from langsmith.run_helpers import traceable
from uuid import uuid4
import os
# Initialize LangSmith
unique_id = uuid4().hex[0:8]
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_PROJECT"] = f"Email_Assitant - {unique_id}"
os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGSMITH_API_KEY"] = os.getenv("LANGSMITH_API_KEY")
client = Client()

3. 设置个人资料

profile = {
    "name": "Plaban","name": "Plaban",
    "full_name": "Plaban Nayak",
    "user_profile_background": "Senior software engineer leading a team of 5 developers",
}

4. 提示说明

prompt_instructions = {
    "triage_rules": {"triage_rules": {
        "ignore": "Marketing newsletters, spam emails, mass company announcements",
        "notify": "Team member out sick, build system notifications, project status updates",
        "respond": "Direct questions from team members, meeting requests, critical bug reports",
    },
    "agent_instructions": "Use these tools when appropriate to help manage Plaban's tasks efficiently."
}

5. 示例电子邮件输入

email = {
    "from": "Alice Smith ","from": "Alice Smith ",
    "to": "Plaban Nayak",
    "subject": "Quick question about API documentation",
    "body": """
Hi John,
I was reviewing the API documentation for the new authentication service and noticed a few endpoints seem to be missing from the specs. Could you help clarify if this was intentional or if we should update the docs?
Specifically, I'm looking at:
- /auth/refresh
- /auth/validate
Thanks!
Alice""",
}

6.实例化内存存储

from langgraph.store.memory import InMemoryStore
from langchain_huggingface import HuggingFaceEmbeddings
model_name = "BAAI/bge-small-en"
model_kwargs = {"device": "cpu"}
encode_kwargs = {"normalize_embeddings": True}
embeddings = HuggingFaceEmbeddings(
    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
)
#
store = InMemoryStore(index={"embed": embeddings})

7. 实例化 LLM

from langchain_groq import ChatGroq
llm = ChatGroq(model="llama-3.3-70b-versatile", api_key=os.getenv("GROQ_API_KEY"))

8. 不同的提示

# Agent prompt baseline
agent_system_prompt = """
< Role >
You are {full_name}'s executive assistant. You are a top-notch executive assistant who cares about {name} performing as well as possible.
 Role >
< Tools >
You have access to the following tools to help manage {name}'s communications and schedule:
1. write_email(to, subject, content) - Send emails to specified recipients
2. schedule_meeting(attendees, subject, duration_minutes, preferred_day) - Schedule calendar meetings
3. check_calendar_availability(day) - Check available time slots for a given day
 Tools >
< Instructions >
{instructions}
 Instructions >
"""
# Agent prompt semantic memory
agent_system_prompt_memory = """
< Role >
You are {full_name}'s executive assistant. You are a top-notch executive assistant who cares about {name} performing as well as possible.
 Role >
< Tools >
You have access to the following tools to help manage {name}'s communications and schedule:
1. write_email(to, subject, content) - Send emails to specified recipients
2. schedule_meeting(attendees, subject, duration_minutes, preferred_day) - Schedule calendar meetings
3. check_calendar_availability(day) - Check available time slots for a given day
4. manage_memory("email_assistant", user, "collection") - Store any relevant information about contacts, actions, discussion, etc. in memory for future reference
5. manage_memory("email_assistant", user, "user_profile") - Store any relevant information about the recipient, {name}, in the user profile for future reference the current user profile is shown below
6. search_memory("email_assistant", user, "collection") - Search memory for detail from previous emails
7. manage_memory("email_assistant", user, "instructions") - Update the instructions for agent tool usage based upon the user feedback
 Tools >
< User profile >
{profile}
 User profile >
< Instructions >
{instructions}
 Instructions >
"""
# Triage prompt
triage_system_prompt = """
< Role >
You are {full_name}'s executive assistant. You are a top-notch executive assistant who cares about {name} performing as well as possible.
 Role >
< Background >
{user_profile_background}.
 Background >
< Instructions >
{name} gets lots of emails. Your job is to categorize each email into one of three categories:
1. IGNORE - Emails that are not worth responding to or tracking
2. NOTIFY - Important information that {name} should know about but doesn't require a response
3. RESPOND - Emails that need a direct response from {name}
Classify the below email into one of these categories.
 Instructions >
< Rules >
Emails that are not worth responding to:
{triage_no}
There are also other things that {name} should know about, but don't require an email response. For these, you should notify {name} (using the `notify` response). Examples of this include:
{triage_notify}
Emails that are worth responding to:
{triage_email}
 Rules >
< Few shot examples >
{examples}
 Few shot examples >
"""
triage_user_prompt = """
Please determine how to handle the below email thread:
From: {author}
To: {to}
Subject: {subject}
{email_thread}"""

9. 按照提示的格式输入格式示例

template = """Email Subject: {subject}
Email From: {from_email}
Email To: {to_email}
Email Content:
```
{content}
```
> Triage Result: {result}"""
# Format list of few shots
def format_few_shot_examples(examples):
    strs = ["Here are some previous examples:"]
    for eg in examples:
        strs.append(
            template.format(
                subject=eg.value["email"]["subject"],
                to_email=eg.value["email"]["to"],
                from_email=eg.value["email"]["author"],
                content=eg.value["email"]["email_thread"][:400],
                result=eg.value["label"],
            )
        )
    return "\n\n------------\n\n".join(strs)

10. 定义响应格式

from pydantic import BaseModel, Field
from typing_extensions import TypedDict, Literal, Annotated
#
class Router(BaseModel):
    """Analyze the unread email and route it according to its content."""
    reasoning: str = Field(
        description="Step-by-step reasoning behind the classification."
    )
    classification: Literal["ignore", "respond", "notify"] = Field(
        description="The classification of an email: 'ignore' for irrelevant emails, "
        "'notify' for important information that doesn't need a response, "
        "'respond' for emails that need a reply",
    )
#
llm_router = llm.with_structured_output(Router)

11. 定义状态

from langgraph.graph import add_messages
from langgraph.graph import StateGraph, START, END
from langgraph.types import Command
from typing import Literal
from IPython.display import Image, display
class State(TypedDict):
    email_input: dict
    messages: Annotated[list, add_messages]
    profile: dict

12. 更新 triage_router 获取来自商店的忽略、通知和响应规则

@traceable(name="triage_router")
def triage_router(state: State, config, store) -> Command[
    Literal["response_agent", "__end__"]
]:
    author = state['email_input']['author']
    to = state['email_input']['to']
    subject = state['email_input']['subject']
    email_thread = state['email_input']['email_thread']
    namespace = (
        "email_assistant",
        config['configurable']['langgraph_user_id'],
        "examples"
    )
    examples = store.search(
        namespace,
        query=str({"email": state['email_input']})
    )
    examples=format_few_shot_examples(examples)
    langgraph_user_id = config['configurable']['langgraph_user_id']
    namespace = (langgraph_user_id, )
    result = store.get(namespace, "triage_ignore")
    if result is None:
        store.put(
            namespace,
            "triage_ignore",
            {"prompt": prompt_instructions["triage_rules"]["ignore"]}
        )
        ignore_prompt = prompt_instructions["triage_rules"]["ignore"]
    else:
        ignore_prompt = result.value['prompt']
    result = store.get(namespace, "triage_notify")
    if result is None:
        store.put(
            namespace,
            "triage_notify",
            {"prompt": prompt_instructions["triage_rules"]["notify"]}
        )
        notify_prompt = prompt_instructions["triage_rules"]["notify"]
    else:
        notify_prompt = result.value['prompt']
    result = store.get(namespace, "triage_respond")
    if result is None:
        store.put(
            namespace,
            "triage_respond",
            {"prompt": prompt_instructions["triage_rules"]["respond"]}
        )
        respond_prompt = prompt_instructions["triage_rules"]["respond"]
    else:
        respond_prompt = result.value['prompt']
    system_prompt = triage_system_prompt.format(
        full_name=profile["full_name"],
        name=profile["name"],
        user_profile_background=profile["user_profile_background"],
        triage_no=ignore_prompt,
        triage_notify=notify_prompt,
        triage_email=respond_prompt,
        examples=examples
    )
    user_prompt = triage_user_prompt.format(
        author=author,
        to=to,
        subject=subject,
        email_thread=email_thread
    )
    result = llm_router.invoke(
        [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ]
    )
    if result.classification == "respond":
        print("? Classification: RESPOND - This email requires a response")
        goto = "response_agent"
        update = {
            "messages": [
                {
                    "role": "user",
                    "content": f"Respond to the email {state['email_input']}",
                }
            ]
        }
    elif result.classification == "ignore":
        print("? Classification: IGNORE - This email can be safely ignored")
        update = None
        goto = END
    elif result.classification == "notify":
        # If real life, this would do something else
        print("? Classification: NOTIFY - This email contains important information")
        update = None
        goto = END
    else:
        raise ValueError(f"Invalid classification: {result.classification}")
    return Command(goto=goto, update=update)

13.构建工具

from langchain_core.tools import tool
@tool
def write_email(to: str, subject: str, content: str) -> str:
    """Write and send an email."""
    # Placeholder response - in real app would send email
    return f"Email sent to {to} with subject '{subject}'"
#
@tool
def schedule_meeting(
    attendees: list[str],
    subject: str,
    duration_minutes: int,
    preferred_day: str
) -> str:
    """Schedule a calendar meeting."""
    # Placeholder response - in real app would check calendar and schedule
    return f"Meeting '{subject}' scheduled for {preferred_day} with {len(attendees)} attendees"
#
@tool
def check_calendar_availability(day: str) -> str:
    """Check calendar availability for a given day."""
    # Placeholder response - in real app would check actual calendar
    return f"Available times on {day}: 9:00 AM, 2:00 PM, 4:00 PM"

14. 使用语义记忆

from langmem import create_manage_memory_tool, create_search_memory_tool
manage_memory_tool = create_manage_memory_tool(
    namespace=(
        "email_assistant",
        "{langgraph_user_id}",
        "collection"
    )
)
search_memory_tool = create_search_memory_tool(
    namespace=(
        "email_assistant",
        "{langgraph_user_id}",
        "collection"
    )
)

15. 语义记忆的系统提示

agent_system_prompt_memory = """
< Role >
You are {full_name}'s executive assistant. You are a top-notch executive assistant who cares about {name} performing as well as possible.
 Role >
< Tools >
You have access to the following tools to help manage {name}'s communications and schedule:
1. write_email(to, subject, content) - Send emails to specified recipients
2. schedule_meeting(attendees, subject, duration_minutes, preferred_day) - Schedule calendar meetings
3. check_calendar_availability(day) - Check available time slots for a given day
4. manage_memory - Store any relevant information about contacts, actions, discussion, etc. in memory for future reference
5. search_memory - Search for any relevant information that may have been stored in memory
 Tools >
< Instructions >
{instructions}
 Instructions >
"""

16.更新商店提示

def create_prompt(state, config, store):
    langgraph_user_id = config['configurable']['langgraph_user_id']
    namespace = (langgraph_user_id, )
    result = store.get(namespace, "agent_instructions")
    if result is None:
        store.put(
            namespace,
            "agent_instructions",
            {"prompt": prompt_instructions["agent_instructions"]}
        )
        prompt = prompt_instructions["agent_instructions"]
    else:
        prompt = result.value['prompt']
    return [
        {
            "role": "system",
            "content": agent_system_prompt_memory.format(
                instructions=prompt,
                **profile
            )
        }
    ] + state['messages']

17. 创建电子邮件代理

from langgraph.prebuilt import create_react_agent
#
tools= [
    write_email,
    schedule_meeting,
    check_calendar_availability,
    manage_memory_tool,
    search_memory_tool
]
response_agent = create_react_agent(
    llm,
    tools=tools,
    prompt=create_prompt,
    # Use this to ensure the store is passed to the agent
    store=store
)
response_agent

18. 设置配置参数以访问语义存储

config = {"configurable": {"langgraph_user_id": "Plaban"}}
response_agent.invoke({"messages":[{"role":"user","content":"What is the availability for Tuesday"}]},config=config)
###############RESPONSE###############################################
{'messages': [HumanMessage(content='What is the availability for Tuesday', additional_kwargs={}, response_metadata={}, id='7005b579-a019-46e0-9e6c-d142b075298d'),
  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_12vn', 'function': {'arguments': '{"day": "Tuesday"}', 'name': 'check_calendar_availability'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 933, 'total_tokens': 950, 'completion_time': 0.061818182, 'prompt_time': 0.059918984, 'queue_time': 0.255777121, 'total_time': 0.121737166}, 'model_name': 'llama-3.3-70b-versatile', 'system_fingerprint': 'fp_41c250edc7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0ee36a6c-62ad-4596-a56b-50826ec19877-0', tool_calls=[{'name': 'check_calendar_availability', 'args': {'day': 'Tuesday'}, 'id': 'call_12vn', 'type': 'tool_call'}], usage_metadata={'input_tokens': 933, 'output_tokens': 17, 'total_tokens': 950}),
  ToolMessage(content='Available times on Tuesday: 9:00 AM, 2:00 PM, 4:00 PM', name='check_calendar_availability', id='4de197b1-87f0-48b8-96d7-096b3d2fe91c', tool_call_id='call_12vn'),
  AIMessage(content='Tuesday has available time slots at 9:00 AM, 2:00 PM, and 4:00 PM.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 26, 'prompt_tokens': 982, 'total_tokens': 1008, 'completion_time': 0.094545455, 'prompt_time': 0.063813211, 'queue_time': 0.253129614, 'total_time': 0.158358666}, 'model_name': 'llama-3.3-70b-versatile', 'system_fingerprint': 'fp_c4760ee73b', 'finish_reason': 'stop', 'logprobs': None}, id='run-0f1a2012-b773-48e8-83ee-174ffee406b2-0', usage_metadata={'input_tokens': 982, 'output_tokens': 26, 'total_tokens': 1008})]}

19. 构建工作流程

email_agent = StateGraph(State)
email_agent.add_node(triage_router)
email_agent.add_node("response_agent", response_agent)"response_agent", response_agent)
email_agent.add_edge(START, "triage_router")
email_agent_workflow = email_agent.compile(store=store)
email_agent_workflow

20. 检查代理的当前行为

email_input = {
    "author": "Alice Jones ","author": "Alice Jones ",
    "to": "Plaban Nayak ",
    "subject": "Quick question about API documentation",
    "email_thread": """Hi Plaban,
Urgent issue - your service is down. Is there a reason why""",
}
#
config = {"configurable": {"langgraph_user_id": "Plaban"}}
#
response = email_agent_workflow.invoke(
    {"email_input": email_input},
    config=config
)
for m in response["messages"]:
    m.pretty_print()

###############################RESPONSE##############################
? Classification: RESPOND - This email requires a response
================================ Human Message =================================
Respond to the email {'author': 'Alice Jones <alice.jones@bar.com>', 'to': 'Plaban Nayak <plaban.nayak@company.com>', 'subject': 'Quick question about API documentation', 'email_thread': 'Hi Plaban,\n\nUrgent issue - your service is down. Is there a reason why'}
================================== Ai Message ==================================
Tool Calls:
  write_email (call_37w0)
 Call ID: call_37w0
  Args:
    to: alice.jones@bar.com
    subject: Re: Quick question about API documentation
    content: Hi Alice, 
Thanks for reaching out. Sorry to hear that our service is down. I'll look into this immediately and get back to you with an update. 
Best, 
Plaban Nayak
================================= Tool Message =================================
Name: write_email
Email sent to alice.jones@bar.com with subject 'Re: Quick question about API documentation'
================================== Ai Message ==================================
Tool Calls:
  manage_memory (call_n6ph)
 Call ID: call_n6ph
  Args:
    action: create
    content: Alice Jones from Bar.com contacted Plaban regarding urgent issue with service being down.
================================= Tool Message =================================
Name: manage_memory
created memory fc6cecf6-d266-4ecb-aa49-7ef92ed49834
================================== Ai Message ==================================
<function=search_memory({"query": "service down", "limit": 1})</function>

response['messages'][-4].content

##########################################
"Email sent to alice.jones@bar.com with subject 'Re: Quick question about API documentation'"

response['messages'][1].additional_kwargs['tool_calls']'messages'][1].additional_kwargs['tool_calls']
##################################################################
[{'id': 'call_37w0',
  'function': {'arguments': '{"to": "alice.jones@bar.com", "subject": "Re: Quick question about API documentation", "content": "Hi Alice, \\n\\nThanks for reaching out. Sorry to hear that our service is down. I\'ll look into this immediately and get back to you with an update. \\n\\nBest, \\nPlaban Nayak"}',
   'name': 'write_email'},
  'type': 'function'}]

21. 使用 LLM 来更新说明。

指示代理始终使用发件人姓名签署电子邮件

from langmem import create_multi_prompt_optimizer
conversations = [
    (
        response['messages'],
        "Always sign your email `Plaban Nayak`"
    )
]
### Update the Instructions again
prompts = [
    {
        "name": "main_agent",
        "prompt": store.get(("Plaban",), "agent_instructions").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on how the agent should write emails or schedule events"
    },
    {
        "name": "triage-ignore",
        "prompt": store.get(("Plaban",), "triage_ignore").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails should be ignored"
    },
    {
        "name": "triage-notify",
        "prompt": store.get(("Plaban",), "triage_notify").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails the user should be notified of"
    },
    {
        "name": "triage-respond",
        "prompt": store.get(("Plaban",), "triage_respond").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails should be responded to"
    },
]

22. 实例化多提示编排器

optimizer = create_multi_prompt_optimizer(
    "groq:llama-3.3-70b-versatile","groq:llama-3.3-70b-versatile",
    kind="prompt_memory",
)
#
updated = optimizer.invoke(
    {"trajectories": conversations, "prompts": prompts}
)
updated
#############################RESPONSE###################################
[{'name': 'main_agent',
  'prompt': "Use these tools when appropriate to help manage Plaban's tasks efficiently. Always sign your email as 'Plaban Nayak'.",
  'update_instructions': 'keep the instructions short and to the point',
  'when_to_update': 'Update this prompt whenever there is feedback on how the agent should write emails or schedule events'},
 {'name': 'triage-ignore',
  'prompt': 'Marketing newsletters, spam emails, mass company announcements',
  'update_instructions': 'keep the instructions short and to the point',
  'when_to_update': 'Update this prompt whenever there is feedback on which emails should be ignored'},
 {'name': 'triage-notify',
  'prompt': 'Team member out sick, build system notifications, project status updates',
  'update_instructions': 'keep the instructions short and to the point',
  'when_to_update': 'Update this prompt whenever there is feedback on which emails the user should be notified of'},
 {'name': 'triage-respond',
  'prompt': 'Direct questions from team members, meeting requests, critical bug reports. Always sign your email as Plaban Nayak',
  'update_instructions': 'keep the instructions short and to the point',
  'when_to_update': 'Update this prompt whenever there is feedback on which emails should be responded to'}]

#json dumps is a bit easier to read
import json
print(json.dumps(updated, indent=4))
################################RESPONSE################################
[
    {
        "name": "main_agent",
        "prompt": "Use these tools when appropriate to help manage Plaban's tasks efficiently. Always sign your email as 'Plaban Nayak'.",
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on how the agent should write emails or schedule events"
    },
    {
        "name": "triage-ignore",
        "prompt": "Marketing newsletters, spam emails, mass company announcements",
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails should be ignored"
    },
    {
        "name": "triage-notify",
        "prompt": "Team member out sick, build system notifications, project status updates",
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails the user should be notified of"
    },
    {
        "name": "triage-respond",
        "prompt": "Direct questions from team members, meeting requests, critical bug reports. Always sign your email as Plaban Nayak",
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails should be responded to"
    }
]

23. 更新商店并修改说明

for i, updated_prompt in enumerate(updated):
    old_prompt = prompts[i]
    if updated_prompt['prompt'] != old_prompt['prompt']:
        name = old_prompt['name']
        print(f"updated {name}")
        if name == "main_agent":
              store.put(
                  ("lance",),
                  "agent_instructions",
                  {"prompt":updated_prompt['prompt']}
              )
        else:
              store.put(
                  ("Plaban",),
                  name,
                  {"prompt":updated_prompt['prompt']}
              )
        print(f"{name} prompt updated in the store Successfully")
    else:
        print(f"{name} prompt not updated in the store")
#########################RESPONSE##############################
updated main_agent
main_agent prompt updated in the store Successfully
main_agent prompt not updated in the store
main_agent prompt not updated in the store
updated triage-respond
triage-respond prompt updated in the store Successfully

查看商店

store.get(("Plaban",), "agent_instructions").value['prompt']"Plaban",), "agent_instructions").value['prompt']
################RESPONSE#############################
"Use these tools when appropriate to help manage Plaban's tasks efficiently."

24. 调用电子邮件助手

response = email_agent_workflow.invoke(
    {"email_input": email_input},"email_input": email_input},
    config=config
)
#
for m in response["messages"]:
    m.pretty_print()
#########################RESPONSE#####################################
================================ Human Message =================================
Respond to the email {'author': 'Alice Jones <alice.jones@bar.com>', 'to': 'Plaban Nayak <plaban.nayak@company.com>', 'subject': 'Quick question about API documentation', 'email_thread': 'Hi Plaban,\n\nUrgent issue - your service is down. Is there a reason why'}
================================== Ai Message ==================================
Tool Calls:
  write_email (call_tsk5)
 Call ID: call_tsk5
  Args:
    to: alice.jones@bar.com
    subject: Re: Quick question about API documentation
    content: Hi Alice, 
I'm so sorry to hear that our service is down. I'll look into this immediately and get back to you with an update. 
Best, 
Plaban Nayak
================================= Tool Message =================================
Name: write_email
Email sent to alice.jones@bar.com with subject 'Re: Quick question about API documentation'
================================== Ai Message ==================================
Tool Calls:
  manage_memory (call_n4q8)
 Call ID: call_n4q8
  Args:
    action: create
    content: Alice Jones from bar.com reached out about an urgent issue with the service being down.
================================= Tool Message =================================
Name: manage_memory
created memory 3b9605de-13b7-4573-8e61-a443d2eba6e0
================================== Ai Message ==================================
Tool Calls:
  write_email (call_f1dp)
 Call ID: call_f1dp
  Args:
    to: dev.team@company.com
    subject: Service Down - Urgent Attention Required
    content: Hi Team, 
We have an urgent issue - our service is currently down. Please look into this and get back to me with an update as soon as possible. 
Best, 
Plaban Nayak
================================= Tool Message =================================
Name: write_email
Email sent to dev.team@company.com with subject 'Service Down - Urgent Attention Required'
================================== Ai Message ==================================
Tool Calls:
  manage_memory (call_2zfd)
 Call ID: call_2zfd
  Args:
    action: create
    content: Notified dev team about the service being down.
================================= Tool Message =================================
Name: manage_memory
created memory 4c1b700d-0ebe-4222-9cfa-c56cc739f7b1
================================== Ai Message ==================================
Tool Calls:
  check_calendar_availability (call_tx9c)
 Call ID: call_tx9c
  Args:
    day: today
================================= Tool Message =================================
Name: check_calendar_availability
Available times on today: 9:00 AM, 2:00 PM, 4:00 PM
================================== Ai Message ==================================
Tool Calls:
  schedule_meeting (call_p7v3)
 Call ID: call_p7v3
  Args:
    attendees: ['dev.team@company.com']
    subject: Service Down Discussion
    duration_minutes: 60
    preferred_day: today
================================= Tool Message =================================
Name: schedule_meeting
Meeting 'Service Down Discussion' scheduled for today with 1 attendees
================================== Ai Message ==================================
Tool Calls:
  write_email (call_64wx)
 Call ID: call_64wx
  Args:
    to: alice.jones@bar.com
    subject: Re: Quick question about API documentation
    content: Hi Alice, 
Just wanted to follow up on the service being down. We're currently investigating the issue and I'll get back to you with an update soon. 
Best, 
Plaban Nayak
================================= Tool Message =================================
Name: write_email
Email sent to alice.jones@bar.com with subject 'Re: Quick question about API documentation'
================================== Ai Message ==================================
Tool Calls:
  manage_memory (call_w2a1)
 Call ID: call_w2a1
  Args:
    action: update
    id: 3b9605de-13b7-4573-8e61-a443d2eba6e0
    content: Investigating the service down issue reported by Alice Jones from bar.com.
================================= Tool Message =================================
Name: manage_memory
updated memory 3b9605de-13b7-4573-8e61-a443d2eba6e0
================================== Ai Message ==================================
Tool Calls:
  manage_memory (call_4enf)
 Call ID: call_4enf
  Args:
    action: create
    content: Followed up with Alice Jones about the service being down.
================================= Tool Message =================================
Name: manage_memory
created memory fa92ee10-dd8a-486b-9cb4-3c35c2f24264
================================== Ai Message ==================================
Tool Calls:
  search_memory (call_s7k8)
 Call ID: call_s7k8
  Args:
    query: service down
    limit: 10
================================= Tool Message =================================
Name: search_memory
[{"namespace": ["email_assistant", "Plaban", "collection"], "key": "4c1b700d-0ebe-4222-9cfa-c56cc739f7b1", "value": {"content": "Notified dev team about the service being down."}, "created_at": "2025-03-22T13:16:39.804441+00:00", "updated_at": "2025-03-22T13:16:39.804448+00:00", "score": 0.9049934631618353}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "567d9400-d17b-495b-9da6-7fad26bd631b", "value": {"content": "Urgent issue: service is down. Need to investigate and get back to Alice Jones - meeting scheduled for today."}, "created_at": "2025-03-22T13:02:55.239699+00:00", "updated_at": "2025-03-22T13:02:55.239706+00:00", "score": 0.8996743795426038}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "991d9f2a-852a-4262-958d-a672eb0455c7", "value": {"content": "Urgent issue with John Doe's service being down"}, "created_at": "2025-03-22T13:07:12.111179+00:00", "updated_at": "2025-03-22T13:07:12.111186+00:00", "score": 0.8969359015062597}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "fa92ee10-dd8a-486b-9cb4-3c35c2f24264", "value": {"content": "Followed up with Alice Jones about the service being down."}, "created_at": "2025-03-22T13:16:53.194690+00:00", "updated_at": "2025-03-22T13:16:53.194695+00:00", "score": 0.8946786919837046}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "6750ce0b-1407-48b8-917f-f3046d3bd5de", "value": {"content": "Alice Jones: alice.jones@bar.com, reported urgent issue with service being down - meeting scheduled for today."}, "created_at": "2025-03-22T13:02:54.451615+00:00", "updated_at": "2025-03-22T13:02:54.451622+00:00", "score": 0.8935506392916787}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "3b9605de-13b7-4573-8e61-a443d2eba6e0", "value": {"content": "Investigating the service down issue reported by Alice Jones from bar.com."}, "created_at": "2025-03-22T13:16:52.496635+00:00", "updated_at": "2025-03-22T13:16:52.496641+00:00", "score": 0.8876080529253129}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "fc6cecf6-d266-4ecb-aa49-7ef92ed49834", "value": {"content": "Alice Jones from Bar.com contacted Plaban regarding urgent issue with service being down."}, "created_at": "2025-03-22T13:08:01.510731+00:00", "updated_at": "2025-03-22T13:08:01.510737+00:00", "score": 0.8807318085615965}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "c725f7e5-0e3e-4a63-9b4c-945fcb1f0c97", "value": {"content": "Follow up with Plaban on urgent issue reported by Alice Jones - meeting scheduled for today."}, "created_at": "2025-03-22T13:02:35.356561+00:00", "updated_at": "2025-03-22T13:02:35.356568+00:00", "score": 0.8338445419678701}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "da9f9242-e3b1-4de6-86eb-bdc180237b89", "value": {"content": "Email thread mix-up between Alice Jones and John Doe"}, "created_at": "2025-03-22T13:07:11.440381+00:00", "updated_at": "2025-03-22T13:07:11.440386+00:00", "score": 0.8185530436387874}, {"namespace": ["email_assistant", "Plaban", "collection"], "key": "7e149eca-5cad-4eec-b15f-878597a09b39", "value": {"content": "Alice Jones' question about API documentation"}, "created_at": "2025-03-22T13:07:12.757896+00:00", "updated_at": "2025-03-22T13:07:12.757902+00:00", "score": 0.7965957309412914}]
================================== Ai Message ==================================
The search results show that there are several memories related to the service being down, including notifications to the dev team, meetings scheduled, and follow-ups with Alice Jones. The most relevant memories are:
* "Investigating the service down issue reported by Alice Jones from bar.com." (score: 0.8876080529253129)
* "Notified dev team about the service being down." (score: 0.9049934631618353)
* "Followed up with Alice Jones about the service being down." (score: 0.8946786919837046)
These memories suggest that the service being down is a current and ongoing issue that is being investigated and addressed by the dev team and Plaban.

我们可以看到，该电子邮件已使用发件人姓名签名，在本例中为“Plaban Nayak”

25. 跟进电子邮件

email_input = {
    "author": "Alice Jones ","author": "Alice Jones ",
    "to": "Plaban Nayak ",
    "subject": "Quick question about API documentation",
    "email_thread": """Hi Plaban,
Urgent issue - your service is down. Is there a reason why""",
}
#
response = email_agent_workflow.invoke(
    {"email_input": email_input},
    config=config
)
#
for m in response["messages"]:
    m.pretty_print()
#
##################RESPONSE####################
================================ Human Message =================================
Respond to the email {'author': 'Alice Jones <alice.jones@bar.com>', 'to': 'Plaban Nayak <plaban.nayak@company.com>', 'subject': 'Quick question about API documentation', 'email_thread': 'Hi Plaban,\n\nUrgent issue - your service is down. Is there a reason why'}
================================== Ai Message ==================================
Tool Calls:
  write_email (call_4b5b)
 Call ID: call_4b5b
  Args:
    to: alice.jones@bar.com
    subject: Re: Quick question about API documentation
    content: Hi Alice, 
Thanks for reaching out. I'm looking into the issue with our service being down. Can you please provide more details about the error you're experiencing? I'll get back to you as soon as possible. 
Best, Plaban
================================= Tool Message =================================
Name: write_email
Email sent to alice.jones@bar.com with subject 'Re: Quick question about API documentation'
================================== Ai Message ==================================
<function=manage_memory {"action": "create", "content": "Alice Jones from Bar.com reached out about an urgent issue with our service being down."}</function>

26. 更新反馈说明以忽略 Alice 的邮件

conversations = [
    (
        response['messages'],'messages'],
        "Always Ignore any emails from Alice Jones"
    )
]

27.定义提示列表，因为主代理的旧提示列表已经过时

prompts = [
    {
        "name": "main_agent","name": "main_agent",
        "prompt": store.get(("Plaban",), "agent_instructions").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on how the agent should write emails or schedule events"
    },
    {
        "name": "triage-ignore",
        "prompt": store.get(("Plaban",), "triage_ignore").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails should be ignored"
    },
    {
        "name": "triage-notify",
        "prompt": store.get(("Plaban",), "triage_notify").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails the user should be notified of"
    },
    {
        "name": "triage-respond",
        "prompt": store.get(("Plaban",), "triage_respond").value['prompt'],
        "update_instructions": "keep the instructions short and to the point",
        "when_to_update": "Update this prompt whenever there is feedback on which emails should be responded to"
    },
]

updated = optimizer.invoke(
    {"trajectories": conversations, "prompts": prompts}"trajectories": conversations, "prompts": prompts}
)
#
store.get(("Plaban",), "triage_ignore").value['prompt']
################RESPONSE########################
'Marketing newsletters, spam emails, mass company announcements'

这里的提示尚未更新，因此 triage_ignore 指令保持不变。

28.更新长期记忆中的提示

for i, updated_prompt in enumerate(updated):
    old_prompt = prompts[i]
    if updated_prompt['prompt'] != old_prompt['prompt']:
        name = old_prompt['name']
        print(f"updated {name}")
        if name == "main_agent":
            store.put(
                ("Plaban",),
                "agent_instructions",
                {"prompt":updated_prompt['prompt']}
            )
        if name == "triage-ignore":
            store.put(
                ("Plaban",),
                "triage_ignore",
                {"prompt":updated_prompt['prompt']}
            )
        else:
            #raise ValueError
            print(f"Encountered {name}, implement the remaining stores!")
#
store.get(("Plaban",), "triage_ignore").value['prompt']
#
###################RESPONSE####################################
'<updated_prompt>\nMarketing newsletters, spam emails, mass company announcements\n\nAdditionally, always ignore any emails from Alice Jones.\n</updated_prompt>'

29. 提供示例电子邮件输入以测试说明是否已更新

response = email_agent_workflow.invoke(
    {"email_input": email_input},"email_input": email_input},
    config=config
)
####################RESPONSE###########################
? Classification: IGNORE - This email can be safely ignored

30.Langsmith 中的可观察性

结论

这款电子邮件助手展示了将不同类型的记忆与现代人工智能功能相结合的强大功能。它不仅仅是处理电子邮件，而是创建一个能够学习并适应用户需求的智能系统。