智能体中的上下文工程

概述

构建智能体（或任何 LLM 应用）的难点在于使其足够可靠。虽然它们可能在原型阶段有效，但在实际用例中往往会失败。

为什么智能体会失败？

当智能体失败时，通常是因为智能体内的 LLM 调用了错误的操作 / 没有按预期执行。LLM 失败的原因主要有两个：

1. 底层 LLM 能力不足
2. 未向 LLM 传递“正确”的上下文

大多数情况下——实际上是第二个原因导致智能体不可靠。

上下文工程是指以正确的格式提供正确的信息和工具，以便 LLM 能够完成任务。这是 AI 工程师的首要工作。缺乏“正确”的上下文是更可靠智能体的首要障碍，而 LangChain 的智能体抽象设计独特，旨在促进上下文工程。

初次接触上下文工程？请从 概念概述 开始，了解不同类型的上下文及其使用时机。

智能体循环

典型的智能体循环包含两个主要步骤：

1. 模型调用 - 使用提示词和可用工具调用 LLM，返回响应或执行工具的请求
2. 工具执行 - 执行 LLM 请求的工具，返回工具结果

此循环持续进行，直到 LLM 决定结束。

你可以控制的内容

要构建可靠的智能体，你需要控制智能体循环中每一步发生的情况，以及步骤之间发生的情况。

上下文类型	你控制的内容	临时或持久
模型上下文	进入模型调用的内容（指令、消息历史、工具、响应格式）	临时
工具上下文	工具可以访问和生成的内容（读写状态、存储、运行时上下文）	持久
生命周期上下文	模型调用和工具调用之间发生的情况（摘要、护栏、日志记录等）	持久

临时上下文

LLM 单次调用所看到的内容。你可以修改消息、工具或提示词，而无需更改保存在状态中的内容。

持久上下文

跨轮次保存在状态中的内容。生命周期钩子和工具写入会永久修改此内容。

数据源

在此过程中，你的智能体会访问（读取/写入）不同的数据源：

数据源	也称为	范围	示例
运行时上下文	静态配置	会话范围	用户 ID、API 密钥、数据库连接、权限、环境设置
状态	短期记忆	会话范围	当前消息、上传的文件、认证状态、工具结果
存储	长期记忆	跨会话	用户偏好、提取的见解、记忆、历史数据

工作原理

LangChain 中间件 是幕后机制，使上下文工程对使用 LangChain 的开发人员变得实用。

中间件允许你挂钩到智能体生命周期的任何步骤并：

• 更新上下文
• 跳转到智能体生命周期中的不同步骤

在本指南中，你将频繁看到中间件 API 的使用，作为实现上下文工程的手段。

模型上下文

控制每个模型调用的内容——指令、可用工具、使用的模型以及输出格式。这些决策直接影响可靠性和成本。

系统提示词

开发者给 LLM 的基础指令。

消息

发送给 LLM 的完整消息列表（对话历史）。

工具

智能体可用于采取行动的工具。

模型

实际调用的模型（包括配置）。

响应格式

模型最终响应的模式规范。

所有这些类型的模型上下文都可以从 状态（短期记忆）、存储（长期记忆）或 运行时上下文（静态配置）中获取。

系统提示词

系统提示词设定 LLM 的行为和能力。不同的用户、上下文或对话阶段需要不同的指令。成功的智能体会利用记忆、偏好和配置来为当前对话状态提供正确的指令。

状态

从状态中访问消息计数或对话上下文：

from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest

@dynamic_prompt
def state_aware_prompt(request: ModelRequest) -> str:
    # request.messages is a shortcut for request.state["messages"]
    message_count = len(request.messages)

    base = "You are a helpful assistant."

    if message_count > 10:
        base += "\nThis is a long conversation - be extra concise."

    return base

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[state_aware_prompt]
)

存储

从长期记忆中访问用户偏好：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

@dynamic_prompt
def store_aware_prompt(request: ModelRequest) -> str:
    user_id = request.runtime.context.user_id

    # Read from Store: get user preferences
    store = request.runtime.store
    user_prefs = store.get(("preferences",), user_id)

    base = "You are a helpful assistant."

    if user_prefs:
        style = user_prefs.value.get("communication_style", "balanced")
        base += f"\nUser prefers {style} responses."

    return base

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[store_aware_prompt],
    context_schema=Context,
    store=InMemoryStore()
)

运行时上下文

从运行时上下文中访问用户 ID 或配置：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest

@dataclass
class Context:
    user_role: str
    deployment_env: str

@dynamic_prompt
def context_aware_prompt(request: ModelRequest) -> str:
    # Read from Runtime Context: user role and environment
    user_role = request.runtime.context.user_role
    env = request.runtime.context.deployment_env

    base = "You are a helpful assistant."

    if user_role == "admin":
        base += "\nYou have admin access. You can perform all operations."
    elif user_role == "viewer":
        base += "\nYou have read-only access. Guide users to read operations only."

    if env == "production":
        base += "\nBe extra careful with any data modifications."

    return base

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[context_aware_prompt],
    context_schema=Context
)

消息

消息构成了发送给 LLM 的提示词。 管理消息内容至关重要，以确保 LLM 拥有正确信息以做出良好响应。

状态

在相关时将上传文件的上下文注入到状态中：

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable

@wrap_model_call
def inject_file_context(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Inject context about files user has uploaded this session."""
    # Read from State: get uploaded files metadata
    uploaded_files = request.state.get("uploaded_files", [])

    if uploaded_files:
        # Build context about available files
        file_descriptions = []
        for file in uploaded_files:
            file_descriptions.append(
                f"- {file['name']} ({file['type']}): {file['summary']}"
            )

        file_context = f"""Files you have access to in this conversation:
{chr(10).join(file_descriptions)}

Reference these files when answering questions."""

        # Inject file context before recent messages
        messages = [
            *request.messages,
            {"role": "user", "content": file_context},
        ]
        request = request.override(messages=messages)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[inject_file_context]
)

存储

从存储中注入用户的电子邮件写作风格以指导起草：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

@wrap_model_call
def inject_writing_style(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Inject user's email writing style from Store."""
    user_id = request.runtime.context.user_id  

    # Read from Store: get user's writing style examples
    store = request.runtime.store  
    writing_style = store.get(("writing_style",), user_id)

    if writing_style:
        style = writing_style.value
        # Build style guide from stored examples
        style_context = f"""Your writing style:
- Tone: {style.get('tone', 'professional')}
- Typical greeting: "{style.get('greeting', 'Hi')}"
- Typical sign-off: "{style.get('sign_off', 'Best')}"
- Example email you've written:
{style.get('example_email', '')}"""

        # Append at end - models pay more attention to final messages
        messages = [
            *request.messages,
            {"role": "user", "content": style_context}
        ]
        request = request.override(messages=messages)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[inject_writing_style],
    context_schema=Context,
    store=InMemoryStore()
)

运行时上下文

根据用户的司法管辖区从运行时上下文中注入合规规则：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable

@dataclass
class Context:
    user_jurisdiction: str
    industry: str
    compliance_frameworks: list[str]

@wrap_model_call
def inject_compliance_rules(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Inject compliance constraints from Runtime Context."""
    # Read from Runtime Context: get compliance requirements
    jurisdiction = request.runtime.context.user_jurisdiction  
    industry = request.runtime.context.industry  
    frameworks = request.runtime.context.compliance_frameworks  

    # Build compliance constraints
    rules = []
    if "GDPR" in frameworks:
        rules.append("- Must obtain explicit consent before processing personal data")
        rules.append("- Users have right to data deletion")
    if "HIPAA" in frameworks:
        rules.append("- Cannot share patient health information without authorization")
        rules.append("- Must use secure, encrypted communication")
    if industry == "finance":
        rules.append("- Cannot provide financial advice without proper disclaimers")

    if rules:
        compliance_context = f"""Compliance requirements for {jurisdiction}:
{chr(10).join(rules)}"""

        # Append at end - models pay more attention to final messages
        messages = [
            *request.messages,
            {"role": "user", "content": compliance_context}
        ]
        request = request.override(messages=messages)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[inject_compliance_rules],
    context_schema=Context
)

临时与持久消息更新：

上面的示例使用 wrap_model_call 进行临时更新——修改发送给模型的单条调用中的消息，而不更改保存在状态中的内容。

对于修改状态的持久更新，你可以：

• 从 wrap_model_call 返回带有 [Command] 的 [ExtendedModelResponse]，以从模型调用层注入状态更新。
• 使用生命周期钩子如 before_model、after_model 或 wrap_tool_call（用于工具返回）来更新对话历史。有关更多详细信息，请参阅 中间件文档。

有关更多信息，请参阅 状态更新。

工具

工具让模型能够与数据库、API 和外部系统交互。你如何定义和选择工具直接影响模型能否有效完成任务。

定义工具

每个工具都需要清晰的名字、描述、参数名称和参数描述。这些不仅仅是元数据——它们指导模型关于何时以及如何使用该工具的推理。

from langchain.tools import tool

@tool(parse_docstring=True)
def search_orders(
    user_id: str,
    status: str,
    limit: int = 10
) -> str:
    """Search for user orders by status.

    Use this when the user asks about order history or wants to check
    order status. Always filter by the provided status.

    Args:
        user_id: Unique identifier for the user
        status: Order status: 'pending', 'shipped', or 'delivered'
        limit: Maximum number of results to return
    """
    # Implementation here
    pass

选择工具

并非所有工具都适用于每种情况。过多的工具可能会使模型不堪重负（上下文过载）并增加错误；过少的工具则限制能力。动态工具选择会根据认证状态、用户权限、功能标志或对话阶段调整可用工具集。

状态

仅在达到特定对话里程碑后启用高级工具：

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable

@wrap_model_call
def state_based_tools(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Filter tools based on conversation State."""
    # Read from State: check if user has authenticated
    state = request.state  
    is_authenticated = state.get("authenticated", False)
    message_count = len(state["messages"])

    # Only enable sensitive tools after authentication
    if not is_authenticated:
        tools = [t for t in request.tools if t.name.startswith("public_")]
        request = request.override(tools=tools)
    elif message_count < 5:
        # Limit tools early in conversation
        tools = [t for t in request.tools if t.name != "advanced_search"]
        request = request.override(tools=tools)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[public_search, private_search, advanced_search],
    middleware=[state_based_tools]
)

存储

根据存储中的用户偏好或功能标志过滤工具：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

@wrap_model_call
def store_based_tools(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Filter tools based on Store preferences."""
    user_id = request.runtime.context.user_id

    # Read from Store: get user's enabled features
    store = request.runtime.store
    feature_flags = store.get(("features",), user_id)

    if feature_flags:
        enabled_features = feature_flags.value.get("enabled_tools", [])
        # Only include tools that are enabled for this user
        tools = [t for t in request.tools if t.name in enabled_features]
        request = request.override(tools=tools)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[search_tool, analysis_tool, export_tool],
    middleware=[store_based_tools],
    context_schema=Context,
    store=InMemoryStore()
)

运行时上下文

根据运行时上下文中的用户权限过滤工具：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from typing import Callable

@dataclass
class Context:
    user_role: str

@wrap_model_call
def context_based_tools(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Filter tools based on Runtime Context permissions."""
    # Read from Runtime Context: get user role
    user_role = request.runtime.context.user_role

    if user_role == "admin":
        # Admins get all tools
        pass
    elif user_role == "editor":
        # Editors can't delete
        tools = [t for t in request.tools if t.name != "delete_data"]
        request = request.override(tools=tools)
    else:
        # Viewers get read-only tools
        tools = [t for t in request.tools if t.name.startswith("read_")]
        request = request.override(tools=tools)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[read_data, write_data, delete_data],
    middleware=[context_based_tools],
    context_schema=Context
)

有关过滤预注册工具和运行时注册工具（例如来自 MCP 服务器）的更多信息，请参阅 动态工具。

模型

不同的模型具有不同的优势、成本和上下文窗口。为手头的任务选择合适的模型，这可能在智能体运行期间发生变化。

状态

根据状态中的对话长度使用不同的模型：

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from langchain.chat_models import init_chat_model
from typing import Callable

# Initialize models once outside the middleware
large_model = init_chat_model("claude-sonnet-4-6")
standard_model = init_chat_model("gpt-4.1")
efficient_model = init_chat_model("gpt-4.1-mini")

@wrap_model_call
def state_based_model(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select model based on State conversation length."""
    # request.messages is a shortcut for request.state["messages"]
    message_count = len(request.messages)

    if message_count > 20:
        # Long conversation - use model with larger context window
        model = large_model
    elif message_count > 10:
        # Medium conversation
        model = standard_model
    else:
        # Short conversation - use efficient model
        model = efficient_model

    request = request.override(model=model)

    return handler(request)

agent = create_agent(
    model="gpt-4.1-mini",
    tools=[...],
    middleware=[state_based_model]
)

存储

使用存储中的用户首选模型：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from langchain.chat_models import init_chat_model
from typing import Callable
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

# Initialize available models once
MODEL_MAP = {
    "gpt-4.1": init_chat_model("gpt-4.1"),
    "gpt-4.1-mini": init_chat_model("gpt-4.1-mini"),
    "claude-sonnet": init_chat_model("claude-sonnet-4-6"),
}

@wrap_model_call
def store_based_model(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select model based on Store preferences."""
    user_id = request.runtime.context.user_id

    # Read from Store: get user's preferred model
    store = request.runtime.store
    user_prefs = store.get(("preferences",), user_id)

    if user_prefs:
        preferred_model = user_prefs.value.get("preferred_model")
        if preferred_model and preferred_model in MODEL_MAP:
            request = request.override(model=MODEL_MAP[preferred_model])

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[store_based_model],
    context_schema=Context,
    store=InMemoryStore()
)

运行时上下文

根据运行时上下文中的成本限制或环境选择模型：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from langchain.chat_models import init_chat_model
from typing import Callable

@dataclass
class Context:
    cost_tier: str
    environment: str

# Initialize models once outside the middleware
premium_model = init_chat_model("claude-sonnet-4-6")
standard_model = init_chat_model("gpt-4.1")
budget_model = init_chat_model("gpt-4.1-mini")

@wrap_model_call
def context_based_model(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select model based on Runtime Context."""
    # Read from Runtime Context: cost tier and environment
    cost_tier = request.runtime.context.cost_tier
    environment = request.runtime.context.environment

    if environment == "production" and cost_tier == "premium":
        # Production premium users get best model
        model = premium_model
    elif cost_tier == "budget":
        # Budget tier gets efficient model
        model = budget_model
    else:
        # Standard tier
        model = standard_model

    request = request.override(model=model)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[context_based_model],
    context_schema=Context
)

有关更多示例，请参阅 动态模型。

响应格式

结构化输出将非结构化文本转换为经过验证的结构化数据。当提取特定字段或为下游系统返回数据时，自由文本是不够的。

工作原理： 当你提供模式作为响应格式时，模型的最终响应保证符合该模式。智能体运行模型/工具调用循环，直到模型完成工具调用，然后将最终响应强制转换为提供的格式。

定义格式

模式定义指导模型。字段名、类型和描述指定输出应遵循的确切格式。

from pydantic import BaseModel, Field

class CustomerSupportTicket(BaseModel):
    """Structured ticket information extracted from customer message."""

    category: str = Field(
        description="Issue category: 'billing', 'technical', 'account', or 'product'"
    )
    priority: str = Field(
        description="Urgency level: 'low', 'medium', 'high', or 'critical'"
    )
    summary: str = Field(
        description="One-sentence summary of the customer's issue"
    )
    customer_sentiment: str = Field(
        description="Customer's emotional tone: 'frustrated', 'neutral', or 'satisfied'"
    )

选择格式

动态响应格式选择根据用户偏好、对话阶段或角色调整模式——早期返回简单格式，随着复杂性增加返回详细格式。

状态

根据对话状态配置结构化输出：

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from pydantic import BaseModel, Field
from typing import Callable

class SimpleResponse(BaseModel):
    """Simple response for early conversation."""
    answer: str = Field(description="A brief answer")

class DetailedResponse(BaseModel):
    """Detailed response for established conversation."""
    answer: str = Field(description="A detailed answer")
    reasoning: str = Field(description="Explanation of reasoning")
    confidence: float = Field(description="Confidence score 0-1")

@wrap_model_call
def state_based_output(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select output format based on State."""
    # request.messages is a shortcut for request.state["messages"]
    message_count = len(request.messages)

    if message_count < 3:
        # Early conversation - use simple format
        request = request.override(response_format=SimpleResponse)
    else:
        # Established conversation - use detailed format
        request = request.override(response_format=DetailedResponse)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[state_based_output]
)

存储

根据存储中的用户偏好配置输出格式：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from pydantic import BaseModel, Field
from typing import Callable
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

class VerboseResponse(BaseModel):
    """Verbose response with details."""
    answer: str = Field(description="Detailed answer")
    sources: list[str] = Field(description="Sources used")

class ConciseResponse(BaseModel):
    """Concise response."""
    answer: str = Field(description="Brief answer")

@wrap_model_call
def store_based_output(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select output format based on Store preferences."""
    user_id = request.runtime.context.user_id

    # Read from Store: get user's preferred response style
    store = request.runtime.store
    user_prefs = store.get(("preferences",), user_id)

    if user_prefs:
        style = user_prefs.value.get("response_style", "concise")
        if style == "verbose":
            request = request.override(response_format=VerboseResponse)
        else:
            request = request.override(response_format=ConciseResponse)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[store_based_output],
    context_schema=Context,
    store=InMemoryStore()
)

运行时上下文

根据运行时上下文（如用户角色或环境）配置输出格式：

from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.agents.middleware import wrap_model_call, ModelRequest, ModelResponse
from pydantic import BaseModel, Field
from typing import Callable

@dataclass
class Context:
    user_role: str
    environment: str

class AdminResponse(BaseModel):
    """Response with technical details for admins."""
    answer: str = Field(description="Answer")
    debug_info: dict = Field(description="Debug information")
    system_status: str = Field(description="System status")

class UserResponse(BaseModel):
    """Simple response for regular users."""
    answer: str = Field(description="Answer")

@wrap_model_call
def context_based_output(
    request: ModelRequest,
    handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
    """Select output format based on Runtime Context."""
    # Read from Runtime Context: user role and environment
    user_role = request.runtime.context.user_role
    environment = request.runtime.context.environment

    if user_role == "admin" and environment == "production":
        # Admins in production get detailed output
        request = request.override(response_format=AdminResponse)
    else:
        # Regular users get simple output
        request = request.override(response_format=UserResponse)

    return handler(request)

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[context_based_output],
    context_schema=Context
)

工具上下文

工具的特殊之处在于它们既读取又写入上下文。

在最基本的情况下，当工具执行时，它接收 LLM 的请求参数并返回工具消息。工具执行其工作并产生结果。

工具还可以为模型获取重要信息，使其能够执行和完成任务。

读取

大多数现实世界的工具不仅需要 LLM 的参数。它们需要用户 ID 进行数据库查询、外部服务的 API 密钥或当前会话状态以做出决策。工具从状态、存储和运行时上下文中读取以访问这些信息。

状态

从状态中读取以检查当前会话信息：

from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent

@tool
def check_authentication(
    runtime: ToolRuntime
) -> str:
    """Check if user is authenticated."""
    # Read from State: check current auth status
    current_state = runtime.state
    is_authenticated = current_state.get("authenticated", False)

    if is_authenticated:
        return "User is authenticated"
    else:
        return "User is not authenticated"

agent = create_agent(
    model="gpt-4.1",
    tools=[check_authentication]
)

存储

从存储中读取以访问持久化用户偏好：

from dataclasses import dataclass
from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

@tool
def get_preference(
    preference_key: str,
    runtime: ToolRuntime[Context]
) -> str:
    """Get user preference from Store."""
    user_id = runtime.context.user_id

    # Read from Store: get existing preferences
    store = runtime.store
    existing_prefs = store.get(("preferences",), user_id)

    if existing_prefs:
        value = existing_prefs.value.get(preference_key)
        return f"{preference_key}: {value}" if value else f"No preference set for {preference_key}"
    else:
        return "No preferences found"

agent = create_agent(
    model="gpt-4.1",
    tools=[get_preference],
    context_schema=Context,
    store=InMemoryStore()
)

运行时上下文

从运行时上下文中读取配置，如 API 密钥和用户 ID：

from dataclasses import dataclass
from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent

@dataclass
class Context:
    user_id: str
    api_key: str
    db_connection: str

@tool
def fetch_user_data(
    query: str,
    runtime: ToolRuntime[Context]
) -> str:
    """Fetch data using Runtime Context configuration."""
    # Read from Runtime Context: get API key and DB connection
    user_id = runtime.context.user_id
    api_key = runtime.context.api_key
    db_connection = runtime.context.db_connection

    # Use configuration to fetch data
    results = perform_database_query(db_connection, query, api_key)

    return f"Found {len(results)} results for user {user_id}"

agent = create_agent(
    model="gpt-4.1",
    tools=[fetch_user_data],
    context_schema=Context
)

# Invoke with runtime context
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Get my data"}]},
    context=Context(
        user_id="user_123",
        api_key="sk-...",
        db_connection="postgresql://..."
    )
)

写入

工具结果可用于帮助智能体完成给定任务。工具既可以直接向模型返回结果，也可以更新智能体的内存，以便将来步骤可以使用重要上下文。

状态

使用 Command 写入状态以跟踪会话特定信息：

from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent
from langgraph.types import Command

@tool
def authenticate_user(
    password: str,
    runtime: ToolRuntime
) -> Command:
    """Authenticate user and update State."""
    # Perform authentication (simplified)
    if password == "correct":
        # Write to State: mark as authenticated using Command
        return Command(
            update={"authenticated": True},
        )
    else:
        return Command(update={"authenticated": False})

agent = create_agent(
    model="gpt-4.1",
    tools=[authenticate_user]
)

存储

写入存储以跨会话持久化数据：

from dataclasses import dataclass
from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent
from langgraph.store.memory import InMemoryStore

@dataclass
class Context:
    user_id: str

@tool
def save_preference(
    preference_key: str,
    preference_value: str,
    runtime: ToolRuntime[Context]
) -> str:
    """Save user preference to Store."""
    user_id = runtime.context.user_id

    # Read existing preferences
    store = runtime.store
    existing_prefs = store.get(("preferences",), user_id)

    # Merge with new preference
    prefs = existing_prefs.value if existing_prefs else {}
    prefs[preference_key] = preference_value

    # Write to Store: save updated preferences
    store.put(("preferences",), user_id, prefs)

    return f"Saved preference: {preference_key} = {preference_value}"

agent = create_agent(
    model="gpt-4.1",
    tools=[save_preference],
    context_schema=Context,
    store=InMemoryStore()
)

有关在工具中访问状态、存储和运行时上下文的全面示例，请参阅 工具。

生命周期上下文

控制核心智能体步骤之间发生的情况——拦截数据流以实现跨领域关注点，如摘要、护栏和日志记录。

正如你在 模型上下文 和 工具上下文 中所见，中间件 是使上下文工程实用的机制。中间件允许你挂钩到智能体生命周期的任何步骤并：

1. 更新上下文 - 修改状态和存储以持久化更改，更新对话历史，或保存见解
2. 跳转生命周期 - 根据上下文移动到智能体循环中的不同步骤（例如，如果满足条件则跳过工具执行，用修改后的上下文重复模型调用）

示例：摘要

最常见的生命周期模式之一是当对话历史过长时自动压缩它。与 模型上下文 中显示的临时消息修剪不同，摘要持久更新状态——永久替换旧消息为摘要，该摘要保存供所有未来轮次使用。

LangChain 为此提供了内置中间件：

from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[
        SummarizationMiddleware(
            model="gpt-4.1-mini",
            trigger={"tokens": 4000},
            keep={"messages": 20},
        ),
    ],
)

当对话超过令牌限制时，SummarizationMiddleware 会自动：

1. 使用单独的 LLM 调用总结旧消息
2. 在状态中用摘要消息替换它们（永久）
3. 保留最近的完整消息以供上下文使用

摘要的对话历史被永久更新——未来的轮次将看到摘要而不是原始消息。

有关内置中间件的完整列表、可用钩子以及如何创建自定义中间件，请参阅 中间件文档。

最佳实践

1. 从简单开始 - 从静态提示词和工具开始，仅在需要时添加动态功能
2. 增量测试 - 一次添加一个上下文工程功能
3. 监控性能 - 跟踪模型调用、令牌使用和延迟
4. 使用内置中间件 - 利用 SummarizationMiddleware、LLMToolSelectorMiddleware 等
5. 记录你的上下文策略 - 明确说明传递了什么上下文以及为什么
6. 理解临时与持久：模型上下文更改是临时的（每次调用），而生命周期上下文更改持久保存到状态

相关资源

• 上下文概念概述 - 了解上下文类型及其使用时机
• 中间件 - 完整的中间件指南
• 工具 - 工具创建和上下文访问
• 记忆 - 短期和长期记忆模式
• 智能体 - 核心智能体概念

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