智能体中的上下文工程

概述

构建智能体（或任何 LLM 应用程序）的困难部分是使它们足够可靠。虽然它们可能适用于原型，但它们通常在实际用例中失败。

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部的 LLM 调用采取了错误的行动/没有做我们期望的事情。LLM 失败有两个原因之一：

底层 LLM 能力不足
“正确”的上下文没有传递给 LLM

通常情况下 - 实际上是第二个原因导致智能体不可靠。 上下文工程是以正确的格式提供正确的信息和工具，以便 LLM 可以完成任务。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 的智能体抽象专门设计为促进上下文工程。

刚接触上下文工程？从概念概述开始，了解不同类型的上下文以及何时使用它们。

智能体循环

典型的智能体循环包括两个主要步骤：

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

此循环继续，直到 LLM 决定完成。

您可以控制什么

要构建可靠的智能体，您需要控制智能体循环的每个步骤以及步骤之间发生的事情。

上下文类型	您控制什么	瞬态或持久
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）	瞬态
工具上下文	工具可以访问和产生的内容（对状态、存储、运行时上下文的读/写）	持久
生命周期上下文	模型和工具调用之间发生的事情（摘要、护栏、日志记录等）	持久

瞬态上下文

LLM 在单次调用中看到的内容。您可以修改消息、工具或提示，而不更改状态中保存的内容。

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永久修改此内容。

数据源

在整个过程中，您的智能体访问（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	对话范围	用户 ID、API 密钥、数据库连接、权限、环境设置
状态	短期记忆	对话范围	当前消息、上传的文件、身份验证状态、工具结果
存储	长期记忆	跨对话	用户偏好、提取的见解、记忆、历史数据

工作原理

LangChain 中间件是使上下文工程对使用 LangChain 的开发人员实用的底层机制。中间件允许您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，您将看到频繁使用中间件 API 作为上下文工程的手段。

模型上下文

控制每次模型调用的内容 - 指令、可用工具、使用的模型和输出格式。这些决策直接影响可靠性和成本。

System Prompt

Base instructions from the developer to the LLM.

Messages

The full list of messages (conversation history) sent to the LLM.

Tools

Utilities the agent has access to to take actions.

Model

The actual model (including configuration) to be called.

Response Format

Schema specification for the model’s final response.

All of these types of model context can draw from state (short-term memory), store (long-term memory), or runtime context (static configuration).

系统提示

系统提示设置 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。成功的智能体利用记忆、偏好和配置来为对话的当前状态提供正确的指令。

State
Store
Runtime Context

Access message count or conversation context from state:

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-4o",
    tools=[...],
    middleware=[state_aware_prompt]
)

Access user preferences from long-term memory:

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-4o",
    tools=[...],
    middleware=[store_aware_prompt],
    context_schema=Context,
    store=InMemoryStore()
)

Access user ID or configuration from Runtime Context:

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-4o",
    tools=[...],
    middleware=[context_aware_prompt],
    context_schema=Context
)

消息

消息构成了发送给 LLM 的提示。管理消息的内容至关重要，以确保 LLM 拥有正确的信息以做出良好的响应。

State
Store
Runtime Context

Inject uploaded file context from State when relevant to current query:

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-4o",
    tools=[...],
    middleware=[inject_file_context]
)

Inject user’s email writing style from Store to guide drafting:

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-4o",
    tools=[...],
    middleware=[inject_writing_style],
    context_schema=Context,
    store=InMemoryStore()
)

Inject compliance rules from Runtime Context based on user’s jurisdiction:

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-4o",
    tools=[...],
    middleware=[inject_compliance_rules],
    context_schema=Context
)

Transient vs Persistent Message Updates:The examples above use wrap_model_call to make transient updates - modifying what messages are sent to the model for a single call without changing what’s saved in state.For persistent updates that modify state (like the summarization example in Life-cycle Context), use life-cycle hooks like before_model or after_model to permanently update the conversation history. See the middleware documentation for more details.

工具

工具让模型与数据库、API 和外部系统交互。您如何定义和选择工具直接影响模型是否能够有效完成任务。

定义工具

每个工具都需要清晰的名称、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

Selecting tools

Not every tool is appropriate for every situation. Too many tools may overwhelm the model (overload context) and increase errors; too few limit capabilities. Dynamic tool selection adapts the available toolset based on authentication state, user permissions, feature flags, or conversation stage.

State
Store
Runtime Context

Enable advanced tools only after certain conversation milestones:

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-4o",
    tools=[public_search, private_search, advanced_search],
    middleware=[state_based_tools]
)

Filter tools based on user preferences or feature flags in Store:

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-4o",
    tools=[search_tool, analysis_tool, export_tool],
    middleware=[store_based_tools],
    context_schema=Context,
    store=InMemoryStore()
)

Filter tools based on user permissions from Runtime Context:

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-4o",
    tools=[read_data, write_data, delete_data],
    middleware=[context_based_tools],
    context_schema=Context
)

See Dynamically selecting tools for more examples.

模型

不同的模型具有不同的优势、成本和上下文窗口。为当前任务选择正确的模型，这在智能体运行期间可能会发生变化。

State
Store
Runtime Context

Use different models based on conversation length from State:

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-4-5-20250929")
standard_model = init_chat_model("gpt-4o")
efficient_model = init_chat_model("gpt-4o-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-4o-mini",
    tools=[...],
    middleware=[state_based_model]
)

Use user’s preferred model from Store:

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-4o": init_chat_model("gpt-4o"),
    "gpt-4o-mini": init_chat_model("gpt-4o-mini"),
    "claude-sonnet": init_chat_model("claude-sonnet-4-5-20250929"),
}

@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-4o",
    tools=[...],
    middleware=[store_based_model],
    context_schema=Context,
    store=InMemoryStore()
)

Select model based on cost limits or environment from Runtime Context:

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-4-5-20250929")
standard_model = init_chat_model("gpt-4o")
budget_model = init_chat_model("gpt-4o-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-4o",
    tools=[...],
    middleware=[context_based_model],
    context_schema=Context
)

See Dynamic model for more examples.

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。在提取特定字段或为下游系统返回数据时，自由格式文本是不够的。 工作原理： 当您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。字段名称、类型和描述精确指定输出应遵循的格式。

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'"
    )

Selecting formats

Dynamic response format selection adapts schemas based on user preferences, conversation stage, or role—returning simple formats early and detailed formats as complexity increases.

State
Store
Runtime Context

Configure structured output based on conversation state:

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-4o",
    tools=[...],
    middleware=[state_based_output]
)

Configure output format based on user preferences in Store:

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-4o",
    tools=[...],
    middleware=[store_based_output],
    context_schema=Context,
    store=InMemoryStore()
)

Configure output format based on Runtime Context like user role or environment:

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-4o",
    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

大多数实际工具需要的不仅仅是 LLM 的参数。它们需要用于数据库查询的用户 ID、用于外部服务的 API 密钥或当前会话状态来做出决策。工具从状态、存储和运行时上下文读取以访问此信息。

State
Store
Runtime Context

Read from State to check current session information:

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-4o",
    tools=[check_authentication]
)

Read from Store to access persisted user preferences:

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-4o",
    tools=[get_preference],
    context_schema=Context,
    store=InMemoryStore()
)

Read from Runtime Context for configuration like API keys and user IDs:

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-4o",
    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记忆，以便为未来步骤提供重要的上下文。

State
Store

Write to State to track session-specific information using Command:

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-4o",
    tools=[authenticate_user]
)

Write to Store to persist data across sessions:

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-4o",
    tools=[save_preference],
    context_schema=Context,
    store=InMemoryStore()
)

See Tools for comprehensive examples of accessing state, store, and runtime context in tools.

生命周期上下文

控制核心智能体步骤之间发生的事情 - 拦截数据流以实现横切关注点，如摘要、护栏和日志记录。正如您在模型上下文和工具上下文中看到的，中间件是使上下文工程实用的机制。中间件允许您挂接到智能体生命周期中的任何步骤，并：

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
在生命周期中跳转 - 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，使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在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 - 永久地用摘要替换旧消息，该摘要会为所有未来的轮次保存。 LangChain offers built-in middleware for this:

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

agent = create_agent(
    model="gpt-4o",
    tools=[...],
    middleware=[
        SummarizationMiddleware(
            model="gpt-4o-mini",
            max_tokens_before_summary=4000,  # Trigger summarization at 4000 tokens
            messages_to_keep=20,  # Keep last 20 messages after summary
        ),
    ],
)

When the conversation exceeds the token limit, SummarizationMiddleware automatically:

Summarizes older messages using a separate LLM call
Replaces them with a summary message in State (permanently)
Keeps recent messages intact for context

The summarized conversation history is permanently updated - future turns will see the summary instead of the original messages.

For a complete list of built-in middleware, available hooks, and how to create custom middleware, see the Middleware documentation.

最佳实践

从简单开始 - 从静态提示和工具开始，仅在需要时添加动态功能
增量测试 - 一次添加一个上下文工程功能
监控性能 - 跟踪模型调用、token 使用和延迟
使用内置中间件 - 利用 SummarizationMiddleware、LLMToolSelectorMiddleware 等
记录您的上下文策略 - 清楚地说明正在传递什么上下文以及为什么
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会持久化到状态

​概述

​为什么智能体会失败？

​智能体循环

​您可以控制什么

瞬态上下文

持久上下文

​数据源

​工作原理

​模型上下文

System Prompt

Messages

Tools

Model

Response Format

​系统提示

​消息

​工具

​定义工具

​Selecting tools

​模型

​响应格式

​定义格式

​Selecting formats

​工具上下文

​读取

​写入

​生命周期上下文

​示例：摘要

​最佳实践

​相关资源

概述

为什么智能体会失败？

智能体循环

您可以控制什么

数据源

工作原理

模型上下文

系统提示

消息

工具

定义工具

Selecting tools

模型

响应格式

定义格式

Selecting formats

工具上下文

读取

写入

生命周期上下文

示例：摘要

最佳实践

相关资源