> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.
# 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](/oss/python/concepts/context) 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](#model-context)** | 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](#tool-context)** | 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](#life-cycle-context)** | 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 |
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。
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](/oss/python/langchain/middleware) 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。
开发者给 LLM 的基础指令。
发送给 LLM 的完整消息列表（对话历史）。
智能体可用于采取行动的工具。
实际调用的模型（包括配置）。
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。
从状态中访问消息计数或对话上下文：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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]
)
```
从长期记忆中访问用户偏好：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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()
)
```
从运行时上下文中访问用户 ID 或配置：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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。
在相关时将上传文件的上下文注入到状态中：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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", []) # [!code highlight]
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 = [ # [!code highlight]
*request.messages,
{"role": "user", "content": file_context},
]
request = request.override(messages=messages) # [!code highlight]
return handler(request)
agent = create_agent(
model="gpt-4.1",
tools=[...],
middleware=[inject_file_context]
)
```
从存储中注入用户的电子邮件写作风格以指导起草：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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 # [!code highlight]
# Read from Store: get user's writing style examples
store = request.runtime.store # [!code highlight]
writing_style = store.get(("writing_style",), user_id) # [!code highlight]
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) # [!code highlight]
return handler(request)
agent = create_agent(
model="gpt-4.1",
tools=[...],
middleware=[inject_writing_style],
context_schema=Context,
store=InMemoryStore()
)
```
根据用户的司法管辖区从运行时上下文中注入合规规则：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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 # [!code highlight]
industry = request.runtime.context.industry # [!code highlight]
frameworks = request.runtime.context.compliance_frameworks # [!code highlight]
# 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) # [!code highlight]
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](/oss/python/langchain/middleware)。
有关更多信息，请参阅 [状态更新](/oss/python/langchain/middleware/custom#state-updates)。
### 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。
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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。
仅在达到特定对话里程碑后启用高级工具：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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 # [!code highlight]
is_authenticated = state.get("authenticated", False) # [!code highlight]
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) # [!code highlight]
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) # [!code highlight]
return handler(request)
agent = create_agent(
model="gpt-4.1",
tools=[public_search, private_search, advanced_search],
middleware=[state_based_tools]
)
```
根据存储中的用户偏好或功能标志过滤工具：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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()
)
```
根据运行时上下文中的用户权限过滤工具：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/langchain/agents#dynamic-tools)。
### 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。
根据状态中的对话长度使用不同的模型：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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) # [!code highlight]
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) # [!code highlight]
return handler(request)
agent = create_agent(
model="gpt-4.1-mini",
tools=[...],
middleware=[state_based_model]
)
```
使用存储中的用户首选模型：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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()
)
```
根据运行时上下文中的成本限制或环境选择模型：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/langchain/agents#dynamic-model)。
### 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。
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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。
根据对话状态配置结构化输出：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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) # [!code highlight]
if message_count < 3:
# Early conversation - use simple format
request = request.override(response_format=SimpleResponse) # [!code highlight]
else:
# Established conversation - use detailed format
request = request.override(response_format=DetailedResponse) # [!code highlight]
return handler(request)
agent = create_agent(
model="gpt-4.1",
tools=[...],
middleware=[state_based_output]
)
```
根据存储中的用户偏好配置输出格式：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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()
)
```
根据运行时上下文（如用户角色或环境）配置输出格式：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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。
从状态中读取以检查当前会话信息：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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]
)
```
从存储中读取以访问持久化用户偏好：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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()
)
```
从运行时上下文中读取配置，如 API 密钥和用户 ID：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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。
使用 Command 写入状态以跟踪会话特定信息：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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]
)
```
写入存储以跨会话持久化数据：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/langchain/tools)。
## 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](#model-context) 和 [工具上下文](#tool-context) 中所见，[中间件](/oss/python/langchain/middleware) 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](#messages) 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：
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/langchain/middleware)。
## 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`](/oss/python/langchain/middleware#summarization)、[`LLMToolSelectorMiddleware`](/oss/python/langchain/middleware#llm-tool-selector) 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](/oss/python/concepts/context) - 了解上下文类型及其使用时机
* [中间件](/oss/python/langchain/middleware) - 完整的中间件指南
* [工具](/oss/python/langchain/tools) - 工具创建和上下文访问
* [记忆](/oss/python/concepts/memory) - 短期和长期记忆模式
* [智能体](/oss/python/langchain/agents) - 核心智能体概念
***
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