智能体

智能体（Agent）是一个循环调用工具的模型，直到给定任务完成。核心智能体循环示意图

脚手架（Harness）是围绕该循环的一切：提示词、工具以及任何塑造模型行为的中间件。

智能体 = 模型 + 脚手架脚手架的职责是：在正确的时间为给定任务提供正确的上下文给模型。

@@MDX_PRESERVE_1@@ create_agent 是一个高度可配置的脚手架。最简单的情况下，你可以用以下方式创建一个：

from langchain.agents import create_agent
    
agent = create_agent(model="google_genai:gemini-3.6-flash", tools=tools)

from langchain.agents import create_agent
    
agent = create_agent(model="openai:gpt-5.5", tools=tools)

from langchain.agents import create_agent
    
agent = create_agent(model="anthropic:claude-sonnet-4-6", tools=tools)

from langchain.agents import create_agent
    
agent = create_agent(model="openrouter:z-ai/glm-5.2", tools=tools)

from langchain.agents import create_agent
    
agent = create_agent(model="fireworks:accounts/fireworks/models/glm-5p2", tools=tools)

from langchain.agents import create_agent
    
agent = create_agent(model="baseten:zai-org/GLM-5.2", tools=tools)

from langchain.agents import create_agent
    
agent = create_agent(model="ollama:north-mini-code-1.0", tools=tools)

在此基础上，你可以直接使用 model=、tools= 和 system_prompt= 参数配置基础功能。要获取更高级的功能，请通过中间件扩展脚手架。

核心组件

模型

传递一个模型标识符字符串 ("provider:model") 或一个已初始化的模型实例来为你的智能体选择模型。有关参数、提供商设置和动态模型选择，请参见模型。

from langchain.agents import create_agent

agent = create_agent(model="google_genai:gemini-3.6-flash", tools=tools)

from langchain.agents import create_agent

agent = create_agent(model="openai:gpt-5.5", tools=tools)

from langchain.agents import create_agent

agent = create_agent(model="anthropic:claude-sonnet-4-6", tools=tools)

from langchain.agents import create_agent

agent = create_agent(model="openrouter:z-ai/glm-5.2", tools=tools)

from langchain.agents import create_agent

agent = create_agent(model="fireworks:accounts/fireworks/models/glm-5p2", tools=tools)

from langchain.agents import create_agent

agent = create_agent(model="baseten:zai-org/GLM-5.2", tools=tools)

from langchain.agents import create_agent

agent = create_agent(model="ollama:north-mini-code-1.0", tools=tools)

工具

要为智能体提供工具，请传递任何可调用的 Python 对象、LangChain 工具或工具字典。有关工具定义、上下文访问和动态工具选择，请参见工具。

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="google_genai:gemini-3.6-flash", tools=[search])

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="openai:gpt-5.5", tools=[search])

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="anthropic:claude-sonnet-4-6", tools=[search])

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="openrouter:z-ai/glm-5.2", tools=[search])

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="fireworks:accounts/fireworks/models/glm-5p2", tools=[search])

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="baseten:zai-org/GLM-5.2", tools=[search])

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


@tool
def search(query: str) -> str:
    """搜索信息。"""
    return f"Results for: {query}"


agent = create_agent(model="ollama:north-mini-code-1.0", tools=[search])

系统提示

塑造智能体处理任务的方式。系统提示参数接受字符串或 SystemMessage。要在运行时动态生成提示，请使用中间件。

agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

agent = create_agent(
    model="openai:gpt-5.5",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=tools,
    system_prompt="你是一个乐于助人的助手。请保持简洁和准确。",
)

结构化输出

使用 response_format= 从智能体返回一个经过验证的模式。有关策略和示例，请参见结构化输出。

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="google_genai:gemini-3.6-flash", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="openai:gpt-5.5", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="anthropic:claude-sonnet-4-6", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="openrouter:z-ai/glm-5.2", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="fireworks:accounts/fireworks/models/glm-5p2", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="baseten:zai-org/GLM-5.2", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

from pydantic import BaseModel
from langchain.agents import create_agent
    
    
class Answer(BaseModel):
    summary: str
    confidence: float
    
    
agent = create_agent(model="ollama:north-mini-code-1.0", tools=tools, response_format=Answer)
result = agent.invoke({"messages": [{"role": "user", "content": "Summarize AI trends"}]})
result["structured_response"]  # Answer(summary=..., confidence=...)

调用

使用 LangSmith 追踪此循环的每一步、调试工具调用并评估智能体输出。请遵循追踪快速入门进行设置。我们建议您同时设置 LangSmith Engine，它可以监控您的追踪记录、检测问题并提出修复建议。

你可以用一条消息来调用智能体。在底层，这会向智能体的 State 传递一个更新。所有智能体的状态中都包含一个消息序列；要调用智能体，请传递一条新消息以及一个 thread_id，以便智能体能够持久化并恢复对话历史：

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="openai:gpt-5.5",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[],
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": str(uuid7())}}

result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config=config,
)

# 在同一对话中进行后续轮次：复用相同的 thread_id 以保留历史记录
result = agent.invoke(
    {"messages": [{"role": "user", "content": "那明天呢？"}]},
    config=config,
)

使用 thread_id 持久化对话历史需要智能体配置一个检查点器。当部署在 LangSmith 上时，系统会自动配置一个检查点器。在本地开发时，请显式传递一个，例如 create_agent(..., checkpointer=InMemorySaver())。

如果你还需要将每次运行的配置（例如用户 ID、API 密钥或功能标志）传递给工具和中间件，请将其与 config 一起作为 context 传递。使用 context_schema 定义该数据的结构，并通过 runtime.context 访问它：

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="openai:gpt-5.5",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

from dataclasses import dataclass
    
from langchain.agents import create_agent
from langchain_core.utils.uuid import uuid7
from langgraph.checkpoint.memory import InMemorySaver
    
    
@dataclass
class Context:
    user_id: str
    
    
agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[],
    context_schema=Context,
    checkpointer=InMemorySaver(),
)
    
result = agent.invoke(
    {"messages": [{"role": "user", "content": "旧金山的天气怎么样？"}]},
    config={"configurable": {"thread_id": str(uuid7())}},
    context=Context(user_id="user-123"),
)

thread_id 定义了对话的范围（消息历史、检查点），而 context 承载了你的工具和中间件在调用时读取的每次运行的数据。两者通常会一起传递。更多信息请参见工具上下文和运行时。

流式传输

invoke 在运行结束时返回最终响应。如果智能体执行了多个工具调用，用户通常需要在完成前获得进度更新。使用流式传输可以在消息和工具活动发生时实时展示它们。

from langchain.messages import AIMessage, HumanMessage


stream = agent.stream_events(
    {"messages": [{"role": "user", "content": "搜索AI相关新闻并总结发现"}]},
    version="v3",
)
for snapshot in stream.values:
    # 每个快照包含此时的完整状态
    latest_message = snapshot["messages"][-1]
    if latest_message.content:
        if isinstance(latest_message, HumanMessage):
            print(f"用户: {latest_message.content}")
        elif isinstance(latest_message, AIMessage):
            print(f"智能体: {latest_message.content}")
    elif latest_message.tool_calls:
        print(f"正在调用工具: {[tc['name'] for tc in latest_message.tool_calls]}")

有关流式模式、事件类型和 UI 模式，请参见流式传输。

配置脚手架

create_agent 高度可扩展。中间件是自定义的基石：每一块处理一个关注点，在合适的时机钩入智能体循环，并且可以自由地与其他任何中间件组合。只取用例所需的部分，跳过其余部分。常见的模式被预构建为一等中间件。其他任何功能都可以构建为自定义中间件。按类别划分的智能体脚手架能力

随着智能体承担复杂的工作，它们需要在几个关键领域获得支持。中间件生态系统提供了：

执行环境

工具、文件系统、沙箱和代码执行

上下文管理

摘要、记忆、技能和提示缓存

规划和委派

待办事项列表和用于并行、隔离工作的子智能体

容错性

重试、回退和调用限制

防护栏

个人信息检测和内容控制

引导

在高影响操作前进行人工审批

create_deep_agent 为长时间运行的编码和研究任务预先组装了这套技术栈（默认包含文件系统、摘要、子智能体和提示缓存）。有关完整的预构建脚手架，请参见深度智能体。

执行环境

当智能体能够采取行动而不仅仅是生成文本时，它们尤其有用。执行环境为智能体提供了一个工作空间：它可以调用的工具、用于在多轮对话中读写文件的文件系统，以及用于运行脚本或 shell 命令的代码执行器。

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="openai:gpt-5.5",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

from langchain.agents import create_agent
from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
    
agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[search],
    middleware=[FilesystemMiddleware(backend=StateBackend())],
)

参见 @@MDX_PRESERVE_2@@ FilesystemMiddleware，沙箱，解释器。

上下文管理

每次模型调用都有固定的上下文窗口。随着智能体运行，该窗口会填满不断累积的历史记录、工具结果和中间步骤。摘要在溢出发生前压缩历史记录；记忆在启动时加载持久化指令，使知识能够跨会话保持；技能按需提供领域知识，而不是预先加载所有内容。

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="google_genai:gemini-3.6-flash"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="openai:gpt-5.5"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="anthropic:claude-sonnet-4-6"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="openrouter:z-ai/glm-5.2"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="fireworks:accounts/fireworks/models/glm-5p2"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="baseten:zai-org/GLM-5.2"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware, MemoryMiddleware, SkillsMiddleware, SummarizationMiddleware
    
backend = StateBackend()
model="ollama:north-mini-code-1.0"
    
agent = create_agent(
    model=model,
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        SummarizationMiddleware(model=model, backend=backend),
        MemoryMiddleware(backend=backend, sources=["./AGENTS.md"]),
        SkillsMiddleware(backend=backend, sources=["./skills/"]),
    ],
)

参见 @@MDX_PRESERVE_3@@ SummarizationMiddleware，@@MDX_PRESERVE_4@@ MemoryMiddleware，技能，上下文工程。

规划和委派

复杂任务常常超出单个上下文窗口的处理能力。委派让主智能体能够将工作分解成若干部分，交给各自运行在独立隔离上下文中的子智能体处理，主智能体则专注于协调而非执行。工作可以并行进行；主智能体的上下文保持整洁。

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="openai:gpt-5.5",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

from deepagents.backends import StateBackend
from deepagents.middleware import FilesystemMiddleware
from deepagents.middleware.subagents import SubAgentMiddleware
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短的摘要。"""
    return f"搜索查询 {query} 的结果如下"


backend = StateBackend()

agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[search],
    middleware=[
        FilesystemMiddleware(backend=backend),
        TodoListMiddleware(),
        SubAgentMiddleware(
            backend=backend,
            subagents=[
                {
                    "name": "researcher",
                    "description": "搜索并返回结构化的摘要。",
                    "system_prompt": "使用搜索工具研究该问题并总结关键点。",
                    "tools": [search],
                    "model": "anthropic:claude-sonnet-4-6",
                    "middleware": [],
                }
            ],
        ),
    ],
)

参见子智能体。

为你的智能体命名

可选地为智能体使用一个标识符。这在将智能体作为子图嵌入多智能体系统时特别有用。

agent = create_agent(model="google_genai:gemini-3.6-flash", tools=tools, name="research_assistant")

agent = create_agent(model="openai:gpt-5.5", tools=tools, name="research_assistant")

agent = create_agent(model="anthropic:claude-sonnet-4-6", tools=tools, name="research_assistant")

agent = create_agent(model="openrouter:z-ai/glm-5.2", tools=tools, name="research_assistant")

agent = create_agent(model="fireworks:accounts/fireworks/models/glm-5p2", tools=tools, name="research_assistant")

agent = create_agent(model="baseten:zai-org/GLM-5.2", tools=tools, name="research_assistant")

agent = create_agent(model="ollama:north-mini-code-1.0", tools=tools, name="research_assistant")

容错性

生产环境中的智能体会遇到开发中很少出现的失败：速率限制、模型超时、瞬时 API 错误。容错中间件在基础设施层面处理这些问题，这样你的工具和业务逻辑就不需要在每个调用周围都写 try/catch。

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="openai:gpt-5.5",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

from langchain.agents import create_agent
from langchain.agents.middleware import ModelRetryMiddleware, ToolRetryMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"查询结果：{query}"


agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[search],
    middleware=[
        ModelRetryMiddleware(max_retries=3),
        ToolRetryMiddleware(max_retries=2),
    ],
)

参见 @@MDX_PRESERVE_5@@ ModelRetryMiddleware，@@MDX_PRESERVE_6@@ ToolRetryMiddleware，预构建中间件。

防护栏

某些策略不能仅放在提示中——它们需要被确定性地强制执行，无论模型做什么。防护栏在数据流经智能体循环时进行拦截，在工具结果到达模型上下文之前应用合规性规则或内容策略。

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="openai:gpt-5.5",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[search],
    middleware=[PIIMiddleware("email")],
)

参见 @@MDX_PRESERVE_9@@ PIIMiddleware，预构建中间件。

引导

完全自主并非总是合适。引导让你能够在特定的决策点——在破坏性写入、昂贵的 API 调用或任何需要判断的操作之前——安排人工介入，而无需重构你的智能体。智能体会暂停等待；人工批准、编辑或拒绝；然后执行继续。

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="google_genai:gemini-3.6-flash",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="openai:gpt-5.5",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="openrouter:z-ai/glm-5.2",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="fireworks:accounts/fireworks/models/glm-5p2",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="baseten:zai-org/GLM-5.2",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool


@tool
def search(query: str) -> str:
    """搜索查询并返回简短摘要。"""
    return f"搜索结果：{query}"


agent = create_agent(
    model="ollama:north-mini-code-1.0",
    tools=[search],
    middleware=[HumanInTheLoopMiddleware(interrupt_on={"write_file": True})],
)

参见 @@MDX_PRESERVE_11@@ HumanInTheLoopMiddleware，人机协同。

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