在 Agent 执行过程中的各个环节暴露出来的钩子函数，使用中间件可以控制和定制 Agent 的执行过程、输出以及增加健壮性（添加一些业务相关的逻辑，比如日志）。
此外中间件还能把一些通用的与业务关系不大的业务抽离出来，让 Agent 具有更好的复用性；带有钩子函数的 Agent 执行逻辑
内置中间件
在 Langchain 中也提供了一系列的内置中间件；
from langchain.agents.middleware import SummarizationMiddleware, ...
from deepagents.middleware.subagents import SubAgentMiddleware, ...
Summarization对历史消息进行摘要总结（避免上下文爆炸）HumanInTheLoop暂停执行、等待人工审核ModelCallLimit | ToolCallLimit限制模型或工具的执行次数ModelFalback当主模型发生故障时，自动回退到备用模型。PIIDetetion敏感信息检测转码TodoList让 Agent 做事前先列计划LLMToolSelector当工具太多时，用子模型筛选最相关的几个工具ToolRetry | ModelRetry通过指数退避算法，设置工具或模型失败时的重试策略LLMToolEmulator当工具未完善时，模拟工具执行 mock 方案ContextEditing上下文编辑，可以控制最终给模型哪些消息FileSearch基于 Glob 和 Grep 检索工具给 Agent 赋予本地文件检索和分析的FileSystem为代理提供文件系统，用于存储上下文和长期记忆；来源于 DeepAgentsSubagent创建子 Agent 来源于 DeepAgents
SummarizationMiddleware
对历史消息进行摘要总结、有压缩上下文的效果；
model摘要模型，字符串或者init_chat_model返回值trigger触发条件，列表嵌套元组messages摘要的消息树tokens摘要 token 数量fraction摘要的百分比模型 token 上限 * 系数百分比，浮点数keep与trigger参数一致，但是仅接受一个值，需要保留的内容summary_prompt总结消息的 prompttrim_tokens_to_summarize总结调用的最大值，超过则会被修剪
model = init_chat_model(
"openai:kimi-k2.6",
base_url=os.environ.get("OPENAI_BASE_URL"),
# 手动设置模型最大 token 数
profile={"max_input_tokens": 262144}
)
messages = [
HumanMessage("你好派大星，我是海绵宝宝"),
AIMessage("你好海绵宝宝"),
HumanMessage("我们一起去抓水母吧~"),
AIMessage("等等我，海绵宝宝！我的水母网... "),
HumanMessage("今天工作日，我要去蟹老板那里打工了~")
]
agent = create_agent(
model,
middleware=[
SummarizationMiddleware(
model,
trigger=[
("messages", 2),
# max token 的 10% 触发
# 有些模型 langchain 无法知道最大 token 数，所以要手动设置
("fraction", 0.1),
("tokens", 100)
],
# 和 trigger 配合，需要保留的消息数
keep=("messages", 2),
# {messages} 一定要有，不然模型也不知道你想摘要什么内容
summary_prompt="用中文繁体总结消息内容 \n{messages}"
)
]
)
result = agent.invoke({"messages": messages})
for message in result["messages"]:
print(message.pretty_print())
HumanInTheLoopMiddleware
中断 Agent 调用工具的过程，审核后再执行；
interrupt_on审核动作，字段类型<tool_name>: boolTrue 表示[approve, reject, edit]False 表示跳过<tool_name>: { allowed_decisions: [approve, reject, edit], description: xxx }可以直接通过数组指定模型和描述
description_prefix中断的描述信息，没有内部description的优先级高
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command
@tool
def get_weather(city: str, is_forcast=False) -> str:
"""获取指定城市天气预报"""
return f"{city} 是晴天 {'明天有雨' if is_forcast else '明天晴天'}"
agent = create_agent(
model,
tools=[get_weather],
middleware=[
HumanInTheLoopMiddleware(
interrupt_on={
# "get_weather": True,
"get_weather": {
"allowed_decisions": ["edit"],
"description": "优先级更高的中断描述信息"
}
},
description_prefix="工具调用中断~"
)
],
# 保证中断和回复在一个会话中，所以要有记忆
checkpointer=InMemorySaver()
)
config = {"configurable": {"thread_id": "1"}}
result = agent.invoke({"messages": [HumanMessage("上海的天气怎么样？")]}, config=config)
# 被中断响应中，拥有 __interrupt__ 属性，数组类型
# rprint(result["__interrupt__"])
# [
# Interrupt(
# value={
# 'action_requests': [{'name': 'get_weather', 'args': {'city': '上海'}, 'description': '优先级更高的中断描述'}],
# 'review_configs': [{'action_name': 'get_weather', 'allowed_decisions': ['edit']}]
# },
# id='481eead7a0f73bd98af8892baa7e7be5'
# )
# ]
if result["__interrupt__"]:
# 通过 command 回复执行
resume_result = agent.invoke(
Command(resume={
"decisions": [
# 编辑的话，需要指定 edited_action 从新调用 tool
# 实际就是参考 result["__interrupt__"][0].value["action_requests"] 中的内容调整参数从新调用一下
{
"type": "edit",
"edited_action": {
"name": "get_weather",
"args": {"city": "上海", "is_forcast": False}
}
}
# 通过
{"type": "approve"}
]
}),
config=config
)
rprint(resume_result)
PIIMiddleware
敏感信息加密
pii_type需要被加密的数据类型，支持自定义；"email" | "credit_card" | "url" | "ip" | "mac_address"strategy加密方案redact默认值，替换为[REDACTED_EMAIL]常量mask部分屏蔽，显示最后几个字符hash转换为 hashblock直接抛异常
detector自定义检测函数正则字符串或者函数apply_to_input模型调用前检查 默认 Trueapply_to_output模型调用后检查 默认 Falseapply_to_tool_results工具执行后检查 默认 False
agent = create_agent(
model,
middleware=[
PIIMiddleware("email", strategy="redact"),
PIIMiddleware("url", strategy="hash"),
PIIMiddleware("credit_card", strategy="mask"),
PIIMiddleware("mac_address", strategy="mask"),
PIIMiddleware("ip", strategy="mask")
],
)
result = agent.invoke({
"messages": HumanMessage("""这是什么邮箱 1234567890@qq.com
这是什么 mac 地址 02:4A:7C:9E:3F:1B
这是什么 ip 192.168.0.1
这是什么银行卡号 6226 1234 5678 9012
这是什么 url http://www.baidu.com"""
)
})
# 这是什么邮箱 [REDACTED_EMAIL]
# 这是什么 mac 地址 **:**:**:**:**:1B
# 这是什么 ip *.*.*.1
# 这是什么银行卡号 6226 1234 5678 9012
# 这是什么 url <url_hash:035db66d>
rprint(result["messages"][0].content)
TodoListMiddleware
应对比较复杂任务时，可以为模型提供规划能力，减缓降智
agent = create_agent(
model,
middleware=[
# 接受两个参数
# system_prompt 系统系统词 内置
# tool_description 工具描述信息，内置
TodoListMiddleware()
],
)
# 实际内部就是通过 write_todos 创建规划任务，创建一个列表一项项的完成
# {
# 'name': 'write_todos',
# 'args': {
# 'todos': [
# {'content': '确定上海一日游的主题和必去景点（如外滩、豫园、陆家嘴等）', 'status': 'in_progress'},
# {'content': '规划合理的游览路线和时间安排，避免走回头路', 'status': 'pending'},
# {'content': '安排用餐时间和地点，体验上海本帮菜或特色小吃', 'status': 'pending'},
# {'content': '考虑交通方式（地铁/打车/步行）及各景点间的通勤时间', 'status': 'pending'},
# {'content': '准备备选方案和注意事项（天气、人流、门票预约等）', 'status': 'pending'}
# ]
# },
# 'id': 'write_todos_0',
# 'type': 'tool_call'
# }
result = agent.invoke({"messages": HumanMessage("使用 write_todos 帮我规划一个上海一日游")})
ModelCallLimitMiddleware
thread_limit线程内模型调用上限run_limit本次运行模型调用上限exit_behaviorend | error优雅退出和抛出异常
agent = create_agent(
model,
middleware=[
ModelCallLimitMiddleware(
thread_limit=1,
# run_limit=1,
# exit_behavior="end",
exit_behavior="error"
)
],
tools=[get_weather],
checkpointer=InMemorySaver()
)
config = {"configurable": {"thread_id": "1"}}
# Model call limits exceeded: run limit (1/1)
# Model call limits exceeded: thread limit (1/1)
# langchain.agents.middleware.model_call_limit.ModelCallLimitExceededError
result = agent.invoke({"messages": HumanMessage("今天上海天气怎么样")}, config=config)
ToolCallLimitMiddleware
tool_name不指定就监控所有工具，否则就指定工具thread_limit线程内工具调用上限run_limit本次运行工具调用上限exit_behavior比较模型中间多出continue表示继续运行 Agent，模型自主决策后续行为
ModelFallbackMiddleware
后备模型，当主模型失败时，会使用该模型
model = init_chat_model(
"openai:kimi-k100",
base_url=os.environ.get("OPENAI_BASE_URL"),
)
agent = create_agent(
model,
middleware=[
ModelFallbackMiddleware("deepseek:deepseek-v4-flash")
],
)
result = agent.invoke({"messages": [HumanMessage("你是什么模型？")]})
LLMToolSelectorMiddleware
工具选择
agent = create_agent(
model,
middleware=[
LLMToolSelectorMiddleware(
model=model, # 选择工具的模型
max_tools=2, # 最多支持选择工具的数量
# always_include=["get_weather"] # 在这里面的工具不会被计数
)
],
tools=[get_news, get_weather]
)
result = agent.invoke({"messages": [HumanMessage("上海天气如何？使用 get_news 看看新闻")]})
ToolRetryMiddleware
基于指数退避算法，设置工具调用失败时的重试策略并且结合 jitter 实现抖动，防止短期迎来并发
class ToolRetryMiddleware(
*,
# 最大重试次数，不包含初次调用 2 + 1 = 3
max_retries: int = 2,
# None 表示所有工具
tools: list[BaseTool | str] | None = None,
# 捕获的异常
retry_on: RetryOn = (Exception, ),
# 返回包含错误详细信息的“ToolMessage” error 则是抛异常
on_failure: OnFailure = "continue",
# 每次重试等待时间 * 2
backoff_factor: float = 2,
# 初始重试等待时间
initial_delay: float = 1,
# 最大等待延迟上限
max_delay: float = 60,
# 增加抖动
jitter: bool = True
)
agent = create_agent(
model,
middleware=[
ToolRetryMiddleware(
max_retries=5,
max_delay=10.0,
retry_on=(TimeoutError,)
)
],
tools=[get_weather]
)
res = agent.invoke({"messages": [HumanMessage("上海天气怎么样")]})
rprint(res["messages"])
# 包含信息
# ToolMessage(
# content="Tool 'get_weather' failed after 6 attempts with TimeoutError: . Please try again.",
# name='get_weather',
# id='12a5e2a3-b044-4dc1-909f-a5339ccb77a3',
# tool_call_id='call_00_D4JALj3Ptmtaj5ogGQy27292',
# status='error'
# ),
ModelRetryMiddleware
参数与 ToolRetryMiddleware 一致
agent = create_agent(
'deepseek:deepseek-cat',
middleware=[
ModelRetryMiddleware(
max_retries=5,
max_delay=10.0,
on_failure="continue"
)
]
)
res = agent.invoke({"messages": [HumanMessage("上海天气怎么样")]})
rprint(res["messages"])
# AIMessage(
# content="Model call failed after 6 attempts with BadRequestError: Error code: 400 - {'error': {'message': 'The supported API model names are deepseek-v4-pro or
# deepseek-v4-flash, but you passed deepseek-cat.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_request_error'}}",
# additional_kwargs={},
# response_metadata={},
# id='aafb281c-234b-4b63-96e5-9775ddc8fabb',
# tool_calls=[],
# invalid_tool_calls=[]
# )
LLMToolEmulator
当工具不完善的时候，可以使用该中间件达到 mock 的效果
agent = create_agent(
model,
middleware=[
# 会默认使用这个去模拟工具的执行
LLMToolEmulator(model=model, tools=['get_weather'])
],
tools=[get_weather]
)
res = agent.invoke({"messages": [HumanMessage("上海天气怎么样")]})
rprint(res["messages"])
中间件的执行顺序
遵循先进后出的策略，
1 -> 2 -> 3 -> 3 -> 2 -> 1
自定义中间件
中间件提供了六个钩子函数，按照功能可划分为两组
Node-style hooks
before_agent智能体调用前after_agent智能体调用后before_model模型调用前after_model模型调用后
Wrap-style hooks
wrap_model_call模型调用前后执行wrap_tool_call工具调用前后执行
每个中间件都会用到其中多个或一个钩子函数，针对中间件的书写也提供了函数式和类两种方案（函数式一般仅使用一个钩子时使用）；函数最终也会被内部包装成类去执行
Node Style -> function
@before_agent
def before_agent_middleware(state: AgentState, runtime: Runtime):
print("=== before_agent ===")
return None
@after_agent
def after_agent_middleware(state: AgentState, runtime: Runtime):
print("=== after_agent ===")
return None
@before_model
def before_model_middleware(state: AgentState, runtime: Runtime):
print("=== before_model ===")
return None
@after_model
def after_model_middleware(state: AgentState, runtime: Runtime):
print("=== after_model ===")
return None
agent = create_agent(
model,
middleware=[before_agent_middleware, after_agent_middleware, before_model_middleware, after_model_middleware],
)
agent.invoke({"messages": [HumanMessage("hello")]})
# === before_agent ===
# === before_model ===
# === after_model ===
# === after_agent ===
接收 AgentState | Runtime 两个参数，返回值为 dict[str, Any] | None。
AgentState是 Agent 运行中的状态，随着 Agent 的运行发行变化，包括消息列表也在其中。Runtime是 Agent 运行中的上下文环境，包括上下文和长期记忆。- 返回值如果是
None表示不做任何操作，返回字典可以状态；返回特定字段可以控制 Agent 流程；
def before_model(self, state, runtime):
if state.get("count", 0) > 10:
# tools 调到工具节点
return {"jump_to": "__end__"} # 跳过模型，直接结束
# 修改 state
@after_agent
def after_agent_middleware(state: AgentState, runtime: Runtime):
state["messages"][-1].content += "after_agent"
return None
state | runtime 的信息如下
# 随着不同的钩子数据也会有变化，比如 before_agent 只有 human 消息, after_agent 就会多一条 AI 消息
{'messages': [HumanMessage(content='hello', additional_kwargs={}, response_metadata={}, id='f577b4bc-722a-4cf7-9891-263d37120559')]}
Runtime(
context=None,
store=None,
stream_writer=<function Pregel.stream.<locals>.stream_writer at 0x10f47c4a0>,
heartbeat=<function _no_op_heartbeat at 0x1084f5620>,
previous=None,
execution_info=ExecutionInfo(
checkpoint_id='1f184dd4-4b99-6b70-8001-aef8a60a67cd',
checkpoint_ns='before_model_middleware.before_model:550f9db1-2052-10ef-6835-8c4766468404',
task_id='550f9db1-2052-10ef-6835-8c4766468404',
thread_id=None,
run_id=None,
node_attempt=1,
node_first_attempt_time=1784622130.085105
),
server_info=None,
control=<langgraph.runtime.RunControl object at 0x10f33ebc0>
)
before_** 通常用于消息修剪、数据脱敏、数据输入验证等操作
after_** 通常用于状态统计、输出验证、格式化响应
can_jump_to 参数
中间件装饰器的参数，决定钩子可以直接跳转到流程的哪些地方
tools跳转至工具节点end跳转至 Agent 流程的末尾，或者第一个after_agent钩子model跳转至模型节点，或者第一个before_model钩子
@after_model(can_jump_to=["tools"])
def after_model_middleware(state: AgentState, runtime: Runtime):
print("=== after_model ===")
return None
Node Style -> class
类实现要继承 AgentMiddleware 这个类
from langchain.agents.middleware from AgentMiddleware, hook_config
class CustomMiddleware(AgentMiddleware):
def __init__(self):
super().__init__()
@hook_config(['end', 'tools', 'model'])
def before_agent(self, state, runtime):
return None
def before_model(self, state, runtime):
return None
def after_agent(self, state, runtime):
return None
def after_model(self, state, runtime):
return None
Wrap Style -> function
@wrap_model_call
def wrap_model_call_middleware(request: ModelRequest, handler: Callable[[ModelRequest], ModelResponse],) -> ModelResponse:
rprint(request)
# ModelRequest(
# model=ChatDeepSeek(
# metadata={'lc_versions': {'langchain-core': '1.4.9', 'langchain': '1.3.12', 'langchain-openai': '1.3.4'}},
# output_version=None,
# profile={
# 'name': 'DeepSeek Chat',
# 'release_date': '2025-12-01',
# 'last_updated': '2026-02-28',
# 'open_weights': True,
# 'max_input_tokens': 1000000,
# 'max_output_tokens': 384000,
# 'text_inputs': True,
# 'image_inputs': False,
# 'audio_inputs': False,
# 'video_inputs': False,
# 'text_outputs': True,
# 'image_outputs': False,
# 'audio_outputs': False,
# 'video_outputs': False,
# 'reasoning_output': False,
# 'tool_calling': True,
# 'attachment': True,
# 'temperature': True
# },
# client=<openai.resources.chat.completions.completions.Completions object at 0x114384980>,
# async_client=<openai.resources.chat.completions.completions.AsyncCompletions object at 0x114385400>,
# root_client=<openai.OpenAI object at 0x111e50ad0>,
# root_async_client=<openai.AsyncOpenAI object at 0x114384ad0>,
# model_name='deepseek-chat',
# model_kwargs={},
# openai_api_key=SecretStr('**********'),
# openai_proxy=None,
# stream_chunk_timeout=120.0,
# extra_body={'thinking': {'type': 'disabled'}},
# api_key=SecretStr('**********'),
# api_base='https://api.deepseek.com/v1'
# ),
# messages=[HumanMessage(content='你好派大星', additional_kwargs={}, response_metadata={}, id='94c72eca-8068-4c30-9a77-731bb6f43af5')],
# system_message=None,
# tool_choice=None,
# tools=[],
# response_format=None,
# state={'messages': [HumanMessage(content='你好派大星', additional_kwargs={}, response_metadata={}, id='94c72eca-8068-4c30-9a77-731bb6f43af5')]},
# runtime=Runtime(
# context=None,
# store=None,
# stream_writer=<function Pregel.stream.<locals>.stream_writer at 0x11454c180>,
# heartbeat=<function _no_op_heartbeat at 0x110641620>,
# previous=None,
# execution_info=ExecutionInfo(
# checkpoint_id='1f184e4e-eab6-694a-8000-417655033d5c',
# checkpoint_ns='model:cecf60ab-f4c0-d4f6-decf-f5ee3248f1c4',
# task_id='cecf60ab-f4c0-d4f6-decf-f5ee3248f1c4',
# thread_id=None,
# run_id=None,
# node_attempt=1,
# node_first_attempt_time=1784625421.681778
# ),
# server_info=None,
# control=<langgraph.runtime.RunControl object at 0x1128d64a0>
# ),
# model_settings={}
# )
# 调用模型的动作
response = handler(request)
return response
@wrap_tool_call
def model_tool_call_middleware(request: ToolCallRequest, handler: Callable[[ToolCallRequest], ToolMessage | Command]) -> ToolMessage | Command:
rprint(request)
# ToolCallRequest(
# tool_call={'name': 'get_weather', 'args': {'city': '上海'}, 'id': 'call_00_WgdCPVqeejoaDfDinENH6195', 'type': 'tool_call'},
# tool=StructuredTool(
# name='get_weather',
# description='获取天气状况',
# args_schema=<class 'langchain_core.utils.pydantic.get_weather'>,
# func=<function get_weather at 0x1076b9b20>
# ),
# state={
# 'messages': [
# HumanMessage(content='上海天气怎么样', additional_kwargs={}, response_metadata={}, id='2662c1f2-f87c-451e-ad29-443ecbd2f3be'),
# AIMessage(
# content='好的，我来查询一下上海的天气情况。',
# additional_kwargs={'refusal': None},
# response_metadata={
# 'token_usage': {
# 'completion_tokens': 53,
# 'prompt_tokens': 273,
# 'total_tokens': 326,
# 'completion_tokens_details': None,
# 'prompt_tokens_details': {'audio_tokens': None, 'cache_write_tokens': None, 'cached_tokens': 256},
# 'prompt_cache_hit_tokens': 256,
# 'prompt_cache_miss_tokens': 17
# },
# 'model_provider': 'deepseek',
# 'model_name': 'deepseek-v4-flash',
# 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402',
# 'id': 'd892560b-e329-4bcb-8f81-9a30db3776c3',
# 'finish_reason': 'tool_calls',
# 'logprobs': None
# },
# id='lc_run--019f83fe-5ee8-70b3-9edc-389b4917dd67-0',
# tool_calls=[{'name': 'get_weather', 'args': {'city': '上海'}, 'id': 'call_00_WgdCPVqeejoaDfDinENH6195', 'type': 'tool_call'}],
# invalid_tool_calls=[],
# usage_metadata={'input_tokens': 273, 'output_tokens': 53, 'total_tokens': 326, 'input_token_details': {'cache_read': 256}, 'output_token_details': {}}
# )
# ]
# },
# runtime=ToolRuntime(
# state={
# 'messages': [
# HumanMessage(content='上海天气怎么样', additional_kwargs={}, response_metadata={}, id='2662c1f2-f87c-451e-ad29-443ecbd2f3be'),
# AIMessage(
# content='好的，我来查询一下上海的天气情况。',
# additional_kwargs={'refusal': None},
# response_metadata={
# 'token_usage': {
# 'completion_tokens': 53,
# 'prompt_tokens': 273,
# 'total_tokens': 326,
# 'completion_tokens_details': None,
# 'prompt_tokens_details': {'audio_tokens': None, 'cache_write_tokens': None, 'cached_tokens': 256},
# 'prompt_cache_hit_tokens': 256,
# 'prompt_cache_miss_tokens': 17
# },
# 'model_provider': 'deepseek',
# 'model_name': 'deepseek-v4-flash',
# 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402',
# 'id': 'd892560b-e329-4bcb-8f81-9a30db3776c3',
# 'finish_reason': 'tool_calls',
# 'logprobs': None
# },
# id='lc_run--019f83fe-5ee8-70b3-9edc-389b4917dd67-0',
# tool_calls=[{'name': 'get_weather', 'args': {'city': '上海'}, 'id': 'call_00_WgdCPVqeejoaDfDinENH6195', 'type': 'tool_call'}],
# invalid_tool_calls=[],
# usage_metadata={'input_tokens': 273, 'output_tokens': 53, 'total_tokens': 326, 'input_token_details': {'cache_read': 256}, 'output_token_details': {}}
# )
# ]
# },
# context=None,
# config={
# 'tags': [],
# 'metadata': {
# 'ls_integration': 'langchain_create_agent',
# 'langgraph_step': 2,
# 'langgraph_node': 'tools',
# 'langgraph_triggers': ('__pregel_push',),
# 'langgraph_path': ('__pregel_push', 0, False),
# 'langgraph_checkpoint_ns': 'tools:3de491c7-7a2b-4851-d2d4-910543884caf',
# 'checkpoint_ns': 'tools:3de491c7-7a2b-4851-d2d4-910543884caf'
# },
# 'callbacks': <langchain_core.callbacks.manager.CallbackManager object at 0x110a0c6d0>,
# 'recursion_limit': 9999,
# 'configurable': {
# '__pregel_runtime': Runtime(
# context=None,
# store=None,
# stream_writer=<function Pregel.stream.<locals>.stream_writer at 0x1107ad6c0>,
# heartbeat=<function _no_op_heartbeat at 0x105ce5620>,
# previous=None,
# execution_info=ExecutionInfo(
# checkpoint_id='1f184e61-4810-6b56-8001-fb803c7cb82d',
# checkpoint_ns='tools:3de491c7-7a2b-4851-d2d4-910543884caf',
# task_id='3de491c7-7a2b-4851-d2d4-910543884caf',
# thread_id=None,
# run_id=None,
# node_attempt=1,
# node_first_attempt_time=1784625914.654541
# ),
# server_info=None,
# control=<langgraph.runtime.RunControl object at 0x11066ce80>
# ),
# '__pregel_replay_state': None,
# '__pregel_task_id': '3de491c7-7a2b-4851-d2d4-910543884caf',
# '__pregel_send': <built-in method extend of collections.deque object at 0x11076c4f0>,
# '__pregel_read': functools.partial(<function local_read at 0x105ce6520>, PregelScratchpad(step=2, stop=9999, call_counter=<langgraph.pregel._algo.LazyAtomicCounter
# object at 0x1101f1f90>, interrupt_counter=<langgraph.pregel._algo.LazyAtomicCounter object at 0x1107f48e0>, get_null_resume=<function _scratchpad.<locals>.get_null_resume at
# 0x1107adbc0>, resume=[], subgraph_counter=<langgraph.pregel._algo.LazyAtomicCounter object at 0x1107f4280>), {'messages': <langgraph.channels.binop.BinaryOperatorAggregate object
# at 0x1107cb080>, 'jump_to': <langgraph.channels.ephemeral_value.EphemeralValue object at 0x1107cb180>, 'structured_response': <langgraph.channels.last_value.LastValue object at
# 0x1107caec0>, '__start__': <langgraph.channels.ephemeral_value.EphemeralValue object at 0x1107cab80>, '__pregel_tasks': <langgraph.channels.topic.Topic object at 0x1107cad40>,
# 'branch:to:model': <langgraph.channels.ephemeral_value.EphemeralValue object at 0x1107cac40>, 'branch:to:tools': <langgraph.channels.ephemeral_value.EphemeralValue object at
# 0x1107caac0>}, {}, PregelTaskWrites(path=('__pregel_push', 0, False), name='tools', writes=deque([]), triggers=('__pregel_push',))),
# '__pregel_checkpointer': None,
# 'checkpoint_map': {'': '1f184e61-4810-6b56-8001-fb803c7cb82d'},
# 'checkpoint_id': None,
# 'checkpoint_ns': 'tools:3de491c7-7a2b-4851-d2d4-910543884caf',
# '__pregel_scratchpad': PregelScratchpad(
# step=2,
# stop=9999,
# call_counter=<langgraph.pregel._algo.LazyAtomicCounter object at 0x1101f1f90>,
# interrupt_counter=<langgraph.pregel._algo.LazyAtomicCounter object at 0x1107f48e0>,
# get_null_resume=<function _scratchpad.<locals>.get_null_resume at 0x1107adbc0>,
# resume=[],
# subgraph_counter=<langgraph.pregel._algo.LazyAtomicCounter object at 0x1107f4280>
# ),
# '__pregel_call': functools.partial(<function _call at 0x105d1fb00>, <weakref at 0x1107e76a0; to 'langgraph.types.PregelExecutableTask' at 0x1107ad9f0>,
# retry_policy=None, futures=<weakref at 0x1107a2200; to 'langgraph.pregel._runner.FuturesDict' at 0x1107e74d0>, schedule_task=<bound method SyncPregelLoop.accept_push of
# <langgraph.pregel._loop.SyncPregelLoop object at 0x1107cc690>>, submit=<weakref at 0x1107fc450; to 'langgraph.pregel._executor.BackgroundExecutor' at 0x1107cc7d0>)
# }
# },
# stream_writer=<function Pregel.stream.<locals>.stream_writer at 0x1107ad6c0>,
# tool_call_id='call_00_WgdCPVqeejoaDfDinENH6195',
# store=None,
# tools=[
# StructuredTool(
# name='get_weather',
# description='获取天气状况',
# args_schema=<class 'langchain_core.utils.pydantic.get_weather'>,
# func=<function get_weather at 0x1076b9b20>
# )
# ],
# execution_info=ExecutionInfo(
# checkpoint_id='1f184e61-4810-6b56-8001-fb803c7cb82d',
# checkpoint_ns='tools:3de491c7-7a2b-4851-d2d4-910543884caf',
# task_id='3de491c7-7a2b-4851-d2d4-910543884caf',
# thread_id=None,
# run_id=None,
# node_attempt=1,
# node_first_attempt_time=1784625914.654541
# ),
# server_info=None
# )
# )
# 调用工具
request.tool_call["args"]["is_forcast"] = True
result = handler(request)
return result
Wrap Style -> class
class WrapMiddleware(AgentMiddleware):
def __init__(self):
super().__init__()
def wrap_model_call(self, request, handler):
return None
def wrap_tool_call(self, request, handler):
return None
中间件执行顺序
class CustomMiddleware(AgentMiddleware):
def __init__(self):
super().__init__()
def before_agent(self, state, runtime):
print("====== before_agent ======")
return None
def before_model(self, state, runtime):
print("====== before_model ======")
return None
def after_agent(self, state, runtime):
print("====== after_agent ======")
return None
def after_model(self, state, runtime):
print("====== after_model ======")
return None
def wrap_model_call(self, request, handler):
print("====== wrap_model_call ====== before")
result = handler(request)
print("====== wrap_model_call ====== after")
return result
def wrap_tool_call(self, request, handler):
print("====== wrap_tool_call ====== before")
result = handler(request)
print("====== wrap_tool_call ====== after")
return result
agent = create_agent(
model,
middleware=[CustomMiddleware()],
tools=[get_weather]
)
# ====== before_agent ======
# ====== before_model ======
# ====== wrap_model_call ====== before
# ====== wrap_model_call ====== after
# ====== after_model ======
# ====== wrap_tool_call ====== before
# ====== wrap_tool_call ====== after
# ====== before_model ======
# ====== wrap_model_call ====== before
# ====== wrap_model_call ====== after
# ====== after_model ======
# ====== after_agent ======
