概览
LangChain 代理使用 LangGraph 持久化来启用长期记忆。这是一个更高级的主题，需要 LangGraph 知识才能使用。记忆存储
LangGraph 将长期记忆作为 JSON 文档存储在存储中。 每个记忆都组织在一个自定义的namespace（类似于文件夹）和一个独特的 key（像文件名）下。命名空间通常包括用户或组织 ID 或其他标签，以便于组织信息。 这种结构支持记忆的分层组织。然后通过内容过滤器支持跨命名空间搜索。复制
向 AI 提问
from langgraph.store.memory import InMemoryStore
def embed(texts: list[str]) -> list[list[float]]:
# Replace with an actual embedding function or LangChain embeddings object
return [[1.0, 2.0] * len(texts)]
# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production use.
store = InMemoryStore(index={"embed": embed, "dims": 2})
user_id = "my-user"
application_context = "chitchat"
namespace = (user_id, application_context)
store.put(
namespace,
"a-memory",
{
"rules": [
"User likes short, direct language",
"User only speaks English & python",
],
"my-key": "my-value",
},
)
# get the "memory" by ID
item = store.get(namespace, "a-memory")
# search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity
items = store.search(
namespace, filter={"my-key": "my-value"}, query="language preferences"
)
在工具中读取长期记忆
代理可以用来查找用户信息的一个工具
复制
向 AI 提问
from dataclasses import dataclass
from langchain_core.runnables import RunnableConfig
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langgraph.store.memory import InMemoryStore
@dataclass
class Context:
user_id: str
# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production.
store = InMemoryStore()
# Write sample data to the store using the put method
store.put(
("users",), # Namespace to group related data together (users namespace for user data)
"user_123", # Key within the namespace (user ID as key)
{
"name": "John Smith",
"language": "English",
} # Data to store for the given user
)
@tool
def get_user_info(runtime: ToolRuntime[Context]) -> str:
"""Look up user info."""
# Access the store - same as that provided to `create_agent`
store = runtime.store
user_id = runtime.context.user_id
# Retrieve data from store - returns StoreValue object with value and metadata
user_info = store.get(("users",), user_id)
return str(user_info.value) if user_info else "Unknown user"
agent = create_agent(
model="claude-sonnet-4-5-20250929",
tools=[get_user_info],
# Pass store to agent - enables agent to access store when running tools
store=store,
context_schema=Context
)
# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "look up user information"}]},
context=Context(user_id="user_123")
)
从工具中写入长期记忆
更新用户信息的工具示例
复制
向 AI 提问
from dataclasses import dataclass
from typing_extensions import TypedDict
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langgraph.store.memory import InMemoryStore
# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production.
store = InMemoryStore()
@dataclass
class Context:
user_id: str
# TypedDict defines the structure of user information for the LLM
class UserInfo(TypedDict):
name: str
# Tool that allows agent to update user information (useful for chat applications)
@tool
def save_user_info(user_info: UserInfo, runtime: ToolRuntime[Context]) -> str:
"""Save user info."""
# Access the store - same as that provided to `create_agent`
store = runtime.store
user_id = runtime.context.user_id
# Store data in the store (namespace, key, data)
store.put(("users",), user_id, user_info)
return "Successfully saved user info."
agent = create_agent(
model="claude-sonnet-4-5-20250929",
tools=[save_user_info],
store=store,
context_schema=Context
)
# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "My name is John Smith"}]},
# user_id passed in context to identify whose information is being updated
context=Context(user_id="user_123")
)
# You can access the store directly to get the value
store.get(("users",), "user_123").value
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。